rOpenSci | rOpenSci Packages

rOpenSci Packages

All of our packages in one place
Showing 10 of 12

Working with Sets the Tidy Way

Lluís Revilla Sancho
Description

Implements a class and methods to work with sets, doing intersection, union, complementary sets, power sets, cartesian product and other set operations in a “tidy” way. These set operations are available for both classical sets and fuzzy sets. Import sets from several formats or from other several data structures.

View Documentation
opentripplanner
CRAN Peer-reviewed

Setup and connect to OpenTripPlanner

Malcolm Morgan
Description

Setup and connect to OpenTripPlanner (OTP) http://www.opentripplanner.org/. OTP is an open source platform for multi-modal and multi-agency journey planning written in Java. The package allows you to manage a local version or connect to remote OTP server. This package has been peer-reviewed by rOpenSci (v. 0.2.0.0).

View Documentation
prism
CRAN

Access Data from the Oregon State Prism Climate Project

Alan Butler
Description

Allows users to access the Oregon State Prism climate data (http://www.prism.oregonstate.edu/). Using the web service API data can easily downloaded in bulk and loaded into R for spatial analysis. Some user friendly visualizations are also provided.

View Documentation
mauricer
CRAN

Install BEAST2 Packages

Richèl J.C. Bilderbeek
Description

BEAST2 (https://www.beast2.org) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAST2 is commonly accompanied by BEAUti 2 (https://www.beast2.org), which, among others, allows one to install BEAST2 package. This package allows to install BEAST2 packages from R.

View Documentation
beastier
CRAN

Call BEAST2

Richèl J.C. Bilderbeek
Description

BEAST2 (https://www.beast2.org) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAST2 is a command-line tool. This package provides a way to call BEAST2 from an R function call.

View Documentation
plotly
CRAN

Create Interactive Web Graphics via plotly.js

Carson Sievert
Description

Create interactive web graphics from ggplot2 graphs and/or a custom interface to the (MIT-licensed) JavaScript library plotly.js inspired by the grammar of graphics.

Scientific use cases
  1. Rothman, A. M. K., Arnold, N. D., Chang, W., Watson, O., Swift, A. J., Condliffe, R., … Lawrie, A. (2015). Pulmonary Artery Denervation Reduces Pulmonary Artery Pressure and Induces Histological Changes in an Acute Porcine Model of Pulmonary Hypertension. Circulation: Cardiovascular Interventions, 8(11), e002569–e002569. https://doi.org/10.1161/circinterventions.115.002569
  2. Doyle, J. M., Merovitch, N., Wyeth, R. C., Stoyek, M. R., Schmidt, M., Wilfart, F., … Croll, R. P. (2017). A simple automated system for appetitive conditioning of zebrafish in their home tanks. Behavioural Brain Research, 317, 444–452. https://doi.org/10.1016/j.bbr.2016.09.044
  3. Hertler, B., Buitrago, M. M., Luft, A. R., & Hosp, J. A. (2016). Temporal course of gene expression during motor memory formation in primary motor cortex of rats. Neurobiology of Learning and Memory, 136, 105–115. https://doi.org/10.1016/j.nlm.2016.09.018
  4. Meyer, A. G. (2016). Analysis of infection biomarkers within a Bayesian framework reveals their role in pneumococcal pneumonia diagnosis in HIV patients. https://doi.org/10.1101/070144
  5. Walker, Kyle. (in press). tigris: An R Package to Access and Work with Geographic Data from the US Census Bureau. https://journal.r-project.org/archive/accepted/walker.pdf
  6. Rastrojo, A., García-Hernández, R., Vargas, P., Camacho, E., Corvo, L., Imamura, H., … Requena, J. M. (2018). Genomic and transcriptomic alterations in Leishmania donovani lines experimentally resistant to antileishmanial drugs. International Journal for Parasitology: Drugs and Drug Resistance. https://doi.org/10.1016/j.ijpddr.2018.04.002
  7. Tang, Y. (2018). autoplotly: An R package for automatic generation of interactive visualizations for statistical results. Journal of Open Source Software, 3(24), 657. https://doi.org/10.21105/joss.00657
  8. Rastrojo, A., García-Hernández, R., Vargas, P., Camacho, E., Corvo, L., Imamura, H., … Requena, J. M. (2018). Genomic and transcriptomic alterations in Leishmania donovani lines experimentally resistant to antileishmanial drugs. International Journal for Parasitology: Drugs and Drug Resistance, 8(2), 246–264. https://doi.org/10.1016/j.ijpddr.2018.04.002
  9. Sun, B. B., Maranville, J. C., Peters, J. E., Stacey, D., Staley, J. R., Blackshaw, J., … Butterworth, A. S. (2018). Genomic atlas of the human plasma proteome. Nature, 558(7708), 73–79. https://doi.org/10.1038/s41586-018-0175-2
  10. Hsu, Lawrence. 2018. Linking Traditional Chinese Medicinal Herbs to Cancer Related Pathways. Scholar Archive. 4054. https://digitalcommons.ohsu.edu/etd/4054
  11. Krogsgaard, L. R., Andersen, L. O. ‘Brien, Johannesen, T. B., Engsbro, A. L., Stensvold, C. R., Nielsen, H. V., & Bytzer, P. (2018). Characteristics of the bacterial microbiome in association with common intestinal parasites in irritable bowel syndrome. Clinical and Translational Gastroenterology, 9(6). https://doi.org/10.1038/s41424-018-0027-2
  12. Sanford, T., Gadzinski, A. J., Gaither, T., Osterberg, E. C., Murphy, G. P., Carroll, P. R., & Breyer, B. N. (2018). Effect of Oscillation on Perineal Pressure in Cyclists: Implications for Micro-Trauma. Sexual Medicine. https://doi.org/10.1016/j.esxm.2018.05.002
  13. Koc, A., Henriksson, T., & Chawade, A. (2018). Specalyzer—an interactive online tool to analyze spectral reflectance measurements. PeerJ, 6, e5031. https://doi.org/10.7717/peerj.5031
  14. Devlin, J. C., Battaglia, T., Blaser, M. J., & Ruggles, K. V. (2018). WHAM!: a web-based visualization suite for user-defined analysis of metagenomic shotgun sequencing data. BMC Genomics, 19(1). https://doi.org/10.1186/s12864-018-4870-z
  15. Václav Brázda, Jiri Lysek, Martin Bartas, and Miroslav Fojta. 2018. Complex analyses of short inverted repeats in all sequenced chloroplast DNAs. BioMed Research International. https://www.hindawi.com/journals/bmri/aip/1097018/
  16. Fontaine, A., Lequime, S., Moltini-Conclois, I., Jiolle, D., Leparc-Goffart, I., Reiner, R. C., & Lambrechts, L. (2018). Epidemiological significance of dengue virus genetic variation in mosquito infection dynamics. PLOS Pathogens, 14(7), e1007187. https://doi.org/10.1371/journal.ppat.1007187
  17. Lawrence, T. N., & Bhalla, R. S. (2018). Spatially explicit action research for coastal fisheries management. PLOS ONE, 13(7), e0199841. https://doi.org/10.1371/journal.pone.0199841
  18. Zhang, Y., Oates, L. G., Serate, J., Xie, D., Pohlmann, E., Bukhman, Y. V., … Ong, R. G. (2018). Diverse lignocellulosic feedstocks can achieve high field-scale ethanol yields while providing flexibility for the biorefinery and landscape-level environmental benefits. GCB Bioenergy. https://doi.org/10.1111/gcbb.12533
  19. Wang, C., Moya, L., Clements, J. A., Nelson, C. C., & Batra, J. (2018). Mining human cancer datasets for kallikrein expression in cancer: the “KLK-CANMAP” Shiny web tool. Biological Chemistry, 0(0). https://doi.org/10.1515/hsz-2017-0322
  20. Locard-Paulet, M., Parra, J., Albigot, R., Mouton-Barbosa, E., Bardi, L., Burlet-Schiltz, O., & Marcoux, J. (2018). VisioProt-MS: interactive 2D maps from intact protein mass spectrometry. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty680
  21. Horvatić, A., Guillemin, N., Kaab, H., McKeegan, D., O’Reilly, E., Bain, M., … Eckersall, P. D. (2018). Quantitative proteomics using tandem mass tags in relation to the acute phase protein response in chicken challenged with Escherichia coli lipopolysaccharide endotoxin. Journal of Proteomics. https://doi.org/10.1016/j.jprot.2018.08.009
  22. Bharanidharan, R., Arokiyaraj, S., Kim, E. B., Lee, C. H., Woo, Y. W., Na, Y., … Kim, K. H. (2018). Ruminal methane emissions, metabolic, and microbial profile of Holstein steers fed forage and concentrate, separately or as a total mixed ration. PLOS ONE, 13(8), e0202446. https://doi.org/10.1371/journal.pone.0202446
  23. Schieffer, K. M., Kline, B. P., Harris, L. R., Deiling, S., Koltun, W. A., & Yochum, G. S. (2018). A Differential Host Response to Viral Infection Defines a Subset of Earlier-Onset Diverticulitis Patients. J Gastrointestin Liver Dis, 27(3), 249-255. https://doi.org/10.15403/jgld.2014.1121.273.sch
  24. Longuespée, R., Kriegsmann, K., Cremer, M., Zgorzelski, C., Casadonte, R., Kazdal, D., … Kriegsmann, M. (2018). In MALDI mass spectrometry imaging on formalin-fixed paraffin-embedded tissue specimen section thickness significantly influences m/z peak intensity. PROTEOMICS - Clinical Applications, 1800074. https://doi.org/10.1002/prca.20180007
  25. Tong, M., Deng, Z., Yang, M., Xu, C., Zhang, X., Zhang, Q., … Liu, Q. (2018). Transcriptomic but not genomic variability confers phenotype of breast cancer stem cells. Cancer Communications, 38(1). https://doi.org/10.1186/s40880-018-0326-8
  26. Denecker, T., & Lelandais, G. (2018). Empowering the detection of ChIP-seq “basic peaks” (bPeaks) in small eukaryotic genomes with a web user-interactive interface. BMC Research Notes, 11(1). https://doi.org/10.1186/s13104-018-3802-y
  27. Wylie, K. M., Blankenship, S. A., Tuuli, M. G., Macones, G. A., & Stout, M. J. (2018). Evaluation of patient- versus provider-collected vaginal swabs for microbiome analysis during pregnancy. BMC Research Notes, 11(1). https://doi.org/10.1186/s13104-018-3809-4
  28. Johnson, E. C. B., Dammer, E. B., Duong, D. M., Yin, L., Thambisetty, M., Troncoso, J. C., … Seyfried, N. T. (2018). Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease. Molecular Neurodegeneration, 13(1). https://doi.org/10.1186/s13024-018-0282-4
  29. Haddaway, N. R., & Westgate, M. J. (2018). Predicting the time needed for environmental systematic reviews and systematic maps. Conservation Biology. https://doi.org/10.1111/cobi.1323
  30. Kollar, B., Shubin, A., Borges, T. J., Tasigiorgos, S., Win, T. S., Lian, C. G., … Riella, L. V. (2018). Increased levels of circulating MMP3 correlate with severe rejection in face transplantation. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-33272-7
  31. Rahman, R., Ung, P. M.-U., & Schlessinger, A. (2018). KinaMetrix: a web resource to investigate kinase conformations and inhibitor space. Nucleic Acids Research. https://doi.org/10.1093/nar/gky916
  32. Horvatić, A., Guillemin, N., Kaab, H., McKeegan, D., O’Reilly, E., Bain, M., … Eckersall, P. D. (2018). Integrated dataset on acute phase protein response in chicken challenged with Escherichia coli lipopolysaccharide endotoxin. Data in Brief. https://doi.org/10.1016/j.dib.2018.09.103
  33. Barra, M., Labberton, A. S., Faiz, K. W., Lindstrøm, J. C., Rønning, O. M., Viana, J., … Rand, K. (2018). Stroke incidence in the young: evidence from a Norwegian register study. Journal of Neurology. https://doi.org/10.1007/s00415-018-9102-6
  34. Orwoll, E. S., Fino, N. F., Gill, T. M., Cauley, J. A., Strotmeyer, E. S., … Ensrud, K. E. (2018). The relationships between physical performance, activity levels and falls in older men. The Journals of Gerontology: Series A. https://doi.org/10.1093/gerona/gly248
  35. Lynd, A., Oruni, A., van’t Hof, A. E., Morgan, J. C., Naego, L. B., Pipini, D., … Weetman, D. (2018). Insecticide resistance in Anopheles gambiae from the northern Democratic Republic of Congo, with extreme knockdown resistance (kdr) mutation frequencies revealed by a new diagnostic assay. Malaria Journal, 17(1). https://doi.org/10.1186/s12936-018-2561-5
  36. Soul, J., Hardingham, T., Boot-Handford, R., & Schwartz, J. M. (2018). SkeletalVis: An exploration and meta-analysis data portal of cross-species skeletal transcriptomics data. Bioinformatics. https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/bty947/26770069/bty947.pdf
  37. Kline, B. P., Schieffer, K. M., Choi, C. S., Connelly, T., Chen, J., Harris, L., … Koltun, W. A. (2018). Multifocal Versus Conventional Unifocal Diverticulitis: A Comparison of Clinical and Transcriptomic Characteristics. Digestive Diseases and Sciences. https://doi.org/10.1007/s10620-018-5403-y
  38. Rehbach, F., Stork, J., & Bartz-Beielstein, T. (2018). Bridging Theory and Practice Through Modular Graphical User Interfaces. Journal of Multimedia Processing and Technologies, 9(4), 134. https://doi.org/10.6025/jmpt/2018/9/4/134-140
  39. Singer, R. A., Love, K. J., & Page, L. M. (2018). A survey of digitized data from U.S. fish collections in the iDigBio data aggregator. PLOS ONE, 13(12), e0207636. https://doi.org/10.1371/journal.pone.0207636
  40. Duan, J., Wei Shi, Nathaniel K Jue, Zongliang Jiang, Lynn Kuo, Rachel O’Neill, Eckhard Wolf, Hong Dong, Xinbao Zheng, Jingbo Chen, Xiuchun (Cindy) Tian. 2018. Dosage Compensation of the X Chromosomes in Bovine Germline Early Embryos and Somatic Tissues. Genome Biology and Evolution. https://academic.oup.com/gbe/advance-article/doi/10.1093/gbe/evy270/5253178
  41. Shen, Z., & Spruit, M. (2019). A Systematic Review of Open Source Clinical Software on GitHub for Improving Software Reuse in Smart Healthcare. Applied Sciences, 9(1), 150. https://www.mdpi.com/2076-3417/9/1/150/pdf
  42. Łącki, M. K., Lermyte, F., Miasojedow, B., Startek, M. P., Sobott, F., Valkenborg, D., & Gambin, A. (2019). masstodon: A tool for assigning peaks and modeling electron transfer reactions in top-down mass spectrometry. Analytical Chemistry. https://doi.org/10.1021/acs.analchem.8b01479
  43. SHANG, D., & GHRIGA, M. (2018). EXPLORING SOCIAL MEDIA ANALYTICS ON COMMUNITY DEVELOPMENT PRACTICES. Journal of Information Technology Management, 29(4), 39. http://jitm.ubalt.edu/XXIX-4/article3.pdf
  44. Waltz, F., Nguyen, T.-T., Arrivé, M., Bochler, A., Chicher, J., Hammann, P., … Giegé, P. (2019). Small is big in Arabidopsis mitochondrial ribosome. Nature Plants, 5(1), 106–117. https://doi.org/10.1038/s41477-018-0339-y
  45. Hofmann, A., Cross, M., Karow, M. A., Straub, J. H., Clemen, C. S., & Eichinger, L. (2019). A convenient tool for bivariate data analysis and bar graph plotting with R. Biochemistry and Molecular Biology Education. https://doi.org/10.1002/bmb.21205
  46. Sellgren, C. M., Gracias, J., Jungholm, O., Perlis, R. H., Engberg, G., Schwieler, L., … Erhardt, S. (2019). Peripheral and central levels of kynurenic acid in bipolar disorder subjects and healthy controls. Translational Psychiatry, 9(1). https://doi.org/10.1038/s41398-019-0378-9
  47. Jovanović, G., Romanić, S. H., Stojić, A., Klinčić, D., Sarić, M. M., Letinić, J. G., & Popović, A. (2019). Introducing of modeling techniques in the research of POPs in breast milk – A pilot study. Ecotoxicology and Environmental Safety, 172, 341–347. https://doi.org/10.1016/j.ecoenv.2019.01.087
  48. Kay, S., Graves, A., Palma, J. H. N., Moreno, G., Roces-Díaz, J. V., Aviron, S., … Herzog, F. (2019). Agroforestry is paying off – Economic evaluation of ecosystem services in European landscapes with and without agroforestry systems. Ecosystem Services, 36, 100896. https://doi.org/10.1016/j.ecoser.2019.100896
  49. Martins, J., Magalhaes, C., Vieira, V., Rocha, M., & Osorio, N. S. (2019). HABIT - a webserver for interactive T cell neoepitope discovery. https://doi.org/10.1101/535716
  50. Nadal-Ribelles, M., Islam, S., Wei, W., Latorre, P., Nguyen, M., de Nadal, E., … Steinmetz, L. M. (2019). Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations. Nature Microbiology. https://doi.org/10.1038/s41564-018-0346-9
  51. Joish, V. N., Shah, S., Tierce, J. C., Patel, D., McKee, C., Lapuerta, P., & Zacks, J. (2019). Serotonin levels and 1-year mortality in patients with neuroendocrine tumors: a systematic review and meta-analysis. Future Oncology https://doi.org/10.2217/fon-2018-0960
  52. Wheeler, D. L., Scott, J., Dung, J. K. S., & Johnson, D. A. (2019). Evidence of a trans-kingdom plant disease complex between a fungus and plant-parasitic nematodes. PLOS ONE, 14(2), e0211508. https://doi.org/10.1371/journal.pone.0211508
  53. Aiello, M., Terenzi, D., Furlanis, G., Catalan, M., Manganotti, P., Eleopra, R., … Rumiati, R. I. (2019). Deep brain stimulation of the subthalamic nucleus and the temporal discounting of primary and secondary rewards. Journal of Neurology. https://doi.org/10.1007/s00415-019-09240-0
  54. Michalak, W., Tsiamis, V., Schwämmle, V., & Rogowska-Wrzesińska, A. (2019). ComplexBrowser: a tool for identification and quantification of protein complexes in large scale proteomics datasets. https://doi.org/10.1101/573774
  55. Su, W., Sun, J., Shimizu, K., & Kadota, K. (2019). TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data. BMC Research Notes, 12(1). https://doi.org/10.1186/s13104-019-4179-2
  56. Ravenhall, M., Campino, S., & Clark, T. G. (2019). SV-Pop: population-based structural variant analysis and visualization. BMC Bioinformatics, 20(1). https://doi.org/10.1186/s12859-019-2718-4
  57. Kelly, M. J., So, J., Rogers, A. J., Gregory, G., Li, J., Zethoven, M., … Kats, L. M. (2019). Bcor loss perturbs myeloid differentiation and promotes leukaemogenesis. Nature Communications, 10(1). https://doi.org/10.1038/s41467-019-09250-6
  58. Chakroborty, D., Kurppa, K. J., Paatero, I., Ojala, V. K., Koivu, M., Tamirat, M. Z., … & Elenius, K. (2019). An unbiased in vitro screen for activating epidermal growth factor receptor mutations. Journal of Biological Chemistry, jbc-RA118. http://www.jbc.org/content/early/2019/04/05/jbc.RA118.006336
  59. Campbell, M. (2019). Learn RStudio IDE. https://doi.org/10.1007/978-1-4842-4511-8
  60. Seyednasrollah, B., Milliman, T., & Richardson, A. D. (2019). Data extraction from digital repeat photography using xROI: An interactive framework to facilitate the process. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 132–144. https://doi.org/10.1016/j.isprsjprs.2019.04.009
  61. Van Strien, M. J., Huber, S. H., Anderies, J. M., & Grêt-Regamey, A. (2019). Resilience in social-ecological systems: identifying stable and unstable equilibria with agent-based models. Ecology and Society, 24(2). https://doi.org/10.5751/es-10899-240208
  62. Piccione, P. M., Baumeister, J., Salvesen, T., Grosjean, C., Flores, Y., Groelly, E., … Lothschütz, C. (2019). Solvent Selection Methods and Tool. Organic Process Research & Development, 23(5), 998–1016. https://doi.org/10.1021/acs.oprd.9b00065
  63. Łagód, G., Duda, S. M., Majerek, D., Szutt, A., & Dołhańczuk-Śródka, A. (2019). Application of Electronic Nose for Evaluation of Wastewater Treatment Process Effects at Full-Scale WWTP. Processes, 7(5), 251. https://doi.org/10.3390/pr7050251
  64. Kirsch, S. A., & Böckmann, R. A. (2019). Coupling of Membrane Nanodomain Formation and Enhanced Electroporation near Phase Transition. Biophysical Journal. https://doi.org/10.1016/j.bpj.2019.04.024
  65. Germon, A., Jourdan, C., Bordron, B., Robin, A., Nouvellon, Y., Chapuis-Lardy, L., … Laclau, J.-P. (2019). Consequences of clear-cutting and drought on fine root dynamics down to 17 m in coppice-managed eucalypt plantations. Forest Ecology and Management, 445, 48–59. https://doi.org/10.1016/j.foreco.2019.05.010
  66. Best, B. D., & Halpin, P. N. (2019). Minimizing wildlife impacts for offshore wind energy development: Winning tradeoffs for seabirds in space and cetaceans in time. PLOS ONE, 14(5), e0215722. https://doi.org/10.1371/journal.pone.0215722
  67. Ogłuszka, M., Orzechowska, M., Jędroszka, D., Witas, P., & Bednarek, A. K. (2019). Evaluate Cutpoints: Adaptable continuous data distribution system for determining survival in Kaplan-Meier estimator. Computer Methods and Programs in Biomedicine, 177, 133–139. https://doi.org/10.1016/j.cmpb.2019.05.023
  68. Kortz, A. R., & Magurran, A. E. (2019). Increases in local richness (α-diversity) following invasion are offset by biotic homogenization in a biodiversity hotspot. Biology Letters, 15(5), 20190133. https://doi.org/10.1098/rsbl.2019.0133
  69. Pérez-Palma, E., Gramm, M., Nürnberg, P., May, P., & Lal, D. (2019). Simple ClinVar: an interactive web server to explore and retrieve gene and disease variants aggregated in ClinVar database. Nucleic Acids Research. https://doi.org/10.1093/nar/gkz411
  70. Aspillaga, E., Safi, K., Hereu, B., & Bartumeus, F. (2019). Modelling the three‐dimensional space use of aquatic animals combining topography and Eulerian telemetry data. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13232
  71. Gibson, K. M., Nguyen, B. N., Neumann, L. M., Miller, M., Buss, P., Daniels, S., … Pukazhenthi, B. (2019). Gut microbiome differences between wild and captive black rhinoceros – implications for rhino health. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-43875-3
  72. Wright, R. J., Gibson, M. I., & Christie-Oleza, J. A. (2019). Understanding microbial community dynamics to improve optimal microbiome selection. Microbiome, 7(1). https://doi.org/10.1186/s40168-019-0702-x
  73. Bailey, L. D., Ens, B. J., Both, C., Heg, D., Oosterbeek, K., & van de Pol, M. (2019). Habitat selection can reduce effects of extreme climatic events in a long‐lived shorebird. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.13041
  74. Nourbakhsh, M., Mansoor, A., Koro, K., Zhang, Q., & Minoo, P. (2019). Expression Profiling Reveals Involvement of WNT Pathway in the Malignant Progression of Sessile Serrated Adenomas. The American Journal of Pathology. https://doi.org/10.1016/j.ajpath.2019.05.009
  75. Glicksberg, B. S., Oskotsky, B., Thangaraj, P. M., Giangreco, N., Badgeley, M. A., Johnson, K. W., … Butte, A. J. (2019). PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model. Bioinformatics. https://doi.org/10.1093/bioinformatics/btz409
  76. Xia, Liu, Zhang, & Guo. (2019). GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins. Cells, 8(7), 675. https://doi.org/10.3390/cells8070675
  77. Carlström, K. E., Ewing, E., Granqvist, M., Gyllenberg, A., Aeinehband, S., Enoksson, S. L., … Piehl, F. (2019). Therapeutic efficacy of dimethyl fumarate in relapsing-remitting multiple sclerosis associates with ROS pathway in monocytes. Nature Communications, 10(1). https://doi.org/10.1038/s41467-019-11139-3
  78. Dag, O., Karabulut, E., & Alpar, R. (2019). GMDH2: Binary Classification via GMDH-Type Neural Network Algorithms—R Package and Web-Based Tool. International Journal of Computational Intelligence Systems, 12(2), 649. https://doi.org/10.2991/ijcis.d.190618.001
  79. Abhilash, L., & Sheeba, V. (2019). RhythmicAlly: Your R and Shiny–Based Open-Source Ally for the Analysis of Biological Rhythms. Journal of Biological Rhythms, 074873041986247. https://doi.org/10.1177/0748730419862474
  80. Van der Veer, C., Bruisten, S. M., van Houdt, R., Matser, A. A., Tachedjian, G., van de Wijgert, J. H. H. M., … van der Helm, J. J. (2019). Effects of an over-the-counter lactic-acid containing intra-vaginal douching product on the vaginal microbiota. BMC Microbiology, 19(1). https://doi.org/10.1186/s12866-019-1545-0
  81. Brionne, A., Juanchich, A., & Hennequet-Antier, C. (2019). ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity. BioData Mining, 12(1). https://doi.org/10.1186/s13040-019-0204-1
  82. Stewart, D. B., Wright, J. R., Fowler, M., McLimans, C. J., Tokarev, V., Amaniera, I., … Lamendella, R. (2019). Integrated Meta-omics Reveals a Fungus-Associated Bacteriome and Distinct Functional Pathways in Clostridioides difficile Infection. mSphere, 4(4). https://doi.org/10.1128/msphere.00454-19
  83. Lewis, M. J., Barnes, M. R., Blighe, K., Goldmann, K., Rana, S., Hackney, J. A., … Pitzalis, C. (2019). Molecular Portraits of Early Rheumatoid Arthritis Identify Clinical and Treatment Response Phenotypes. Cell Reports, 28(9), 2455–2470.e5. https://doi.org/10.1016/j.celrep.2019.07.091
  84. Gonçalves, B., Coutinho, D., Exel, J., Travassos, B., Lago, C., & Sampaio, J. (2019). Extracting spatial-temporal features that describe a team match demands when considering the effects of the quality of opposition in elite football. PLOS ONE, 14(8), e0221368. https://doi.org/10.1371/journal.pone.0221368
  85. Scharmüller, A., Schreiner, V. C., & Schäfer, R. B. (2020). Standartox: Standardizing Toxicity Data. Data, 5(2), 46. https://doi.org/10.3390/data5020046
View Documentation

Interface to the Open Tree of Life API

Francois Michonneau
Description

An interface to the Open Tree of Life API to retrieve phylogenetic trees, information about studies used to assemble the synthetic tree, and utilities to match taxonomic names to ‘Open Tree identifiers. The Open Tree of Life’ aims at assembling a comprehensive phylogenetic tree for all named species.

Scientific use cases
  1. Michonneau, F., Brown, J. W., & Winter, D. J. (2016). rotl: an R package to interact with the Open Tree of Life data. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.12593
  2. Killen, S. S., Norin, T., & Halsey, L. G. (2016). Do method and species lifestyle affect measures of maximum metabolic rate in fishes? Journal of Fish Biology. https://doi.org/10.1111/jfb.13195
  3. Estrada-Peña, A., & de la Fuente, J. (2016). Species interactions in occurrence data for a community of tick-transmitted pathogens. Scientific Data, 3, 160056. https://doi.org/10.1038/sdata.2016.56
  4. Matthews, A. E., Klimov, P. B., Proctor, H. C., Dowling, A. P. G., Diener, L., Hager, S. B., … Boves, T. J. (2017). Cophylogenetic assessment of New World warblers (Parulidae) and their symbiotic feather mites (Proctophyllodidae). Journal of Avian Biology. https://doi.org/10.1111/jav.01580
  5. Santorelli, S., Magnusson, W. E., & Deus, C. P. (2018). Most species are not limited by an Amazonian river postulated to be a border between endemism areas. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-20596-7
  6. Farquharson, K. A., Hogg, C. J., & Grueber, C. E. (2018). A meta-analysis of birth-origin effects on reproduction in diverse captive environments. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-03500-9
  7. Portugal, S. J., & White, C. R. (2018). Miniaturisation of biologgers is not alleviating the 5% rule. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13013
  8. Barneche, D. R., Robertson, D. R., White, C. R., & Marshall, D. J. (2018). Fish reproductive-energy output increases disproportionately with body size. Science, 360(6389), 642–645. https://doi.org/10.1126/science.aao6868
  9. Morais, R. A., & Bellwood, D. R. (2018). Global drivers of reef fish growth. Fish and Fisheries. https://doi.org/10.1111/faf.12297
  10. Gastauer, M., Caldeira, C. F., Trotter, I., Ramos, S. J., & Neto, J. A. A. M. (2018). Optimizing community trees using the open tree of life increases the reliability of phylogenetic diversity and dispersion indices. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2018.06.008
  11. Paseka, R. E., & Grunberg, R. L. (2018). Allometric and trait-based patterns in parasite stoichiometry. Oikos. https://doi.org/10.1111/oik.05339
  12. Barneche, D. R., Burgess, S. C., & Marshall, D. J. (2018). Global environmental drivers of marine fish egg size. Global Ecology and Biogeography, 27(8), 890–898. https://doi.org/10.1111/geb.12748
  13. Merkling, T., Nakagawa, S., Lagisz, M., & Schwanz, L. E. (2017). Maternal Testosterone and Offspring Sex-Ratio in Birds and Mammals: A Meta-Analysis. Evolutionary Biology, 45(1), 96–104. https://doi.org/10.1007/s11692-017-9432-9
  14. Becker, D., Czirják, G., Rynda-Apple, A., & Plowright, R. (2018). Handling stress and sample storage are associated with weaker complement-mediated bactericidal ability in birds but not bats. Physiological and Biochemical Zoology. https://doi.org/10.1086/701069
  15. O’Dea, R. E., Lagisz, M., Hendry, A. P., & Nakagawa, S. (2018). Developmental temperature affects phenotypic means and variability: a meta-analysis of fish data. https://doi.org/10.32942/osf.io/ge7f8
  16. Tresch, S., Frey, D., Le Bayon, R.-C., Zanetta, A., Rasche, F., Fliessbach, A., & Moretti, M. (2018). Litter decomposition driven by soil fauna, plant diversity and soil management in urban gardens. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2018.12.235
  17. Green, D. M. (2019). Rarity of Size-Assortative Mating in Animals: Assessing the Evidence with Anuran Amphibians. The American Naturalist, 193(2) https://www.journals.uchicago.edu/doi/abs/10.1086/701124
  18. Mathot, K. J., Dingemanse, N. J., & Nakagawa, S. (2018). The covariance between metabolic rate and behaviour varies across behaviours and thermal types: meta-analytic insights. Biological Reviews. https://doi.org/10.1111/brv.12491
  19. Pettersen, A. K., White, C. R., Bryson-Richardson, R. J., & Marshall, D. J. (2019). Linking life-history theory and metabolic theory explains the offspring size-temperature relationship. Ecology Letters. https://doi.org/10.1111/ele.13213
  20. Halsey, L. G., & White, C. R. (2019). Terrestrial locomotion energy costs vary considerably between species: no evidence that this is explained by rate of leg force production or ecology. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-018-36565-z
  21. Ohmer, M. E. B., Cramp, R. L., White, C. R., Harlow, P. S., McFadden, M. S., Merino-Viteri, A., … Franklin, C. E. (2019). Phylogenetic investigation of skin sloughing rates in frogs: relationships with skin characteristics and disease-driven declines. Proceedings of the Royal Society B: Biological Sciences, 286(1896), 20182378. https://doi.org/10.1098/rspb.2018.2378
  22. Shefferson, R. P., Bunch, W., Cowden, C. C., Lee, Y., Kartzinel, T. R., Yukawa, T., … Jiang, H. (2019). Does evolutionary history determine specificity in broad ecological interactions? Journal of Ecology. https://doi.org/10.1111/1365-2745.13170
  23. Pinto, N. S., Palaoro, A. V., & Peixoto, P. E. C. (2019). All by myself? Meta‐analysis of animal contests shows stronger support for self than for mutual assessment models. Biological Reviews. https://doi.org/10.1111/brv.12509
  24. Kovacevic, A., Latombe, G., & Chown, S. L. (2019). Rate dynamics of ectotherm responses to thermal stress. Proceedings of the Royal Society B: Biological Sciences, 286(1902), 20190174. https://doi.org/10.1098/rspb.2019.0174
  25. Mihalitsis, M., & Bellwood, D. R. (2019). Morphological and functional diversity of piscivorous fishes on coral reefs. Coral Reefs. https://doi.org/10.1007/s00338-019-01820-w
  26. Tetzlaff, S. J., Sperry, J. H., & DeGregorio, B. A. (2019). Effects of antipredator training, environmental enrichment, and soft release on wildlife translocations: A review and meta-analysis. Biological Conservation, 236, 324–331. https://doi.org/10.1016/j.biocon.2019.05.054
  27. McTavish, E. J. (2019). Linking Biodiversity Data Using Evolutionary History. Biodiversity Information Science and Standards, 3. https://doi.org/10.3897/biss.3.36207
  28. Peters, A., Delhey, K., Nakagawa, S., Aulsebrook, A., & Verhulst, S. (2019). Immunosenescence in wild animals: meta‐analysis and outlook. Ecology Letters. https://doi.org/10.1111/ele.13343
  29. Park, A. W. (2019). Food web structure selects for parasite host range. Proceedings of the Royal Society B: Biological Sciences, 286(1908), 20191277. https://doi.org/10.1098/rspb.2019.1277
  30. Mihalitsis, M., & Bellwood, D. (2019). Functional implications of dentition-based morphotypes in piscivorous fishes. Royal Society Open Science, 6(9), 190040. https://doi.org/10.1098/rsos.190040
  31. Sánchez-Tójar, A., Moran, N. P., O’Dea, R. E., Reinhold, K., & Nakagawa, S. (2019). Illustrating the importance of meta-analysing variances alongside means in ecology and evolution. https://doi.org/10.32942/osf.io/yhfvk
  32. Li, X., Zhu, H., Geisen, S., Bellard, C., Hu, F., Li, H., … Liu, M. (2019). Agriculture erases climate constraints on soil nematode communities across large spatial scales. Global Change Biology. https://doi.org/10.1111/gcb.14821
  33. Maherali, H. (2019). Mutualism as a plant functional trait: linking variation in the mycorrhizal symbiosis to climatic tolerance, geographic range and population dynamics. International Journal of Plant Sciences. https://doi.org/10.1086/706187
  34. Defolie, C., Merkling, T., & Fichtel, C. (2019). Patterns and variation in the mammal parasite–glucorticoid relationship. Biological Reviews. https://doi.org/10.1111/brv.12555
  35. Estrada-Peña, A., Nava, S., Tarragona, E., Bermúdez, S., de la Fuente, J., Domingos, A., … Guglielmone, A. A. (2019). Species occurrence of ticks in South America, and interactions with biotic and abiotic traits. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0314-0
  36. Godfrey, J. M., Riggio, J., Orozco, J., Guzmán‐Delgado, P., Chin, A. R. O., & Zwieniecki, M. A. (2020). Ray fractions and carbohydrate dynamics of tree species along a 2750 m elevation gradient indicate climate response, not spatial storage limitation. New Phytologist, 225(6), 2314–2330. https://doi.org/10.1111/nph.16361
  37. Clark, T. J., & Luis, A. D. (2019). Nonlinear population dynamics are ubiquitous in animals. Nature Ecology & Evolution, 4(1), 75–81. https://doi.org/10.1038/s41559-019-1052-6
  38. Shan, S., Soltis, P. S., Soltis, D. E., & Yang, B. (2020). Considerations in adapting CRISPR/Cas9 in nongenetic model plant systems. Applications in Plant Sciences, 8(1). https://doi.org/10.1002/aps3.11314
  39. Horne, C. R., Hirst, A. G., & Atkinson, D. (2020). Selection for increased male size predicts variation in sexual size dimorphism among fish species. Proceedings of the Royal Society B: Biological Sciences, 287(1918), 20192640. https://doi.org/10.1098/rspb.2019.2640
  40. Walczyńska, A., Gudowska, A., & Sobczyk, Ł. (2020). Should I shrink or should I flow? – body size adjustment to thermo-oxygenic niche. https://doi.org/10.1101/2020.01.14.905901
  41. Gomez Isaza, D. F., Cramp, R. L., & Franklin, C. E. (2020). Living in polluted waters: A meta-analysis of the effects of nitrate and interactions with other environmental stressors on freshwater taxa. Environmental Pollution, 114091. https://doi.org/10.1016/j.envpol.2020.114091
  42. Finoshin, A. D., Adameyko, K. I., Mikhailov, K. V., Kravchuk, O. I., Georgiev, A. A., Gornostaev, N. G., … Lyupina, Y. V. (2020). Iron metabolic pathways in the processes of sponge plasticity. PLOS ONE, 15(2), e0228722. https://doi.org/10.1371/journal.pone.0228722
  43. Jhwueng, D.-C., & O’Meara, B. C. (2020). On the Matrix Condition of Phylogenetic Tree. Evolutionary Bioinformatics, 16, 117693432090172. https://doi.org/10.1177/1176934320901721
  44. Perez‐Lamarque, B., Selosse, M., Öpik, M., Morlon, H., & Martos, F. (2020). Cheating in arbuscular mycorrhizal mutualism: a network and phylogenetic analysis of mycoheterotrophy. New Phytologist. https://doi.org/10.1111/nph.16474
  45. Marshall, D. J., Pettersen, A. K., Bode, M., & White, C. R. (2020). Developmental cost theory predicts thermal environment and vulnerability to global warming. Nature Ecology & Evolution, 4(3), 406–411. https://doi.org/10.1038/s41559-020-1114-9
  46. Wei, N., Kaczorowski, R. L., Arceo-Gómez, G., O’Neill, E. M., Hayes, R. A., & Ashman, T.-L. (2020). Pollinator niche partitioning and asymmetric facilitation contribute to the maintenance of diversity. https://doi.org/10.1101/2020.03.02.974022
  47. Allen, D., & Kim, A. Y. (2020). A permutation test and spatial cross-validation approach to assess models of interspecific competition between trees. PLOS ONE, 15(3), e0229930. https://doi.org/10.1371/journal.pone.0229930
  48. Moran, N. P., Sánchez-Tójar, A., Schielzeth, H., & Reinhold, K. (2020). Poor condition promotes high-risk behaviours but context-dependency is key: A systematic review and meta-analysis. Ecorxiv preprint. https://ecoevorxiv.org/xsehd/
  49. Lindner, M., Gilhooley, M. J., Palumaa, T., Morton, A. J., Hughes, S., & Hankins, M. W. (2020). Expression and Localization of Kcne2 in the Vertebrate Retina. Investigative Opthalmology & Visual Science, 61(3), 33. https://doi.org/10.1167/iovs.61.3.33
  50. Cui, X., Paterson, A. M., Wyse, S. V., Alam, M. A., Maurin, K. J. L., Pieper, R., … Curran, T. J. (2020). Shoot flammability of vascular plants is phylogenetically conserved and related to habitat fire-proneness and growth form. Nature Plants, 6(4), 355–359. https://doi.org/10.1038/s41477-020-0635-1
  51. Morand, S., Chaisiri, K., Kritiyakan, A., & Kumlert, R. (2020). Disease Ecology of Rickettsial Species: A Data Science Approach. Tropical Medicine and Infectious Disease, 5(2), 64. https://doi.org/10.3390/tropicalmed5020064
  52. Bubac, C. M., Miller, J. M., & Coltman, D. W. (2020). The genetic basis of animal behavioural diversity in natural populations. Molecular Ecology, 29(11), 1957–1971. https://doi.org/10.1111/mec.15461
  53. Crowley, D., Becker, D., Washburne, A., & Plowright, R. (2020). Identifying Suspect Bat Reservoirs of Emerging Infections. Vaccines, 8(2), 228. https://doi.org/10.3390/vaccines8020228
  54. Estrada-Peña, A., Nava, S., Tarragona, E., de la Fuente, J., & Guglielmone, A. A. (2020). A community approach to the Neotropical ticks-hosts interactions. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-66400-3
  55. Burda, P.-C., Crosskey, T., Lauk, K., Zurborg, A., Söhnchen, C., Liffner, B., … Gilberger, T.-W. (2020). Structure-Based Identification and Functional Characterization of a Lipocalin in the Malaria Parasite Plasmodium falciparum. Cell Reports, 31(12), 107817. https://doi.org/10.1016/j.celrep.2020.107817
  56. Álvarez-Noriega, M., Burgess, S. C., Byers, J. E., Pringle, J. M., Wares, J. P., & Marshall, D. J. (2020). Global biogeography of marine dispersal potential. Nature Ecology & Evolution, 4(9), 1196–1203. https://doi.org/10.1038/s41559-020-1238-y
View Documentation

Model Comparison Using babette

Richèl J.C. Bilderbeek
Description

BEAST2 (https://www.beast2.org) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. mcbette allows to do a Bayesian model comparison over some site and clock models, using babette (https://www.github.com/ropensci/babette/).

View Documentation

Group Animal Relocation Data by Spatial and Temporal Relationship

Alec L. Robitaille
Description

Detects spatial and temporal groups in GPS relocations (Robitaille et al. (2020) doi:10.1111/2041-210X.13215). It can be used to convert GPS relocations to gambit-of-the-group format to build proximity-based social networks In addition, the randomizations function provides data-stream randomization methods suitable for GPS data.

Scientific use cases
  1. Robitaille, A. L., Webber, Q. M. R., & Vander Wal, E. (2018). Conducting social network analysis with animal telemetry data: applications and methods using spatsoc. https://doi.org/10.1101/447284
  2. Webber, Q. M. R., & Vander Wal, E. (2019). Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach. Animal Behaviour, 149, 77–87. https://doi.org/10.1016/j.anbehav.2019.01.010
  3. Peignier, M., Webber, Q. M. R., Koen, E. L., Laforge, M. P., Robitaille, A. L., & Vander Wal, E. (2019). Space use and social association in a gregarious ungulate: Testing the conspecific attraction and resource dispersion hypotheses. Ecology and Evolution. https://doi.org/10.1002/ece3.5071
  4. Gilbertson, M. L. J., White, L. A., & Craft, M. E. (2020). Trade‐offs with telemetry‐derived contact networks for infectious disease studies in wildlife. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13355
View Documentation

Casts (R)Markdown files to XML and back

Maëlle Salmon
Description

Casts (R)Markdown files to XML and back to allow their editing via XPat.

View Documentation

Bindings to OpenCV Computer Vision Library

Jeroen Ooms
Description

Experimenting with computer vision and machine learning in R. This package exposes some of the available OpenCV https://opencv.org/ algorithms, such as edge, body or face detection. These can either be applied to analyze static images, or to filter live video footage from a camera device.

View Documentation

Functions to Automate Downloading Geospatial Data Available from Several Federated Data Sources

R. Kyle Bocinsky
Description

Functions to automate downloading geospatial data available from several federated data sources (mainly sources maintained by the US Federal government). Currently, the package enables extraction from seven datasets: The National Elevation Dataset digital elevation models (1 and 1/3 arc-second; USGS); The National Hydrography Dataset (USGS); The Soil Survey Geographic (SSURGO) database from the National Cooperative Soil Survey (NCSS), which is led by the Natural Resources Conservation Service (NRCS) under the USDA; the Global Historical Climatology Network (GHCN), coordinated by National Climatic Data Center at NOAA; the Daymet gridded estimates of daily weather parameters for North America, version 3, available from the Oak Ridge National Laboratory’s Distributed Active Archive Center (DAAC); the International Tree Ring Data Bank; and the National Land Cover Database (NLCD).

Scientific use cases
  1. McAfee, S. A., McCabe, G. J., Gray, S. T., & Pederson, G. T. (2018). Changing station coverage impacts temperature trends in the Upper Colorado River Basin. International Journal of Climatology. https://doi.org/10.1002/joc.5898
  2. Medury, A., Griswold, J. B., Huang, L., & Grembek, O. (2019). Pedestrian Count Expansion Methods: Bridging the Gap between Land Use Groups and Empirical Clusters. Transportation Research Record: Journal of the Transportation Research Board, 036119811983826. https://doi.org/10.1177/0361198119838266
  3. Meisner, J., Clifford, W. R., Wohrle, R. D., Kangiser, D., & Rabinowitz, P. (2019). Soil and climactic predictors of canine coccidioidomycosis seroprevalence in Washington State: an ecological cross‐sectional study. Transboundary and Emerging Diseases. https://doi.org/10.1111/tbed.13265
  4. Saadi, M., Oudin, L., & Ribstein, P. (2019). Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters. Water, 11(8), 1540. https://doi.org/10.3390/w11081540
  5. Martinez-Feria, R. A., & Basso, B. (2020). Unstable crop yields reveal opportunities for site-specific adaptations to climate variability. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-59494-2
View Documentation

Automatic Package Testing

Mark Padgham
Description

Automatic testing of R packages via a simple YAML schema.

View Documentation

Taxonomic Information from Around the Web

Scott Chamberlain
Description

Interacts with a suite of web APIs for taxonomic tasks, such as getting database specific taxonomic identifiers, verifying species names, getting taxonomic hierarchies, fetching downstream and upstream taxonomic names, getting taxonomic synonyms, converting scientific to common names and vice versa, and more.

Scientific use cases
  1. Baden, H. M., Särkinen, T., Conde, D. A., Matthews, A. C., Vandrot, H., Chicas, S., Harris, D. J. (2015). A botanical inventory of forest on karstic limestone and metamorphic substrate in the Chiquibul Forest, Belize, with focus on woody taxa. Edinburgh Journal of Botany, 73(01), 39–81. https://doi.org/10.1017/s0960428615000256
  2. Vanden Berghe, E., Coro, G., Bailly, N., Fiorellato, F., Aldemita, C., Ellenbroek, A., & Pagano, P. (2015). Retrieving taxa names from large biodiversity data collections using a flexible matching workflow. Ecological Informatics, 28, 29–41. https://doi.org/10.1016/j.ecoinf.2015.05.004
  3. Bocci, G. (2015). TR8: an R package for easily retrieving plant species traits. Methods in Ecology and Evolution, 6(3), 347–350. https://doi.org/10.1111/2041-210x.12327
  4. Bradie, J., Pietrobon, A., & Leung, B. (2015). Beyond species-specific assessments: an analysis and validation of environmental distance metrics for non-indigenous species risk assessment. Biological Invasions, 17(12), 3455–3465. https://doi.org/10.1007/s10530-015-0970-8
  5. Dodd, A. J., Burgman, M. A., McCarthy, M. A., & Ainsworth, N. (2015). The changing patterns of plant naturalization in Australia. Diversity Distrib., 21(9), 1038–1050. https://doi.org/10.1111/ddi.12351
  6. Drozd, P., & Šipoš, J. (2013). R for all (I): Introduction to the new age of biological analyses. Casopis Slezskeho Zemskeho Muzea A, 62(1). https://doi.org/10.2478/cszma-2013-0004
  7. Chamberlain, S. A., & Szöcs, E. (2013). taxize: taxonomic search and retrieval in R. F1000Research, 2, 191. https://doi.org/10.12688/f1000research.2-191.v1
  8. Hodgins, K. A., Bock, D. G., Hahn, M. A., Heredia, S. M., Turner, K. G., & Rieseberg, L. H. (2015). Comparative genomics in the Asteraceae reveals little evidence for parallel evolutionary change in invasive taxa. Mol Ecol, 24(9), 2226–2240. https://doi.org/10.1111/mec.13026
  9. Lapatas, V., Stefanidakis, M., Jimenez, R. C., Via, A., & Schneider, M. V. (2015). Data integration in biological research: an overview. J of Biol Res-Thessaloniki, 22(1). https://doi.org/10.1186/s40709-015-0032-5
  10. Niedballa, J., Sollmann, R., Courtiol, A., & Wilting, A. (2016). camtrapR: an R package for efficient camera trap data management. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.12600
  11. Ningthoujam, S. S., Choudhury, M. D., Potsangbam, K. S., Chetia, P., Nahar, L., Sarker, S. D., … Talukdar, A. D. (2014). NoSQL Data Model for Semi-automatic Integration of Ethnomedicinal Plant Data from Multiple Sources. Phytochemical Analysis, 25(6), 495–507. https://doi.org/10.1002/pca.2520
  12. Pérez-Luque, A. J., Barea-Azcón, J. M., Álvarez-Ruiz, L., Bonet-García, F. J., & Zamora, R. (2016). Dataset of Passerine bird communities in a Mediterranean high mountain (Sierra Nevada, Spain). ZK, 552, 137–154. https://doi.org/10.3897/zookeys.552.6934
  13. Poisot, T. (2015). Best publishing practices to improve user confidence in scientific software. IEE, 8. https://doi.org/10.4033/iee.2015.8.8.f
  14. Pos, E., Guevara Andino, J. E., Sabatier, D., Molino, J.-F., Pitman, N., Mogollón, H., … ter Steege, H. (2014). Are all species necessary to reveal ecologically important patterns? Ecology and Evolution, 4(24), 4626–4636. https://doi.org/10.1002/ece3.1246
  15. Bachelot, B., Uriarte, M., Zimmerman, J. K., Thompson, J., Leff, J. W., Asiaii, A., … McGuire, K. (2016). Long-lasting effects of land use history on soil fungal communities in second-growth tropical rain forests. Ecol Appl. https://doi.org/10.1890/15-1397.1
  16. Pérez-Luque, A. J., Sánchez-Rojas, C. P., Zamora, R., Pérez-Pérez, R., & Bonet, F. J. (2015). Dataset of Phenology of Mediterranean high-mountain meadows flora (Sierra Nevada, Spain). PhytoKeys, 46, 89–107. https://doi.org/10.3897/phytokeys.46.9116
  17. Poisot, T., Gravel, D., Leroux, S., Wood, S. A., Fortin, M.-J., Baiser, B., … Stouffer, D. B. (2015). Synthetic datasets and community tools for the rapid testing of ecological hypotheses. Ecography, 39(4), 402–408. https://doi.org/10.1111/ecog.01941
  18. Wagner, F. H., Hérault, B., Bonal, D., Stahl, C., Anderson, L. O., Baker, T. R., … Botosso, P. C. (2016). Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests. Biogeosciences, 13(8), 2537–2562. https://doi.org/10.5194/bg-13-2537-2016
  19. Schwery, O., & O’Meara, B. C. (2016). MonoPhy : a simple R package to find and visualize monophyly issues . PeerJ Computer Science, 2, e56. https://doi.org/10.7717/peerj-cs.56
  20. Bradie, J., & Leung, B. (2016). A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. Journal of Biogeography. https://doi.org/10.1111/jbi.12894
  21. Bufford, J. L., Hulme, P. E., Sikes, B. A., Cooper, J. A., Johnston, P. R., & Duncan, R. P. (2016). Taxonomic similarity, more than contact opportunity, explains novel plant-pathogen associations between native and alien taxa. New Phytol. https://doi.org/10.1111/nph.14077
  22. Cramer, M. D., & Verboom, G. A. (2016). Measures of biologically relevant environmental heterogeneity improve prediction of regional plant species richness. Journal of Biogeography. https://doi.org/10.1111/jbi.12911
  23. Foster, Z. S. L., Sharpton, T., & Grunwald, N. J. (2016). MetacodeR: An R package for manipulation and heat tree visualization of community taxonomic data from metabarcoding. https://doi.org/10.1101/071019
  24. Halse-Gramkow, M., Ernst, M., Rønsted, N., Dunn, R. R., & Saslis-Lagoudakis, C. H. (2016). Using evolutionary tools to search for novel psychoactive plants. Plant Genetic Resources, 1–10. https://doi.org/10.1017/s1479262116000344
  25. Liang, J., Crowther, T. W., Picard, N., Wiser, S., Zhou, M., Alberti, G., et al. (2016). Positive biodiversity-productivity relationship predominant in global forests. Science, 354(6309), aaf8957–aaf8957. https://doi.org/10.1126/science.aaf8957
  26. Nath, C. D., Munoz, F., Pélissier, R., Burslem, D. F. R. P., & Muthusankar, G. (2016). Growth rings in tropical trees: role of functional traits, environment, and phylogeny. Trees. https://doi.org/10.1007/s00468-016-1442-1
  27. Sclavi, B., & Herrick, J. (2016). Genome size variation and species diversity in salamander families. https://doi.org/10.1101/065425
  28. Vincze, O. (2016). Light enough to travel or wise enough to stay? Brain size evolution and migratory behaviour in birds. Evolution. https://doi.org/10.1111/evo.13012
  29. Wagner, V. (2016). A review of software tools for spell-checking taxon names in vegetation databases. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12432
  30. Weber, M. G., Porturas, L. D., & Taylor, S. A. (2016). Foliar nectar enhances plant–mite mutualisms: the effect of leaf sugar on the control of powdery mildew by domatia-inhabiting mites. Annals of Botany, mcw118. https://doi.org/10.1093/aob/mcw118
  31. Wiser, S. K. (2016). Achievements and challenges in the integration, reuse and synthesis of vegetation plot data. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12419
  32. Galata, V., Backes, C., Laczny, C. C., Hemmrich-Stanisak, G., Li, H., Smoot, L., et al. (2016). Comparing genome versus proteome-based identification of clinical bacterial isolates. Briefings in Bioinformatics, bbw122. https://doi.org/10.1093/bib/bbw122
  33. Réjou-Méchain, M., Tanguy, A., Piponiot, C., Chave, J., & Hérault, B. (2017). BIOMASS: An R Package for estimating aboveground biomass and its uncertainty in tropical forests. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.12753
  34. O’Donnell JL, Kelly RP, Shelton AO, Samhouri JF, Lowell NC, Williams GD. (2017) Spatial distribution of environmental DNA in a nearshore marine habitat. PeerJ 5:e3044 https://doi.org/10.7717/peerj.3044
  35. Mohiuddin, M. M., Salama, Y., Schellhorn, H. E., & Golding, G. B. (2017). Shotgun metagenomic sequencing reveals freshwater beach sands as reservoir of bacterial pathogens. Water Research. https://doi.org/10.1016/j.watres.2017.02.057
  36. Andruszkiewicz, E. A., Starks, H. A., Chavez, F. P., Sassoubre, L. M., Block, B. A., & Boehm, A. B. (2017). Biomonitoring of marine vertebrates in Monterey Bay using eDNA metabarcoding. PLOS ONE, 12(4), e0176343. https://doi.org/10.1371/journal.pone.0176343
  37. Olson, N. D., Zook, J. M., Morrow, J. B., & Lin, N. J. (2017). Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data. PeerJ, 5, e3729. https://doi.org/10.7717/peerj.3729
  38. Ordano, M., Blendinger, P. G., Lomáscolo, S. B., Chacoff, N. P., Sánchez, M. S., Núñez Montellano, M. G., … Valoy, M. (2017). The role of trait combination in the conspicuousness of fruit display among bird-dispersed plants. Functional Ecology. https://doi.org/10.1111/1365-2435.12899
  39. Bartomeus, I., Cariveau, D. P., Harrison, T., & Winfree, R. (2017). On the inconsistency of pollinator species traits for predicting either response to land-use change or functional contribution. Oikos. https://doi.org/10.1111/oik.04507
  40. Bartomeus, I., Cariveau, D., Harrison, T., & Winfree, R. (2016). On the inconsistency of pollinator species traits for predicting either response to agricultural intensification or functional contribution. https://doi.org/10.1101/072132
  41. Leung, W. T. M., Thomas-Walters, L., Garner, T. W. J., Balloux, F., Durrant, C., & Price, S. J. (2017). A quantitative-PCR based method to estimate ranavirus viral load following normalisation by reference to an ultraconserved vertebrate target. Journal of Virological Methods. https://doi.org/10.1016/j.jviromet.2017.08.016
  42. Malcolm F. Rosenthal, Matthew Gertler, Angela D. Hamilton, Sonal Prasad, Maydianne C.B. Andrade, Taxonomic bias in animal behaviour publications. Animal Behaviour, Volume 127, 2017, pgs. 83-89. https://doi.org/10.1016/j.anbehav.2017.02.017
  43. Reznik, E., Christodoulou, D., Goldford, J. E., Briars, E., Sauer, U., Segrè, D., & Noor, E. (2017). Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity. Cell Reports, 20(11), 2666–2677. https://doi.org/10.1016/j.celrep.2017.08.066
  44. Power, S. C., Anthony Verboom, G., Bond, W. J., & Cramer, M. D. (2017). Environmental correlates of biome-level floristic turnover in South Africa. Journal of Biogeography. https://doi.org/10.1111/jbi.12971
  45. Branoff, B. L. (2017). Quantifying the influence of urban land use on mangrove biology and ecology: A meta-analysis. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12638
  46. Berlemont, R. (2017). Distribution and diversity of enzymes for polysaccharide degradation in fungi. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-00258-w
  47. Dallas, T., Decker, R. R., & Hastings, A. (2017). Species are not most abundant in the centre of their geographic range or climatic niche. Ecology Letters. https://doi.org/10.1111/ele.12860
  48. Hutchinson, M. C., Cagua, E. F., & Stouffer, D. B. (2017). Cophylogenetic signal is detectable in pollination interactions across ecological scales. Ecology. https://doi.org/10.1002/ecy.1955
  49. Chalmandrier, L., Albouy, C., & Pellissier, L. (2017). Species pool distributions along functional trade-offs shape plant productivity–diversity relationships. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-15334-4
  50. Drost, H.-G., Gabel, A., Liu, J., Quint, M., & Grosse, I. (2017). myTAI: evolutionary transcriptomics with R. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx835
  51. Emer, C., Galetti, M., Pizo, M. A., Guimarães, P. R., Moraes, S., Piratelli, A., & Jordano, P. (2018). Seed-dispersal interactions in fragmented landscapes - a metanetwork approach. Ecology Letters. https://doi.org/10.1111/ele.12909
  52. Surabhi, S., Avvaru, A. K., Sowpati, D. T., & Mishra, R. K. (2018). Patterns of microsatellite distribution reflect the evolution of biological complexity. https://doi.org/10.1101/253930
  53. Khorramdelazad, M., Bar, I., Whatmore, P., Smetham, G., Bhaaskaria, V., Yang, Y., … Ford, R. (2018). Transcriptome profiling of lentil (Lens culinaris) through the first 24 hours of Ascochyta lentis infection reveals key defence response genes. BMC Genomics, 19(1). https://doi.org/10.1186/s12864-018-4488-1
  54. Vieilledent, G., Fischer, F. J., Chave, J., Guibal, D., Langbour, P., & Gérard, J. (2018). New formula and conversion factor to compute tree species basic wood density from a global wood technology database. bioRxiv, 274068. https://doi.org/10.1101/274068
  55. Foster, Z. S. L., Chamberlain, S., & Grünwald, N. J. (2018). Taxa: An R package implementing data standards and methods for taxonomic data. F1000Research, 7, 272. https://doi.org/10.12688/f1000research.14013.1
  56. Bennett, J. M., Calosi, P., Clusella-Trullas, S., Martínez, B., Sunday, J., Algar, A. C., … Morales-Castilla, I. (2018). GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms. Scientific Data, 5, 180022. https://doi.org/10.1038/sdata.2018.22
  57. Correia, R. A., Jarić, I., Jepson, P., Malhado, A. C. M., Alves, J. A., & Ladle, R. J. (2018). Nomenclature instability in species culturomic assessments: Why synonyms matter. Ecological Indicators, 90, 74–78. https://doi.org/10.1016/j.ecolind.2018.02.059
  58. Holmes, I., & Davis Rabosky, A. R. (2018). Natural history bycatch: a pipeline for identifying metagenomic sequences in RADseq data. PeerJ, 6, e4662. https://doi.org/10.7717/peerj.4662
  59. Ondei, S., Brook, B. W., & Buettel, J. C. (2018). Nature’s untold stories: an overview on the availability and type of on-line data on long-term biodiversity monitoring. Biodiversity and Conservation. https://doi.org/10.1007/s10531-018-1582-2
  60. Tsuboi, M., van der Bijl, W., Kopperud, B. T., Erritzøe, J., Voje, K. L., Kotrschal, A., … Kolm, N. (2018). Breakdown of brain–body allometry and the encephalization of birds and mammals. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-018-0632-1
  61. Grenié, M., Mouillot, D., Villéger, S., Denelle, P., Tucker, C. M., Munoz, F., & Violle, C. (2018). Functional rarity of coral reef fishes at the global scale: Hotspots and challenges for conservation. Biological Conservation, 226, 288–299. https://doi.org/10.1016/j.biocon.2018.08.011
  62. Morzaria-Luna, H. N., Cruz-Piñón, G., Brusca, R. C., López-Ortiz, A. M., Moreno-Báez, M., Reyes-Bonilla, H., & Turk-Boyer, P. (2018). Biodiversity hotspots are not congruent with conservation areas in the Gulf of California. Biodiversity and Conservation. https://doi.org/10.1007/s10531-018-1631-x
  63. Vieilledent, G., Fischer, F. J., Chave, J., Guibal, D., Langbour, P., & Gérard, J. (2018). New formula and conversion factor to compute basic wood density of tree species using a global wood technology database. American Journal of Botany. https://doi.org/10.1002/ajb2.1175
  64. Milla, R., Bastida, J. M., Turcotte, M. M., Jones, G., Violle, C., Osborne, C. P., … Byun, C. (2018). Phylogenetic patterns and phenotypic profiles of the species of plants and mammals farmed for food. Nature Ecology & Evolution, 2(11), 1808–1817. https://doi.org/10.1038/s41559-018-0690-4
  65. Kandlikar, G. S., Gold, Z. J., Cowen, M. C., Meyer, R. S., Freise, A. C., Kraft, N. J. B., … Curd, E. E. (2018). ranacapa: An R package and Shiny web app to explore environmental DNA data with exploratory statistics and interactive visualizations. F1000Research, 7, 1734. https://doi.org/10.12688/f1000research.16680.1
  66. Bartomeus, I., Stavert, J. R., Ward, D., & Aguado, O. (2018). Historical collections as a tool for assessing the global pollination crisis. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1763), 20170389. https://doi.org/10.1098/rstb.2017.0389
  67. Pelletier, T. A., Carstens, B. C., Tank, D. C., Sullivan, J., & Espíndola, A. (2018). Predicting plant conservation priorities on a global scale. Proceedings of the National Academy of Sciences, 201804098. https://doi.org/10.1073/pnas.1804098115
  68. Da Silva, R., Pearce Kelly, P., Zimmerman, B., Knott, M., Foden, W., & Conde, D. A. (2018). Assessing the Conservation Potential of Fish and Corals in Aquariums Globally. Journal for Nature Conservation. https://doi.org/10.1016/j.jnc.2018.12.001
  69. Da Silva, R., & Conde, D. A. (2018). Data on the conservation potential of fish and coral populations in aquariums. Data in Brief. https://doi.org/10.1016/j.dib.2018.12.083
  70. Sclavi, B., & Herrick, J. (2018). Genome size variation and species diversity in salamanders. Journal of Evolutionary Biology. https://doi.org/10.1111/jeb.13412
  71. Muñoz, G., Trøjelsgaard, K., & Kissling, W. D. (2019). A synthesis of animal-mediated seed dispersal of palms reveals distinct biogeographical differences in species interactions. Journal of Biogeography. https://doi.org/10.1111/jbi.13493
  72. Muñoz, G., Kissling, W. D., & van Loon, E. E. (2019). Biodiversity Observations Miner: A web application to unlock primary biodiversity data from published literature. Biodiversity Data Journal, 7. https://doi.org/10.3897/bdj.7.e28737
  73. Smith, T. P., Thomas, T. J., Garcia-Carreras, B., Sal, S., Yvon-Durocher, G., Bell, T., & Pawar, S. (2019). Metabolic rates of prokaryotic microbes may inevitably rise with global warming. bioRxiv, 524264. https://doi.org/10.1101/524264
  74. Srivastava, S., Avvaru, A. K., Sowpati, D. T., & Mishra, R. K. (2019). Patterns of microsatellite distribution across eukaryotic genomes. BMC Genomics, 20(1). https://doi.org/10.1186/s12864-019-5516-5
  75. Thomsen, P. F., & Sigsgaard, E. E. (2019). Environmental DNA metabarcoding of wild flowers reveals diverse communities of terrestrial arthropods. Ecology and Evolution. https://doi.org/10.1002/ece3.4809
  76. König, C., Weigelt, P., Schrader, J., Taylor, A., Kattge, J., & Kreft, H. (2019). Biodiversity data integration–The significance of data resolution and domain. PLOS Biology, 17(3), e3000183. https://doi.org/10.1371/journal.pbio.3000183
  77. Higino, G., & Vital, M. V. C. (2019). Mapping and understanding the digital biodiversity knowledge about vertebrates in the Atlantic Rainforest. https://doi.org/10.32942/osf.io/c63vj
  78. Jo, J., Lee, H.-G., Kim, K. Y., & Park, C. (2019). SoEM: a novel PCR-free biodiversity assessment method based on small-organelles enriched metagenomics. ALGAE, 34(1), 57–70. https://doi.org/10.4490/algae.2019.34.2.26
  79. Axtner, J., Crampton-Platt, A., Hörig, L. A., Mohamed, A., Xu, C. C. Y., Yu, D. W., & Wilting, A. (2019). An efficient and robust laboratory workflow and tetrapod database for larger scale environmental DNA studies. GigaScience, 8(4). https://doi.org/10.1093/gigascience/giz029
  80. Lin, B. Y., Chan, P. P., & Lowe, T. M. (2019). tRNAviz: explore and visualize tRNA sequence features. Nucleic Acids Research. https://doi.org/10.1093/nar/gkz438
  81. Sporbert, M., Bruelheide, H., Seidler, G., Keil, P., Jandt, U., Austrheim, G., … Welk, E. (2019). Assessing sampling coverage of species distribution in biodiversity databases. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12763
  82. Steidinger, B. S., Crowther, T. W., Liang, J., Van Nuland, M. E., Werner, G. D. A., … Peay, K. G. (2019). Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature, 569(7756), 404–408. https://doi.org/10.1038/s41586-019-1128-0
  83. Bagley, M., Pilgrim, E., Knapp, M., Yoder, C., Santo Domingo, J., & Banerji, A. (2019). High-throughput environmental DNA analysis informs a biological assessment of an urban stream. Ecological Indicators, 104, 378–389. https://doi.org/10.1016/j.ecolind.2019.04.088
  84. Foisy, M. R., Albert, L. P., Hughes, D. W. W., & Weber, M. G. (2019). Do latex and resin canals spur plant diversification? Re‐examining a classic example of escape and radiate coevolution. Journal of Ecology. https://doi.org/10.1111/1365-2745.13203
  85. Boggs, Scheible, Machado, & Meiklejohn. (2019). Single Fragment or Bulk Soil DNA Metabarcoding: Which is Better for Characterizing Biological Taxa Found in Surface Soils for Sample Separation? Genes, 10(6), 431. https://doi.org/10.3390/genes10060431
  86. Palacios-Abrantes, J., Cisneros-Montemayor, A. M., Cisneros-Mata, M. A., Rodríguez, L., Arreguín-Sánchez, F., Aguilar, V., … Cheung, W. W. L. (2019). A metadata approach to evaluate the state of ocean knowledge: Strengths, limitations, and application to Mexico. PLOS ONE, 14(6), e0216723. https://doi.org/10.1371/journal.pone.0216723
  87. Grattarola, F., Botto, G., da Rosa, I., Gobel, N., González, E., González, J., … Pincheira-Donoso, D. (2019). Biodiversidata: An Open-Access Biodiversity Database for Uruguay. Biodiversity Data Journal, 7. https://doi.org/10.3897/bdj.7.e36226
  88. Danella Figo, D., De Amicis, K., Neiva Santos de Aquino, D., Pomiecinski, F., Gadermaier, G., Briza, P., … Souza Santos, K. (2019). Cashew Tree Pollen: An Unknown Source of IgE-Reactive Molecules. International Journal of Molecular Sciences, 20(10), 2397. https://doi.org/10.3390/ijms20102397
  89. Hagen, O., Vaterlaus, L., Albouy, C., Brown, A., Leugger, F., Onstein, R. E., … Pellissier, L. (2019). Mountain building, climate cooling and the richness of cold‐adapted plants in the Northern Hemisphere. Journal of Biogeography. https://doi.org/10.1111/jbi.13653
  90. Alhajeri, B. H., Porto, L., & Maestri, R. (2019). Habitat productivity is a poor predictor of body size in rodents. Current Zoology. https://doi.org/10.1093/cz/zoz037
  91. Lennox, R. J., Veríssimo, D., Twardek, W. M., Davis, C. R., & Jarić, I. (2019). Sentiment analysis as a measure of conservation culture in scientific literature. Conservation Biology. https://doi.org/10.1111/cobi.13404
  92. Esperon‐Rodriguez, M., Power, S. A., Tjoelker, M. G., Beaumont, L. J., Burley, H., Caballero‐Rodriguez, D., & Rymer, P. D. (2019). Assessing the vulnerability of Australia’s urban forests to climate extremes. Plants, People, Planet. https://doi.org/10.1002/ppp3.10064
  93. Cazelles, K., Bartley, T., Guzzo, M. M., Brice, M., MacDougall, A. S., Bennett, J. R., … McCann, K. S. (2019). Homogenization of freshwater lakes: recent compositional shifts in fish communities are explained by gamefish movement and not climate change. Global Change Biology. https://doi.org/10.1111/gcb.14829
  94. Bufford, J. L., Hulme, P. E., Sikes, B. A., Cooper, J. A., Johnston, P. R., & Duncan, R. P. (2019). Novel interactions between alien pathogens and native plants increase plant‐pathogen network connectance and decrease specialization. Journal of Ecology. https://doi.org/10.1111/1365-2745.13293
  95. Sydenham, M. A. K., Moe, S. R., & Eldegard, K. (2020). When context matters: Spatial prediction models of environmental conditions can identify target areas for wild bee habitat management interventions. Landscape and Urban Planning, 193, 103673. https://doi.org/10.1016/j.landurbplan.2019.103673
  96. Bottin, M., Peyre, G., Vargas, C., Raz, L., Richardson, J. E., & Sanchez, A. (2019). Phytosociological data and herbarium collections show congruent large scale patterns but differ in their local descriptions of community composition. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12825
  97. Millard, J. W., Freeman, R., & Newbold, T. (2019). Text‐analysis reveals taxonomic and geographic disparities in animal pollination literature. Ecography. https://doi.org/10.1111/ecog.04532
  98. Hung, T., Rosales, M., Kurobe, T., Stevenson, T., Ellison, L., Tigan, G., … Teh, S. (2019). A pilot study of the performance of captive‐reared delta smelt Hypomesus transpacificus in a semi‐natural environment. Journal of Fish Biology. https://doi.org/10.1111/jfb.14162
  99. Chalmandrier, L., Pansu, J., Zinger, L., Boyer, F., Coissac, E., Génin, A., … Thuiller, W. (2019). Environmental and biotic drivers of soil microbial β‐diversity across spatial and phylogenetic scales. Ecography. https://doi.org/10.1111/ecog.04492
  100. Gryseels, S., Watts, T. D., Kabongo, J.-M. M., Larsen, B. B., Lemey, P., Muyembe-Tamfum, J.-J., … Worobey, M. (2019). A near-full-length HIV-1 genome from 1966 recovered from formalin-fixed paraffin-embedded tissue. https://doi.org/10.1101/687863
  101. Zheleznova, G., Shubina, T., Degteva, S., Chadin, I., & Rubtsov, M. (2019). Moss occurrences in Yugyd Va National Park, Subpolar and Northern Urals, European North-East Russia. Biodiversity Data Journal, 7. https://doi.org/10.3897/bdj.7.e32307
  102. Outhwaite, C. L., Powney, G. D., August, T. A., Chandler, R. E., Rorke, S., Pescott, O. L., … Isaac, N. J. B. (2019). Annual estimates of occupancy for bryophytes, lichens and invertebrates in the UK, 1970–2015. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0269-1
  103. Smith, T. P., Thomas, T. J. H., García-Carreras, B., Sal, S., Yvon-Durocher, G., Bell, T., & Pawar, S. (2019). Community-level respiration of prokaryotic microbes may rise with global warming. Nature Communications, 10(1). https://doi.org/10.1038/s41467-019-13109-1
  104. Mancinelli, G., Mali, S., & Belmonte, G. (2019). Species Richness and Taxonomic Distinctness of Zooplankton in Ponds and Small Lakes from Albania and North Macedonia: The Role of Bioclimatic Factors. Water, 11(11), 2384. https://doi.org/10.3390/w11112384
  105. Sigsgaard, E. E., Torquato, F., Frøslev, T. G., Moore, A. B. M., Sørensen, J. M., Range, P., … Thomsen, P. F. (2019). Using vertebrate environmental DNA from seawater in biomonitoring of marine habitats. Conservation Biology. https://doi.org/10.1111/cobi.13437
  106. Toussaint, A., Bueno, G., Davison, J., Moora, M., Tedersoo, L., Zobel, M., … Pärtel, M. (2019). Asymmetric patterns of global diversity among plants and mycorrhizal fungi. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12837
  107. Jin, J., & Yang, J. (2020). BDcleaner: A workflow for cleaning taxonomic and geographic errors in occurrence data archived in biodiversity databases. Global Ecology and Conservation, 21, e00852. https://doi.org/10.1016/j.gecco.2019.e00852
  108. Geary, W. L., Doherty, T. S., Nimmo, D. G., Tulloch, A. I. T., & Ritchie, E. G. (2020). Predator responses to fire: A global systematic review and meta‐analysis. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.13153
  109. Marshall, B. M., & Strine, C. T. (2019). Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media. PeerJ, 7, e8059. https://doi.org/10.7717/peerj.8059
  110. Champagne, E., Royo, A. A., Tremblay, J.-P., & Raymond, P. (2019). Phytochemicals Involved in Plant Resistance to Leporids and Cervids: a Systematic Review. Journal of Chemical Ecology, 46(1), 84–98. https://doi.org/10.1007/s10886-019-01130-z
  111. Burrows, M. T., Hawkins, S. J., Moore, J. J., Adams, L., Sugden, H., Firth, L., & Mieszkowska, N. (2020). Global‐scale species distributions predict temperature‐related changes in species composition of rocky shore communities in Britain. Global Change Biology, 26(4), 2093–2105. https://doi.org/10.1111/gcb.14968
  112. Kim, H. M., Jo, J., Park, C., Choi, B.-J., Lee, H.-G., & Kim, K. Y. (2019). Epibionts associated with floating Sargassum horneri in the Korea Strait. ALGAE, 34(4), 303–313. https://doi.org/10.4490/algae.2019.34.12.10
  113. Hansen, O. L. P., Svenning, J., Olsen, K., Dupont, S., Garner, B. H., Iosifidis, A., … Høye, T. T. (2019). Species‐level image classification with convolutional neural network enables insect identification from habitus images. Ecology and Evolution, 10(2), 737–747. https://doi.org/10.1002/ece3.5921
  114. Quintero, E., Pizo, M. A., & Jordano, P. (2020). Fruit resource provisioning for avian frugivores: The overlooked side of effectiveness in seed dispersal mutualisms. Journal of Ecology. https://doi.org/10.1111/1365-2745.13352
  115. Cirtwill, A. R., Dalla Riva, G. V., Baker, N. J., Ohlsson, M., Norström, I., Wohlfarth, I., … Stouffer, D. B. (2020). Related plants tend to share pollinators and herbivores, but strength of phylogenetic signal varies among plant families. New Phytologist. https://doi.org/10.1111/nph.16420
  116. Akpınar, B. A., Carlson, P. O., Paavilainen, V. O., & Dunn, C. D. (2020). Pathogenicity of human mtDNA variants is revealed by combining a novel phylogenetic analysis with machine learning. https://doi.org/10.1101/2020.01.10.902239
  117. Bachman, S., Walker, B., Barrios, S., Copeland, A., & Moat, J. (2020). Rapid Least Concern: towards automating Red List assessments. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e47018
  118. Mooney, A., Conde, D. A., Healy, K., & Buckley, Y. M. (2020). A system wide approach to managing zoo collections for visitor attendance and in situ conservation. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-14303-2
  119. Gagné, T. O., Reygondeau, G., Jenkins, C. N., Sexton, J. O., Bograd, S. J., Hazen, E. L., & Van Houtan, K. S. (2020). Towards a global understanding of the drivers of marine and terrestrial biodiversity. PLOS ONE, 15(2), e0228065. https://doi.org/10.1371/journal.pone.0228065
  120. Cederwall, J., Black, T. A., Blais, J. M., Hanson, M. L., Hollebone, B. P., Palace, V. P., … Orihel, D. M. (2020). Life under an oil slick: response of a freshwater food web to simulated spills of diluted bitumen in field mesocosms. Canadian Journal of Fisheries and Aquatic Sciences, 77(5), 779–788. https://doi.org/10.1139/cjfas-2019-0224
  121. Mossion, V., Dauphin, B., Grant, J., Zemp, N., & Croll, D. (2020). A reference transcriptome for the early-branching fern Botrychium lunaria enables fine-grained resolution of population structure. https://doi.org/10.1101/2020.02.17.952283
  122. Verde Arregoitia, L. D., Teta, P., & D’Elía, G. (2020). Patterns in research and data sharing for the study of form and function in caviomorph rodents. Journal of Mammalogy. https://doi.org/10.1093/jmammal/gyaa002
  123. Rodrigues, B. N., & Boscolo, D. (2020). Do bipartite binary antagonistic and mutualistic networks have different responses to the taxonomic resolution of nodes? Ecological Entomology. https://doi.org/10.1111/een.12844
  124. Thompson, K. A. (2020). Experimental hybridization studies suggest that pleiotropic alleles commonly underlie adaptive divergence between natural populations. The American Naturalist. https://doi.org/10.1086/708722
  125. Kaczvinsky, C., & Hardy, N. B. (2020). Do major host shifts spark diversification in butterflies? Ecology and Evolution, 10(8), 3636–3646. https://doi.org/10.1002/ece3.6116
  126. Zizka, A., Carvalho‐Sobrinho, J. G., Pennington, R. T., Queiroz, L. P., Alcantara, S., Baum, D. A., … Antonelli, A. (2020). Transitions between biomes are common and directional in Bombacoideae (Malvaceae). Journal of Biogeography. https://doi.org/10.1111/jbi.13815
  127. Young, N. E., Jarnevich, C. S., Sofaer, H. R., Pearse, I., Sullivan, J., Engelstad, P., & Stohlgren, T. J. (2020). A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales. PLOS ONE, 15(3), e0229253. https://doi.org/10.1371/journal.pone.0229253
  128. Martins, P. T., & Boeckx, C. (2020). Vocal learning: Beyond the continuum. PLOS Biology, 18(3), e3000672. https://doi.org/10.1371/journal.pbio.3000672
  129. Timpano, E. K., Scheible, M. K. R., & Meiklejohn, K. A. (2020). Optimization of the second internal transcribed spacer (ITS2) for characterizing land plants from soil. PLOS ONE, 15(4), e0231436. https://doi.org/10.1371/journal.pone.0231436
  130. Mishler, B. D., Guralnick, R., Soltis, P. S., Smith, S. A., Soltis, D. E., Barve, N., … Laffan, S. W. (2020). Spatial phylogenetics of the North American flora. Journal of Systematics and Evolution. https://doi.org/10.1111/jse.12590
  131. Chandler, J. O., Haas, F. B., Khan, S., Bowden, L., Ignatz, M., Enfissi, E. M. A., … Leubner-Metzger, G. (2020). Rocket Science: The Effect of Spaceflight on Germination Physiology, Ageing, and Transcriptome of Eruca sativa Seeds. Life, 10(4), 49. https://doi.org/10.3390/life10040049
  132. Verhoeven, M. R., Glisson, W. J., & Larkin, D. J. (2020). Niche Models Differentiate Potential Impacts of Two Aquatic Invasive Plant Species on Native Macrophytes. Diversity, 12(4), 162. https://doi.org/10.3390/d12040162
  133. Ladwig, L. M., Zirbel, C. R., Sorenson, Q. M., & Damschen, E. I. (2020). A taxonomic, phylogenetic, and functional comparison of restoration seed mixes and historical plant communities in Midwestern oak savannas. Forest Ecology and Management, 466, 118122. https://doi.org/10.1016/j.foreco.2020.118122
  134. Van den Berg, S. J. P., Rendal, C., Focks, A., Butler, E., Peeters, E. T. H. M., De Laender, F., & Van den Brink, P. J. (2020). Potential impact of chemical stress on freshwater invertebrates: A sensitivity assessment on continental and national scale based on distribution patterns, biological traits, and relatedness. Science of The Total Environment, 731, 139150. https://doi.org/10.1016/j.scitotenv.2020.139150
  135. Scharmüller, A., Schreiner, V. C., & Schäfer, R. B. (2020). Standartox: Standardizing Toxicity Data. Data, 5(2), 46. https://doi.org/10.3390/data5020046
  136. Crowley, D., Becker, D., Washburne, A., & Plowright, R. (2020). Identifying Suspect Bat Reservoirs of Emerging Infections. Vaccines, 8(2), 228. https://doi.org/10.3390/vaccines8020228
  137. Lenoir, J., Bertrand, R., Comte, L., Bourgeaud, L., Hattab, T., Murienne, J., & Grenouillet, G. (2020). Species better track climate warming in the oceans than on land. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-020-1198-2
  138. Stringham, O., Toomes, A., Kanishka, A. M., Mitchell, L., Heinrich, S., Ross, J. V., & Cassey, P. (2020). A guide to using the Internet to monitor and quantify the wildlife trade. https://ecoevorxiv.org/5yzw9/download?format=pdf
  139. Szöcs, E., Stirling, T., Scott, E. R., Scharmüller, A., & Schäfer, R. B. (2020). webchem: An R Package to Retrieve Chemical Information from the Web. Journal of Statistical Software, 93(1), 1-17. https://www.jstatsoft.org/article/view/v093i13/v93i13.pdf
  140. Monaco, C. J., Bradshaw, C. J. A., Booth, D. J., Gillanders, B. M., Schoeman, D. S., & Nagelkerken, I. (2020). Dietary generalism accelerates arrival and persistence of coral‐reef fishes in their novel ranges under climate change. Global Change Biology. https://doi.org/10.1111/gcb.15221
  141. Pal Negi, A., Singh, R., Sharma, A., & Negi, V. S. (2020). Insights into high mobility group A (HMGA) proteins from Poaceae family: An in silico approach for studying homologs. Computational Biology and Chemistry, 87, 107306. https://doi.org/10.1016/j.compbiolchem.2020.107306
  142. Loewen, C. J. G., Strecker, A. L., Gilbert, B., & Jackson, D. A. (2020). Climate warming moderates the impacts of introduced sportfish on multiple dimensions of prey biodiversity. Global Change Biology, 26(9), 4937–4951. https://doi.org/10.1111/gcb.15225
  143. Li, D., Olden, J. D., Lockwood, J. L., Record, S., McKinney, M. L., & Baiser, B. (2020). Changes in taxonomic and phylogenetic diversity in the Anthropocene. Proceedings of the Royal Society B: Biological Sciences, 287(1929), 20200777. https://doi.org/10.1098/rspb.2020.0777
  144. Arranz, V., Pearman, W. S., Aguirre, J. D., & Liggins, L. (2020). MARES, a replicable pipeline and curated reference database for marine eukaryote metabarcoding. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0549-9
View Documentation
phonfieldwork
CRAN

Linguistic Phonetic Fieldwork Tools

George Moroz
Description

There are a lot of different typical tasks that have to be solved during phonetic research and experiments. This includes creating a presentation that will contain all stimuli, renaming and concatenating multiple sound files recorded during a session, automatic annotation in Praat TextGrids (this is one of the sound annotation standards provided by Praat software, see Boersma & Weenink 2018 http://www.fon.hum.uva.nl/praat/), creating an html table with annotations and spectrograms, and converting multiple formats (Praat TextGrid, ELAN, EXMARaLDA, Audacity, subtitles .srt, and FLEx flextext). All of these tasks can be solved by a mixture of different tools (any programming language has programs for automatic renaming, and Praat contains scripts for concatenating and renaming files, etc.). phonfieldwork provides a functionality that will make it easier to solve those tasks independently of any additional tools. You can also compare the functionality with other packages: rPraat https://CRAN.R-project.org/package=rPraat, textgRid https://CRAN.R-project.org/package=textgRid.

View Documentation
babette
CRAN

Control BEAST2

Richèl J.C. Bilderbeek
Description

BEAST2 (https://www.beast2.org) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAST2 is commonly accompanied by BEAUti 2, Tracer and DensiTree. babette provides for an alternative workflow of using all these tools separately. This allows doing complex Bayesian phylogenetics easily and reproducibly from R.

View Documentation

IUCN Red List Client

Scott Chamberlain
Description

IUCN Red List (http://apiv3.iucnredlist.org/api/v3/docs) client. The IUCN Red List is a global list of threatened and endangered species. Functions cover all of the Red List API routes. An API key is required.

Scientific use cases
  1. Cardoso P (2017) red - an R package to facilitate species red list assessments according to the IUCN criteria. Biodiversity Data Journal 5: e20530. https://doi.org/10.3897/BDJ.5.e20530
  2. Moat, J., Bachman, S. P., Field, R., & Boyd, D. S. (2018). Refining area of occupancy to address the modifiable areal unit problem in ecology and conservation. Conservation Biology. https://doi.org/10.1111/cobi.13139
  3. Lusseau, D., & Mancini, F. (2018). A global assessment of tourism and recreation conservation threats to prioritise interventions. arXiv preprint https://arxiv.org/abs/1808.08399
  4. Van de Perre, F., Leirs, H., & Verheyen, E. (2019). Paleoclimate, ecoregion size, and degree of isolation explain regional biodiversity differences among terrestrial vertebrates within the Congo Basin. Belgian Journal of Zoology, 149(1). https://doi.org/10.26496/bjz.2019.28
  5. Alhajeri, B. H., & Fourcade, Y. (2019). High correlation between species‐level environmental data estimates extracted from IUCN expert range maps and from GBIF occurrence data. Journal of Biogeography. https://doi.org/10.1111/jbi.13619
  6. Nyboer, E. A., Liang, C., & Chapman, L. J. (2019). Assessing the vulnerability of Africa’s freshwater fishes to climate change: A continent-wide trait-based analysis. Biological Conservation, 236, 505–520. https://doi.org/10.1016/j.biocon.2019.05.003
  7. Grattarola, F., Botto, G., da Rosa, I., Gobel, N., González, E., González, J., … Pincheira-Donoso, D. (2019). Biodiversidata: An Open-Access Biodiversity Database for Uruguay. Biodiversity Data Journal, 7. https://doi.org/10.3897/bdj.7.e36226
  8. Lennox, R. J., Veríssimo, D., Twardek, W. M., Davis, C. R., & Jarić, I. (2019). Sentiment analysis as a measure of conservation culture in scientific literature. Conservation Biology. https://doi.org/10.1111/cobi.13404
  9. Dawson, A., Paciorek, C. J., Goring, S. J., Jackson, S. T., McLachlan, J. S., & Williams, J. W. (2019). Quantifying trends and uncertainty in prehistoric forest composition in the upper Midwestern United States. Ecology. https://doi.org/10.1002/ecy.2856
  10. Bager Olsen, M. T., Geldmann, J., Harfoot, M., Tittensor, D. P., Price, B., Sinovas, P., … Burgess, N. D. (2019). Thirty-six years of legal and illegal wildlife trade entering the USA. Oryx, 1–10. https://doi.org/10.1017/s0030605319000541
  11. Scheffers, B. R., Oliveira, B. F., Lamb, I., & Edwards, D. P. (2019). Global wildlife trade across the tree of life. Science, 366(6461), 71–76. https://doi.org/10.1126/science.aav5327
  12. Stévart, T., Dauby, G., Lowry, P. P., Blach-Overgaard, A., Droissart, V., Harris, D. J., … Couvreur, T. L. P. (2019). A third of the tropical African flora is potentially threatened with extinction. Science Advances, 5(11), eaax9444. https://doi.org/10.1126/sciadv.aax9444
  13. Cooke, R. S. C., Eigenbrod, F., & Bates, A. E. (2020). Ecological distinctiveness of birds and mammals at the global scale. Global Ecology and Conservation, 22, e00970. https://doi.org/10.1016/j.gecco.2020.e00970
  14. Ji, Y., Baker, C. C., Li, Y., Popescu, V. D., Wang, Z., Wang, J., … Yu, D. W. (2020). Large-scale Quantification of Vertebrate Biodiversity in Ailaoshan Nature Reserve from Leech iDNA. https://doi.org/10.1101/2020.02.10.941336
View Documentation

Convert Between WKT and GeoJSON

Scott Chamberlain
Description

Convert WKT to GeoJSON and GeoJSON to WKT. Functions included for converting between GeoJSON to WKT, creating both GeoJSON features, and non-features, creating WKT from R objects (e.g., lists, data.frames, vectors), and linting WKT.

View Documentation

Interface to Species Occurrence Data Sources

Scott Chamberlain
Description

A programmatic interface to many species occurrence data sources, including Global Biodiversity Information Facility (GBIF), USGSs Biodiversity Information Serving Our Nation (BISON), iNaturalist, Berkeley Ecoinformatics Engine, eBird, Integrated Digitized Biocollections (iDigBio), VertNet, Ocean Biogeographic Information System (OBIS), and Atlas of Living Australia (ALA). Includes functionality for retrieving species occurrence data, and combining those data.

Scientific use cases
  1. Alfsnes, K., Leinaas, H. P., & Hessen, D. O. (2017). Genome size in arthropods: different roles of phylogeny, habitat and life history in insects and crustaceans. Ecology and Evolution. https://doi.org/10.1002/ece3.3163
  2. Vanderhoeven, S., Adriaens, T., Desmet, P., Strubbe, D., Backeljau, T., Barbier, Y., … Groom, Q. (2017). Tracking Invasive Alien Species (TrIAS): Building a data-driven framework to inform policy. Research Ideas and Outcomes, 3, e13414. https://doi.org/10.3897/rio.3.e13414
  3. Pérez-Escobar, O. A., Rodriguez, L. K., & Martel, C. (2017). A new species of Telipogon (Oncidiinae: Orchidaceae) from the paramos of Colombia. Phytotaxa, 305(4), 262-268. http://www.biotaxa.org/Phytotaxa/article/view/phytotaxa.305.4.2
  4. Dallas, T., Decker, R. R., & Hastings, A. (2017). Species are not most abundant in the centre of their geographic range or climatic niche. Ecology Letters. https://doi.org/10.1111/ele.12860
  5. Oldham, K. A., & Weeks, A. (2017). Varieties of Melampyrum Lineare (Orobanchaceae) Revisited. Rhodora. http://www.rhodorajournal.org/doi/abs/10.3119/16-13
  6. Sales, L. P., Ribeiro, B. R., Hayward, M. W., Paglia, A., Passamani, M., & Loyola, R. (2017). Niche conservatism and the invasive potential of the wild boar. Journal of Animal Ecology, 86(5), 1214–1223. https://doi.org/10.1111/1365-2656.12721
  7. Longbottom, J., Shearer, F. M., Devine, M., Alcoba, G., Chappuis, F., Weiss, D. J., … Pigott, D. M. (2018). Vulnerability to snakebite envenoming: a global mapping of hotspots. The Lancet. https://doi.org/10.1016/S0140-6736(18)31224-8
  8. Samy, A. M., Alkishe, A. A., Thomas, S., Wang, L., & Zhang, W. (2018). Mapping the potential distributions of etiological agent, vectors, and reservoirs of Japanese Encephalitis in Asia and Australia. Acta Tropica. https://doi.org/10.1016/j.actatropica.2018.08.014
  9. Pfeffer, D. A., Lucas, T. C. D., May, D., Harris, J., Rozier, J., Twohig, K. A., … Gething, P. W. (2018). malariaAtlas: an R interface to global malariometric data hosted by the Malaria Atlas Project. Malaria Journal, 17(1). https://doi.org/10.1186/s12936-018-2500-5
  10. Perez, T. M., Valverde-Barrantes, O., Bravo, C., Taylor, T. C., Fadrique, B., Hogan, J. A., … Feeley, K. J. (2018). Botanic gardens are an untapped resource for studying the functional ecology of tropical plants. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1763), 20170390. https://doi.org/10.1098/rstb.2017.0390
  11. Zuquim, G., Costa, F. R. C., Tuomisto, H., Moulatlet, G. M., & Figueiredo, F. O. G. (2019). The importance of soils in predicting the future of plant habitat suitability in a tropical forest. Plant and Soil. https://doi.org/10.1007/s11104-018-03915-9
  12. Myers, E. A., Xue, A. T., Gehara, M., Cox, C., Davis Rabosky, A. R., Lemos‐Espinal, J., … Burbrink, F. T. (2019). Environmental Heterogeneity and Not Vicariant Biogeographic Barriers Generate Community Wide Population Structure in Desert Adapted Snakes. Molecular Ecology. https://doi.org/10.1111/mec.15182
  13. Pender, J. E., Hipp, A. L., Hahn, M., Kartesz, J., Nishino, M., & Starr, J. R. (2019). How sensitive are climatic niche inferences to distribution data sampling? A comparison of Biota of North America Program (BONAP) and Global Biodiversity Information Facility (GBIF) datasets. Ecological Informatics, 100991. https://doi.org/10.1016/j.ecoinf.2019.100991
  14. Báez, J. C., Barbosa, A. M., Pascual, P., Ramos, M. L., & Abascal, F. (2019). Ensemble modeling of the potential distribution of the whale shark in the Atlantic Ocean. Ecology and Evolution, 10(1), 175–184. https://doi.org/10.1002/ece3.5884
  15. Reyes, J. A., & Lira-Noriega, A. (2020). Current and future global potential distribution of the fruit fly Drosophila suzukii (Diptera: Drosophilidae). The Canadian Entomologist, 1–13. https://doi.org/10.4039/tce.2020.3
  16. Sales, L., Culot, L., & Pires, M. M. (2020). Climate niche mismatch and the collapse of primate seed dispersal services in the Amazon. Biological Conservation, 247, 108628. https://doi.org/10.1016/j.biocon.2020.108628
  17. Gaynor, M. L., Fu, C., Gao, L., Lu, L., Soltis, D. E., & Soltis, P. S. (2020). Biogeography and ecological niche evolution in Diapensiaceae inferred from phylogenetic analysis. Journal of Systematics and Evolution. https://doi.org/10.1111/jse.12646
View Documentation

Straightforward BibTeX and BibLaTeX Bibliography Management

Mathew W. McLean
Description

Provides tools for importing and working with bibliographic references. It greatly enhances the bibentry class by providing a class BibEntry which stores BibTeX and BibLaTeX references, supports UTF-8 encoding, and can be easily searched by any field, by date ranges, and by various formats for name lists (author by last names, translator by full names, etc.). Entries can be updated, combined, sorted, printed in a number of styles, and exported. BibTeX and BibLaTeX .bib files can be read into R and converted to BibEntry objects. Interfaces to NCBI Entrez, CrossRef, and Zotero are provided for importing references and references can be created from locally stored PDF files using Poppler. Includes functions for citing and generating a bibliography with hyperlinks for documents prepared with RMarkdown or RHTML.

View Documentation

eBird Data Extraction and Processing in R

Matthew Strimas-Mackey
Description

Extract and process bird sightings records from eBird (http://ebird.org), an online tool for recording bird observations. Public access to the full eBird database is via the eBird Basic Dataset (EBD; see http://ebird.org/ebird/data/download for access), a downloadable text file. This package is an interface to AWK for extracting data from the EBD based on taxonomic, spatial, or temporal filters, to produce a manageable file size that can be imported into R.

View Documentation

Full Text of Scholarly Articles Across Many Data Sources

Scott Chamberlain
Description

Provides a single interface to many sources of full text scholarly data, including Biomed Central, Public Library of Science, Pubmed Central, eLife, F1000Research, PeerJ, Pensoft, Hindawi, arXiv preprints, and more. Functionality included for searching for articles, downloading full or partial text, downloading supplementary materials, converting to various data formats.

Scientific use cases
  1. Bauer, P. C., Barbera, P., & Munzert, S. (2016). The Quality of Citations: Towards Quantifying Qualitative Impact in Social Science Research. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2874549
  2. Piper, A. M., Batovska, J., Cogan, N. O. I., Weiss, J., Cunningham, J. P., Rodoni, B. C., & Blacket, M. J. (2019). Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance. GigaScience, 8(8). https://doi.org/10.1093/gigascience/giz092
  3. Mishra, P., & Narayan Tripathi, L. (2019). Characterization of two‐dimensional materials from Raman spectral data. Journal of Raman Spectroscopy. https://doi.org/10.1002/jrs.5744
  4. Vitale, O., Preste, R., Palmisano, D., & Attimonelli, M. (2019). A data and text mining pipeline to annotate human mitochondrial variants with functional and clinical information. Molecular Genetics & Genomic Medicine, 8(2). https://doi.org/10.1002/mgg3.1085
  5. Joo, R., Picardi, S., Boone, M. E., Clay, T. A., Patrick, S. C., Romero-Romero, V. S., & Basille, M. (2020). A decade of movement ecology. arXiv preprint arXiv:2006.00110 https://arxiv.org/pdf/2006.00110.pdf
View Documentation

A Tool for Automating Download and Preprocessing of MODIS Land Products Data

Lorenzo Busetto
Description

Allows automating the creation of time series of rasters derived from MODIS Satellite Land Products data. It performs several typical preprocessing steps such as download, mosaicking, reprojection and resize of data acquired on a specified time period. All processing parameters can be set using a user-friendly GUI. Users can select which layers of the original MODIS HDF files they want to process, which additional Quality Indicators should be extracted from aggregated MODIS Quality Assurance layers and, in the case of Surface Reflectance products , which Spectral Indexes should be computed from the original reflectance bands. For each output layer, outputs are saved as single-band raster files corresponding to each available acquisition date. Virtual files allowing access to the entire time series as a single file are also created. Command-line execution exploiting a previously saved processing options file is also possible, allowing to automatically update time series related to a MODIS product whenever a new image is available.

Scientific use cases
  1. Busetto, L., & Ranghetti, L. (2016). MODIStsp : An R package for automatic preprocessing of MODIS Land Products time series. Computers & Geosciences, 97, 40–48. https://doi.org/10.1016/j.cageo.2016.08.020
  2. Bellón, B., Bégué, A., Lo Seen, D., de Almeida, C., & Simões, M. (2017). A Remote Sensing Approach for Regional-Scale Mapping of Agricultural Land-Use Systems Based on NDVI Time Series. Remote Sensing, 9(6), 600. https://doi.org/10.3390/rs9060600
  3. Hurtado, L. A., Calzada, J. E., Rigg, C. A., Castillo, M., & Chaves, L. F. (2018). Climatic fluctuations and malaria transmission dynamics, prior to elimination, in Guna Yala, República de Panamá. Malaria Journal, 17(1). https://doi.org/10.1186/s12936-018-2235-3
  4. Ranghetti, L., Cardarelli, E., Boschetti, M., Busetto, L., & Fasola, M. (2018). Assessment of Water Management Changes in the Italian Rice Paddies from 2000 to 2016 Using Satellite Data: A Contribution to Agro-Ecological Studies. Remote Sensing, 10(3), 416. https://doi.org/10.3390/rs10030416
  5. Bellón, B., Bégué, A., Lo Seen, D., Lebourgeois, V., Evangelista, B. A., Simões, M., & Demonte Ferraz, R. P. (2018). Improved regional-scale Brazilian cropping systems’ mapping based on a semi-automatic object-based clustering approach. International Journal of Applied Earth Observation and Geoinformation, 68, 127–138. https://doi.org/10.1016/j.jag.2018.01.019
  6. Manfron, G., Delmotte, S., Busetto, L., Hossard, L., Ranghetti, L., Brivio, P. A., & Boschetti, M. (2017). Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue, France. International Journal of Applied Earth Observation and Geoinformation, 57, 190–201. https://doi.org/10.1016/j.jag.2017.01.001
  7. Araya, S., Ostendorf, B., Lyle, G., & Lewis, M. (2018). CropPhenology: An R package for extracting crop phenology from time series remotely sensed vegetation index imagery. Ecological Informatics, 46, 45–56. https://doi.org/10.1016/j.ecoinf.2018.05.006
  8. Adisa, O., Botai, J., Hassen, A., Darkey, D., Adeola, A., Tesfamariam, E., … Adisa, A. (2018). Variability of Satellite Derived Phenological Parameters across Maize Producing Areas of South Africa. Sustainability, 10(9), 3033. https://doi.org/10.3390/su10093033
  9. Granell, C., Miralles, I., Rodríguez-Pupo, L., González-Pérez, A., Casteleyn, S., Busetto, L., … Huerta, J. (2017). Conceptual Architecture and Service-Oriented Implementation of a Regional Geoportal for Rice Monitoring. ISPRS International Journal of Geo-Information, 6(7), 191. https://doi.org/10.3390/ijgi6070191
  10. Boschetti, M., Busetto, L., Manfron, G., Laborte, A., Asilo, S., Pazhanivelan, S., & Nelson, A. (2017). PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series. Remote Sensing of Environment, 194, 347–365. https://doi.org/10.1016/j.rse.2017.03.029
  11. Nutini, F., Stroppiana, D., Busetto, L., Bellingeri, D., Corbari, C., Mancini, M., … Boschetti, M. (2017). A Weekly Indicator of Surface Moisture Status from Satellite Data for Operational Monitoring of Crop Conditions. Sensors, 17(6), 1338. https://doi.org/10.3390/s17061338
  12. Moura, M. M., dos Santos, A. R., Pezzopane, J. E. M., Alexandre, R. S., da Silva, S. F., Pimentel, S. M., … de Carvalho, J. R. (2019). Relation of El Niño and La Niña phenomena to precipitation, evapotranspiration and temperature in the Amazon basin. Science of The Total Environment, 651, 1639–1651. https://doi.org/10.1016/j.scitotenv.2018.09.242
  13. Hurtado, L., Rigg, C., Calzada, J., Dutary, S., Bernal, D., Koo, S., & Chaves, L. (2018). Population Dynamics of Anopheles albimanus (Diptera: Culicidae) at Ipetí-Guna, a Village in a Region Targeted for Malaria Elimination in Panamá. Insects, 9(4), 164. https://doi.org/10.3390/insects9040164
  14. Sodnomov, B. V., Ayurzhanaev, A. A., Tsydypov, B. Z., Garmaev, E. Z., & Tulokhonov, A. K. (2018). Software for analysis of vegetation indices dynamics. IOP Conference Series: Earth and Environmental Science, 211, 012083. https://doi.org/10.1088/1755-1315/211/1/012083
  15. Marcos, B., Gonçalves, J., Alcaraz-Segura, D., Cunha, M., & Honrado, J. P. (2019). Improving the detection of wildfire disturbances in space and time based on indicators extracted from MODIS data: a case study in northern Portugal. International Journal of Applied Earth Observation and Geoinformation, 78, 77–85. https://doi.org/10.1016/j.jag.2018.12.003
  16. Rigg, C. A., Hurtado, L. A., Calzada, J. E., & Chaves, L. F. (2019). Malaria infection rates in Anopheles albimanus (Diptera: Culicidae) at Ipetí-Guna, a village within a region targeted for malaria elimination in Panamá. Infection, Genetics and Evolution, 69, 216–223. https://doi.org/10.1016/j.meegid.2019.02.003
  17. Nghiem, J., Potter, C., & Baiman, R. (2019). Detection of Vegetation Cover Change in Renewable Energy Development Zones of Southern California Using MODIS NDVI Time Series Analysis, 2000 to 2018. Environments, 6(4), 40. https://doi.org/10.3390/environments6040040
  18. Marcos, B., Gonçalves, J., Alcaraz-Segura, D., Cunha, M., & Honrado, J. P. (2019). Improving the detection of wildfire disturbances in space and time based on indicators extracted from MODIS data: a case study in northern Portugal. International Journal of Applied Earth Observation and Geoinformation, 78, 77-85. https://doi.org/10.1016/j.jag.2018.12.003
  19. Bhattarai, N., Mallick, K., Stuart, J., Vishwakarma, B. D., Niraula, R., Sen, S., & Jain, M. (2019). An automated multi-model evapotranspiration mapping framework using remotely sensed and reanalysis data. Remote Sensing of Environment, 229, 69–92. https://doi.org/10.1016/j.rse.2019.04.026
  20. Adeola, A. M., Botai, J. O., Mukarugwiza Olwoch, J., DeW. Rautenbach, H. C. J., Adisa, O. M., De Jager, C., … Aaron, M. (2019). Predicting malaria cases using remotely sensed environmental variables in Nkomazi, South Africa. Geospatial Health, 14(1). https://doi.org/10.4081/gh.2019.676
  21. Nelli, L., Ferguson, H. M., & Matthiopoulos, J. (2019). Achieving explanatory depth and spatial breadth in infectious disease modelling: Integrating active and passive case surveillance. Statistical Methods in Medical Research, 096228021985638. https://doi.org/10.1177/0962280219856380
  22. Verstraeten, W. W., Dujardin, S., Hoebeke, L., Bruffaerts, N., Kouznetsov, R., Dendoncker, N., … Delcloo, A. W. (2019). Spatio-temporal monitoring and modelling of birch pollen levels in Belgium. Aerobiologia. https://doi.org/10.1007/s10453-019-09607-w
  23. Yoo, B. H., Kim, K. S., & Lee, J. (2019). MODIS 대기자료를 활용한 남북한 기상관측소에서의 냉방도일 추정. 한국농림기상학회지, 21(2), 97–109. https://doi.org/10.5532/KJAFM.2019.21.2.97
  24. Mpandeli, S., Nhamo, L., Moeletsi, M., Masupha, T., Magidi, J., Tshikolomo, K., … Mabhaudhi, T. (2019). Assessing climate change and adaptive capacity at local scale using observed and remotely sensed data. Weather and Climate Extremes, 26, 100240. https://doi.org/10.1016/j.wace.2019.100240
  25. Badreldin, N., Abu Hatab, A., & Lagerkvist, C.-J. (2019). Spatiotemporal dynamics of urbanization and cropland in the Nile Delta of Egypt using machine learning and satellite big data: implications for sustainable development. Environmental Monitoring and Assessment, 191(12). https://doi.org/10.1007/s10661-019-7934-x
  26. Estrada-Peña, A., Nava, S., Tarragona, E., Bermúdez, S., de la Fuente, J., Domingos, A., … Guglielmone, A. A. (2019). Species occurrence of ticks in South America, and interactions with biotic and abiotic traits. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0314-0
  27. Fatikhunnada, A., Liyantono, Solahudin, M., Buono, A., Kato, T., & Seminar, K. B. (2020). Assessment of pre-treatment and classification methods for Java paddy field cropping pattern detection on MODIS images. Remote Sensing Applications: Society and Environment, 17, 100281. https://doi.org/10.1016/j.rsase.2019.100281
  28. Akpoti, K., Kabo-bah, A. T., Dossou-Yovo, E. R., Groen, T. A., & Zwart, S. J. (2020). Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling. Science of The Total Environment, 709, 136165. https://doi.org/10.1016/j.scitotenv.2019.136165
  29. Pérez-Goya, U., Montesino-SanMartin, M., Militino, A. F., & Ugarte, M. D. (2020). RGISTools: Downloading, Customizing, and Processing Time Series of Remote Sensing Data in R. arXiv preprint arXiv:2002.01859 https://arxiv.org/pdf/2002.01859.pdf
  30. Barela, I., Burger, L. M., Taylor, J., Evans, K. O., Ogawa, R., McClintic, L., & Wang, G. (2020). Relationships between survival and habitat suitability of semi‐aquatic mammals. Ecology and Evolution, 10(11), 4867–4875. https://doi.org/10.1002/ece3.6239
  31. Nguyen, C. T., Nguyen, D. T. H., & Phan, D. K. (2020). Factors affecting urban electricity consumption: a case study in the Bangkok Metropolitan Area using an integrated approach of earth observation data and data analysis. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-020-09157-6
  32. Fernández-Ruiz, N., & Estrada-Peña, A. (2020). Could climate trends disrupt the contact rates between Ixodes ricinus (Acari, Ixodidae) and the reservoirs of Borrelia burgdorferi s.l.? PLOS ONE, 15(5), e0233771. https://doi.org/10.1371/journal.pone.0233771
  33. Liu, L., Huang, J., Xiong, Q., Zhang, H., Song, P., Huang, Y., … Wang, X. (2020). Optimal MODIS data processing for accurate multi-year paddy rice area mapping in China. GIScience & Remote Sensing, 57(5), 687–703. https://doi.org/10.1080/15481603.2020.1773012
  34. Anton, C. B., Smith, D. W., Suraci, J. P., Stahler, D. R., Duane, T. P., & Wilmers, C. C. (2020). Gray wolf habitat use in response to visitor activity along roadways in Yellowstone National Park. Ecosphere, 11(6). https://doi.org/10.1002/ecs2.3164
  35. Jayawardhana, W. G. N. N., & Chathurange, V. M. I. (2020). Investigate the sensitivity of the satellite-based agricultural drought indices to monitor the drought condition of paddy and introduction to enhanced multi-temporal agricultural drought indices. J Remote Sens GIS, 9, 271. https://www.longdom.org/open-access/investigate-the-sensitivity-of-the-satellitebased-agricultural-drought-indices-to-monitor-the-drought-condition-of-paddy.pdf
View Documentation

Label Creation for Tracking and Collecting Data from Biological Samples

Yihan Wu
Description

Tools to generate unique identifier codes and printable barcoded labels for the management of biological samples. The creation of unique ID codes and printable PDF files can be initiated by standard commands, user prompts, or through a GUI addin for R Studio. Biologically informative codes can be included for hierarchically structured sampling designs.

View Documentation

An R Client for the ODK Central API

Florian W. Mayer
Description

Utilities to access and tidy up data from ODK Centrals API. ODK Central is OpenDataKits clearinghouse for digitally captured data https://docs.opendatakit.org/central-intro/. ODK Central’s API is documented at https://odkcentral.docs.apiary.io/.

View Documentation

rOpenSci's blog guidance

Maëlle Salmon
Description

It provides templates for roweb2 blogging and help for a GitHub forking workflow.

View Documentation

Tools for Spell Checking in R

Jeroen Ooms
Description

Spell checking common document formats including latex, markdown, manual pages, and description files. Includes utilities to automate checking of documentation and vignettes as a unit test during R CMD check. Both British and American English are supported out of the box and other languages can be added. In addition, packages may define a wordlist to allow custom terminology without having to abuse punctuation.

Scientific use cases
  1. Luc, A., Lê, S., & Philippe, M. (2019). Nudging consumers for relevant data using Free JAR profiling: an application to product development. Food Quality and Preference, 103751. https://doi.org/10.1016/j.foodqual.2019.103751
View Documentation
dataspice

Create Lightweight Schema.org Descriptions of Data

Bryce Mecum
Description

The goal of dataspice is to make it easier for researchers to create basic, lightweight, and concise metadata files for their datasets. These basic files can then be used to make useful information available during analysis, create a helpful dataset “README” webpage, and produce more complex metadata formats to aid dataset discovery. Metadata fields are based on the Schema.org and Ecological Metadata Language standards.

View Documentation

Handling Taxonomic Lists

Miguel Alvarez
Description

Handling taxonomic lists through objects of class taxlist. This package provides functions to import species lists from Turboveg (https://www.synbiosys.alterra.nl/turboveg/) and the possibility to create backups from resulting R-objects. Also quick displays are implemented as summary-methods.

View Documentation

Advanced Graphics and Image-Processing in R

Jeroen Ooms
Description

Bindings to ImageMagick: the most comprehensive open-source image processing library available. Supports many common formats (png, jpeg, tiff, pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio images are automatically previewed when printed to the console, resulting in an interactive editing environment. The latest version of the package includes a native graphics device for creating in-memory graphics or drawing onto images using pixel coordinates.

Scientific use cases
  1. Stachelek, J., Ford, C., Kincaid, D., King, K., Miller, H., & Nagelkirk, R. (2017). The National Eutrophication Survey: lake characteristics and historical nutrient concentrations. Earth System Science Data Discussions, 1–11. https://doi.org/10.5194/essd-2017-52
  2. Mendez, P. K., Lee, S., & Venter, C. E. (2018). Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys, 795, 49–65. https://doi.org/10.3897/zookeys.795.28416
  3. Weishäupl, D., Schneider, J., Peixoto Pinheiro, B., Ruess, C., Dold, S. M., von Zweydorf, F., … Schmidt, T. (2018). Physiological and pathophysiological characteristics of ataxin-3 isoforms. Journal of Biological Chemistry, jbc.RA118.005801. https://doi.org/10.1074/jbc.ra118.005801
  4. Evans, L. K., & Nishioka, J. (2018). Accumulation processes of trace metals into Arctic sea ice: distribution of Fe, Mn and Cd associated with ice structure. Marine Chemistry. https://doi.org/10.1016/j.marchem.2018.11.011
  5. Maia, R., Gruson, H., Endler, J. A., & White, T. E. (2018). pavo 2: new tools for the spectral and spatial analysis of colour in R. https://doi.org/10.1101/427658
  6. Salazar, P. C., Navarro-Cerrillo, R. M., Cruz, G., Grados, N., & Villar, R. (2019). Variability in growth and biomass allocation and the phenotypic plasticity of seven Prosopis pallida populations in response to water availability. Trees. https://doi.org/10.1007/s00468-019-01868-9
  7. Logemann, A., Schafberg, M., & Brockmeyer, B. (2019). Using the HPTLC-bioluminescence bacteria assay for the determination of acute toxicities in marine sediments and its eligibility as a monitoring assessment tool. Chemosphere. https://doi.org/10.1016/j.chemosphere.2019.05.246
  8. Upham, N. S., Esselstyn, J. A., & Jetz, W. (2019). Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLOS Biology, 17(12), e3000494. https://doi.org/10.1371/journal.pbio.3000494
  9. Mowinckel, A. M., & Vidal-Piñeiro, D. (2019). Visualisation of Brain Statistics with R-packages ggseg and ggseg3d. arXiv preprint arXiv:1912.08200 https://arxiv.org/abs/1912.08200
  10. Schwalb‐Willmann, J., Remelgado, R., Safi, K., & Wegmann, M. (2020). moveVis: Animating movement trajectories in synchronicity with static or temporally dynamic environmental data in R. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13374
  11. Michaels, I. H., Pirani, S. J., & Carrascal, A. (2020). Visualizing 50 Years of Cancer Mortality Rates Across the US at Multiple Geographic Levels Using a Synchronized Map and Graph Animation. Preventing Chronic Disease, 17. https://doi.org/10.5888/pcd17.190286
  12. Feldmann, M. J., Hardigan, M. A., Famula, R. A., López, C. M., Tabb, A., Cole, G. S., & Knapp, S. J. (2020). Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry. GigaScience, 9(5). https://doi.org/10.1093/gigascience/giaa030
  13. Biber‐Freudenberger, L., Ergeneman, C., Förster, J. J., Dietz, T., & Börner, J. (2020). Bioeconomy futures: Expectation patterns of scientists and practitioners on the sustainability of bio‐based transformation. Sustainable Development. https://doi.org/10.1002/sd.2072
View Documentation

Comprehensive TIFF I/O with Full Support for ImageJ TIFF Files

Rory Nolan
Description

General purpose TIFF file I/O for R users. Currently the only such package with read and write support for TIFF files with floating point (real-numbered) pixels, and the only package that can correctly import TIFF files that were saved from ImageJ and write TIFF files than can be correctly read by ImageJ https://imagej.nih.gov/ij/. Also supports text image I/O.

Scientific use cases
  1. Nolan, R., & Padilla-Parra, S. (2018). ijtiff: An R package providing TIFF I/O for ImageJ users. Journal of Open Source Software, 3(23), 633. https://doi.org/10.21105/joss.00633
  2. Hoffman, M. M., Zylla, J. S., Bhattacharya, S., Calar, K., Hartman, T. W., Bhardwaj, R. D., … Messerli, S. M. (2020). Analysis of Dual Class I Histone Deacetylase and Lysine Demethylase Inhibitor Domatinostat (4SC-202) on Growth and Cellular and Genomic Landscape of Atypical Teratoid/Rhabdoid. Cancers, 12(3), 756. https://doi.org/10.3390/cancers12030756
View Documentation

Download Weather Data from Environment and Climate Change Canada

Steffi LaZerte
Description

Provides means for downloading historical weather data from the Environment and Climate Change Canada website (https://climate.weather.gc.ca/historical_data/search_historic_data_e.html). Data can be downloaded from multiple stations and over large date ranges and automatically processed into a single dataset. Tools are also provided to identify stations either by name or proximity to a location.

Scientific use cases
  1. Konzen, E., Shi, J. Q., & Wang, Z. (2019). Modelling Function-Valued Processes with Nonseparable Covariance Structure. arXiv preprint arXiv:1903.09981. https://arxiv.org/pdf/1903.09981.pdf
  2. Hanes, C., Wotton, M., Woolford, D. G., Martell, D. L., & Flannigan, M. (2020). Preceding Fall Drought Conditions and Overwinter Precipitation Effects on Spring Wildland Fire Activity in Canada. Fire, 3(2), 24. https://www.mdpi.com/2571-6255/3/2/24/pdf
View Documentation
wateRinfo

Download Time Series Data from Waterinfo.be

Stijn Van Hoey
Description

wateRinfo facilitates access to waterinfo.be (https://www.waterinfo.be), a website managed by the Flanders Environment Agency (VMM) and Flanders Hydraulics Research. The website provides access to real-time water and weather related environmental variables for Flanders (Belgium), such as rainfall, air pressure, discharge, and water level. The package provides functions to search for stations and variables, and download time series.

View Documentation
beautier
CRAN

BEAUti from R

Richèl J.C. Bilderbeek
Description

BEAST2 (https://www.beast2.org) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAUti 2 (which is part of BEAST2) is a GUI tool that allows users to specify the many possible setups and generates the XML file BEAST2 needs to run. This package provides a way to create BEAST2 input files without active user input, but using R function calls instead.

View Documentation

Catalogue of Life Plus Client

Scott Chamberlain
Description

Client for the Catalogue of Life Plus (CoL+) webservice (https://github.com/CatalogueOfLife/general). The CoL+ webservice is a new interface to the Catalogue of Life. Includes functions for each of the API methods, including searching for names, and more.

View Documentation

Archive and Unarchive Databases Using Flat Files

Carl Boettiger
Description

Flat text files provide a robust, compressible, and portable way to store tables from databases. This package provides convenient functions for exporting tables from relational database connections into compressed text files and streaming those text files back into a database without requiring the whole table to fit in working memory.

View Documentation
clifro
CRAN

Easily Download and Visualise Climate Data from CliFlo

Blake Seers
Description

CliFlo is a web portal to the New Zealand National Climate Database and provides public access (via subscription) to around 6,500 various climate stations (see https://cliflo.niwa.co.nz/ for more information). Collating and manipulating data from CliFlo (hence clifro) and importing into R for further analysis, exploration and visualisation is now straightforward and coherent. The user is required to have an internet connection, and a current CliFlo subscription (free) if data from stations, other than the public Reefton electronic weather station, is sought.

Scientific use cases
  1. Chambault, P., Baudena, A., Bjorndal, K. A., AR Santos, M., Bolten, A. B., & Vandeperre, F. (2019). Swirling in the ocean: immature loggerhead turtles seasonally target old anticyclonic eddies at the fringe of the North Atlantic gyre. Progress in Oceanography. https://doi.org/10.1016/j.pocean.2019.05.005
  2. Atalah, J., & Forrest, B. (2019). Forecasting mussel settlement using historical data and boosted regression trees. Aquaculture Environment Interactions, 11, 625–638. https://doi.org/10.3354/aei00337
View Documentation

CI-Agnostic Workflow Definitions

Kirill Müller
Description

Provides a way to describe common build and deployment workflows for R-based projects: packages, websites (e.g. blogdown, pkgdown), or data processing (e.g. research compendia). The recipe is described independent of the continuous integration tool used for processing the workflow (e.g. Travis CI or AppVeyor). This package has been peer-reviewed by rOpenSci (v. 0.3.0.9004).

View Documentation

A Pipeline Toolkit for Reproducible Computation at Scale

William Michael Landau
Description

A general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website https://docs.ropensci.org/drake/ and the online manual https://books.ropensci.org/drake/.

View Documentation
datapack
CRAN

A Flexible Container to Transport and Manipulate Data and Associated Resources

Matthew B. Jones
Description

Provides a flexible container to transport and manipulate complex sets of data. These data may consist of multiple data files and associated meta data and ancillary files. Individual data objects have associated system level meta data, and data files are linked together using the OAI-ORE standard resource map which describes the relationships between the files. The OAI- ORE standard is described at https://www.openarchives.org/ore. Data packages can be serialized and transported as structured files that have been created following the BagIt specification. The BagIt specification is described at https://tools.ietf.org/html/draft-kunze-bagit-08.

View Documentation

Convert Among Citation Formats

Scott Chamberlain
Description

Converts among many citation formats, including BibTeX, Citeproc, Codemeta, RDF XML, RIS, Schema.org, and Citation File Format. A low level R6 class is provided, as well as stand-alone functions for each citation format for both read and write.

View Documentation

Simple Git Client for R

Jeroen Ooms
Description

Simple git client for R based on libgit2 with support for SSH and HTTPS remotes. All functions in gert use basic R data types (such as vectors and data-frames) for their arguments and return values. User credentials are shared with command line git through the git-credential store and ssh keys stored on disk or ssh-agent. On Linux, a somewhat recent version of libgit2 is required; we provide a PPA for older Ubuntu LTS versions.

View Documentation
CoordinateCleaner
CRAN Peer-reviewed

Automated Cleaning of Occurrence Records from Biological Collections

Alexander Zizka
Description

Automated flagging of common spatial and temporal errors in biological and paleontological collection data, for the use in conservation, ecology and paleontology. Includes automated tests to easily flag (and exclude) records assigned to country or province centroid, the open ocean, the headquarters of the Global Biodiversity Information Facility, urban areas or the location of biodiversity institutions (museums, zoos, botanical gardens, universities). Furthermore identifies per species outlier coordinates, zero coordinates, identical latitude/longitude and invalid coordinates. Also implements an algorithm to identify data sets with a significant proportion of rounded coordinates. Especially suited for large data sets. The reference for the methodology is: Zizka et al. (2019) doi:10.1111/2041-210X.13152.

Scientific use cases
  1. Milla, R., Bastida, J. M., Turcotte, M. M., Jones, G., Violle, C., Osborne, C. P., … Byun, C. (2018). Phylogenetic patterns and phenotypic profiles of the species of plants and mammals farmed for food. Nature Ecology & Evolution, 2(11), 1808–1817. https://doi.org/10.1038/s41559-018-0690-4
  2. Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C., Edler, D., … Antonelli, A. (2019). CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13152
  3. Rice, A., Šmarda, P., Novosolov, M., Drori, M., Glick, L., Sabath, N., … Mayrose, I. (2019). The global biogeography of polyploid plants. Nature Ecology & Evolution, 3(2), 265–273. https://doi.org/10.1038/s41559-018-0787-9
  4. Karger, D. N., Kessler, M., Conrad, O., Weigelt, P., Kreft, H., König, C., & Zimmermann, N. E. (2019). Why tree lines are lower on islands-Climatic and biogeographic effects hold the answer. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12897
  5. De Frenne, P., Zellweger, F., Rodríguez-Sánchez, F., Scheffers, B. R., Hylander, K., Luoto, M., … Lenoir, J. (2019). Global buffering of temperatures under forest canopies. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-019-0842-1
  6. Colli‐Silva, M., Vasconcelos, T. N. C., & Pirani, J. R. (2019). Outstanding plant endemism levels strongly support the recognition of campo rupestre provinces in mountaintops of eastern South America. Journal of Biogeography. https://doi.org/10.1111/jbi.13585
  7. Waller, J. (2019). Data Location Quality at GBIF. Biodiversity Information Science and Standards, 3. https://doi.org/10.3897/biss.3.35829
  8. Butterfield, B. J., Holmgren, C. A., Anderson, R. S., & Betancourt, J. L. (2019). Life history traits predict colonization and extinction lags of desert plant species since the Last Glacial Maximum. Ecology. https://doi.org/10.1002/ecy.2817
  9. Wüest, R. O., Zimmermann, N. E., Zurell, D., Alexander, J. M., Fritz, S. A., Hof, C., … Karger, D. N. (2019). Macroecology in the age of Big Data – Where to go from here? Journal of Biogeography. https://doi.org/10.1111/jbi.13633
  10. Pender, J. E., Hipp, A. L., Hahn, M., Kartesz, J., Nishino, M., & Starr, J. R. (2019). How sensitive are climatic niche inferences to distribution data sampling? A comparison of Biota of North America Program (BONAP) and Global Biodiversity Information Facility (GBIF) datasets. Ecological Informatics, 100991. https://doi.org/10.1016/j.ecoinf.2019.100991
  11. Feng, X., Park, D. S., Walker, C., Peterson, A. T., Merow, C., & Papeş, M. (2019). A checklist for maximizing reproducibility of ecological niche models. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-019-0972-5
  12. Espinosa, B. S., D’Apolito, C., Silva-Caminha, S. A. F., Ferreira, M. G., & Absy, M. L. (2020). Neogene paleoecology and biogeography of a Malvoid pollen in northwestern South America. Review of Palaeobotany and Palynology, 273, 104131. https://doi.org/10.1016/j.revpalbo.2019.104131
  13. Jin, J., & Yang, J. (2020). BDcleaner: A workflow for cleaning taxonomic and geographic errors in occurrence data archived in biodiversity databases. Global Ecology and Conservation, 21, e00852. https://doi.org/10.1016/j.gecco.2019.e00852
  14. Zizka, A., Azevedo, J., Leme, E., Neves, B., Costa, A. F., Caceres, D., & Zizka, G. (2019). Biogeography and conservation status of the pineapple family (Bromeliaceae). Diversity and Distributions, 26(2), 183–195. https://doi.org/10.1111/ddi.13004
  15. Marshall, B. M., & Strine, C. T. (2019). Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media. PeerJ, 7, e8059. https://doi.org/10.7717/peerj.8059
  16. Asevedo, L., D’Apolito, C., Misumi, S. Y., Barros, M. A. de, Barth, O. M., & Avilla, L. dos S. (2020). Palynological analysis of dental calculus from Pleistocene proboscideans of southern Brazil: A new approach for paleodiet and paleoenvironmental reconstructions. Palaeogeography, Palaeoclimatology, Palaeoecology, 540, 109523. https://doi.org/10.1016/j.palaeo.2019.109523
  17. Léveillé-Bourret, É., Chen, B.-H., Garon-Labrecque, M.-È., Ford, B. A., & Starr, J. R. (2020). RAD sequencing resolves the phylogeny, taxonomy and biogeography of Trichophoreae despite a recent rapid radiation (Cyperaceae). Molecular Phylogenetics and Evolution, 145, 106727. https://doi.org/10.1016/j.ympev.2019.106727
  18. Moudrý, V., & Devillers, R. (2020). Quality and usability challenges of global marine biodiversity databases: An example for marine mammal data. Ecological Informatics, 56, 101051. https://doi.org/10.1016/j.ecoinf.2020.101051
  19. Alfaro-Ramírez, F. U., Ramírez-Albores, J. E., Vargas-Hernández, J. J., Franco-Maass, S., & Pérez-Suárez, M. (2020). Potential reduction of Hartweg´s Pine (Pinus hartwegii Lindl.) geographic distribution. PLOS ONE, 15(2), e0229178. https://doi.org/10.1371/journal.pone.0229178
  20. Armitage, D. W., & Jones, S. E. (2020). Barriers to coexistence limit the poleward range of a globally-distributed plant. https://doi.org/10.1101/2020.02.24.946574
  21. Zizka, A., Carvalho‐Sobrinho, J. G., Pennington, R. T., Queiroz, L. P., Alcantara, S., Baum, D. A., … Antonelli, A. (2020). Transitions between biomes are common and directional in Bombacoideae (Malvaceae). Journal of Biogeography. https://doi.org/10.1111/jbi.13815
  22. Bernardi, A. P., Lauterjung, M. B., Mantovani, A., & dos Reis, M. S. (2020). Phylogeography and species distribution modeling reveal a historic disjunction for the conifer Podocarpus lambertii. Tree Genetics & Genomes, 16(3). https://doi.org/10.1007/s11295-020-01434-2
  23. Gaynor, M. L., Fu, C., Gao, L., Lu, L., Soltis, D. E., & Soltis, P. S. (2020). Biogeography and ecological niche evolution in Diapensiaceae inferred from phylogenetic analysis. Journal of Systematics and Evolution. https://doi.org/10.1111/jse.12646
  24. Pacifico, R., Almeda, F., Frota, A., & Fidanza, K. (2020). Areas of endemism on Brazilian mountaintops revealed by taxonomically verified records of Microlicieae (Melastomataceae). Phytotaxa, 450(2), 119–148. https://doi.org/10.11646/phytotaxa.450.2.1
View Documentation

Parse Scientific Names

Scott Chamberlain
Description

Parse scientific names using gnparser (https://gitlab.com/gogna/gnparser), written in Go. gnparser parses scientific names into their component parts; it utilizes a Parsing Expression Grammar specifically for scientific names.

View Documentation

NOAA Weather Data from R

Scott Chamberlain
Description

Client for many NOAA data sources including the NCDC climate API at https://www.ncdc.noaa.gov/cdo-web/webservices/v2, with functions for each of the API endpoints: data, data categories, data sets, data types, locations, location categories, and stations. In addition, we have an interface for NOAA sea ice data, the NOAA severe weather inventory, NOAA Historical Observing Metadata Repository (HOMR) data, NOAA storm data via IBTrACS, tornado data via the NOAA storm prediction center, and more.

Scientific use cases
  1. Bowman, D. C., & Lees, J. M. (2015). Near real time weather and ocean model data access with rNOMADS. Computers & Geosciences, 78, 88–95. https://doi.org/10.1016/j.cageo.2015.02.013
  2. Grosser, S., Scofield, R. P., & Waters, J. M. (2017). Multivariate skeletal analyses support a taxonomic distinction between New Zealand and Australian Eudyptula penguins (Sphenisciformes: Spheniscidae). Emu - Austral Ornithology, 117(3), 276–283. https://doi.org/10.1080/01584197.2017.1315310
  3. Fitzpatrick, M. C., & Dunn, R. R. (2019). Contemporary climatic analogs for 540 North American urban areas in the late 21st century. Nature Communications, 10(1). https://doi.org/10.1038/s41467-019-08540-3
  4. Blakey, R. V., Webb, E. B., Kesler, D. C., Siegel, R. B., Corcoran, D., & Johnson, M. (2019). Bats in a changing landscape: Linking occupancy and traits of a diverse montane bat community to fire regime. Ecology and Evolution. https://doi.org/10.1002/ece3.5121
  5. Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L., & Sunday, J. M. (2019). Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature, 569(7754), 108–111. https://doi.org/10.1038/s41586-019-1132-4
  6. Saunders, K. R., Stephenson, A. G., & Karoly, D. J. (2019). A Regionalisation Approach for Rainfall based on Extremal Dependence. arXiv preprint https://arxiv.org/pdf/1907.05750.pdf
  7. Dumitrescu, A., Cheval, S., & Guijarro, J. A. (2019). Homogenization of a combined hourly air temperature dataset over Romania. International Journal of Climatology. https://doi.org/10.1002/joc.6353
  8. Zhong, B. H. W., Wiersma, J. J., Sheaffer, C. C., Steffenson, B. J., & Smith, K. P. (2019). Assessment of Winter Barley in Minnesota: Relationships among Cultivar, Fall Seeding Date, Winter Survival, and Grain Yield. Cftm, 5(1), 0. https://doi.org/10.2134/cftm2019.07.0055
  9. Kearney, M. R., Gillingham, P. K., Bramer, I., Duffy, J. P., & Maclean, I. M. D. (2019). A method for computing hourly, historical, terrain‐corrected microclimate anywhere on earth. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13330
  10. Da Silva, R. G., Ribeiro, M. H. D. M., Mariani, V. C., & Coelho, L. dos S. (2020). Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables. Chaos, Solitons & Fractals, 139, 110027. https://doi.org/10.1016/j.chaos.2020.110027
  11. Charalampopoulos, I. (2020). The R Language as a Tool for Biometeorological Research. Atmosphere, 11(7), 682. https://doi.org/10.3390/atmos11070682
View Documentation

Interactive, Complex Heatmaps

Alicia Schep
Description

Make complex, interactive heatmaps. iheatmapr includes a modular system for iteratively building up complex heatmaps, as well as the iheatmap() function for making relatively standard heatmaps.

Scientific use cases
  1. Gershanov, S., Toledano, H., Michowiz, S., Barinfeld, O., Pinhasov, A., Goldenberg-Cohen, N., & Salmon-Divon, M. (2018). MicroRNA&ndash,mRNA expression profiles associated with medulloblastoma subgroup 4. Cancer Management and Research, Volume 10, 339–352. https://doi.org/10.2147/cmar.s156709
  2. Ruiz, J. L., Tena, J. J., Bancells, C., Cortés, A., Gómez-Skarmeta, J. L., & Gómez-Díaz, E. (2018). Characterization of the accessible genome in the human malaria parasite Plasmodium falciparum. Nucleic Acids Research. https://doi.org/10.1093/nar/gky643
  3. Ott, C. J., Federation, A. J., Schwartz, L. S., Kasar, S., Klitgaard, J. L., Lenci, R., … Bradner, J. E. (2018). Enhancer Architecture and Essential Core Regulatory Circuitry of Chronic Lymphocytic Leukemia. Cancer Cell. https://doi.org/10.1016/j.ccell.2018.11.001
  4. Kim, K. W., Allen, D. W., Briese, T., Couper, J. J., Barry, S. C., … Colman, P. G. (2019). Distinct gut virome profile of pregnant women with type 1 diabetes in the ENDIA study. Open Forum Infectious Diseases. https://doi.org/10.1093/ofid/ofz025
  5. Reyes, A. L. P., Silva, T. C., Coetzee, S. G., Plummer, J. T., Davis, B. D., Chen, S., … Jones, M. R. (2019). GENAVi: a shiny web application for gene expression normalization, analysis and visualization. BMC Genomics, 20(1). https://doi.org/10.1186/s12864-019-6073-7
  6. Kim, K. W., Allen, D. W., Briese, T., Couper, J. J., Barry, S. C., … Colman, P. G. (2020). Higher frequency of vertebrate‐infecting viruses in the gut of infants born to mothers with type 1 diabetes. Pediatric Diabetes, 21(2), 271–279. https://doi.org/10.1111/pedi.12952
  7. Meng, S., Zhan, S., Dou, W., & Ge, W. (2019). The interactome and proteomic responses of ALKBH7 in cell lines by in-depth proteomics analysis. Proteome Science, 17(1). https://doi.org/10.1186/s12953-019-0156-x
  8. Shi, L., Tian, H., Wang, P., Li, L., Zhang, Z., Zhang, J., & Zhao, Y. (2020). Spaceflight and simulated microgravity suppresses macrophage development via altered RAS/ERK/NFκB and metabolic pathways. Cellular & Molecular Immunology. https://doi.org/10.1038/s41423-019-0346-6
  9. Caseys, C., Gongjun Shi, Nicole Soltis, Raoni Gwinner, Jason Corwin, Susanna Atwell, Daniel Kliebenstein. 2020. Quantitative interactions drive Botrytis cinerea disease outcome across the plant kingdom. bioRxiv preprint 507491; https://doi.org/10.1101/507491
  10. Wang, Y., Zhang, X., Song, Q., Hou, Y., Liu, J., Sun, Y., & Wang, P. (2020). Characterization of the chromatin accessibility in an Alzheimer’s disease (AD) mouse model. Alzheimer’s Research & Therapy, 12(1). https://doi.org/10.1186/s13195-020-00598-2
View Documentation

Interface to the Global Biodiversity Information Facility API

Scott Chamberlain
Description

A programmatic interface to the Web Service methods provided by the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/developer/summary). GBIF is a database of species occurrence records from sources all over the globe. rgbif includes functions for searching for taxonomic names, retrieving information on data providers, getting species occurrence records, getting counts of occurrence records, and using the GBIF tile map service to make rasters summarizing huge amounts of data.

Scientific use cases
  1. Amano, T., Lamming, J. D. L., & Sutherland, W. J. (2016). Spatial Gaps in Global Biodiversity Information and the Role of Citizen Science. BioScience, 66(5), 393–400. https://doi.org/10.1093/biosci/biw022
  2. Bartomeus, I., Park, M. G., Gibbs, J., Danforth, B. N., Lakso, A. N., & Winfree, R. (2013). Biodiversity ensures plant-pollinator phenological synchrony against climate change. Ecol Lett, 16(11), 1331–1338. https://doi.org/10.1111/ele.12170
  3. Barve, V. (2014). Discovering and developing primary biodiversity data from social networking sites: A novel approach. Ecological Informatics, 24, 194–199. https://doi.org/10.1016/j.ecoinf.2014.08.008
  4. Bone, R. E., Smith, J. A. C., Arrigo, N., & Buerki, S. (2015). A macro-ecological perspective on crassulacean acid metabolism (CAM) photosynthesis evolution in Afro-Madagascan drylands: Eulophiinae orchids as a case study. New Phytol, 208(2), 469–481. https://doi.org/10.1111/nph.13572
  5. Collins, R., Duarte Ribeiro, E., Nogueira Machado, V., Hrbek, T., & Farias, I. (2015). A preliminary inventory of the catfishes of the lower Rio Nhamundá, Brazil (Ostariophysi, Siluriformes). Biodiversity Data Journal, 3, e4162. https://doi.org/10.3897/bdj.3.e4162
  6. Drozd, P., & Šipoš, J. (2013). R for all (I): Introduction to the new age of biological analyses. Casopis Slezskeho Zemskeho Muzea A, 62(1). https://doi.org/10.2478/cszma-2013-0004
  7. Kong, X., Huang, M., & Duan, R. (2015). SDMdata: A Web-Based Software Tool for Collecting Species Occurrence Records. PLoS ONE, 10(6), e0128295. https://doi.org/10.1371/journal.pone.0128295
  8. Richardson, D. M., Le Roux, J. J., & Wilson, J. R. (2015). Australian acacias as invasive species: lessons to be learnt from regions with long planting histories. Southern Forests: a Journal of Forest Science, 77(1), 31–39. https://doi.org/10.2989/20702620.2014.999305
  9. Turner, K. G., Fréville, H., & Rieseberg, L. H. (2015). Adaptive plasticity and niche expansion in an invasive thistle. Ecology and Evolution, 5(15), 3183–3197. https://doi.org/10.1002/ece3.1599
  10. Verheijen, L. M., Aerts, R., Bönisch, G., Kattge, J., & Van Bodegom, P. M. (2015). Variation in trait trade-offs allows differentiation among predefined plant functional types: implications for predictive ecology. New Phytol, 209(2), 563–575. https://doi.org/10.1111/nph.13623
  11. Zizka, A., & Antonelli, A. (2015). speciesgeocodeR: An R package for linking species occurrences, user-defined regions and phylogenetic trees for biogeography, ecology and evolution. https://doi.org/10.1101/032755
  12. Butterfield, B. J., Copeland, S. M., Munson, S. M., Roybal, C. M., & Wood, T. E. (2016). Prestoration: using species in restoration that will persist now and into the future. Restoration Ecology. https://doi.org/10.1111/rec.12381
  13. Dellinger, A. S., Essl, F., Hojsgaard, D., Kirchheimer, B., Klatt, S., Dawson, W., … Dullinger, S. (2015). Niche dynamics of alien species do not differ among sexual and apomictic flowering plants. New Phytol, 209(3), 1313–1323. https://doi.org/10.1111/nph.13694
  14. Feitosa, Y. O., Absy, M. L., Latrubesse, E. M., & Stevaux, J. C. (2015). Late Quaternary vegetation dynamics from central parts of the Madeira River in Brazil. Acta Botanica Brasilica, 29(1), 120–128. https://doi.org/10.1590/0102-33062014abb3711
  15. Malhado, A. C. M., Oliveira-Neto, J. A., Stropp, J., Strona, G., Dias, L. C. P., Pinto, L. B., & Ladle, R. J. (2015). Climatological correlates of seed size in Amazonian forest trees. Journal of Vegetation Science, 26(5), 956–963. https://doi.org/10.1111/jvs.12301
  16. Werner, G. D. A., Cornwell, W. K., Cornelissen, J. H. C., & Kiers, E. T. (2015). Evolutionary signals of symbiotic persistence in the legume–rhizobia mutualism. Proc Natl Acad Sci USA, 112(33), 10262–10269. https://doi.org/10.1073/pnas.1424030112
  17. Robertson, M. P., Visser, V., & Hui, C. (2016). Biogeo: an R package for assessing and improving data quality of occurrence record datasets. Ecography, 39(4), 394–401. https://doi.org/10.1111/ecog.02118
  18. Davison, J., Moora, M., Opik, M., Adholeya, A., Ainsaar, L., Ba, A., … Zobel, M. (2015). Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science, 349(6251), 970–973. https://doi.org/10.1126/science.aab1161
  19. Curtis, C. A., & Bradley, B. A. (2016). Plant Distribution Data Show Broader Climatic Limits than Expert-Based Climatic Tolerance Estimates. PLOS ONE, 11(11), e0166407. https://doi.org/10.1371/journal.pone.0166407
  20. Dullinger, I., Wessely, J., Bossdorf, O., Dawson, W., Essl, F., Gattringer, A., … Dullinger, S. (2016). Climate change will increase the naturalization risk from garden plants in Europe. Global Ecol. Biogeogr. https://doi.org/10.1111/geb.12512
  21. Groom, Q., Weatherdon, L., & Geijzendorffer, I. R. (2016). Is citizen science an open science in the case of biodiversity observations? Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.12767
  22. Janssens, S. B., Vandelook, F., De Langhe, E., Verstraete, B., Smets, E., Vandenhouwe, I., & Swennen, R. (2016). Evolutionary dynamics and biogeography of Musaceae reveal a correlation between the diversification of the banana family and the geological and climatic history of Southeast Asia. New Phytol, 210(4), 1453–1465. https://doi.org/10.1111/nph.13856
  23. Sanyal, A., & Decocq, G. (2016). Adaptive evolution of seed oil content in angiosperms: accounting for the global patterns of seed oils. BMC Evolutionary Biology, 16(1). https://doi.org/10.1186/s12862-016-0752-7
  24. Gilles, D., Zaiss, R., Blach-Overgaard, A., Catarino, L., Damen, T., Deblauwe, V., et al. (2016). RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys, 74, 1–18. https://doi.org/10.3897/phytokeys.74.9723
  25. Lundgren, M. R., & Christin, P.-A. (2016). Despite phylogenetic effects, C3-C4 lineages bridge the ecological gap to C4 photosynthesis. Journal of Experimental Botany, erw451. https://doi.org/10.1093/jxb/erw451
  26. Rai, K., Bhattarai, N. R., Vanaerschot, M., Imamura, H., Gebru, G., Khanal, B., … Van der Auwera, G. (2017). Single locus genotyping to track Leishmania donovani in the Indian subcontinent: Application in Nepal. PLOS Neglected Tropical Diseases, 11(3), e0005420. https://doi.org/10.1371/journal.pntd.0005420
  27. Balao, F., Trucchi, E., Wolfe, T., Hao, B.-H., Lorenzo, M. T., Baar, J., … Paun, O. (2017). Adaptive sequence evolution is driven by biotic stress in a pair of orchid species (Dactylorhiza) with distinct ecological optima. Molecular Ecology. https://doi.org/10.1111/mec.14123
  28. Carvajal-Endara, S., Hendry, A. P., Emery, N. C., & Davies, T. J. (2017). Habitat filtering not dispersal limitation shapes oceanic island floras: species assembly of the Galápagos archipelago. Ecology Letters, 20(4), 495–504. https://doi.org/10.1111/ele.12753
  29. Mounce, R., Smith, P., & Brockington, S. (2017). Ex situ conservation of plant diversity in the world’s botanic gardens. Nature Plants, 3(10), 795–802. https://doi.org/10.1038/s41477-017-0019-3
  30. Alfsnes, K., Leinaas, H. P., & Hessen, D. O. (2017). Genome size in arthropods: different roles of phylogeny, habitat and life history in insects and crustaceans. Ecology and Evolution. https://doi.org/10.1002/ece3.3163
  31. Chamberlain SA, Boettiger C. (2017) R Python, and Ruby clients for GBIF species occurrence data. PeerJ Preprints 5:e3304v1 https://doi.org/10.7287/peerj.preprints.3304v1
  32. Ludt, W. B., Morgan, L., Bishop, J., & Chakrabarty, P. (2017). A quantitative and statistical biological comparison of three semi-enclosed seas: the Red Sea, the Persian (Arabian) Gulf, and the Gulf of California. Marine Biodiversity. https://doi.org/10.1007/s12526-017-0740-1
  33. Vanderhoeven, S., Adriaens, T., Desmet, P., Strubbe, D., Backeljau, T., Barbier, Y., … Groom, Q. (2017). Tracking Invasive Alien Species (TrIAS): Building a data-driven framework to inform policy. Research Ideas and Outcomes, 3, e13414. https://doi.org/10.3897/rio.3.e13414
  34. Aedo, C., & Pando, F. (2017). A distribution and taxonomic reference dataset of Geranium in the New World. Scientific Data, 4, 170049. https://doi.org/10.1038/sdata.2017.49
  35. Cardoso, D., Särkinen, T., Alexander, S., Amorim, A. M., Bittrich, V., Celis, M., … Forzza, R. C. (2017). Amazon plant diversity revealed by a taxonomically verified species list. Proceedings of the National Academy of Sciences, 201706756. https://doi.org/10.1073/pnas.1706756114
  36. Duffy, G. A., Coetzee, B. W. T., Latombe, G., Akerman, A. H., McGeoch, M. A., & Chown, S. L. (2017). Barriers to globally invasive species are weakening across the Antarctic. Diversity and Distributions. https://doi.org/10.1111/ddi.12593
  37. Pereira, A. G., Sterli, J., Moreira, F. R. R., & Schrago, C. G. (2017). Multilocus phylogeny and statistical biogeography clarify the evolutionary history of major lineages of turtles. Molecular Phylogenetics and Evolution. https://doi.org/10.1016/j.ympev.2017.05.008
  38. Mayer, K., Haeuser, E., Dawson, W., Essl, F., Kreft, H., Pergl, J., … van Kleunen, M. (2017). Naturalization of ornamental plant species in public green spaces and private gardens. Biological Invasions. https://doi.org/10.1007/s10530-017-1594-y
  39. Chalmandrier, L., Albouy, C., & Pellissier, L. (2017). Species pool distributions along functional trade-offs shape plant productivity–diversity relationships. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-15334-4
  40. Serra-Diaz, J. M., Enquist, B. J., Maitner, B., Merow, C., & Svenning, J.-C. (2017). Big data of tree species distributions: how big and how good? Forest Ecosystems, 4(1). https://doi.org/10.1186/s40663-017-0120-0
  41. Sanyal, A., Lenoir, J., O’Neill, C., Dubois, F., & Decocq, G. (2018). Intraspecific and interspecific adaptive latitudinal cline in Brassicaceae seed oil traits. American Journal of Botany, 105(1), 85–94. https://doi.org/10.1002/ajb2.1014
  42. Bemmels, J. B., Wright, S. J., Garwood, N. C., Queenborough, S. A., Valencia, R., & Dick, C. W. (2018). Filter-dispersal assembly of lowland Neotropical rainforests across the Andes. Ecography. https://doi.org/10.1111/ecog.03473
  43. Schweiger, A. H., & Svenning, J.-C. (2018). Down-sizing of dung beetle assemblages over the last 53 000 years is consistent with a dominant effect of megafauna losses. Oikos. https://doi.org/10.1111/oik.04995
  44. Saad, N. J., Lynch, V. D., Antillón, M., Yang, C., Crump, J. A., & Pitzer, V. E. (2018). Seasonal dynamics of typhoid and paratyphoid fever. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-25234-w
  45. De Oliveira, H., Oprea, M., & Dias, R. (2018). Distributional Patterns and Ecological Determinants of Bat Occurrence Inside Caves: A Broad Scale Meta-Analysis. Diversity, 10(3), 49. https://doi.org/10.3390/d10030049
  46. Lortie, C. J., Filazzola, A., Kelsey, R., Hart, A. K., & Butterfield, H. S. (2018). Better late than never: a synthesis of strategic land retirement and restoration in California. Ecosphere, 9(8), e02367. https://doi.org/10.1002/ecs2.2367
  47. Boria, R. A., & Blois, J. L. (2018). The effect of large sample sizes on ecological niche models: Analysis using a North American rodent, Peromyscus maniculatus. Ecological Modelling, 386, 83–88. https://doi.org/10.1016/j.ecolmodel.2018.08.013
  48. Lusseau, D., & Mancini, F. (2018). A global assessment of tourism and recreation conservation threats to prioritise interventions. arXiv preprint https://arxiv.org/abs/1808.08399
  49. Dallas, T. A., & Hastings, A. (2018). Habitat suitability estimated by niche models is largely unrelated to species abundance. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12820
  50. Gadelha Jr, L. M., de Siracusa, P. C., Ziviani, A., Dalcin, E. C., Affe, H. M., de Siqueira, M. F., … & Costa, R. L. (2018). A Survey of e-Biodiversity: Concepts, Practices, and Challenges. arXiv preprint arXiv:1810.00224 https://arxiv.org/abs/1810.00224
  51. Testo, W. L., Sessa, E., & Barrington, D. S. (2018). The rise of the Andes promoted rapid diversification in Neotropical Phlegmariurus (Lycopodiaceae). New Phytologist. https://doi.org/10.1111/nph.15544
  52. Milla, R., Bastida, J. M., Turcotte, M. M., Jones, G., Violle, C., Osborne, C. P., … Byun, C. (2018). Phylogenetic patterns and phenotypic profiles of the species of plants and mammals farmed for food. Nature Ecology & Evolution, 2(11), 1808–1817. https://doi.org/10.1038/s41559-018-0690-4
  53. Smith, J. R., Letten, A. D., Ke, P.-J., Anderson, C. B., Hendershot, J. N., Dhami, M. K., … Daily, G. C. (2018). A global test of ecoregions. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-018-0709-x
  54. Collins, R. A., Wangensteen, O. S., O’Gorman, E. J., Mariani, S., Sims, D. W., & Genner, M. J. (2018). Persistence of environmental DNA in marine systems. Communications Biology, 1(1). https://doi.org/10.1038/s42003-018-0192-6
  55. Bentz, C., Dediu, D., Verkerk, A., & Jäger, G. (2018). The evolution of language families is shaped by the environment beyond neutral drift. Nature Human Behaviour, 2(11), 816–821. https://doi.org/10.1038/s41562-018-0457-6
  56. Menegotto, A., & Rangel, T. F. (2018). Mapping knowledge gaps in marine diversity reveals a latitudinal gradient of missing species richness. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-07217-7
  57. Bartomeus, I., Stavert, J. R., Ward, D., & Aguado, O. (2018). Historical collections as a tool for assessing the global pollination crisis. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1763), 20170389. https://doi.org/10.1098/rstb.2017.0389
  58. Hanson, J. O., Fuller, R. A., & Rhodes, J. R. (2018). Conventional methods for enhancing connectivity in conservation planning do not always maintain gene flow. Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.13315
  59. Vidal, J. de D., de Souza, A. P., & Koch, I. (2018). Impacts of landscape composition, marginality of distribution, soil fertility, and climatic stability on the patterns of woody plant endemism in the Cerrado. https://doi.org/10.1101/362475
  60. López-Jurado, J., Mateos-Naranjo, E., & Balao, F. (2018). Niche divergence and limits to expansion in the high polyploid Dianthus broteri complex. New Phytologist. https://doi.org/10.1111/nph.15663
  61. Spalink, D., MacKay, R., & Sytsma, K. J. (2019). Phylogeography, population genetics, and distribution modeling reveal vulnerability of Scirpus longii (Cyperaceae) and the Atlantic Coastal Plain Flora to climate change. Molecular Ecology. https://doi.org/10.1111/mec.15006
  62. Lee, C. K. F., Keith, D. A., Nicholson, E., & Murray, N. J. (2019). REDLISTR: Tools for the IUCN Red Lists of Ecosystems and Threatened Species in R. Ecography. https://doi.org/10.1111/ecog.04143
  63. Ladwig, L. M., Chandler, J. L., Guiden, P. W., & Henn, J. J. (2019). Extreme winter warm event causes exceptionally early bud break for many woody species. Ecosphere, 10(1), e02542. https://doi.org/10.1002/ecs2.2542
  64. Lu, M., & Hedin, L. O. (2019). Global plant–symbiont organization and emergence of biogeochemical cycles resolved by evolution-based trait modelling. Nature Ecology & Evolution, 3(2), 239–250. https://doi.org/10.1038/s41559-018-0759-0
  65. Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C., Edler, D., … Antonelli, A. (2019). CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13152
  66. Rice, A., Šmarda, P., Novosolov, M., Drori, M., Glick, L., Sabath, N., … Mayrose, I. (2019). The global biogeography of polyploid plants. Nature Ecology & Evolution, 3(2), 265–273. https://doi.org/10.1038/s41559-018-0787-9
  67. Mittermeier, T. et al. 2019. A season for all things: Phenological imprints in Wikipedia usage and their relevance toconservation. PLoS Biology https://research.birmingham.ac.uk/portal/files/58082037/pbio.3000146_1.pdf
  68. Daru, B. H., le Roux, P. C., Gopalraj, J., Park, D. S., Holt, B. G., & Greve, M. (2019). Spatial overlaps between the global protected areas network and terrestrial hotspots of evolutionary diversity. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12888
  69. Dillen, M., Groom, Q., Chagnoux, S., Güntsch, A., Hardisty, A., Haston, E., … Phillips, S. (2019). A benchmark dataset of herbarium specimen images with label data. Biodiversity Data Journal, 7. https://10.3897/bdj.7.e31817
  70. Piñar, G., Poyntner, C., Tafer, H., & Sterflinger, K. (2019). A time travel story: metagenomic analyses decipher the unknown geographical shift and the storage history of possibly smuggled antique marble statues. Annals of Microbiology. https://doi.org/10.1007/s13213-019-1446-3
  71. Dreyer, J. B. B., Higuchi, P., & Silva, A. C. (2019). Ligustrum lucidum W. T. Aiton (broad-leaf privet) demonstrates climatic niche shifts during global-scale invasion. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-40531-8
  72. Ludt, W. B., Bernal, M. A., Kenworthy, E., Salas, E., & Chakrabarty, P. (2019). Genomic, ecological, and morphological approaches to investigating species limits: A case study in modern taxonomy from Tropical Eastern Pacific surgeonfishes. Ecology and Evolution. https://doi.org/10.1002/ece3.5029
  73. Medina, I. (2019). The role of the environment in the evolution of nest shape in Australian passerines. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-41948-x
  74. Miranda, L. S., Imperatriz-Fonseca, V. L., & Giannini, T. C. (2019). Climate change impact on ecosystem functions provided by birds in southeastern Amazonia. PLOS ONE, 14(4), e0215229. https://doi.org/10.1371/journal.pone.0215229
  75. Van de Perre, F., Leirs, H., & Verheyen, E. (2019). Paleoclimate, ecoregion size, and degree of isolation explain regional biodiversity differences among terrestrial vertebrates within the Congo Basin. Belgian Journal of Zoology, 149(1). https://doi.org/10.26496/bjz.2019.28
  76. Hoban, S., Dawson, A., Robinson, J. D., Smith, A. B., & Strand, A. E. (2019). Inference of biogeographic history by formally integrating distinct lines of evidence: genetic, environmental niche, and fossil. Ecography. https://doi.org/10.1111/ecog.04327
  77. Bacci, L. F., Michelangeli, F. A., & Goldenberg, R. (2019). Revisiting the classification of Melastomataceae: implications for habit and fruit evolution. Botanical Journal of the Linnean Society, 190(1), 1–24. https://doi.org/10.1093/botlinnean/boz006
  78. Baliga, V. B., & Mehta, R. S. (2019). Morphology, ecology, and biogeography of independent origins of cleaning behavior around the world. Integrative and comparative biology. https://doi.org/10.1093/icb/icz030
  79. Kadereit, J. W., Lauterbach, M., Kandziora, M., Spillmann, J., & Nyffeler, R. (2019). Dual colonization of European high-altitude areas from Asia by Callianthemum (Ranunculaceae). Plant Systematics and Evolution. https://doi.org/10.1007/s00606-019-01583-5
  80. Schubert, M., Marcussen, T., Meseguer, A. S., & Fjellheim, S. (2019). The grass subfamily Pooideae: Cretaceous–Palaeocene origin and climate‐driven Cenozoic diversification. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12923
  81. Westmeijer, G., Everaert, G., Pirlet, H., De Clerck, O., & Vandegehuchte, M. B. (2019). Mechanistic niche modelling to identify favorable growth sites of temperate macroalgae. Algal Research, 41, 101529. https://doi.org/10.1016/j.algal.2019.101529
  82. Alhajeri, B. H., & Fourcade, Y. (2019). High correlation between species‐level environmental data estimates extracted from IUCN expert range maps and from GBIF occurrence data. Journal of Biogeography. https://doi.org/10.1111/jbi.13619
  83. Ros-Candeira, A., Pérez-Luque, A. J., Suárez-Muñoz, M., Bonet-García, F. J., Hódar, J. A., Giménez de Azcárate, F., & Ortega-Díaz, E. (2019). Dataset of occurrence and incidence of pine processionary moth in Andalusia, south Spain. ZooKeys, 852, 125–136. https://doi.org/10.3897/zookeys.852.28567
  84. McTavish, E. J. (2019). Linking Biodiversity Data Using Evolutionary History. Bio/diversity Information Science and Standards, 3. https://doi.org/10.3897/biss.3.36207
  85. Uzma, Jiménez-Mejías, P., Amir, R., Hayat, M. Q., & Hipp, A. L. (2019). Timing and ecological priority shaped the diversification of sedges in the Himalayas. PeerJ, 7, e6792. https://doi.org/10.7717/peerj.6792
  86. Butterfield, B. J., Holmgren, C. A., Anderson, R. S., & Betancourt, J. L. (2019). Life history traits predict colonization and extinction lags of desert plant species since the Last Glacial Maximum. Ecology. https://doi.org/10.1002/ecy.2817
  87. Correia, R. A., Ruete, A., Stropp, J., Malhado, A. C. M., dos Santos, J. W., Lessa, T., … Ladle, R. J. (2019). Using ignorance scores to explore biodiversity recording effort for multiple taxa in the Caatinga. Ecological Indicators, 106, 105539. https://doi.org/10.1016/j.ecolind.2019.105539
  88. Collins, R. A., Bakker, J., Wangensteen, O. S., Soto, A. Z., Corrigan, L., Sims, D. W., … Mariani, S. (2019). Non‐specific amplification compromises environmental DNA metabarcoding with COI. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13276
  89. Jaganathan, G. K., & Dalrymple, S. E. (2019). Internal Seed Structure of Alpine Plants and Extreme Cold Exposure. Data, 4(3), 107. https://doi.org/10.3390/data4030107
  90. Hayden, B., Palomares, M. L. D., Smith, B. E., & Poelen, J. H. (2019). Biological and environmental drivers of trophic ecology in marine fishes - a global perspective. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-47618-2
  91. Pender, J. E., Hipp, A. L., Hahn, M., Kartesz, J., Nishino, M., & Starr, J. R. (2019). How sensitive are climatic niche inferences to distribution data sampling? A comparison of Biota of North America Program (BONAP) and Global Biodiversity Information Facility (GBIF) datasets. Ecological Informatics, 100991. https://doi.org/10.1016/j.ecoinf.2019.100991
  92. Esperon‐Rodriguez, M., Power, S. A., Tjoelker, M. G., Beaumont, L. J., Burley, H., Caballero‐Rodriguez, D., & Rymer, P. D. (2019). Assessing the vulnerability of Australia’s urban forests to climate extremes. Plants, People, Planet. https://doi.org/10.1002/ppp3.10064
  93. De Luca, D., Kooistra, W. H. C. F., Sarno, D., Gaonkar, C. C., & Piredda, R. (2019). Global distribution and diversity of Chaetoceros (Bacillariophyta, Mediophyceae): integration of classical and novel strategies. PeerJ, 7, e7410. https://doi.org/10.7717/peerj.7410
  94. Havinga, I., Hein, L., Vega-Araya, M., & Languillaume, A. (2020). Spatial quantification to examine the effectiveness of payments for ecosystem services: A case study of Costa Rica’s Pago de Servicios Ambientales. Ecological Indicators, 108, 105766. https://doi.org/10.1016/j.ecolind.2019.105766
  95. Ahmad, S., Yang, L., Khan, T. U., Wanghe, K., Li, M., & Luan, X. (2020). Using an ensemble modelling approach to predict the potential distribution of Himalayan gray goral (Naemorhedus goral bedfordi) in Pakistan. Global Ecology and Conservation, 21, e00845. https://doi.org/10.1016/j.gecco.2019.e00845
  96. Faltýnek Fric, Z., Rindoš, M., & Konvička, M. (2019). Phenology responses of temperate butterflies to latitude depend on ecological traits. Ecology Letters, 23(1), 172–180. https://doi.org/10.1111/ele.13419
  97. Stévart, T., Dauby, G., Lowry, P. P., Blach-Overgaard, A., Droissart, V., Harris, D. J., … Couvreur, T. L. P. (2019). A third of the tropical African flora is potentially threatened with extinction. Science Advances, 5(11), eaax9444. https://doi.org/10.1126/sciadv.aax9444
  98. D’Amen, M., & Azzurro, E. (2019). Lessepsian fish invasion in Mediterranean marine protected areas: a risk assessment under climate change scenarios. ICES Journal of Marine Science, 77(1), 388–397. https://doi.org/10.1093/icesjms/fsz207
  99. Yusri, S., Siregar, V. P., & Suharsono. (2019). Distribution Modelling of Porites (Poritidae) in Indonesia. IOP Conference Series: Earth and Environmental Science, 363, 012025. https://doi.org/10.1088/1755-1315/363/1/012025
  100. Ekroos, J., Kleijn, D., Batáry, P., Albrecht, M., Báldi, A., Blüthgen, N., … Smith, H. G. (2020). High land-use intensity in grasslands constrains wild bee species richness in Europe. Biological Conservation, 241, 108255. https://doi.org/10.1016/j.biocon.2019.108255
  101. Marshall, B. M., & Strine, C. T. (2019). Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media. PeerJ, 7, e8059. https://doi.org/10.7717/peerj.8059
  102. Mienna, I. M., Speed, J. D. M., Bendiksby, M., Thornhill, A. H., Mishler, B. D., & Martin, M. D. (2019). Differential patterns of floristic phylogenetic diversity across a post‐glacial landscape. Journal of Biogeography. https://doi.org/10.1111/jbi.13789
  103. D’Amen, M., & Azzurro, E. (2019). Integrating univariate niche dynamics in species distribution models: A step forward for marine research on biological invasions. Journal of Biogeography, 47(3), 686–697. https://doi.org/10.1111/jbi.13761
  104. Alves, D. M. C. C., Eduardo, A. A., da Silva Oliveira, E. V., Villalobos, F., Dobrovolski, R., Pereira, T. C., … Gouveia, S. F. (2020). Unveiling geographical gradients of species richness from scant occurrence data. Global Ecology and Biogeography, 29(4), 748–759. https://doi.org/10.1111/geb.13055
  105. Léveillé-Bourret, É., Chen, B.-H., Garon-Labrecque, M.-È., Ford, B. A., & Starr, J. R. (2020). RAD sequencing resolves the phylogeny, taxonomy and biogeography of Trichophoreae despite a recent rapid radiation (Cyperaceae). Molecular Phylogenetics and Evolution, 145, 106727. https://doi.org/10.1016/j.ympev.2019.106727
  106. Wraith, J., Norman, P., & Pickering, C. (2020). Orchid conservation and research: An analysis of gaps and priorities for globally Red Listed species. Ambio. https://doi.org/10.1007/s13280-019-01306-7
  107. Bachman, S., Walker, B., Barrios, S., Copeland, A., & Moat, J. (2020). Rapid Least Concern: towards automating Red List assessments. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e47018
  108. Ceschin, D. G., Pires, N. S., Mardirosian, M. N., Lascano, C. I., & Venturino, A. (2020). The Rhinella arenarum transcriptome: de novo assembly, annotation and gene prediction. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-57961-4
  109. Van Zonneveld, M., Rakha, M., Tan, S. yee, Chou, Y.-Y., Chang, C.-H., Yen, J.-Y., … Solberg, S. Ø. (2020). Mapping patterns of abiotic and biotic stress resilience uncovers conservation gaps and breeding potential of Vigna wild relatives. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-58646-8
  110. Benhadi‐Marín, J., Santos, S. A. P., Baptista, P., & Pereira, J. A. (2020). Distribution of Bactrocera oleae (Rossi, 1790) throughout the Iberian Peninsula based on a maximum entropy modeling approach. Annals of Applied Biology. https://doi.org/10.1111/aab.12584
  111. Shivambu, T. C., Shivambu, N., & Downs, C. T. (2020). Impact assessment of seven alien invasive bird species already introduced to South Africa. Biological Invasions. https://doi.org/10.1007/s10530-020-02221-9
  112. Li, X., & Guo, B. (2020). Substantially adaptive potential in polyploid cyprinid fishes: evidence from biogeographic, phylogenetic and genomic studies. Proceedings of the Royal Society B: Biological Sciences, 287(1920), 20193008. https://doi.org/10.1098/rspb.2019.3008
  113. Hannah, L., Roehrdanz, P. R., Marquet, P. A., Enquist, B. J., Midgley, G., Foden, W., … Svenning, J. (2020). 30% land conservation and climate action reduces tropical extinction risk by more than 50%. Ecography. https://doi.org/10.1111/ecog.05166
  114. Zizka, A., Carvalho‐Sobrinho, J. G., Pennington, R. T., Queiroz, L. P., Alcantara, S., Baum, D. A., … Antonelli, A. (2020). Transitions between biomes are common and directional in Bombacoideae (Malvaceae). Journal of Biogeography. https://doi.org/10.1111/jbi.13815
  115. Jung, E.-Y., Gaviria, J., Sun, S., & Engelbrecht, B. M. J. (2020). Comparative drought resistance of temperate grassland species: testing performance trade-offs and the relation to distribution. Oecologia, 192(4), 1023–1036. https://doi.org/10.1007/s00442-020-04625-9
  116. Howard, C. C., & Cellinese, N. (2020). Tunicate bulb size variation in monocots explained by temperature and phenology. Ecology and Evolution, 10(5), 2299–2309. https://doi.org/10.1002/ece3.5996
  117. Du, C., Chen, J., Jiang, L., & Qiao, G. (2020). High correlation of species diversity patterns between specialist herbivorous insects and their specific hosts. Journal of Biogeography. https://doi.org/10.1111/jbi.13816
  118. Kusumoto, B., Costello, M. J., Kubota, Y., Shiono, T., Wei, C., Yasuhara, M., & Chao, A. (2020). Global distribution of coral diversity: Biodiversity knowledge gradients related to spatial resolution. Ecological Research, 35(2), 315–326. https://doi.org/10.1111/1440-1703.12096
  119. Young, N. E., Jarnevich, C. S., Sofaer, H. R., Pearse, I., Sullivan, J., Engelstad, P., & Stohlgren, T. J. (2020). A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales. PLOS ONE, 15(3), e0229253. https://doi.org/10.1371/journal.pone.0229253
  120. Chapman, A., Belbin, L., Zermoglio, P., Wieczorek, J., Morris, P., Nicholls, M., … Schigel, D. (2020). Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards, 4. https://doi.org/10.3897/biss.4.50889
  121. Stropp, J., Umbelino, B., Correia, R. A., Campos-Silva, J. V., Ladle, R. J., & Malhado, A. C. M. (2020). The ghosts of forests past and future: deforestation and botanical sampling in the Brazilian Amazon. Ecography. https://doi.org/10.1111/ecog.05026
  122. Hernández‐Rojas, A. C., Kluge, J., Krömer, T., Carvajal‐Hernández, C., Silva‐Mijangos, L., Miehe, G., … Kessler, M. (2020). Latitudinal patterns of species richness and range size of ferns along elevational gradients at the transition from tropics to subtropics. Journal of Biogeography, 47(6), 1383–1397. https://doi.org/10.1111/jbi.13841
  123. Scharmüller, A., Schreiner, V. C., & Schäfer, R. B. (2020). Standartox: Standardizing Toxicity Data. Data, 5(2), 46. https://doi.org/10.3390/data5020046
  124. Bohora Schlickmann, M., da Silva, A. C., de Oliveira, L. M., Oliveira Matteucci, D., Domingos Machado, F., Cuchi, T., … Higuchi, P. (2020). Specific leaf area is a potential indicator of tree species sensitive to future climate change in the mixed Subtropical Forests of southern Brazil. Ecological Indicators, 116, 106477. https://doi.org/10.1016/j.ecolind.2020.106477
  125. Joyce, E., Thiele, K., Slik, F., & Crayn, D. (2020). Checklist of the vascular flora of the Sunda-Sahul Convergence Zone. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e51094
  126. Petersen, T. K., Speed, J. D. M., Grøtan, V., & Austrheim, G. (2020). Urban aliens and threatened near-naturals: Land-cover affects the species richness of alien- and threatened species in an urban-rural setting. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-65459-2
  127. Lindberg, C. L., Hanslin, H. M., Schubert, M., Marcussen, T., Trevaskis, B., Preston, J. C., & Fjellheim, S. (2020). Increased above‐ground resource allocation is a likely precursor for independent evolutionary origins of annuality in the Pooideae grass subfamily. New Phytologist. https://doi.org/10.1111/nph.16666
  128. Lenoir, J., Bertrand, R., Comte, L., Bourgeaud, L., Hattab, T., Murienne, J., & Grenouillet, G. (2020). Species better track climate warming in the oceans than on land. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-020-1198-2
  129. Sun, M., Folk, R. A., Gitzendanner, M. A., Soltis, P. S., Chen, Z., Soltis, D. E., & Guralnick, R. P. (2020). Recent accelerated diversification in rosids occurred outside the tropics. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17116-5
  130. Hock, M., Hofmann, R., Essl, F., Pyšek, P., Bruelheide, H., & Erfmeier, A. (2020). Native distribution characteristics rather than functional traits explain preadaptation of invasive species to high‐UV‐B environments. Diversity and Distributions. https://doi.org/10.1111/ddi.13113
View Documentation
rinat
CRAN

Access iNaturalist Data Through APIs

Stéphane Guillou
Description

A programmatic interface to the API provided by the iNaturalist website https://www.inaturalist.org/ to download species occurrence data submitted by citizen scientists.

View Documentation

Bielefeld Academic Search Engine (BASE) Client

Scott Chamberlain
Description

Interface to the API for the Bielefeld Academic Search Engine (BASE) (https://www.base-search.net/). BASE is a search engine for more than 150 million scholarly documents from more than 7000 sources. Methods are provided for searching for documents, as well as getting information on higher level groupings of documents: collections and repositories within collections. Search includes faceting, so you can get a high level overview of number of documents across a given variable (e.g., year). BASE asks users to respect a rate limit, but does not enforce it themselves; we enforce that rate limit.

View Documentation

Parse Messy Geographic Coordinates

Scott Chamberlain
Description

Parse geographic coordinates from various formats to decimal degree numeric values. Parse coordinates into their parts (degree, minutes, seconds); calculate hemisphere from coordinates; pull out individually degrees, minutes, or seconds; add and subtract degrees, minutes, and seconds. C++ code herein originally inspired from code written by Jeffrey D. Bogan, but then completely re-written.

View Documentation
rrricanes
Peer-reviewed

Web scraper for Atlantic and east Pacific hurricanes and tropical storms

Tim Trice
Description

Get archived data of past and current hurricanes and tropical storms for the Atlantic and eastern Pacific oceans. Data is available for storms since 1998. Datasets are updated via the rrricanesdata package. Currently, this package is about 6MB of datasets. See the README or view vignette("drat") for more information.

View Documentation

Visualize Species Occurrence Data

Scott Chamberlain
Description

Utilities for visualizing species occurrence data. Includes functions to visualize occurrence data from spocc, rgbif, and other packages. Mapping options included for base R plots, ggplot2, leaflet and GitHub gists.

View Documentation

Clean Biological Occurrence Records

Scott Chamberlain
Description

Clean biological occurrence records. Includes functionality for cleaning based on various aspects of spatial coordinates, unlikely values due to political centroids, coordinates based on where collections of specimens are held, and more.

Scientific use cases
  1. Abdala-Roberts, L., Galmán, A., Petry, W. K., Covelo, F., de la Fuente, M., Glauser, G., & Moreira, X. (2018). Interspecific variation in leaf functional and defensive traits in oak species and its underlying climatic drivers. PLOS ONE, 13(8), e0202548. https://doi.org/10.1371/journal.pone.0202548
  2. Dallas, T. A., & Hastings, A. (2018). Habitat suitability estimated by niche models is largely unrelated to species abundance. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12820
  3. Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C., Edler, D., … Antonelli, A. (2019). CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13152
  4. Jin, J., & Yang, J. (2020). BDcleaner: A workflow for cleaning taxonomic and geographic errors in occurrence data archived in biodiversity databases. Global Ecology and Conservation, 21, e00852. https://doi.org/10.1016/j.gecco.2019.e00852
  5. Staude, I. R., Waller, D. M., Bernhardt-Römermann, M., Bjorkman, A. D., Brunet, J., De Frenne, P., … Baeten, L. (2020). Replacements of small- by large-ranged species scale up to diversity loss in Europe’s temperate forest biome. Nature Ecology & Evolution, 4(6), 802–808. https://doi.org/10.1038/s41559-020-1176-8
View Documentation

Mangal Client

Steve Vissault
Description

An interface to the Mangal database - a collection of ecological networks. This package includes functions to work with the Mangal RESTful API methods (https://mangal.io/doc/api/).

View Documentation

Accesses Weather Data from the Iowa Environment Mesonet

Maëlle Salmon
Description

Allows to get weather data from Automated Surface Observing System (ASOS) stations (airports) in the whole world thanks to the Iowa Environment Mesonet website.

Scientific use cases
  1. Hagerman, A. D., South, D. D., Sondgerath, T. C., Patyk, K. A., Sanson, R. L., Schumacher, R. S., … Magzamen, S. (2018). Temporal and geographic distribution of weather conditions favorable to airborne spread of foot-and-mouth disease in the coterminous United States. Preventive Veterinary Medicine, 161, 41–49. https://doi.org/10.1016/j.prevetmed.2018.10.016
  2. Milà, C., Curto, A., Dimitrova, A., Sreekanth, V., Kinra, S., Marshall, J. D., & Tonne, C. (2020). Identifying predictors of personal exposure to air temperature in peri-urban India. Science of The Total Environment, 707, 136114. https://doi.org/10.1016/j.scitotenv.2019.136114
View Documentation

Australian Government Bureau of Meteorology (BOM) Data Client

Adam H. Sparks
Description

Provides functions to interface with Australian Government Bureau of Meteorology (BOM) data, fetching data and returning a data frame of precis forecasts, historical and current weather data from stations, agriculture bulletin data, BOM 0900 or 1500 weather bulletins and downloading and importing radar and satellite imagery files. Data (c) Australian Government Bureau of Meteorology Creative Commons (CC) Attribution 3.0 licence or Public Access Licence (PAL) as appropriate. See http://www.bom.gov.au/other/copyright.shtml for further details.

Scientific use cases
  1. H Sparks, A., Padgham, M., Parsonage, H., & Pembleton, K. (2017). bomrang: Fetch Australian Government Bureau of Meteorology Data in R. The Journal of Open Source Software, 2(17). https://doi.org/10.21105/joss.00411
View Documentation

NASA POWER API Client

Adam H. Sparks
Description

Client for NASA POWER global meteorology, surface solar energy and climatology data API. POWER (Prediction Of Worldwide Energy Resource) data are freely available global meteorology and surface solar energy climatology data for download with a resolution of 1/2 by 1/2 arc degree longitude and latitude and are funded through the NASA Earth Science Directorate Applied Science Program. For more on the data themselves, a web-based data viewer and web access, please see https://power.larc.nasa.gov/.

Scientific use cases
  1. Charalampopoulos, I. (2020). The R Language as a Tool for Biometeorological Research. Atmosphere, 11(7), 682. https://doi.org/10.3390/atmos11070682
View Documentation

Global Surface Summary of the Day (GSOD) Weather Data Client

Adam Sparks
Description

Provides automated downloading, parsing, cleaning, unit conversion and formatting of Global Surface Summary of the Day (GSOD) weather data from the from the USA National Centers for Environmental Information (NCEI). Units are converted from from United States Customary System (USCS) units to International System of Units (SI). Stations may be individually checked for number of missing days defined by the user, where stations with too many missing observations are omitted. Only stations with valid reported latitude and longitude values are permitted in the final data. Additional useful elements, saturation vapour pressure (es), actual vapour pressure (ea) and relative humidity (RH) are calculated from the original data using the improved August-Roche-Magnus approximation (Alduchov & Eskridge 1996) and included in the final data set. The resulting metadata include station identification information, country, state, latitude, longitude, elevation, weather observations and associated flags. For information on the GSOD data from NCEI, please see the GSOD readme.txt file available from, https://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt.

Scientific use cases
  1. H Sparks, A., Hengl, T., & Nelson, A. (2017). GSODR: Global Summary Daily Weather Data in R. The Journal of Open Source Software, 2(10). https://doi.org/10.21105/joss.00177
View Documentation

API Client for CHIRPS

Kauê de Sousa
Description

API Client for the Climate Hazards Group InfraRed Precipitation with Station Data CHIRPS. The CHIRPS data is a 35+ year quasi-global rainfall data set, which incorporates 0.05 arc-degrees resolution satellite imagery, and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. For more details on CHIRPS data please visit its official home page https://www.chc.ucsb.edu/data/chirps. Requests from large time series (> 10 years) and large geographic coverage (global scale) may take several minutes.

View Documentation
getCRUCLdata
CRAN Peer-reviewed

CRU CL v. 2.0 Climatology Client

Adam Sparks
Description

Provides functions that automate downloading and importing University of East Anglia Climate Research Unit (CRU) CL v. 2.0 climatology data, facilitates the calculation of minimum temperature and maximum temperature and formats the data into a tidy data frame as a tibble or a list of raster stack objects for use. CRU CL v. 2.0 data are a gridded climatology of 1961-1990 monthly means released in 2002 and cover all land areas (excluding Antarctica) at 10 arcminutes (0.1666667 degree) resolution. For more information see the description of the data provided by the University of East Anglia Climate Research Unit, https://crudata.uea.ac.uk/cru/data/hrg/tmc/readme.txt.

View Documentation
dittodb
CRAN

A Test Environment for Database Requests

Jonathan Keane
Description

Testing and documenting code that communicates with remote databases can be painful. Although the interaction with R is usually relatively simple (e.g. data(frames) passed to and from a database), because they rely on a separate service and the data there, testing them can be difficult to set up, unsustainable in a continuous integration environment, or impossible without replicating an entire production cluster. This package addresses that by allowing you to make recordings from your database interactions and then play them back while testing (or in other contexts) all without needing to spin up or have access to the database your code would typically connect to.

View Documentation
patentsview
CRAN Peer-reviewed

An R Client to the PatentsView API

Christopher Baker
Description

Provides functions to simplify the PatentsView API (http://www.patentsview.org/api/doc.html) query language, send GET and POST requests to the API’s seven endpoints, and parse the data that comes back.

View Documentation

Access and Search MedRxiv and BioRxiv Preprint Data

Luke McGuinness
Description

An increasingly important source of health-related bibliographic content are preprints - preliminary versions of research articles that have yet to undergo peer review. The two preprint repositories most relevant to health-related sciences are medRxiv https://www.medrxiv.org/ and bioRxiv https://www.biorxiv.org/, both of which are operated by the Cold Spring Harbor Laboratory. medrxivr provides programmatic access to the Cold Spring Harbour Laboratory (CSHL) API https://api.biorxiv.org/, allowing users to easily download medRxiv and bioRxiv preprint metadata (e.g. title, abstract, publication date, author list, etc) into R. medrxivr also provides functions to search the downloaded preprint records using regular expressions and Boolean logic, as well as helper functions that allow users to export their search results to a .BIB file for easy import to a reference manager and to download the full-text PDFs of preprints matching their search criteria.

View Documentation

Extract and Tidy Canadian Hydrometric Data

Sam Albers
Description

Provides functions to access historical and real-time national hydrometric data from Water Survey of Canada data sources (https://dd.weather.gc.ca/hydrometric/csv/ and https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/) and then applies tidy data principles.

Scientific use cases
  1. Albers, S. (2017). tidyhydat: Extract and Tidy Canadian Hydrometric Data. The Journal of Open Source Software, 2(20), 511. https://doi.org/10.21105/joss.00511
  2. Beaton, A., Whaley, R., Corston, K., & Kenny, F. (2019). Identifying historic river ice breakup timing using MODIS and Google Earth Engine in support of operational flood monitoring in Northern Ontario. https://doi.org/10.1016/j.rse.2019.02.011
View Documentation
circle

R client package for the Circle CI API

Patrick Schratz
Description

Tools for interacting with the Circle CI API. Besides executing common tasks such as querying build logs and restarting builds, this package also helps setting up permissions to deploy from builds.

View Documentation

Interface to the OpenCage API

Maëlle Salmon
Description

Tool for accessing the OpenCage API, which provides forward geocoding (from placename to longitude and latitude) and reverse geocoding (from longitude and latitude to placename).

Scientific use cases
  1. Cano, J., Rodríguez, A., Simpson, H., Tabah, E. N., Gómez, J. F., & Pullan, R. L. (2018). Modelling the spatial distribution of aquatic insects (Order Hemiptera) potentially involved in the transmission of Mycobacterium ulcerans in Africa. Parasites & Vectors, 11(1). http://doi.org/10.1186/s13071-018-3066-3
  2. Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C., Edler, D., … Antonelli, A. (2019). CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13152
  3. Deribe, K., Simpson, H., Pullan, R. L., Bosco, M. J., Wanji, S., Weaver, N. D., … Cano, J. (2020). Predicting the Environmental Suitability and Population at Risk of Podoconiosis in Africa. https://doi.org/10.1101/2020.03.04.977827
View Documentation

Interface to the Search API for PLoS Journals

Scott Chamberlain
Description

A programmatic interface to the SOLR based search API (http://api.plos.org/) provided by the Public Library of Science journals to search their articles. Functions are included for searching for articles, retrieving articles, making plots, doing faceted searches, highlight searches, and viewing results of highlighted searches in a browser.

Scientific use cases
  1. Hartgerink, C. H. J., van Aert, R. C. M., Nuijten, M. B., Wicherts, J. M., & van Assen, M. A. L. M. (2016). Distributions ofp-values smaller than .05 in psychology: what is going on? PeerJ, 4, e1935. https://doi.org/10.7717/peerj.1935
  2. White, E. (2015). Some thoughts on best publishing practices for scientific software. IEE, 8. https://doi.org/10.4033/iee.2015.8.9.c
  3. Gálvez, R. H. (2017). Assessing author self-citation as a mechanism of relevant knowledge diffusion. Scientometrics. https://doi.org/10.1007/s11192-017-2330-1
  4. Li, K., Yan, E., & Feng, Y. (2017). How is R cited in research outputs? Structure, impacts, and citation standard. Journal of Informetrics, 11(4), 989–1002. https://doi.org/10.1016/j.joi.2017.08.003
  5. Federer LM, Belter CW, Joubert DJ, Livinski A, Lu YL, et al. (2018) Data sharing in PLOS ONE: An analysis of Data Availability Statements. PLOS ONE 13(5): e0194768. https://doi.org/10.1371/journal.pone.0194768
  6. Jaspers, S., De Troyer, E., & Aerts, M. (2018). Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA. EFSA Supporting Publications, 15(6), 1427E. https://doi.org/10.2903/sp.efsa.2018.EN-1427
  7. Nuijten, M. B. (2018, April 30). Research on Research: A Meta-Scientific Study of Problems and Solutions in Psychological Science. https://doi.org/10.31234/osf.io/qtk7e
  8. Enkhbayar, A., Haustein, S., Barata, G., & Alperin, J. P. (2019). How much research shared on Facebook is hidden from public view? A comparison of public and private online activity around PLOS ONE papers. arXiv preprint arXiv:1909.01476. https://arxiv.org/abs/1909.01476
  9. Mishra, P., & Narayan Tripathi, L. (2019). Characterization of two‐dimensional materials from Raman spectral data. Journal of Raman Spectroscopy. https://doi.org/10.1002/jrs.5744
  10. Vílchez-Román, C., Huamán-Delgado, F., & Alhuay-Quispe, J. (2020). Social dimension activates the usage and academic impact of Open Access publications in Andean countries: a structural modeling-based approach. Information Development, 026666692090184. https://doi.org/10.1177/0266666920901849
  11. Enkhbayar, A., Haustein, S., Barata, G., & Alperin, J. P. (2020). How much research shared on Facebook happens outside of public pages and groups? A comparison of public and private online activity around PLOS ONE papers. Quantitative Science Studies, 1–22. https://doi.org/10.1162/qss_a_00044
View Documentation

Interface to the Pleiades Archeological Database

Scott Chamberlain
Description

Provides a set of functions for interacting with the Pleiades (https://pleiades.stoa.org/) API, including getting status data, places data, and creating a GeoJSON based map on GitHub gists.

View Documentation

Import OpenStreetMap Data as Simple Features or Spatial Objects

Mark Padgham
Description

Download and import of OpenStreetMap (OSM) data as sf or sp objects. OSM data are extracted from the Overpass web server (http://overpass-api.de/) and processed with very fast C++ routines for return to R.

Scientific use cases
  1. Hawker, L., Rougier, J., Neal, J., Bates, P., Archer, L., & Yamazaki, D. (2018). Implications of Simulating Global Digital Elevation Models for Flood Inundation Studies. Water Resources Research. https://doi.org/10.1029/2018wr023279
  2. Briz-Redón, Á. (2019). SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes, 5. https://doi.org/10.3897/rio.5.e33521
  3. Morelle, K., Jezek, M., Licoppe, A., & Podgorski, T. (2019). Deathbed choice by ASF‐infected wild boar can help find carcasses. Transboundary and Emerging Diseases. https://doi.org/10.1111/tbed.13267
  4. Lara-Lizardi, F., Hoyos-Padilla, M., Hearn, A., Klimley, A. P., Galván-Magaña, F., Arauz, R., … Ketchum, J. T. (2020). Shark movements in the Revillagigedo Archipelago and connectivity with the Eastern Tropical Pacific. https://doi.org/10.1101/2020.03.02.972844
  5. Borgoni, R., Gilardi, A., & Zappa, D. (2020). Assessing the Risk of Car Crashes in Road Networks. Social Indicators Research. https://doi.org/10.1007/s11205-020-02295-x
  6. Dunnett, S., Sorichetta, A., Taylor, G., & Eigenbrod, F. (2020). Harmonised global datasets of wind and solar farm locations and power. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0469-8
View Documentation

Stubbing and Setting Expectations on HTTP Requests

Scott Chamberlain
Description

Stubbing and setting expectations on HTTP requests. Includes tools for stubbing HTTP requests, including expected request conditions and response conditions. Match on HTTP method, query parameters, request body, headers and more. Can be used for unit tests or outside of a testing context.

View Documentation

Client for the cranchecks.info API

Scott Chamberlain
Description

Client for the cranchecks.info API.

View Documentation
fingertipsR
Peer-reviewed

Fingertips Data for Public Health

Sebastian Fox
Description

Fingertips (http://fingertips.phe.org.uk/) contains data for many indicators of public health in England. The underlying data is now more easily accessible by making use of the API.

Scientific use cases
  1. Van Schaik, P., Peng, Y., Ojelabi, A., & Ling, J. (2019). Explainable statistical learning in public health for policy development: the case of real-world suicide data. BMC medical research methodology, 19(1), 152. https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-019-0796-7
  2. Rebolj, M., Parmar, D., Maroni, R., Blyuss, O., & Duffy, S. W. (In press). Concurrent participation in screening for cervical, breast, and bowel cancer in England. Journal of Medical Screening. https://doi.org/10.1177/0969141319871977
  3. Senior, S. (2020, February 4). Does Sure Start spending improve school readiness? An ecological longitudinal study. https://doi.org/10.31235/osf.io/rbcz5
  4. van Wieringen, W. N., & Binder, H. (2020). Transfer learning of regression models from a sequence of datasets by penalized estimation. arXiv preprint arXiv:2007.02117. https://arxiv.org/pdf/2007.02117
View Documentation

Make Fake Data

Scott Chamberlain
Description

Make fake data, supporting addresses, person names, dates, times, colors, coordinates, currencies, digital object identifiers (DOIs), jobs, phone numbers, DNA sequences, doubles and integers from distributions and within a range.

View Documentation

Downloading Supplementary Data from Published Manuscripts

William D. Pearse
Description

Downloads data supplementary materials from manuscripts, using papers’ DOIs as references. Facilitates open, reproducible research workflows: scientists re-analyzing published datasets can work with them as easily as if they were stored on their own computer, and others can track their analysis workflow painlessly. The main function suppdata() returns a (temporary) location on the user’s computer where the file is stored, making it simple to use suppdata() with standard functions like read.csv().

Scientific use cases
  1. D Pearse, W., & A Chamberlain, S. (2018). Suppdata: Downloading Supplementary Data from Published Manuscripts. Journal of Open Source Software, 3(25), 721. https://doi.org/10.21105/joss.00721
View Documentation

Sustainable Transport Planning

Robin Lovelace
Description

Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. Enables common transport planning tasks including: downloading and cleaning transport datasets; creating geographic “desire lines” from origin-destination (OD) data; route assignment, locally and via interfaces to routing services such as https://cyclestreets.net/; calculation of route segment attributes such as bearing and aggregate flow; and travel watershed analysis. See Lovelace and Ellison (2018) doi:10.32614/RJ-2018-053 and vignettes for details.

Scientific use cases
  1. Lovelace, R., Goodman, A., Aldred, R., Berkoff, N., Abbas, A., & Woodcock, J. (2015). The Propensity to Cycle Tool: An open source online system for sustainable transport planning. arXiv preprint arXiv:1509.04425 http://arxiv.org/abs/1509.04425
  2. Lovelace, R., Morgan, M., Hama, L., & Padgham, M. (2019). stats19: A package for working with open road crash data. Journal of Open Source Software, 4(33), 1181. https:://doi.org/10.21105/joss.01181
  3. Yen, Y., Zhao, P., & Sohail, M. T. (2019). The morphology and circuity of walkable, bikeable, and drivable street networks in Phnom Penh, Cambodia. Environment and Planning B: Urban Analytics and City Science, 239980831985772. https://doi.org/10.1177/2399808319857726
  4. Zhao, P., & Cao, Y. (2020). Commuting inequity and its determinants in Shanghai: New findings from big-data analytics. Transport Policy, 92, 20–37. https://doi.org/10.1016/j.tranpol.2020.03.006
View Documentation

Generates Networks from BTS Data

Filipe Teixeira
Description

A flexible tool that allows generating bespoke air transport statistics for urban studies based on publicly available data from the Bureau of Transport Statistics (BTS) in the United States https://www.transtats.bts.gov/databases.asp?Mode_ID=1&Mode_Desc=Aviation&Subject_ID2=0.

Scientific use cases
  1. Teixeira, F., & Derudder, B. (2018). SKYNET: An R package for generating air passenger networks for urban studies. Urban Studies, 004209801880325. https://doi.org/10.1177/0042098018803258
View Documentation
treedata.table
CRAN Peer-reviewed

Manipulation of Matched Phylogenies and Data using data.table

Cristian Roman-Palacios
Description

An implementation that combines trait data and a phylogenetic tree (or trees) into a single object of class treedata.table. The resulting object can be easily manipulated to simultaneously change the trait- and tree-level sampling. Currently implemented functions allow users to use a data.table syntax when performing operations on the trait dataset within the treedata.table object.

View Documentation
lingtypology
CRAN Peer-reviewed

Linguistic Typology and Mapping

George Moroz
Description

Provides R with the Glottolog database https://glottolog.org/ and some more abilities for purposes of linguistic mapping. The Glottolog database contains the catalogue of languages of the world. This package helps researchers to make a linguistic maps, using philosophy of the Cross-Linguistic Linked Data project https://clld.org/, which allows for while at the same time facilitating uniform access to the data across publications. A tutorial for this package is available on GitHub pages https://docs.ropensci.org/lingtypology/ and package vignette. Maps created by this package can be used both for the investigation and linguistic teaching. In addition, package provides an ability to download data from typological databases such as WALS, AUTOTYP and some others and to create your own database website.

Scientific use cases
  1. Maisak, T. (2017). Repetitive prefix in Agul: Morphological copy from a closely related language. International Journal of Bilingualism, 136700691774006. https://doi.org/10.1177/1367006917740060
  2. Roettger, T., & Gordon, M. (2017). Methodological issues in the study of word stress correlates. Linguistics Vanguard, 3(1). http://www.linguistics.ucsb.edu/faculty/gordon/Roettger&Gordon_AcousticMethodologoy.pdf
  3. Hantgan-Sonko, A. (2020). Synchronic and diachronic strategies of mora preservation in Gújjolaay Eegimaa. Journal of African Languages and Literatures, (1), 1-25. http://www.politics.unina.it/index.php/jalalit/article/download/6732/7790
  4. Ye, J. (2020). Independent and dependent possessive person forms. Studies in Language, 44(2), 363–406. https://doi.org/10.1075/sl.19020.ye
View Documentation
binman
CRAN

A Binary Download Manager

Ju Yeong Kim
Description

Tools and functions for managing the download of binary files. Binary repositories are defined in YAML format. Defining new pre-download, download and post-download templates allow additional repositories to be added.

View Documentation
taxadb
CRAN

A High-Performance Local Taxonomic Database Interface

Carl Boettiger
Description

Creates a local database of many commonly used taxonomic authorities and provides functions that can quickly query this data.

View Documentation
webchem
CRAN

Chemical Information from the Web

Tamás Stirling
Description

Chemical information from around the web. This package interacts with a suite of web services for chemical information. Sources include: Alan Wood’s Compendium of Pesticide Common Names, Chemical Identifier Resolver, ChEBI, Chemical Translation Service, ChemIDplus, ChemSpider, ETOX, Flavornet, NIST Chemistry WebBook, OPSIN, PAN Pesticide Database, PubChem, SRS, Wikidata.

Scientific use cases
  1. Pirhadi, S., Sunseri, J., & Koes, D. R. (2016). Open Source Molecular Modeling. Journal of Molecular Graphics and Modelling. https://doi.org/10.1016/j.jmgm.2016.07.008
  2. Bergmann, A. J., Scott, R. P., Wilson, G., & Anderson, K. A. (2018). Development of quantitative screen for 1550 chemicals with GC-MS. Analytical and Bioanalytical Chemistry, 1-10. https://link.springer.com/article/10.1007/s00216-018-0997-7
  3. Robert J. Allaway, Sara J. Gosline, Marco Nievo, Salvatore La Rosa, Annette Bakker and Justin Guinney 2018. Abstract 4643: Drug-Target Explorer: An interactive tool for examining chemical-biological interactions. Cancer Res July 1 2018 (78) (13 Supplement) 4643, https://doi.org/10.1158/1538-7445.AM2018-4643
  4. Stanstrup, J., Broeckling, C., Helmus, R., Hoffmann, N., Mathé, E., Naake, T., … Neumann, S. (2019). The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites, 9(10), 200. https://doi.org/10.3390/metabo9100200
  5. Tada, I., Tsugawa, H., Meister, I., Zhang, P., Shu, R., Katsumi, R., … Chaleckis, R. (2019). Creating a Reliable Mass Spectral–Retention Time Library for All Ion Fragmentation-Based Metabolomics. Metabolites, 9(11), 251. https://doi.org/10.3390/metabo9110251
  6. Malaj, E., Liber, K., & Morrissey, C. A. (2019). Spatial distribution of agricultural pesticide use and predicted wetland exposure in the Canadian Prairie Pothole Region. Science of The Total Environment, 134765. https://doi.org/10.1016/j.scitotenv.2019.134765
  7. Zushi, Y., Hanari, N., Nabi, D., & Lin, B.-L. (2020). Mixture Touch: A Web Platform for the Evaluation of Complex Chemical Mixtures. ACS Omega, 5(14), 8121–8126. https://doi.org/10.1021/acsomega.0c00340
  8. Scharmüller, A., Schreiner, V. C., & Schäfer, R. B. (2020). Standartox: Standardizing Toxicity Data. Data, 5(2), 46. https://doi.org/10.3390/data5020046
View Documentation

Client for Various CrossRef APIs

Scott Chamberlain
Description

Client for various CrossRef APIs, including metadata search with their old and newer search APIs, get citations in various formats (including bibtex, citeproc-json, rdf-xml, etc.), convert DOIs to PMIDs, and vice versa, get citations for DOIs, and get links to full text of articles when available.

Scientific use cases
  1. Jahn, N., & Tullney, M. (2016). A study of institutional spending on open access publication fees in Germany. PeerJ, 4, e2323. https://doi.org/10.7717/peerj.2323
  2. Lammey, R. (2016). Using the Crossref Metadata API to explore publisher content. Sci Ed, 3(2), 109–111. https://doi.org/10.6087/kcse.75
  3. Bauer, P. C., Barbera, P., & Munzert, S. (2016). The Quality of Citations: Towards Quantifying Qualitative Impact in Social Science Research. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2874549
  4. Cho, H., & Yu, Y. (2018). Link prediction for interdisciplinary collaboration via co-authorship network. arXiv preprint arXiv:1803.06249. https://arxiv.org/pdf/1803.06249.pdf
  5. Jaspers, S., De Troyer, E., & Aerts, M. (2018). Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA. EFSA Supporting Publications, 15(6), 1427E. https://doi.org/10.2903/sp.efsa.2018.EN-1427
  6. Hicks, D. J., Coil, D. A., Stahmer, C. G., & Eisen, J. A. (2019). Network analysis to evaluate the impact of research funding on research community consolidation. https://doi.org/10.1101/534495
  7. Olsson-Collentine, A., van Assen, M. A. L. M., & Hartgerink, C. H. J. (2019). The Prevalence of Marginally Significant Results in Psychology Over Time. Psychological Science, 095679761983032. https://doi.org/10.1177/0956797619830326
  8. Matthias, L., Jahn, N., & Laakso, M. (2019). The Two-Way Street of Open Access Journal Publishing - Flip It and Reverse It. Publications. 7(2), 23. https://doi.org/10.3390/publications7020023
  9. Mishra, P., & Narayan Tripathi, L. (2019). Characterization of two‐dimensional materials from Raman spectral data. Journal of Raman Spectroscopy. https://doi.org/10.1002/jrs.5744
  10. Fu, D. Y., & Hughey, J. J. (2019). Releasing a preprint is associated with more attention and citations for the peer-reviewed article. eLife, 8. https://doi.org/10.7554/elife.52646
  11. Fraser, N., Momeni, F., Mayr, P., & Peters, I. (2020). The relationship between bioRxiv preprints, citations and altmetrics. Quantitative Science Studies, 1–21. https://doi.org/10.1162/qss_a_00043
View Documentation

Print Maps, Draw on Them, Scan Them Back in

Mark Padgham
Description

Print maps, draw on them, scan them back in, and convert to spatial objects.

View Documentation
UCSCXenaTools
CRAN Peer-reviewed

Download and Explore Datasets from UCSC Xena Data Hubs

Shixiang Wang
Description

Download and explore datasets from UCSC Xena data hubs, which are a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Databases are normalized so they can be combined, linked, filtered, explored and downloaded.

Scientific use cases
  1. Wang, S., He, Z., Wang, X., Li, H., & Liu, X.-S. (2019). Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction. eLife, 8. https://doi.org/10.7554/elife.49020
  2. Li, Y., Ge, D., & Lu, C. (2019). The SMART App: an interactive web application for comprehensive DNA methylation analysis and visualization. Epigenetics & Chromatin, 12(1). https://doi.org/10.1186/s13072-019-0316-3
  3. Kang, W., Zhang, M., Wang, Q., Gu, D., Huang, Z., Wang, H., … Jin, X. (2020). The SLC Family Are Candidate Diagnostic and Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma. BioMed Research International, 2020, 1–17. https://doi.org/10.1155/2020/1932948
  4. Liu, Y., Wang, L., Lo, K.-W., & Lui, V. W. Y. (2020). Omics-wide quantitative B-cell infiltration analyses identify GPR18 for human cancer prognosis with superiority over CD20. Communications Biology, 3(1). https://doi.org/10.1038/s42003-020-0964-7
View Documentation
DataPackageR
Peer-reviewed

Construct Reproducible Analytic Data Sets as R Packages

Greg Finak
Description

A framework to help construct R data packages in a reproducible manner. Potentially time consuming processing of raw data sets into analysis ready data sets is done in a reproducible manner and decoupled from the usual R CMD build process so that data sets can be processed into R objects in the data package and the data package can then be shared, built, and installed by others without the need to repeat computationally costly data processing. The package maintains data provenance by turning the data processing scripts into package vignettes, as well as enforcing documentation and version checking of included data objects. Data packages can be version controlled in github, and used to share data for manuscripts, collaboration and general reproducibility.

Scientific use cases
  1. Finak, G., Mayer, B., Fulp, W., Obrecht, P., Sato, A., Chung, E., … Gottardo, R. (2018). DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis. Gates Open Research, 2, 31. https://doi.org/10.12688/gatesopenres.12832.2
View Documentation

R Interface to FishBase

Carl Boettiger
Description

A programmatic interface to http://www.fishbase.org, re-written based on an accompanying RESTful API. Access tables describing over 30,000 species of fish, their biology, ecology, morphology, and more. This package also supports experimental access to http://www.sealifebase.org data, which contains nearly 200,000 species records for all types of aquatic life not covered by FishBase.

Scientific use cases
  1. Drozd, P., & Šipoš, J. (2013). R for all (I): Introduction to the new age of biological analyses. Casopis Slezskeho Zemskeho Muzea A, 62(1). https://doi.org/10.2478/cszma-2013-0004
  2. Froehlich, H. E., Gentry, R. R., & Halpern, B. S. (2016). Synthesis and comparative analysis of physiological tolerance and life-history growth traits of marine aquaculture species. Aquaculture, 460, 75–82. https://doi.org/10.1016/j.aquaculture.2016.04.018
  3. McGee, M. D., Borstein, S. R., Neches, R. Y., Buescher, H. H., Seehausen, O., & Wainwright, P. C. (2015). A pharyngeal jaw evolutionary innovation facilitated extinction in Lake Victoria cichlids. Science, 350(6264), 1077–1079. https://doi.org/10.1126/science.aab0800
  4. Plank, M. J., Pitchford, J. W., & James, A. (2016). Evolutionarily Stable Strategies for Fecundity and Swimming Speed of Fish. Bull Math Biol, 78(2), 280–292. https://doi.org/10.1007/s11538-016-0143-7
  5. Price, S. A., Friedman, S. T., & Wainwright, P. C. (2015). How predation shaped fish: the impact of fin spines on body form evolution across teleosts. Proc. R. Soc. B, 282(1819), 20151428. https://doi.org/10.1098/rspb.2015.1428
  6. Sagouis, A., Cucherousset, J., Villéger, S., Santoul, F., & Boulêtreau, S. (2015). Non-native species modify the isotopic structure of freshwater fish communities across the globe. Ecography, 38(10), 979–985. https://doi.org/10.1111/ecog.01348
  7. Boeger, W. A., Marteleto, F. M., Zagonel, L., & Braga, M. P. (2014). Tracking the history of an invasion: the freshwater croakers (Teleostei: Sciaenidae) in South America. Zool Scr, 44(3), 250–262. https://doi.org/10.1111/zsc.12098
  8. Mindel, B. L., Webb, T. J., Neat, F. C., & Blanchard, J. L. (2016). A trait-based metric sheds new light on the nature of the body size-depth relationship in the deep sea. J Anim Ecol, 85(2), 427–436. https://doi.org/10.1111/1365-2656.12471
  9. Miya, M., Friedman, M., Satoh, T. P., Takeshima, H., Sado, T., Iwasaki, W., … Nishida, M. (2013). Evolutionary Origin of the Scombridae (Tunas and Mackerels): Members of a Paleogene Adaptive Radiation with 14 Other Pelagic Fish Families. PLoS ONE, 8(9), e73535. https://doi.org/10.1371/journal.pone.0073535
  10. Price, S. A., Claverie, T., Near, T. J., & Wainwright, P. C. (2015). Phylogenetic insights into the history and diversification of fishes on reefs. Coral Reefs, 34(4), 997–1009. https://doi.org/10.1007/s00338-015-1326-7
  11. Collins, R. A., Britz, R., & Rüber, L. (2015). Phylogenetic systematics of leaffishes - Teleostei: Polycentridae, Nandidae. Journal of Zoological Systematics and Evolutionary Research. 53(4), 259–272. https://doi.org/10.1111/jzs.12103
  12. Schaefer, J., Frazier, N., & Barr, J. (2015). Dynamics of Near-Coastal Fish Assemblages following the Deepwater Horizon Oil Spill in the Northern Gulf of Mexico. Transactions of the American Fisheries Society, 145(1), 108–119. https://doi.org/10.1080/00028487.2015.1111253
  13. Bezerra, L. A. V., Padial, A. A., Mariano, F. B., Garcez, D. S., & Sánchez-Botero, J. I. (2017). Fish diversity in tidepools: assembling effects of environmental heterogeneity. Environmental Biology of Fishes. https://doi.org/10.1007/s10641-017-0584-3
  14. Tedesco, P. A., Beauchard, O., Bigorne, R., Blanchet, S., Buisson, L., Conti, L., … Oberdorff, T. (2017). A global database on freshwater fish species occurrence in drainage basins. Scientific Data, 4, 170141. https://doi.org/10.1038/sdata.2017.141
  15. Dulvy, N. K., & Kindsvater, H. K. (2017). The Future Species of Anthropocene Seas. Conservation for the Anthropocene Ocean, 39–64. https://doi.org/10.1016/b978-0-12-805375-1.00003-9
  16. Pedersen, E. J., Thompson, P. L., Ball, R. A., Fortin, M.-J., Gouhier, T. C., Link, H., … Pepin, P. (2017). Signatures of the collapse and incipient recovery of an overexploited marine ecosystem. Royal Society Open Science, 4(7), 170215. https://doi.org/10.1098/rsos.170215
  17. Martin, B. T., Heintz, R., Danner, E. M., & Nisbet, R. M. (2017). Integrating lipid storage into general representations of fish energetics. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.12667
  18. McCurry, M. R., Fitzgerald, E. M. G., Evans, A. R., Adams, J. W., & Mchenry, C. R. (2017). Skull shape reflects prey size niche in toothed whales. Biological Journal of the Linnean Society. https://doi.org/10.1093/biolinnean/blx032
  19. Neubauer, P., Thorson, J. T., Melnychuk, M. C., Methot, R., & Blackhart, K. (2018). Drivers and rates of stock assessments in the United States. PLOS ONE, 13(5), e0196483. https://doi.org/10.1371/journal.pone.0196483
  20. Babcock, E. A., Tewfik, A., & Burns-Perez, V. (2018). Fish community and single-species indicators provide evidence of unsustainable practices in a multi-gear reef fishery. Fisheries Research, 208, 70–85. https://doi.org/10.1016/j.fishres.2018.07.003
  21. Van Gemert, R., & Andersen, K. H. (2018). Challenges to fisheries advice and management due to stock recovery. ICES Journal of Marine Science. https://doi.org/10.1093/icesjms/fsy084
  22. Sánchez-Hernández, J., & Amundsen, P.-A. (2018). Ecosystem type shapes trophic position and omnivory in fishes. Fish and Fisheries. https://doi.org/10.1111/faf.12308
  23. Degen, R., & Faulwetter, S. (2018). The Arctic Traits Database: A repository of arctic benthic invertebrate traits. Earth System Science Data Discussions, 1–25. https://doi.org/10.5194/essd-2018-97
  24. Jarić, I., Lennox, R. J., Kalinkat, G., Cvijanović, G., & Radinger, J. (2018). Susceptibility of European freshwater fish to climate change: species profiling based on life-history and environmental characteristics. Global Change Biology. https://doi.org/10.1111/gcb.14518
  25. Borstein, S. R., Fordyce, J. A., O’Meara, B. C., Wainwright, P. C., & McGee, M. D. (2018). Reef fish functional traits evolve fastest at trophic extremes. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-018-0725-x
  26. West, C. D., Hobbs, E., Croft, S. A., Green, J. M. H., Schmidt, S. Y., & Wood, R. (2018). Improving consumption based accounting for global capture fisheries. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2018.11.298
  27. Leaf, R. T., & Oshima, M. C. (2019). Construction and evaluation of a robust trophic network model for the northern Gulf of Mexico ecosystem. Ecological Informatics, 50, 13–23. https://doi.org/10.1016/j.ecoinf.2018.12.005
  28. Pimiento, C., Cantalapiedra, J. L., Shimada, K., Field, D. J., & Smaers, J. B. (2019). Evolutionary pathways toward gigantism in sharks and rays. Evolution. https://doi.org/10.1111/evo.13680
  29. Free, C. M., Thorson, J. T., Pinsky, M. L., Oken, K. L., Wiedenmann, J., & Jensen, O. P. (2019). Impacts of historical warming on marine fisheries production. Science, 363(6430), 979–983. https://doi.org/10.1126/science.aau1758
  30. Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L., & Sunday, J. M. (2019). Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature, 569(7754), 108–111. https://doi.org/10.1038/s41586-019-1132-4
  31. Goodman, M. C., Hannah, S. M., & Ruttenberg, B. I. (2019). The relationship between geographic range extent, sea surface temperature and adult traits in coastal temperate fishes. Journal of Biogeography. https://doi.org/10.1111/jbi.13595
  32. Van Denderen, D., Gislason, H., & Andersen, K. H. (2019). Little difference in average fish growth and maximum size across temperatures. EcoEvoRxiv. https://doi.org/10.32942/osf.io/8cu4y
  33. Nyboer, E. A., Liang, C., & Chapman, L. J. (2019). Assessing the vulnerability of Africa’s freshwater fishes to climate change: A continent-wide trait-based analysis. Biological Conservation, 236, 505–520. https://doi.org/10.1016/j.biocon.2019.05.003
  34. Petrik, C. M., Stock, C. A., Andersen, K. H., van Denderen, P. D., & Watson, J. R. (2019). Bottom-up drivers of global patterns of demersal, forage, and pelagic fishes. Progress in Oceanography, 176, 102124. https://doi.org/10.1016/j.pocean.2019.102124
  35. Alfaro, M. E., Karan, E., Schwartz, S. T., & Shultz, A. J. (2019). The Evolution of Color Pattern in Butterflyfishes (Chaetodontidae). Integrative and Comparative Biology. https://doi.org/10.1093/icb/icz119
  36. Valdez, J. W., & Mandrekar, K. (2019). Assessing the Species in the CARES Preservation Program and the Role of Aquarium Hobbyists in Freshwater Fish Conservation. https://doi.org/10.20944/preprints201907.0030.v1
  37. Collins, R. A., Bakker, J., Wangensteen, O. S., Soto, A. Z., Corrigan, L., Sims, D. W., … Mariani, S. (2019). Non‐specific amplification compromises environmental DNA metabarcoding with COI. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13276
  38. Hayden, B., Palomares, M. L. D., Smith, B. E., & Poelen, J. H. (2019). Biological and environmental drivers of trophic ecology in marine fishes - a global perspective. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-47618-2
  39. Lacy, S. N., Corcoran, D., Alò, D., Lessmann, J., Meza, F., & Marquet, P. A. (2019). Main drivers of freshwater fish diversity across extra-tropical Southern Hemisphere rivers. Hydrobiologia. https://doi.org/10.1007/s10750-019-04044-9
  40. Bayley, D. T. I., Mogg, A. O. M., Purvis, A., & Koldewey, H. J. (2019). Evaluating the efficacy of small‐scale marine protected areas for preserving reef health: A case study applying emerging monitoring technology. Aquatic Conservation: Marine and Freshwater Ecosystems. https://doi.org/10.1002/aqc.3215
  41. Friedman, M., Feilich, K. L., Beckett, H. T., Alfaro, M. E., Faircloth, B. C., Černý, D., … Harrington, R. C. (2019). A phylogenomic framework for pelagiarian fishes (Acanthomorpha: Percomorpha) highlights mosaic radiation in the open ocean. Proceedings of the Royal Society B: Biological Sciences, 286(1910), 20191502. https://doi.org/10.1098/rspb.2019.1502
  42. Cazelles, K., Bartley, T., Guzzo, M. M., Brice, M., MacDougall, A. S., Bennett, J. R., … McCann, K. S. (2019). Homogenization of freshwater lakes: recent compositional shifts in fish communities are explained by gamefish movement and not climate change. Global Change Biology. https://doi.org/10.1111/gcb.14829
  43. Benun Sutton, F., & Wilson, A. B. (2019). Where are all the moms? External fertilization predicts the rise of male parental care in bony fishes. Evolution. https://doi.org/10.1111/evo.13846
  44. Thorson, J. T. (2019). Predicting recruitment density dependence and intrinsic growth rate for all fishes worldwide using a data‐integrated life‐history model. Fish and Fisheries. <https://doi.org/10.1111/faf.12427
  45. Lecocq, T., Benard, A., Pasquet, A., Nahon, S., Ducret, A., Dupont-Marin, K., … Thomas, M. (2019). TOFF, a database of traits of fish to promote advances in fish aquaculture. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0307-z
  46. Blowes, S. A., Chase, J. M., Di Franco, A., Frid, O., Gotelli, N. J., Guidetti, P., … Belmaker, J. (2020). Mediterranean marine protected areas have higher biodiversity via increased evenness, not abundance. Journal of Applied Ecology, 57(3), 578–589. https://doi.org/10.1111/1365-2664.13549
  47. Burns, M. D., & Bloom, D. D. (2020). Migratory lineages rapidly evolve larger body sizes than non-migratory relatives in ray-finned fishes. Proceedings of the Royal Society B: Biological Sciences, 287(1918), 20192615. https://doi.org/10.1098/rspb.2019.2615
  48. Pimiento, C., & Benton, M. J. (2020). The impact of the Pull of the Recent on extant elasmobranchs. Palaeontology. https://doi.org/10.1111/pala.12478
  49. Manel, S., Guerin, P.-E., Mouillot, D., Blanchet, S., Velez, L., Albouy, C., & Pellissier, L. (2020). Global determinants of freshwater and marine fish genetic diversity. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-14409-7
  50. Parravicini, V., Casey, J. M., Schiettekatte, N. M. D., Brandl, S. J., Pozas-Schacre, C., Carlot, J., … Vii, J. (2020). Global gut content data synthesis and phylogeny delineate reef fish trophic guilds. https://doi.org/10.1101/2020.03.04.977116
  51. Jézéquel, C., Tedesco, P. A., Bigorne, R., Maldonado-Ocampo, J. A., Ortega, H., Hidalgo, M., … Oberdorff, T. (2020). A database of freshwater fish species of the Amazon Basin. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0436-4
  52. Färber, L., van Gemert, R., Langangen, Ø., Durant, J. M., & Andersen, K. H. (2020). Population variability under stressors is dependent on body mass growth and asymptotic body size. Royal Society Open Science, 7(2), 192011. https://doi.org/10.1098/rsos.192011
  53. Siqueira, A. C., Morais, R. A., Bellwood, D. R., & Cowman, P. F. (2020). Trophic innovations fuel reef fish diversification. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-16498-w
  54. Monaco, C. J., Bradshaw, C. J. A., Booth, D. J., Gillanders, B. M., Schoeman, D. S., & Nagelkerken, I. (2020). Dietary generalism accelerates arrival and persistence of coral‐reef fishes in their novel ranges under climate change. Global Change Biology. https://doi.org/10.1111/gcb.15221
  55. Griffiths, D. (2020). Foraging habitat determines predator–prey size relationships in marine fishes. Journal of Fish Biology. https://doi.org/10.1111/jfb.14451
View Documentation

HTTP Client

Scott Chamberlain
Description

A simple HTTP client, with tools for making HTTP requests, and mocking HTTP requests. The package is built on R6, and takes inspiration from Rubys faraday’ gem (https://rubygems.org/gems/faraday). The package name is a play on curl, the widely used command line tool for HTTP, and this package is built on top of the R package curl, an interface to libcurl (https://curl.haxx.se/libcurl).

View Documentation

Interface to the MODIS Land Products Subsets Web Services

Hufkens Koen
Description

Programmatic interface to the Oak Ridge National Laboratories MODIS Land Products Subsets web services (https://modis.ornl.gov/data/modis_webservice.html). Allows for easy downloads of MODIS time series directly to your R workspace or your computer.

Scientific use cases
  1. Fecchio, A., Bell, J. A., Bosholn, M., Vaughan, J. A., Tkach, V. V., Lutz, H. L., … Clark, N. J. (2019). An inverse latitudinal gradient in infection probability and phylogenetic diversity for Leucocytozoon blood parasites in New World birds. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.13117
  2. Nguyen, V. T., Dietrich, J., & Uniyal, B. (2020). Modeling interbasin groundwater flow in karst areas: Model development, application, and calibration strategy. Environmental Modelling & Software, 124, 104606. https://doi.org/10.1016/j.envsoft.2019.104606
  3. Torregroza-Espinosa, A. C., Restrepo, J. C., Correa-Metrio, A., Hoyos, N., Escobar, J., Pierini, J., & Martínez, J.-M. (2020). Fluvial and oceanographic influences on suspended sediment dispersal in the Magdalena River Estuary. Journal of Marine Systems, 204, 103282. https://doi.org/10.1016/j.jmarsys.2019.103282
  4. Nguyen, H. N., Hung, C.-M., Yang, M.-Y., & Lin, S.-M. (2020). Sympatric competitors have driven the evolution of temporal activity patterns in Cnemaspis geckos in Southeast Asia. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-019-56549-x
  5. Yuan, M. L., Jung, C., Wake, M. H., & Wang, I. J. (2020). Habitat use, interspecific competition and phylogenetic history shape the evolution of claw and toepad morphology in Lesser Antillean anoles. Biological Journal of the Linnean Society, 129(3), 630–643. https://doi.org/10.1093/biolinnean/blz203
  6. Benito, B. M., & Birks, H. J. B. (2020). distantia: an open‐source toolset to quantify dissimilarity between multivariate ecological time‐series. Ecography. https://doi.org/10.1111/ecog.04895
  7. Pyle, P., Foster, K. R., Godwin, C. M., Kaschube, D. R., & Saracco, J. F. (2020). Yearling proportion correlates with habitat structure in a boreal forest landbird community. PeerJ, 8, e8898. https://doi.org/10.7717/peerj.8898
  8. Trinka, J., Haghbin, H., & Maadooliat, M. (2020). Multivariate Functional Singular Spectrum Analysis Over Different Dimensional Domains. arXiv preprint arXiv:2006.03933. https://arxiv.org/pdf/2006.03933
View Documentation

Work with Open Road Traffic Casualty Data from Great Britain

Robin Lovelace
Description

Tools to help download, process and analyse the UK road collision data collected using the STATS19 form. The data are provided as CSV files with detailed road safety data about the circumstances of car crashes and other incidents on the roads resulting in casualties in Great Britain from 1979, the types (including make and model) of vehicles involved and the consequential casualties. The statistics relate only to personal casualties on public roads that are reported to the police, and subsequently recorded, using the STATS19 accident reporting form. See the Department for Transport website https://data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data for more information on these data.

View Documentation

Setup, Run and Analyze NetLogo Model Simulations from R via XML

Jan Salecker
Description

Setup, run and analyze NetLogo (https://ccl.northwestern.edu/netlogo/) model simulations in R. nlrx experiments use a similar structure as NetLogos Behavior Space experiments. However, nlrx offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses. The user defines all information that is needed in an intuitive framework, using class objects. Experiments are submitted from R to NetLogo via XML files that are dynamically written, based on specifications defined by the user. By nesting model calls in future environments, large simulation design with many runs can be executed in parallel. This also enables simulating NetLogo experiments on remote high performance computing machines. In order to use this package, Java and NetLogo (>= 5.3.1) need to be available on the executing system.

Scientific use cases
  1. Kaaronen, R. O., & Strelkovskii, N. (2019). Cultural Evolution of Sustainable Behaviours: Pro-Environmental Tipping Points in an Agent-Based Model. https://doi.org/10.31234/osf.io/w6dpa
  2. Wesener, F., Szymczak, A., Rillig, M. C., & Tietjen, B. (2020). Stress priming affects fungal competition – evidence from a combined experimental and modeling study. https://doi.org/10.1101/2020.03.04.976357
  3. Adams, R. I., Bhangar, S., Dannemiller, K. C., Eisen, J. A., Fierer, N., Gilbert, J. A., … Bibby, K. (2016). Ten questions concerning the microbiomes of buildings. Building and Environment, 109, 224–234. https://doi.org/10.1016/j.buildenv.2016.09.001
  4. D’Orazio, M., Bernardini, G., & Quagliarini, E. (2020). Sustainable and resilient strategies for touristic cities against COVID-19: an agent-based approach. arXiv preprint arXiv:2005.12547. https://arxiv.org/pdf/2005.12547.pdf
View Documentation

Compact and Flexible Summaries of Data

Elin Waring
Description

A simple to use summary function that can be used with pipes and displays nicely in the console. The default summary statistics may be modified by the user as can the default formatting. Support for data frames and vectors is included, and users can implement their own skim methods for specific object types as described in a vignette. Default summaries include support for inline spark graphs. Instructions for managing these on specific operating systems are given in the “Using skimr” vignette and the README.

Scientific use cases
  1. Sinval, J., Marques-Pinto, A., Queirós, C., & Marôco, J. (2018). Work Engagement among Rescue Workers: Psychometric Properties of the Portuguese UWES. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.02229
  2. Sinval, J., Pasian, S., Queirós, C., & Marôco, J. (2018). Brazil-Portugal Transcultural Adaptation of the UWES-9: Internal Consistency, Dimensionality, and Measurement Invariance. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.00353
  3. Almeida, L. S., Pérez Fuentes, M. del C., Casanova, J. R., Gázquez Linares, J. J., & Molero Jurado, M. del M. (2018). Alcohol Expectancy-Adolescent Questionnaire (AEQ-AB): Validation for portuguese college students. Health and Addictions/Salud y Drogas, 18(2), 155. https://doi.org/10.21134/haaj.v18i2.389
  4. António, N., de Almeida, A., & Nunes, L. (2018). Hotel booking demand datasets. Data in Brief. https://doi.org/10.1016/j.dib.2018.11.126
  5. Sinval, J., Casanova, J. R., Marôco, J., & Almeida, L. S. (2018). University student engagement inventory (USEI): Psychometric properties. Current Psychology. https://doi.org/10.1007/s12144-018-0082-6
  6. Rodrigues, S., Sinval, J., Queirós, C., Marôco, J., & Kaiseler, M. (2019). Transitioning from recruit to officer: An investigation of how stress appraisal and coping influence work engagement. International Journal of Selection and Assessment. https://doi.org/10.1111/ijsa.12238
  7. Sinval, J., Sirgy, M. J., Lee, D.-J., & Marôco, J. (2019). The Quality of Work Life Scale: Validity Evidence from Brazil and Portugal. Applied Research in Quality of Life. https://doi.org/10.1007/s11482-019-09730-3
  8. Nalborczyk, L., Grandchamp, R., Koster, E. H. W., Perrone-Bertolotti, M., & Loevenbruck, H. (2019). Can we decode phonetic features in inner speech using surface electromyography? https://doi.org/10.31234/osf.io/8v5yd
  9. Correia, C. N., McLoughlin, K. E., Nalpas, N. C., Magee, D. A., Browne, J. A., Rue-Albrecht, K., … MacHugh, D. E. (2018). RNA Sequencing (RNA-Seq) Reveals Extremely Low Levels of Reticulocyte-Derived Globin Gene Transcripts in Peripheral Blood From Horses (Equus caballus) and Cattle (Bos taurus). Frontiers in Genetics, 9. https://doi.org/10.3389/fgene.2018.00278
  10. Long, J. D., & Turner, D. (2020). Applied R in the Classroom. Australian Economic Review, 53(1), 139–157. https://doi.org/10.1111/1467-8462.12362
  11. Sinval, J., & Marôco, J. (2020). Short Index of Job Satisfaction: Validity evidence from Portugal and Brazil. PLOS ONE, 15(4), e0231474. https://doi.org/10.1371/journal.pone.0231474
  12. Lam, K.-L., Cheng, W.-Y., Su, Y., Li, X., Wu, X., Wong, K.-H., … Cheung, P. C.-K. (2020). Use of random forest analysis to quantify the importance of the structural characteristics of beta-glucans for prebiotic development. Food Hydrocolloids, 108, 106001. https://doi.org/10.1016/j.foodhyd.2020.106001
  13. McKnelly, K. J., Howitz, W. J., Lam, S., & Link, R. D. (2020). Extraction on Paper Activity: An Active Learning Technique to Facilitate Student Understanding of Liquid–Liquid Extraction. Journal of Chemical Education, 97(7), 1960–1965. https://doi.org/10.1021/acs.jchemed.9b00975
View Documentation

A High-Performance Database of Shipment-Level CITES Trade Data

Noam Ross
Description

Provides convenient access to over 40 years and 20 million records of endangered wildlife trade data from the Convention on International Trade in Endangered Species of Wild Fauna and Flora, stored on a local on-disk, out-of memory DuckDB database for bulk analysis.

Scientific use cases
  1. Hierink, F., Bolon, I., Durso, A. M., Ruiz de Castañeda, R., Zambrana-Torrelio, C., Eskew, E. A., & Ray, N. (2020). Forty-four years of global trade in CITES-listed snakes: Trends and implications for conservation and public health. Biological Conservation, 248, 108601. https://doi.org/10.1016/j.biocon.2020.108601
View Documentation

Working with Audio and Video in R

Jeroen Ooms
Description

Bindings to FFmpeg http://www.ffmpeg.org/ AV library for working with audio and video in R. Generates high quality video from images or R graphics with custom audio. Also offers high performance tools for reading raw audio, creating spectrograms, and converting between countless audio / video formats. This package interfaces directly to the C API and does not require any command line utilities.

View Documentation

Read Spectrometric Data and Metadata

Hugo Gruson
Description

Parse various reflectance/transmittance/absorbance spectra file formats to extract spectral data and metadata, as described in Gruson, White & Maia (2019) doi:10.21105/joss.01857. Among other formats, it can import files from Avantes https://www.avantes.com/, CRAIC http://www.microspectra.com/, and OceanInsight (formerly OceanOptics) https://www.oceaninsight.com/ brands.

View Documentation

Ecological Metadata as Linked Data

Carl Boettiger
Description

This is a utility for transforming Ecological Metadata Language (EML) files into JSON-LD and back into EML. Doing so creates a list-based representation of EML in R, so that EML data can easily be manipulated using standard R tools. This makes this package an effective backend for other R-based tools working with EML. By abstracting away the complexity of XML Schema, developers can build around native R list objects and not have to worry about satisfying many of the additional constraints of set by the schema (such as element ordering, which is handled automatically). Additionally, the JSON-LD representation enables the use of developer-friendly JSON parsing and serialization that may facilitate the use of EML in contexts outside of R, as well as the informatics-friendly serializations such as RDF and SPARQL queries.

View Documentation
bibtex
CRAN

Bibtex Parser

James Joseph Balamuta
Description

Utility to parse a bibtex file.

View Documentation

Record HTTP Calls to Disk

Scott Chamberlain
Description

Record test suite HTTP requests and replays them during future runs. A port of the Ruby gem of the same name (https://github.com/vcr/vcr/). Works by hooking into the webmockr R package for matching HTTP requests by various rules (HTTP method, URL, query parameters, headers, body, etc.), and then caching real HTTP responses on disk in cassettes. Subsequent HTTP requests matching any previous requests in the same cassette use a cached HTTP response.

View Documentation
rdataretriever
CRAN

R Interface to the Data Retriever

Henry Senyondo
Description

Provides an R interface to the Data Retriever https://retriever.readthedocs.io/en/latest/ via the Data Retriever’s command line interface. The Data Retriever automates the tasks of finding, downloading, and cleaning public datasets, and then stores them in a local database.

View Documentation

Fetch Scholary Full Text from Crossref

Scott Chamberlain
Description

Text mining client for Crossref (https://crossref.org). Includes functions for getting getting links to full text of articles, fetching full text articles from those links or Digital Object Identifiers (DOIs), and text extraction from PDFs.

View Documentation

Extract Scientific Names from Text

Scott Chamberlain
Description

Extract scientific names from text using the Golang tool gnfinder https://github.com/gnames/gnfinder.

View Documentation
tracerer
CRAN

Tracer from R

Richèl J.C. Bilderbeek
Description

BEAST2 (https://www.beast2.org) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. Tracer (http://tree.bio.ed.ac.uk/software/tracer/) is a GUI tool to parse and analyze the files generated by BEAST2. This package provides a way to parse and analyze BEAST2 input files without active user input, but using R function calls instead.

View Documentation

Download and Process Public Domain Works from Project Gutenberg

David Robinson
Description

Download and process public domain works in the Project Gutenberg collection http://www.gutenberg.org/. Includes metadata for all Project Gutenberg works, so that they can be searched and retrieved.

View Documentation
neotoma
CRAN

Access to the Neotoma Paleoecological Database Through R

Simon J. Goring
Description

Access paleoecological datasets from the Neotoma Paleoecological Database using the published API (http://wnapi.neotomadb.org/). The functions in this package access various pre-built API functions and attempt to return the results from Neotoma in a usable format for researchers and the public.

Scientific use cases
  1. Nanavati, W. P., Whitlock, C., Iglesias, V., & de Porras, M. E. (2019). Postglacial vegetation, fire, and climate history along the eastern Andes, Argentina and Chile (lat. 41–55°S). Quaternary Science Reviews, 207, 145–160. https://doi.org/10.1016/j.quascirev.2019.01.014
  2. Wang, Y., Goring, S. J., & McGuire, J. L. (2019). Bayesian ages for pollen records since the last glaciation in North America. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0182-7
  3. Elmslie, B. G., Gushulak, C. A., Boreux, M. P., Lamoureux, S. F., Leavitt, P. R., & Cumming, B. F. (2019). Complex responses of phototrophic communities to climate warming during the Holocene of northeastern Ontario, Canada. The Holocene, 095968361988301. https://doi.org/10.1177/0959683619883014
  4. Deza-Araujo, M., Morales-Molino, C., Tinner, W., Henne, P. D., Heitz, C., Pezzatti, G. B., … Conedera, M. (2020). A critical assessment of human-impact indices based on anthropogenic pollen indicators. Quaternary Science Reviews, 236, 106291. https://doi.org/10.1016/j.quascirev.2020.106291
  5. Carroll, H. M., Wanamaker, A. D., Clark, L. G., & Wilsey, B. J. (2020). Ragweed and sagebrush pollen can distinguish between vegetation types at broad spatial scales. Ecosphere, 11(5). https://doi.org/10.1002/ecs2.3120
View Documentation

Generate CodeMeta Metadata for R Packages

Carl Boettiger
Description

The Codemeta Project defines a JSON-LD format for describing software metadata, as detailed at https://codemeta.github.io. This package provides utilities to generate, parse, and modify codemeta.json files automatically for R packages, as well as tools and examples for working with codemeta.json JSON-LD more generally.

View Documentation

Integrated Taxonomic Information System Client

Scott Chamberlain
Description

An interface to the Integrated Taxonomic Information System (ITIS) (https://www.itis.gov). Includes functions to work with the ITIS REST API methods (https://www.itis.gov/ws_description.html), as well as the Solr web service (https://www.itis.gov/solr_documentation.html).

Scientific use cases
  1. Goring, S., Lacourse, T., Pellatt, M. G., & Mathewes, R. W. (2013). Pollen assemblage richness does not reflect regional plant species richness: a cautionary tale. Journal of Ecology, 101(5), 1137–1145. https://doi.org/10.1111/1365-2745.12135
View Documentation

Access the Global Plant Phenology Data Portal

John Deck
Description

An R interface to the Global Plant Phenology Data Portal, which is accessible online at https://www.plantphenology.org/.

View Documentation

Cache Mocked HTTP Requests

Scott Chamberlain
Description

Cache mocked HTTP requests, leveraging webmockr for the HTTP request matching.

View Documentation

General Purpose GraphQL Client

Scott Chamberlain
Description

A GraphQL client, with an R6 interface for initializing a connection to a GraphQL instance, and methods for constructing queries, including fragments and parameterized queries. Queries are checked with the libgraphqlparser C++ parser via the gaphql package.

View Documentation
trufflesniffer
Staff maintained

Scan Secrets in R Scripts, Packages, or Projects

Scott Chamberlain
Description

Scan secrets in r scripts, packages, or projects.

View Documentation

Time Travel to Test Time Dependent Code

Scott Chamberlain
Description

Time travel to test time dependent code.

View Documentation

Time Classes

Scott Chamberlain
Description

Time classes, with hooks for mocking time.

View Documentation

Convert Complex Objects to and from R Data Structures

Scott Chamberlain
Description

Convert complex objects to and from R data structures.

View Documentation

Tools for Vizualizing Data Taxonomically

Scott Chamberlain
Description

Tools for vizualizing data taxonomically.

View Documentation

Client for Citoid

Scott Chamberlain
Description

Client for Citoid (https://www.mediawiki.org/wiki/Citoid), an API for getting citations for various scholarly work identifiers found on Wikipedia.

View Documentation

NoSQL Database Connector

Scott Chamberlain
Description

Simplified document database manipulation and analysis, including support for many NoSQL databases, including document databases (Elasticsearch, CouchDB, MongoDB), key-value databases (Redis), and (with limitations) SQLite/json1.

View Documentation

Microsoft Academic API Client

Scott Chamberlain
Description

The Microsoft Academic Knowledge API provides programmatic access to scholarly articles in the Microsoft Academic Graph (https://academic.microsoft.com/). Includes methods matching all ‘Microsoft Academic’ API routes, including search, graph search, text similarity, and interpret natural language query string.

View Documentation

Control How Many Times Conditions are Thrown

Scott Chamberlain
Description

Provides ability to control how many times in function calls conditions are thrown (shown to the user). Includes control of warnings and messages.

View Documentation
bowerbird
Peer-reviewed

Keep a Collection of Sparkly Data Resources

Ben Raymond
Description

Tools to get and maintain a data repository from third-party data providers.

View Documentation

Base Classes and Functions for Phylogenetic Tree Input and Output

Guangchuang Yu
Description

treeio is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats.

Scientific use cases
  1. Yu, G., Tsan-Yuk Lam, T., Zhu, H., & Guan, Y. (2018). Two methods for mapping and visualizing associated data on phylogeny using ggtree. Molecular Biology and Evolution. https://doi.org/10.1093/molbev/msy194
  2. Paudyal, N., Pan, H., Elbediwi, M., Zhou, X., Peng, X., Li, X., … Yue, M. (2019). Characterization of Salmonella Dublin isolated from bovine and human hosts. BMC Microbiology, 19(1). https://doi.org/10.1186/s12866-019-1598-0
  3. Callanan, J., Stockdale, S. R., Shkoporov, A., Draper, L. A., Ross, R. P., & Hill, C. (2020). Expansion of known ssRNA phage genomes: From tens to over a thousand. Science Advances, 6(6), eaay5981. https://doi.org/10.1126/sciadv.aay5981
  4. Ahrenfeldt, J., Waisi, M., Loft, I. C., Clausen, P. T. L. C., Allesøe, R., Szarvas, J., … Lund, O. (2020). Metaphylogenetic analysis of global sewage reveals that bacterial strains associated with human disease show less degree of geographic clustering. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-59292-w
  5. Ryt-Hansen, P., Pedersen, A. G., Larsen, I., Kristensen, C. S., Krog, J. S., Wacheck, S., & Larsen, L. E. (2020). Substantial Antigenic Drift in the Hemagglutinin Protein of Swine Influenza A Viruses. Viruses, 12(2), 248. https://doi.org/10.3390/v12020248
  6. Yu, G. (2020). Using ggtree to Visualize Data on Tree‐Like Structures. Current Protocols in Bioinformatics, 69(1). https://doi.org/10.1002/cpbi.96
  7. Lequime, S., Bastide, P., Dellicour, S., Lemey, P., & Baele, G. (2020). nosoi: a stochastic agent-based transmission chain simulation framework in R. https://doi.org/10.1101/2020.03.03.973107
  8. Bastide, P., Ho, L. S. T., Baele, G., Lemey, P., & Suchard, M. A. (2020). Efficient Bayesian Inference of General Gaussian Models on Large Phylogenetic Trees. arXiv preprint arXiv:2003.10336. https://arxiv.org/pdf/2003.10336
  9. Ordynets, A., Liebisch, R., Lysenko, L., Scherf, D., Volobuev, S., Saitta, A., … Langer, E. (2020). Morphologically similar but not closely related: the long-spored species of Subulicystidium (Trechisporales, Basidiomycota). Mycological Progress, 19(7), 691–703. https://doi.org/10.1007/s11557-020-01587-3
View Documentation
tradestatistics
CRAN Peer-reviewed

Open Trade Statistics API Wrapper and Utility Program

Mauricio Vargas
Description

Access Open Trade Statistics API from R to download international trade data.

View Documentation

ZooBank API Client

Scott Chamberlain
Description

Interface to the ZooBank API (http://zoobank.org/Api) client. ZooBank (http://zoobank.org/) is the official registry of zoological nomenclature. Methods are provided for using each of the API endpoints, including for querying by author, querying for publications, get statistics on ZooBank activity, and more.

View Documentation
c14bazAAR

Download and Prepare C14 Dates from Different Source Databases

Clemens Schmid
Description

Query different C14 date databases and apply basic data cleaning, merging and calibration steps. Currently available databases: 14cpalaeolithic, 14sea, adrac, austarch, calpal, context, emedyd, eubar, euroevol, irdd, jomon, katsianis, kiteeastafrica, medafricarbon, mesorad, pacea, palmisano, radon, radonb.

View Documentation

Tools for Working with Taxonomic Databases

Scott Chamberlain
Description

Tools for working with taxonomic databases, including utilities for downloading databases, loading them into various SQL databases, cleaning up files, and providing a SQL connection that can be used to do SQL queries directly or used in dplyr.

Scientific use cases
  1. Jin, J., & Yang, J. (2020). BDcleaner: A workflow for cleaning taxonomic and geographic errors in occurrence data archived in biodiversity databases. Global Ecology and Conservation, 21, e00852. https://doi.org/10.1016/j.gecco.2019.e00852
View Documentation

High-Performance Stemmer, Tokenizer, and Spell Checker

Jeroen Ooms
Description

Low level spell checker and morphological analyzer based on the famous hunspell library https://hunspell.github.io. The package can analyze or check individual words as well as parse text, latex, html or xml documents. For a more user-friendly interface use the spelling package which builds on this package to automate checking of files, documentation and vignettes in all common formats.

Scientific use cases
  1. Cichosz, P. (2018) A case study in text mining of discussion forum posts: classification with bag of words and global vectors Int. J. Appl. Math. Comput. Sci., Vol. 28, No. 4, 787–801. https://www.amcs.uz.zgora.pl/?action=paper&paper=1469
  2. Yeomans, M., Kantor, A., & Tingley, D. (2018). The politeness Package: Detecting Politeness in Natural Language. The R Journal. https://journal.r-project.org/archive/2018/RJ-2018-067/RJ-2018-067.pdf
  3. Lee, A. J., Jones, B. C., & DeBruine, L. M. (2019, January 21). Investigating the association between mating-relevant self-concepts and mate preferences through a data-driven analysis of online personal descriptions. https://doi.org/10.31234/osf.io/38zef
  4. Liu, Crocker H., Nowak, Adam, and Smith, Patrick S. 2018. Does the Asset Pricing Premium Reflect Asymmetric or IncompleteInformation?. Economics Faculty Working Papers Series. 5. https://researchrepository.wvu.edu/econ_working-papers/5
  5. Nicolas, G., Bai, X., & Fiske, S. T. (2019). Automated Dictionary Creation for Analyzing Text: An Illustration from Stereotype Content. https://psyarxiv.com/afm8k/download?format=pdf
  6. Bayer, D., & Michael, S. (2019). Exploring the Daschle Collection using Text Mining. arXiv preprint arXiv:1904.12623 https://arxiv.org/pdf/1904.12623
  7. Green, E. P., Whitcomb, A., Kahumbura, C., Rosen, J. G., Goyal, S., Achieng, D., & Bellows, B. (2019). What is the best method of family planning for me?: a text mining analysis of messages between users and agents of a digital health service in Kenya. Gates Open Research, 3, 1475. https://doi.org/10.12688/gatesopenres.12999.1
  8. Lin, C., Lou, Y.-S., Tsai, D.-J., Lee, C.-C., Hsu, C.-J., Wu, D.-C., … Fang, W.-H. (2019). Projection Word Embedding Model With Hybrid Sampling Training for Classifying ICD-10-CM Codes: Longitudinal Observational Study. JMIR Medical Informatics, 7(3), e14499. https://doi.org/10.2196/14499
  9. Luc, A., Lê, S., & Philippe, M. (2019). Nudging consumers for relevant data using Free JAR profiling: an application to product development. Food Quality and Preference, 103751. https://doi.org/10.1016/j.foodqual.2019.103751
  10. Ramagopalan, S. V., Malcolm, B., Merinopoulou, E., McDonald, L., & Cox, A. (2019). Automated extraction of treatment patterns from social media posts: an exploratory analysis in renal cell carcinoma. Future Oncology. https://doi.org/10.2217/fon-2019-0406
  11. Cinelli, M., Ficcadenti, V., & Riccioni, J. (2019). The interconnectedness of the economic content in the speeches of the US Presidents. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03372-2
  12. Christensen, A. P., & Kenett, Y. (2019, October 22). Semantic Network Analysis (SemNA): A Tutorial on Preprocessing, Estimating, and Analyzing Semantic Networks. https://doi.org/10.31234/osf.io/eht87
  13. Booth, A., Bell, T., Halhol, S., Pan, S., Welch, V., Merinopoulou, E., … Cox, A. (2019). Using Social Media to Uncover Treatment Experiences and Decisions in Patients With Acute Myeloid Leukemia or Myelodysplastic Syndrome Who Are Ineligible for Intensive Chemotherapy: Patient-Centric Qualitative Data Analysis. Journal of Medical Internet Research, 21(11), e14285. https://doi.org.10.2196/14285
  14. Deng, H., Wang, Q., Turner, D. P., Sexton, K. E., Burns, S. M., Eikermann, M., … Houle, T. T. (2020). Sentiment analysis of real-world migraine tweets for population research. Cephalalgia Reports, 3, 251581631989886. https://doi.org/10.1177/2515816319898867
  15. Cinelli, M. (2019). Generalized rich-club ordering in networks. Journal of Complex Networks, 7(5), 702–719. https://doi.org/10.1093/comnet/cnz002
  16. Funk, B., Sadeh-Sharvit, S., Fitzsimmons-Craft, E. E., Trockel, M. T., Monterubio, G. E., Goel, N. J., … Taylor, C. B. (2020). A Framework for Applying Natural Language Processing in Digital Health Interventions. Journal of Medical Internet Research, 22(2), e13855. https://doi.org/10.2196/13855
  17. Cichosz, P. (2020). Unsupervised modeling anomaly detection in discussion forums posts using global vectors for text representation. Natural Language Engineering, 1–28. https://doi.org/10.1017/s1351324920000066
  18. Pruchnik, P. (2020). Identification of Trends in the Polish Media on the Example of the Quarterly Studia Medioznawcze The Use of Big Data Tools. Media Studies, 80(1). http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.desklight-e79ed2c7-fd7d-4a91-8895-c322743c8f48/c/04_Pruchnik_EN.pdf
  19. Hamilton, L. M., & Lahne, J. (2020). Fast and automated sensory analysis: Using natural language processing for descriptive lexicon development. Food Quality and Preference, 83, 103926. https://doi.org/10.1016/j.foodqual.2020.103926
  20. DellaPosta, D., & Nee, V. (2020). Emergence of diverse and specialized knowledge in a metropolitan tech cluster. Social Science Research, 86, 102377. https://doi.org/10.1016/j.ssresearch.2019.102377
  21. Geller, J., Davis, S. D., & Peterson, D. (2020, May 23). Sans forgetica is not desirable for learning. https://doi.org/10.31234/osf.io/ku5bz
  22. Morselli, D., Passini, S., & McGarty, C. (2020). Sos Venezuela: an analysis of the anti-Maduro protest movements using Twitter. Social Movement Studies, 1–22. https://doi.org/10.1080/14742837.2020.1770072
  23. Ficcadenti, V., Cerqueti, R., Ausloos, M., & Dhesi, G. (2020). Words ranking and Hirsch index for identifying the core of the hapaxes in political texts. Journal of Informetrics, 14(3), 101054. https://doi.org/10.1016/j.joi.2020.101054
View Documentation
travis

Set Up Travis for Testing and Deployment

Kirill Müller
Description

Tools for interacting with the Travis API for setting up continuous integration for R packages and other R-based projects.

View Documentation

Fetch Sections of XML Scholarly Articles

Scott Chamberlain
Description

Get chunks of XML scholarly articles without having to know how to work with XML. Custom mappers for each publisher and for each article section pull out the information you want. Works with outputs from package fulltext, xml2 package documents, and file paths to XML documents.

View Documentation

A robots.txt Parser and Webbot/Spider/Crawler Permissions Checker

Peter Meissner
Description

Provides functions to download and parse robots.txt files. Ultimately the package makes it easy to check if bots (spiders, crawler, scrapers, …) are allowed to access specific resources on a domain.

View Documentation

Interface to the Orcid.org API

Scott Chamberlain
Description

Client for the Orcid.org API (https://orcid.org/). Functions included for searching for people, searching by DOI, and searching by Orcid ID.

View Documentation
addressable
Staff maintained

Email Address Validation

Scott Chamberlain
Description

Email Address Validation.

View Documentation

Interface to Virtuoso using ODBC

Carl Boettiger
Description

Provides users with a simple and convenient mechanism to manage and query a Virtuoso database using the DBI (Data-Base Interface) compatible ODBC (Open Database Connectivity) interface. Virtuoso is a high-performance “universal server,” which can act as both a relational database, supporting standard Structured Query Language (SQL) queries, while also supporting data following the Resource Description Framework (RDF) model for Linked Data. RDF data can be queried using SPARQL (SPARQL Protocol and RDF Query Language) queries, a graph-based query that supports semantic reasoning. This allows users to leverage the performance of local or remote Virtuoso servers using popular R packages such as DBI and dplyr, while also providing a high-performance solution for working with large RDF triplestores from R. The package also provides helper routines to install, launch, and manage a Virtuoso server locally on Mac, Windows and Linux platforms using the standard interactive installers from the R command-line. By automatically handling these setup steps, the package can make using Virtuoso considerably faster and easier for a most users to deploy in a local environment. Managing the bulk import of triples from common serializations with a single intuitive command is another key feature of this package. Bulk import performance can be tens to hundreds of times faster than the comparable imports using existing R tools, including rdflib and redland packages.

View Documentation
rsnps
CRAN

Get SNP (Single-Nucleotide Polymorphism) Data on the Web

Julia Gustavsen
Description

A programmatic interface to various SNP datasets on the web: OpenSNP (https://opensnp.org), and NBCIs dbSNP database (https://www.ncbi.nlm.nih.gov/projects/SNP/). Functions are included for searching for NCBI. For OpenSNP, functions are included for getting SNPs, and data for genotypes, phenotypes, annotations, and bulk downloads of data by user.

Scientific use cases
  1. Mackinnon, M. J., Ndila, C., Uyoga, S., Macharia, A., Snow, R. W., Band, G., et al. (2016). Environmental Correlation Analysis for Genes Associated with Protection against Malaria. Molecular Biology and Evolution, 33(5), 1188–1204. https://doi.org/10.1093/molbev/msw004
  2. Roy, A., Ghosal, S., & Choudhury, K. R. (2017). High dimensional Single Index Bayesian Modeling of the Brain Atrophy over time. arXiv preprint arXiv:1712.06743. https://arxiv.org/abs/1712.06743
  3. Amiri Roudbar, M., Mohammadabadi, M. R., Ayatollahi Mehrgardi, A., Abdollahi-Arpanahi, R., Momen, M., Morota, G., … Rosa, G. J. M. (2020). Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls. Heredity, 124(5), 658–674. https://doi.org/10.1038/s41437-020-0301-4
View Documentation

Work with GitHub Gists

Scott Chamberlain
Description

Work with GitHub gists from R (e.g., https://en.wikipedia.org/wiki/GitHub#Gist, https://docs.github.com/en/github/writing-on-github/creating-gists/). A gist is simply one or more files with code/text/images/etc. This package allows the user to create new gists, update gists with new files, rename files, delete files, get and delete gists, star and un-star gists, fork gists, open a gist in your default browser, get embed code for a gist, list gist commits, and get rate limit information when authenticated. Some requests require authentication and some do not. Gists website: https://gist.github.com/.

View Documentation

Species Trait Data from Around the Web

Scott Chamberlain
Description

Species trait data from many different sources, including sequence data from NCBI (https://www.ncbi.nlm.nih.gov/), plant trait data from BETYdb, data from EOL Traitbank, Birdlife International, and more.

Scientific use cases
  1. Michonneau, F., Brown, J. W., & Winter, D. J. (2016). rotl: an R package to interact with the Open Tree of Life data. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.12593
  2. LeBauer, D., Kooper, R., Mulrooney, P., Rohde, S., Wang, D., Long, S. P., & Dietze, M. C. (2017). BETYdb: a yield, trait, and ecosystem service database applied to second‐generation bioenergy feedstock production. GCB Bioenergy. https://doi.org/10.1111/gcbb.12420
View Documentation

Interface to the CAVD DataSpace

Ju Yeong Kim
Description

Provides a convenient API interface to access immunological data within the CAVD DataSpace(https://dataspace.cavd.org), a data sharing and discovery tool that facilitates exploration of HIV immunological data from pre-clinical and clinical HIV vaccine studies.

View Documentation

Interface to the Open Science Framework (OSF)

Aaron Wolen
Description

An interface for interacting with OSF (https://osf.io). osfr enables you to access open research materials and data, or create and manage your own private or public projects.

Scientific use cases
  1. Corput, D. V. D. (2020). Locked in Syndrome Machine Learning Classification using Sentence Comprehension EEG Data. arXiv preprint arXiv:2006.12336 https://arxiv.org/pdf/2006.12336.pdf
View Documentation

Client for CCAFS GCM Data

Scott Chamberlain
Description

Client for Climate Change, Agriculture, and Food Security (CCAFS) General Circulation Models (GCM) data. Data is stored in Amazon S3, from which we provide functions to fetch data.

View Documentation

DrugBank Database XML Parser

Mohammed Ali
Description

This tool is for parsing the DrugBank XML database https://www.drugbank.ca/. The parsed data are then returned in a proper R dataframe with the ability to save them in a given database.

View Documentation

Export Data Frames to Excel xlsx Format

Jeroen Ooms
Description

Zero-dependency data frame to xlsx exporter based on libxlsxwriter. Fast and no Java or Excel required.

Scientific use cases
  1. Garmendia, A., Raigón, M. D., Marques, O., Ferriol, M., Royo, J., & Merle, H. (2018). Effects of nettle slurry (Urtica dioica L.) used as foliar fertilizer on potato (Solanum tuberosum L.) yield and plant growth. PeerJ, 6, e4729. https://doi.org/10.7717/peerj.4729
  2. Garmendia, A., Merle, H., Ruiz, P., & Ferriol, M. (2018). Distribution and ecological segregation on regional and microgeographic scales of the diploid Centaurea aspera L., the tetraploid C. seridis L., and their triploid hybrids (Compositae). PeerJ, 6, e5209. https://doi.org/10.7717/peerj.5209
  3. Garmendia, A., Beltrán, R., Zornoza, C., Breijo, F., Reig, J., Bayona, I., & Merle, H. (2019). Insect repellent and chemical agronomic treatments to reduce seed number in ‘Afourer’ mandarin - Effect on yield and fruit diameter. Scientia Horticulturae. 246, 437–447. https://doi.org/10.1016/j.scienta.2018.11.025
  4. Ktenioudaki, A., O’Donnell, C. P., & do Nascimento Nunes, M. C. (2019). Modelling the biochemical and sensory changes of strawberries during storage under diverse relative humidity conditions. Postharvest Biology and Technology, 154, 148–158. https://doi.org/10.1016/j.postharvbio.2019.04.023
  5. Ayodele Benjamin, E., Vincent, E., Claudius, A., Olatomiwa, L., & Dickson, E. (2019). Data-based investigation on the performance of an independent Gas turbine for electricity generation using real power measurements and other closely related parameters. Data in Brief, 104444. https://doi.org/10.1016/j.dib.2019.104444
  6. Ehlers, M., Nold, J., Kuhn, M., Klingelhöfer-Jens, M., & Lonsdorf, T. (2020). Natural variations in brain morphology do not account for inter-individual differences in defensive responding during fear acquisition training and extinction. https://psyarxiv.com/q2kyf/download?format=pdf
View Documentation

Client for the Open Citations Corpus

Scott Chamberlain
Description

Client for the Open Citations Corpus (http://opencitations.net/). Includes a set of functions for getting one identifier type from another, as well as getting references and citations for a given identifier.

View Documentation

Store and Retrieve Data.frames in a Git Repository

Thierry Onkelinx
Description

Make versioning of data.frame easy and efficient using git repositories.

View Documentation

Managing Larger Data on a GitHub Repository

Carl Boettiger
Description

Because larger (> 50 MB) data files cannot easily be committed to git, a different approach is required to manage data associated with an analysis in a GitHub repository. This package provides a simple work-around by allowing larger (up to 2 GB) data files to piggyback on a repository as assets attached to individual GitHub releases. These files are not handled by git in any way, but instead are uploaded, downloaded, or edited directly by calls through the GitHub API. These data files can be versioned manually by creating different releases. This approach works equally well with public or private repositories. Data can be uploaded and downloaded programmatically from scripts. No authentication is required to download data from public repositories.

Scientific use cases
  1. Boettiger, C. (2018). Managing Larger Data on a GitHub Repository. Journal of Open Source Software, 3(29), 971. https://doi.org/10.21105/joss.00971
View Documentation

Marine Regions Data from Marineregions.org

Lennert Schepers
Description

Tools to get marine regions data from http://www.marineregions.org/. Includes tools to get region metadata, as well as data in GeoJSON format, as well as Shape files. Use cases include using data downstream to visualize geospatial data by marine region, mapping variation among different regions, and more.

View Documentation

Parse Darwin Core Files

Scott Chamberlain
Description

Parse and create Darwin Core (http://rs.tdwg.org/dwc/) Simple and Archives. Functionality includes reading and parsing all the files in a Darwin Core Archive, including the datasets and metadata; read and parse simple Darwin Core files; and validation of Darwin Core Archives.

Scientific use cases
  1. Granados, J. E., Ros-Candeira, A., Pérez-Luque, A. J., Moreno-Llorca, R., Cano-Manuel, F. J., Fandos, P., … Zamora, R. (2020). Long-term monitoring of the Iberian ibex population in the Sierra Nevada of the southeast Iberian Peninsula. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0544-1
View Documentation

Search Vertnet, a Database of Vertebrate Specimen Records

Scott Chamberlain
Description

Retrieve, map and summarize data from the VertNet.org archives (http://vertnet.org/). Functions allow searching by many parameters, including taxonomic names, places, and dates. In addition, there is an interface for conducting spatially delimited searches, and another for requesting large datasets via email.

Scientific use cases
  1. Drozd, P., & Šipoš, J. (2013). R for all (I): Introduction to the new age of biological analyses. Casopis Slezskeho Zemskeho Muzea A, 62(1). https://doi.org/10.2478/cszma-2013-0004
View Documentation

Genomic Data Retrieval

Hajk-Georg Drost
Description

Perform large scale genomic data retrieval and functional annotation retrieval. This package aims to provide users with a standardized way to automate genome, proteome, RNA, coding sequence (CDS), GFF, and metagenome retrieval from NCBI RefSeq, NCBI Genbank, ENSEMBL, and UniProt databases. Furthermore, an interface to the BioMart database (Smedley et al. (2009) doi:10.1186/1471-2164-10-22) allows users to retrieve functional annotation for genomic loci. In addition, users can download entire databases such as NCBI RefSeq (Pruitt et al. (2007) doi:10.1093/nar/gkl842), NCBI nr, NCBI nt, NCBI Genbank (Benson et al. (2013) doi:10.1093/nar/gks1195), etc. with only one command.

Scientific use cases
  1. Drost, H.-G., Gabel, A., Liu, J., Quint, M., & Grosse, I. (2017). myTAI: evolutionary transcriptomics with R. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx835
  2. Gogleva, A., Drost, H.-G., & Schornack, S. (2018). SecretSanta: flexible pipelines for functional secretome prediction. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty088
  3. Ng, P. K.-S., Li, J., Jeong, K. J., Shao, S., Chen, H., Tsang, Y. H., … Mills, G. B. (2018). Systematic Functional Annotation of Somatic Mutations in Cancer. Cancer Cell, 33(3), 450–462.e10. https://doi.org/10.1016/j.ccell.2018.01.021
  4. Schwalie, P. C., Dong, H., Zachara, M., Russeil, J., Alpern, D., Akchiche, N., … Deplancke, B. (2018). A stromal cell population that inhibits adipogenesis in mammalian fat depots. Nature. https://doi.org/10.1038/s41586-018-0226-8
  5. Wegrzyn, J. L., Falk, T., Grau, E., Buehler, S., Ramnath, R., & Herndon, N. (2019). Cyberinfrastructure and resources to enable an integrative approach to studying forest trees. Evolutionary Applications. https://doi.org/10.1111/eva.12860
  6. Karakülah, G., Arslan, N., Yandım, C., & Suner, A. (2019). TEffectR: an R package for studying the potential effects of transposable elements on gene expression with linear regression model. PeerJ, 7, e8192. https://doi.org/10.7717/peerj.8192
  7. Noecker, C., Chiu, H. C., McNally, C. P., & Borenstein, E. (2019). Defining and evaluating microbial contributions to metabolite variation in microbiome-metabolome association studies. mSystems, 4(6). https://doi.org/10.1128/mSystems.00579-19
  8. Kim, J., Yoon, S., & Nam, D. (2020). netGO: R-Shiny package for network-integrated pathway enrichment analysis. Bioinformatics. https://doi.org/10.1093/bioinformatics/btaa077
  9. Drost, H.-G. (2020). LTRpred: de novo annotation of intact retrotransposons. Journal of Open Source Software, 5(50), 2170. https://doi.org/10.21105/joss.02170
View Documentation

Open Source OCR Engine

Jeroen Ooms
Description

Bindings to Tesseract https://opensource.google.com/projects/tesseract: a powerful optical character recognition (OCR) engine that supports over 100 languages. The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results.

Scientific use cases
  1. Stachelek, J., Ford, C., Kincaid, D., King, K., Miller, H., & Nagelkirk, R. (2017). The National Eutrophication Survey: lake characteristics and historical nutrient concentrations. Earth System Science Data Discussions, 1–11. https://doi.org/10.5194/essd-2017-52
  2. Bayer, D., & Michael, S. (2019). Exploring the Daschle Collection using Text Mining. arXiv preprint arXiv:1904.12623 https://arxiv.org/pdf/1904.12623
  3. Tennant, W. S. D., Tildesley, M. J., Spencer, S. E. F., & Keeling, M. J. (2020). Climate drivers of plague epidemiology in British India, 1898–1949. Proceedings of the Royal Society B: Biological Sciences, 287(1928), 20200538. https://doi.org/10.1098/rspb.2020.0538
View Documentation
packagemetrics

Collect Metrics on Packages from CRAN, GitHub, and StackOverflow

Sam Firke
Description

This package was designed to address two issues, 78 and 69, for the ROpenSci unconf17 concerning avoiding redundant / overlapping packages and a framework for reproducible tables. As this is a complex topic, the smaller tasks being accomplished is producing a list of metrics that can be used to compare similar packages utilizing information collected from CRAN, GitHub, and StackOverflow.

Scientific use cases
  1. Maljkovic, D. (2019). Modelling Influential Factors of Consumption in Buildings Connected to District Heating Systems. Energies, 12(4), 586. https://doi.org/10.3390/en12040586
View Documentation

Convert Data from and to GeoJSON or TopoJSON

Scott Chamberlain
Description

Convert data to GeoJSON or TopoJSON from various R classes, including vectors, lists, data frames, shape files, and spatial classes. geojsonio does not aim to replace packages like sp, rgdal, rgeos, but rather aims to be a high level client to simplify conversions of data from and to GeoJSON and TopoJSON.

Scientific use cases
  1. von Schmidt, A., Cyganski, R., & Heinrichs, M. 2019. Web-based Visualization of Daily Mobility Patterns in R. International Journal on Advances in Internet Technology, vol 12 (3 & 4). https://elib.dlr.de/133599/1/inttech_v12_n34_2019_2.pdf
  2. Ranghetti, L., Boschetti, M., Nutini, F., & Busetto, L. (2020). “sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data. Computers & Geosciences, 139, 104473. https://doi.org/10.1016/j.cageo.2020.104473
  3. Shrestha, R. K., & Shrestha, R. (2020). Group segmentation and heterogeneity in the choice of cooking fuels in post-earthquake Nepal. arXiv preprint arXiv:2005.09616. https://arxiv.org/pdf/2005.09616.pdf
View Documentation

Client for the Comprehensive Knowledge Archive Network (CKAN) API

Scott Chamberlain
Description

Client for CKAN API (https://ckan.org/). Includes interface to CKAN APIs for search, list, show for packages, organizations, and resources. In addition, provides an interface to the datastore API.

Scientific use cases
  1. White, L., & Santy, S. (2018). DataDepsGenerators.jl: making reusing data easy by automatically generating DataDeps.jl registration code. Journal of Open Source Software, 3(31), 921. https://doi.org/10.21105/joss.00921
View Documentation

Accesses Air Quality Data from the Open Data Platform OpenAQ

Maëlle Salmon
Description

Allows access to air quality data from the API of the OpenAQ platform https://docs.openaq.org/, with the different services the API offers (getting measurements for a given query, getting latest measurements, getting lists of available countries/cities/locations).

Scientific use cases
  1. Selvi, S., & Chandrasekaran, M. (2018). Performance evaluation of mathematical predictive modeling for air quality forecasting. Cluster Computing. https://doi.org/10.1007/s10586-017-1667-9
View Documentation
EML
CRAN

Read and Write Ecological Metadata Language Files

Carl Boettiger
Description

Work with Ecological Metadata Language (EML) files. EML is a widely used metadata standard in the ecological and environmental sciences, described in Jones et al. (2006), doi:10.1146/annurev.ecolsys.37.091305.110031.

View Documentation
qcoder

Lightweight Qualitative Coding

Elin Waring
Description

A free, lightweight, open source option for analyzing text-based qualitative data. Enables analysis of interview transcripts, observation notes, memos, and other sources. Supports the work of social scientists, historians, humanists, and other researchers who use qualitative methods. Addresses the unique challenges faced in analyzing qualitative data analysis. Provides opportunities for researchers who otherwise might not develop software to build software development skills.

View Documentation

General Purpose Interface to Elasticsearch

Scott Chamberlain
Description

Connect to Elasticsearch, a NoSQL database built on the Java Virtual Machine. Interacts with the Elasticsearch HTTP API (https://www.elastic.co/elasticsearch/), including functions for setting connection details to Elasticsearch instances, loading bulk data, searching for documents with both HTTP query variables and JSON based body requests. In addition, elastic provides functions for interacting with API’s for indices, documents, nodes, clusters, an interface to the cat API, and more.

View Documentation
paleobioDB
CRAN

Download and Process Data from the Paleobiology Database

Sara Varela
Description

Includes 19 functions to wrap each endpoint of the PaleobioDB API, plus 8 functions to visualize and process the fossil data. The API documentation for the Paleobiology Database can be found in http://paleobiodb.org/data1.1/.

Scientific use cases
  1. Varela, S., González-Hernández, J., Sgarbi, L. F., Marshall, C., Uhen, M. D., Peters, S., & McClennen, M. (2014). paleobioDB: an R package for downloading, visualizing and processing data from the Paleobiology Database. Ecography, 38(4), 419–425. https://doi.org/10.1111/ecog.01154
  2. Read, J. S., Walker, J. I., Appling, A. P., Blodgett, D. L., Read, E. K., & Winslow, L. A. (2015). geoknife: reproducible web-processing of large gridded datasets. Ecography, 39(4), 354–360. https://doi.org/10.1111/ecog.01880
  3. Springer, M. S., Emerling, C. A., Meredith, R. W., Janečka, J. E., Eizirik, E., & Murphy, W. J. (2016). Waking the undead: implications of a soft explosive model for the timing of placental mammal diversification. Molecular Phylogenetics and Evolution. https://doi.org/10.1016/j.ympev.2016.09.017
  4. Pimiento, C., & Benton, M. J. (2020). The impact of the Pull of the Recent on extant elasmobranchs. Palaeontology. https://doi.org/10.1111/pala.12478
View Documentation

Secure Shell (SSH) Client for R

Jeroen Ooms
Description

Connect to a remote server over SSH to transfer files via SCP, setup a secure tunnel, or run a command or script on the host while streaming stdout and stderr directly to the client.

View Documentation

Fetch Phylogenies from Many Sources

Scott Chamberlain
Description

Includes methods for fetching phylogenies from a variety of sources, including the Phylomatic web service (http://phylodiversity.net/phylomatic), and Phylocom (https://github.com/phylocom/phylocom/).

Scientific use cases
  1. Mayor, J. R., Sanders, N. J., Classen, A. T., Bardgett, R. D., Clément, J.-C., Fajardo, A., et al. (2017). Elevation alters ecosystem properties across temperate treelines globally. Nature, 542(7639), 91–95. https://doi.org/10.1038/nature21027
  2. Giroldo, A. B., Scariot, A., & Hoffmann, W. A. (2017). Trait shifts associated with the subshrub life-history strategy in a tropical savanna. Oecologia. https://doi.org/10.1007/s00442-017-3930-4
  3. Van de Peer, T., Mereu, S., Verheyen, K., María Costa Saura, J., Morillas, L., Roales, J., … Muys, B. (2018). Tree seedling vitality improves with functional diversity in a Mediterranean common garden experiment. Forest Ecology and Management, 409, 614–633. https://doi.org/10.1016/j.foreco.2017.12.001
  4. Bemmels, J. B., Wright, S. J., Garwood, N. C., Queenborough, S. A., Valencia, R., & Dick, C. W. (2018). Filter-dispersal assembly of lowland Neotropical rainforests across the Andes. Ecography. https://doi.org/10.1111/ecog.03473
  5. Gastauer, M., Caldeira, C. F., Trotter, I., Ramos, S. J., & Neto, J. A. A. M. (2018). Optimizing community trees using the open tree of life increases the reliability of phylogenetic diversity and dispersion indices. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2018.06.008
  6. Albert, S., Flores, O., Rouget, M., Wilding, N., & Strasberg, D. (2018). Why are woody plants fleshy-fruited at low elevations? Evidence from a high-elevation oceanic island. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12676
  7. Gill, B. A., Musili, P. M., Kurukura, S., Hassan, A. A., Goheen, J. R., Kress, W. J., … Kartzinel, T. R. (2019). Plant DNA-barcode library and community phylogeny for a semi-arid East African savanna. Molecular Ecology Resources. https://doi.org/10.1111/1755-0998.13001
  8. Redmond, M. D., Morris, T. L., & Cramer, M. C. (2019). The cost of standing tall: wood nutrients associated with tree invasions in nutrient‐poor fynbos soils of South Africa. Ecosphere, 10(9). https://doi.org/10.1002/ecs2.2831
  9. Vidal, M. C., Quinn, T. W., Stireman, J. O., Tinghitella, R. M., & Murphy, S. M. (2019). Geography is more important than host plant use for the population genetic structure of a generalist insect herbivore. Molecular Ecology. https://doi.org/10.1111/mec.15218
  10. Bohner, T., & Diez, J. (2019). Extensive mismatches between species distributions and performance and their relationship to functional traits. Ecology Letters. https://doi.org/10.1111/ele.13396
  11. Roddy, A. B., Théroux-Rancourt, G., Abbo, T., Benedetti, J. W., Brodersen, C. R., Castro, M., … Simonin, K. A. (2019). The Scaling of Genome Size and Cell Size Limits Maximum Rates of Photosynthesis with Implications for Ecological Strategies. International Journal of Plant Sciences. https://doi.org/10.1086/706186>
  12. Herrera, C. M. (2020). Flower traits, habitat, and phylogeny as predictors of pollinator service: a plant community perspective. Ecological Monographs. https://doi.org/10.1002/ecm.1402
  13. Théroux-Rancourt, G., Roddy, A. B., Earles, J. M., Gilbert, M. E., Zwieniecki, M. A., Boyce, C. K., … Brodersen, C. R. (2020). Maximum CO2 diffusion inside leaves is limited by the scaling of cell size and genome size. https://doi.org/10.1101/2020.01.16.904458
  14. Larson, J. E., Anacker, B. L., Wanous, S., & Funk, J. L. (2020). Ecological strategies begin at germination: Traits, plasticity and survival in the first 4 days of plant life. Functional Ecology. https://doi.org/10.1111/1365-2435.13543
  15. Trugman, A. T., Anderegg, L. D. L., Shaw, J. D., & Anderegg, W. R. L. (2020). Trait velocities reveal that mortality has driven widespread coordinated shifts in forest hydraulic trait composition. Proceedings of the National Academy of Sciences, 117(15), 8532–8538. https://doi.org/10.1073/pnas.1917521117
  16. Santana, V. M., Alday, J. G., Adamo, I., Alloza, J. A., & Baeza, M. J. (2020). Climate, and not fire, drives the phylogenetic clustering of species with hard-coated seeds in Mediterranean Basin communities. Perspectives in Plant Ecology, Evolution and Systematics, 45, 125545. https://doi.org/10.1016/j.ppees.2020.125545
View Documentation
landscapetools
CRAN Peer-reviewed

Landscape Utility Toolbox

Marco Sciaini
Description

Provides utility functions for some of the less-glamorous tasks involved in landscape analysis. It includes functions to coerce raster data to the common tibble format and vice versa, it helps with flexible reclassification tasks of raster data and it provides a function to merge multiple raster. Furthermore, landscapetools helps landscape scientists to visualize their data by providing optional themes and utility functions to plot single landscapes, rasterstacks, -bricks and lists of raster.

Scientific use cases
  1. Langhammer, M., Thober, J., Lange, M., Frank, K., & Grimm, V. (2019). Agricultural landscape generators for simulation models: A review of existing solutions and an outline of future directions. Ecological Modelling, 393, 135–151. https://doi.org/10.1016/j.ecolmodel.2018.12.010
  2. Etherington, T., & Omondiagbe, O. (2019). virtualNicheR: generating virtual fundamental and realised niches for use in virtual ecology experiments. Journal of Open Source Software, 4(41), 1661. https://doi.org/10.21105/joss.01661
  3. Betts, M. G., Wolf, C., Pfeifer, M., Banks-Leite, C., Arroyo-Rodríguez, V., Ribeiro, D. B., … Ewers, R. M. (2019). Extinction filters mediate the global effects of habitat fragmentation on animals. Science, 366(6470), 1236–1239. https://doi.org/10.1126/science.aax9387
  4. Scherer, C., Radchuk, V., Franz, M., Thulke, H., Lange, M., Grimm, V., & Kramer‐Schadt, S. (2020). Moving infections: individual movement decisions drive disease persistence in spatially structured landscapes. Oikos. https://doi.org/10.1111/oik.07002
  5. Silva, I., Crane, M., Marshall, B. M., & Strine, C. T. (2020). Revisiting reptile home ranges: moving beyond traditional estimators with dynamic Brownian Bridge Movement Models. https://doi.org/10.1101/2020.02.10.941278
View Documentation

Tools for Managing SSH and Git Credentials

Jeroen Ooms
Description

Setup and retrieve HTTPS and SSH credentials for use with git and other services. For HTTPS remotes the package interfaces the git-credential utility which git uses to store HTTP usernames and passwords. For SSH remotes we provide convenient functions to find or generate appropriate SSH keys. The package both helps the user to setup a local git installation, and also provides a back-end for git/ssh client libraries to authenticate with existing user credentials.

View Documentation

Find Free Versions of Scholarly Publications via Unpaywall

Najko Jahn
Description

This web client interfaces Unpaywall https://unpaywall.org/products/api, formerly oaDOI, a service finding free full-texts of academic papers by linking DOIs with open access journals and repositories. It provides unified access to various data sources for open access full-text links including Crossref and the Directory of Open Access Journals (DOAJ). API usage is free and no registration is required.

Scientific use cases
  1. Ashby, M. P. J. (2020, March 6). Three quarters of new criminological knowledge is hidden from policy makers. https://doi.org/10.31235/osf.io/wnq7h
View Documentation
cricketdata

International Cricket Data

Rob Hyndman
Description

Data on all international cricket matches is provided by ESPNCricinfo. This package provides some scraper functions to download the data into tibbles ready for analysis. Some innings-level data sourced from Howzstat is also included in the package.

View Documentation

Client for Neuroscience Information Framework APIs

Scott Chamberlain
Description

Client for Neuroscience Information Framework (NIF) APIs (https://neuinfo.org/; https://neuinfo.org/about/webservices). Package includes functions for each API route, and gives back data in tidy data.frame format.

View Documentation

General Purpose Oai-PMH Services Client

Scott Chamberlain
Description

A general purpose client to work with any OAI-PMH (Open Archives Initiative Protocol for Metadata Harvesting) service. The OAI-PMH protocol is described at http://www.openarchives.org/OAI/openarchivesprotocol.html. Functions are provided to work with the OAI-PMH verbs: GetRecord, Identify, ListIdentifiers, ListMetadataFormats, ListRecords, and ListSets.

Scientific use cases
  1. Peters, I., Kraker, P., Lex, E., Gumpenberger, C., & Gorraiz, J. I. (2017). Zenodo in the Spotlight of Traditional and New Metrics. Frontiers in Research Metrics and Analytics, 2. https://doi.org/10.3389/frma.2017.00013
View Documentation

Interface to the USGS BISON API

Scott Chamberlain
Description

Interface to the USGS BISON (https://bison.usgs.gov/) API, a database for species occurrence data. Data comes from species in the United States from participating data providers. You can get data via taxonomic and location based queries. A simple function is provided to help visualize data.

Scientific use cases
  1. Young, N. E., Jarnevich, C. S., Sofaer, H. R., Pearse, I., Sullivan, J., Engelstad, P., & Stohlgren, T. J. (2020). A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales. PLOS ONE, 15(3), e0229253. https://doi.org/10.1371/journal.pone.0229253
View Documentation
EndoMineR
Peer-reviewed

Functions to mine endoscopic and associated pathology datasets

Sebastian Zeki
Description

This script comprises the functions that are used to clean up endoscopic reports and pathology reports as well as many of the scripts used for analysis.
The scripts assume the endoscopy and histopathology data set is merged already but it can also be used of course with the unmerged datasets.

View Documentation

General Purpose Client for ERDDAP Servers

Scott Chamberlain
Description

General purpose R client for ERDDAP servers. Includes functions to search for datasets, get summary information on datasets, and fetch datasets, in either csv or netCDF format. ERDDAP information: https://upwell.pfeg.noaa.gov/erddap/information.html.

Scientific use cases
  1. Shabangu, F. W., Yemane, D., Stafford, K. M., Ensor, P., & Findlay, K. P. (2017). Modelling the effects of environmental conditions on the acoustic occurrence and behaviour of Antarctic blue whales. PLOS ONE, 12(2), e0172705. https://doi.org/10.1371/journal.pone.0172705
  2. Mendez, L., Borsa, P., Cruz, S., de Grissac, S., Hennicke, J., Lallemand, J., … Weimerskirch, H. (2017). Geographical variation in the foraging behaviour of the pantropical red-footed booby. Marine Ecology Progress Series, 568, 217–230. https://doi.org/10.3354/meps12052
  3. Abolaffio, M., Reynolds, A. M., Cecere, J. G., Paiva, V. H., & Focardi, S. (2018). Olfactory-cued navigation in shearwaters: linking movement patterns to mechanisms. Scientific Reports, 8(1). http://doi.org/10.1038/s41598-018-29919-0
  4. Baylis, A. M. M., Tierney, M., Orben, R. A., Warwick-Evans, V., Wakefield, E., Grecian, W. J., … Brickle, P. (2019). Important At-Sea Areas of Colonial Breeding Marine Predators on the Southern Patagonian Shelf. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-44695-1
  5. O’Farrell, S., Chollett, I., Sanchirico, J. N., & Perruso, L. (2019). Classifying fishing behavioral diversity using high-frequency movement data. Proceedings of the National Academy of Sciences, 201906766. https://doi.org/10.1073/pnas.1906766116
View Documentation
onekp

Retrieve Data from the 1000 Plants Initiative (1KP)

Zebulun Arendsee
Description

The 1000 Plants Initiative (www.onekp.com) has sequenced the transcriptomes
of over 1000 plant species. This package allows these sequences and
metadata to be retrieved and filtered by code, species or recursively by
clade.  Scientific names and NCBI taxonomy IDs are both supported.

View Documentation
gender
CRAN

Predict Gender from Names Using Historical Data

Lincoln Mullen
Description

Infers state-recorded gender categories from first names and dates of birth using historical datasets. By using these datasets instead of lists of male and female names, this package is able to more accurately infer the gender of a name, and it is able to report the probability that a name was male or female. GUIDELINES: This method must be used cautiously and responsibly. Please be sure to see the guidelines and warnings about usage in the README or the package documentation. See Blevins and Mullen (2015) http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html.

View Documentation

World Register of Marine Species (WoRMS) Client

Scott Chamberlain
Description

Client for World Register of Marine Species (http://www.marinespecies.org/). Includes functions for each of the API methods, including searching for names by name, date and common names, searching using external identifiers, fetching synonyms, as well as fetching taxonomic children and taxonomic classification.

Scientific use cases
  1. O’Hara, C. C., Afflerbach, J. C., Scarborough, C., Kaschner, K., & Halpern, B. S. (2017). Aligning marine species range data to better serve science and conservation. PLOS ONE, 12(5), e0175739. https://doi.org/10.1371/journal.pone.0175739
  2. Clegg, T., Ali, M., & Beckerman, A. P. (2018). The impact of intraspecific variation on food web structure. Ecology. https://doi.org./10.1002/ecy.2523
  3. Webb, T. J., Lines, A., & Howarth, L. M. (2020). Occupancy‐derived thermal affinities reflect known physiological thermal limits of marine species. Ecology and Evolution, 10(14), 7050–7061. https://doi.org/10.1002/ece3.6407
View Documentation

Download Qualtrics Survey Data

Julia Silge
Description

Provides functions to access survey results directly into R using the Qualtrics API. Qualtrics https://www.qualtrics.com/about/ is an online survey and data collection software platform. See https://api.qualtrics.com/ for more information about the Qualtrics API. This package is community-maintained and is not officially supported by Qualtrics.

View Documentation

Client for the CORE API

Scott Chamberlain
Description

Client for the CORE API (https://core.ac.uk/docs/). CORE (https://core.ac.uk) aggregates open access research outputs from repositories and journals worldwide and make them available to the public.

View Documentation

Access Publisher Copyright & Self-Archiving Policies via the SHERPA/RoMEO API

Matthias Grenié
Description

Fetches information from the SHERPA/RoMEO API http://www.sherpa.ac.uk/romeo/apimanual.php which indexes policies of journal regarding the archival of scientific manuscripts before and/or after peer-review as well as formatted manuscripts.

Scientific use cases
  1. Ashby, M. P. J. (2020, March 6). Three quarters of new criminological knowledge is hidden from policy makers. https://doi.org/10.31235/osf.io/wnq7h
View Documentation

Polyhedra Database

Alejandro Baranek
Description

A polyhedra database scraped from various sources as R6 objects and rgl visualizing capabilities.

View Documentation

Directory of Open Access Journals Client

Scott Chamberlain
Description

Client for the Directory of Open Access Journals (DOAJ) (https://doaj.org/). API documentation at https://doaj.org/api/v1/docs. Methods included for working with all DOAJ API routes: fetch article information by identifier, search for articles, fetch journal information by identifier, and search for journals.

View Documentation

Parse a BibTeX File to a Data Frame

Philipp Ottolinger
Description

Parse a BibTeX file to a data.frame to make it accessible for further analysis and visualization.

Scientific use cases
  1. Scharmüller, A., Schreiner, V. C., & Schäfer, R. B. (2020). Standartox: Standardizing Toxicity Data. Data, 5(2), 46. https://doi.org/10.3390/data5020046
  2. LeBeau, B. C., & Aloe, A. M. (2020). Evolution of Statistical Software and Quantitative Methods. https://doi.org/10.17077/pp.005273
View Documentation

R Interface to the Species+ Database

Kevin Cazelles
Description

A programmatic interface to the Species+ https://speciesplus.net/ database via the Species+/CITES Checklist API https://api.speciesplus.net/.

Scientific use cases
  1. Geschke, J., Cazelles, K., & Bartomeus, I. (2018). rcites: An R package to access the CITES Speciesplus database. Journal of Open Source Software, 3(31), 1091. https://doi.org/10.21105/joss.01091
  2. Hierink, F., Bolon, I., Durso, A. M., Ruiz de Castañeda, R., Zambrana-Torrelio, C., Eskew, E. A., & Ray, N. (2020). Forty-four years of global trade in CITES-listed snakes: Trends and implications for conservation and public health. Biological Conservation, 248, 108601. https://doi.org/10.1016/j.biocon.2020.108601
View Documentation

Taxonomic Information from Wikipedia

Scott Chamberlain
Description

Taxonomic information from Wikipedia, Wikicommons, Wikispecies, and Wikidata. Functions included for getting taxonomic information from each of the sources just listed, as well performing taxonomic search.

View Documentation

A Tidy Approach to NetCDF Data Exploration and Extraction

Michael Sumner
Description

Tidy tools for NetCDF data sources. Explore the contents of a NetCDF source (file or URL) presented as variables organized by grid with a database-like interface. The hyper_filter() interactive function translates the filter value or index expressions to array-slicing form. No data is read until explicitly requested, as a data frame or list of arrays via hyper_tibble() or hyper_array().

View Documentation

Connector to CouchDB

Scott Chamberlain
Description

Provides an interface to the NoSQL database CouchDB (http://couchdb.apache.org). Methods are provided for managing databases within CouchDB, including creating/deleting/updating/transferring, and managing documents within databases. One can connect with a local CouchDB instance, or a remote CouchDB databases such as Cloudant. Documents can be inserted directly from vectors, lists, data.frames, and JSON. Targeted at CouchDB v2 or greater.

View Documentation

Access for Dryad Web Services

Scott Chamberlain
Description

Interface to the Dryad “Solr” API, their “OAI-PMH” service, and fetch datasets. Dryad (https://datadryad.org/) is a curated host of data underlying scientific publications.

Scientific use cases
  1. Drozd, P., & Šipoš, J. (2013). R for all (I): Introduction to the new age of biological analyses. Casopis Slezskeho Zemskeho Muzea A, 62(1). https://doi.org/10.2478/cszma-2013-0004
  2. White, L., & Santy, S. (2018). DataDepsGenerators.jl: making reusing data easy by automatically generating DataDeps.jl registration code. Journal of Open Source Software, 3(31), 921. https://doi.org/10.21105/joss.00921
  3. Manning, F., Curtis, P. J., Walker, I., & Pither, J. (2020, June 2). An experimental test of the capacity for long-distance dispersal of freshwater diatoms adhering to waterfowl plumage. https://doi.org/10.32942/osf.io/h97pw
View Documentation

Fast, Consistent Tokenization of Natural Language Text

Lincoln Mullen
Description

Convert natural language text into tokens. Includes tokenizers for shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs, characters, shingled characters, lines, tweets, Penn Treebank, regular expressions, as well as functions for counting characters, words, and sentences, and a function for splitting longer texts into separate documents, each with the same number of words. The tokenizers have a consistent interface, and the package is built on the stringi and Rcpp packages for fast yet correct tokenization in UTF-8.

Scientific use cases
  1. A. Mullen, L., Benoit, K., Keyes, O., Selivanov, D., & Arnold, J. (2018). Fast, Consistent Tokenization of Natural Language Text. Journal of Open Source Software, 3(23), 655. https://doi.org/10.21105/joss.00655
  2. Pajo, J. (2018). Quantitative Falsification for Qualitative Findings. Social Science Computer Review, 089443931876795. https://doi.org/10.1177/0894439318767956
  3. Casey, Jerome (2018). Text Analytics Techniques in the Digital World: a Sentiment Analysis Case Study of the Coverage of Climate Change on US News Networks. Irish Communication Review: Vol. 16: Iss. 1, Article 7. https://arrow.dit.ie/icr/vol16/iss1/7
  4. Gye-Soo, K. 2018. Text Mining and Big Data Analysis in the Relational Database with R. International Journal of Trend in Research and Development. 4(5): 384-386. http://www.ijtrd.com/papers/IJTRD12170.pdf
  5. Ficcadenti, V., Cerqueti, R., & Ausloos, M. (2019). A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2018.12.049
  6. Calderone, A. (2019). A Computational Analysis of Natural Languages to Build a Sentence Structure Aware Artificial Neural Network. arXiv preprint arXiv:1906.05491 https://arxiv.org/pdf/1906.05491.pdf
  7. Ulibarri, N., & Scott, T. A. (2019). Environmental hazards, rigid institutions, and transformative change: How drought affects the consideration of water and climate impacts in infrastructure management. Global Environmental Change, 59, 102005. https://doi.org/10.1016/j.gloenvcha.2019.102005
  8. Claes, M., & Mäntylä, M. (2020). 20-MAD–20 Years of Issues and Commits of Mozilla and Apache Development. arXiv preprint arXiv:2003.14015. https://arxiv.org/pdf/2003.14015.pdf
View Documentation

Classes for GeoJSON

Scott Chamberlain
Description

Classes for GeoJSON to make working with GeoJSON easier. Includes S3 classes for GeoJSON classes with brief summary output, and a few methods such as extracting and adding bounding boxes, properties, and coordinate reference systems; working with newline delimited GeoJSON; linting through the geojsonlint package; and serializing to/from Geobuf binary GeoJSON format.

View Documentation

Simple Jenkins Client for R

Jeroen Ooms
Description

Manage jobs and builds on your Jenkins CI server https://jenkins.io/. Create and edit projects, schedule builds, manage the queue, download build logs, and much more.

View Documentation
RNeXML
CRAN

Semantically Rich I/O for the NeXML Format

Carl Boettiger
Description

Provides access to phyloinformatic data in NeXML format. The package should add new functionality to R such as the possibility to manipulate NeXML objects in more various and refined way and compatibility with ape objects.

Scientific use cases
  1. Stöver, B. C., Wiechers, S., & Müller, K. F. (2019). JPhyloIO: a Java library for event-based reading and writing of different phylogenetic file formats through a common interface. BMC Bioinformatics, 20(1). https://doi.org/10.1186/s12859-019-2982-3
View Documentation

Interface to Bold Systems API

Scott Chamberlain
Description

A programmatic interface to the Web Service methods provided by Bold Systems (http://www.boldsystems.org/) for genetic barcode data. Functions include methods for searching by sequences by taxonomic names, ids, collectors, and institutions; as well as a function for searching for specimens, and downloading trace files.

Scientific use cases
  1. Hassall, C., Owen, J., & Gilbert, F. (2016). Phenological shifts in hoverflies (Diptera: Syrphidae): linking measurement and mechanism. Ecography. https://doi.org/10.1111/ecog.02623
  2. Bowser, M., Morton, J., Hanson, J., Magness, D., & Okuly, M. (2017). Arthropod and oligochaete assemblages from grasslands of the southern Kenai Peninsula, Alaska. Biodiversity Data Journal, 5, e10792. https://doi.org/10.3897/bdj.5.e10792
  3. Divoll, T. J., Brown, V. A., Kinne, J., McCracken, G. F., & O’Keefe, J. M. (2018). Disparities in second-generation DNA metabarcoding results exposed with accessible and repeatable workflows. Molecular Ecology Resources. https://doi.org/10.1111/1755-0998.12770
  4. Cravens, Z. M., Brown, V. A., Divoll, T. J., & Boyles, J. G. (2017). Illuminating prey selection in an insectivorous bat community exposed to artificial light at night. Journal of Applied Ecology, 55(2), 705–713. https://doi.org/10.1111/1365-2664.13036
  5. Collins, R. A., Bakker, J., Wangensteen, O. S., Soto, A. Z., Corrigan, L., Sims, D. W., … Mariani, S. (2019). Non‐specific amplification compromises environmental DNA metabarcoding with COI. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13276
  6. Piper, A. M., Batovska, J., Cogan, N. O. I., Weiss, J., Cunningham, J. P., Rodoni, B. C., & Blacket, M. J. (2019). Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance. GigaScience, 8(8). https://doi.org/10.1093/gigascience/giz092
  7. Arranz, V., Pearman, W. S., Aguirre, J. D., & Liggins, L. (2020). MARES, a replicable pipeline and curated reference database for marine eukaryote metabarcoding. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0549-9
View Documentation

Access Nomis UK Labour Market Data

Evan Odell
Description

Access UK official statistics from the Nomis database. Nomis includes data from the Census, the Labour Force Survey, DWP benefit statistics and other economic and demographic data from the Office for National Statistics, based around statistical geographies. See https://www.nomisweb.co.uk/api/v01/help for full API documentation.

View Documentation

R Bindings for ZeroMQ

Jeroen Ooms
Description

Interface to the ZeroMQ lightweight messaging kernel (see http://www.zeromq.org/ for more information).

View Documentation
outsider.base
CRAN

Base Package for Outsider

Dom Bennett
Description

Base package for outsider https://github.com/ropensci/outsider. The outsider package and its sister packages enable the installation and running of external, command-line software within R. This base package is a key dependency of the user-facing outsider package as it provides the utilities for interfacing between Docker https://www.docker.com and R. It is intended that end-users of outsider do not directly work with this base package.

View Documentation

Install and Run Programs, Outside of R, Inside of R

Dom Bennett
Description

Install and run external command-line programs in R through use of Docker https://www.docker.com/ and online repositories.

View Documentation
git2r
CRAN

Provides Access to Git Repositories

Stefan Widgren
Description

Interface to the libgit2 library, which is a pure C implementation of the Git core methods. Provides access to Git repositories to extract data and running some basic Git commands.

Scientific use cases
  1. Blischak, J. D., Carbonetto, P., & Stephens, M. (2019). Creating and sharing reproducible research code the workflowr way. F1000Research, 8, 1749. https://doi.org/10.12688/f1000research.20843.1
View Documentation

Automated Phylogenetic Sequence Cluster Identification from GenBank

Dom Bennett
Description

A pipeline for the identification, within taxonomic groups, of orthologous sequence clusters from GenBank https://www.ncbi.nlm.nih.gov/genbank/ as the first step in a phylogenetic analysis. The pipeline depends on a local alignment search tool and is, therefore, not dependent on differences in gene naming conventions and naming errors.

Scientific use cases
  1. Evans, K. M., Vidal-García, M., Tagliacollo, V. A., Taylor, S. J., & Fenolio, D. B. (2019). Bony Patchwork: Mosaic Patterns of Evolution in the Skull of Electric Fishes (Apteronotidae: Gymnotiformes). Integrative and Comparative Biology. https://doi.org/10.1093/icb/icz026
  2. Ruiz-Sanchez, E., Maya-Lastra, C. A., Steinmann, V. W., Zamudio, S., Carranza, E., Murillo, R. M., & Rzedowski, J. (2019). Datataxa: a new script to extract metadata sequence information from GenBank, the Flora of Bajío as a case study. Botanical Sciences, 97(4), 754–760. https://doi.org/10.17129/botsci.2226
View Documentation
grainchanger

Moving-Window and Direct Data Aggregation

Laura Graham
Description

Data aggregation via moving window or direct methods. Aggregate a fine-resolution raster to a grid. The moving window method smooths the surface using a specified function within a moving window of a specified size and shape prior to aggregation. The direct method simply aggregates to the grid using the specified function.

View Documentation

Fetch Species Origin Data from the Web

Scott Chamberlain
Description

Get species origin data (whether species is native/invasive) from the following sources on the web: Encyclopedia of Life (http://eol.org), Flora Europaea (http://rbg-web2.rbge.org.uk/FE/fe.html), Global Invasive Species Database (http://www.iucngisd.org/gisd), the Native Species Resolver (https://bien.nceas.ucsb.edu/bien/tools/nsr/), Integrated Taxonomic Information Service (https://www.itis.gov/), and Global Register of Introduced and Invasive Species (http://www.griis.org/).

View Documentation

R Interface to the Europe PubMed Central RESTful Web Service

Najko Jahn
Description

An R Client for the Europe PubMed Central RESTful Web Service (see https://europepmc.org/RestfulWebService for more information). It gives access to both metadata on life science literature and open access full texts. Europe PMC indexes all PubMed content and other literature sources including Agricola, a bibliographic database of citations to the agricultural literature, or Biological Patents. In addition to bibliographic metadata, the client allows users to fetch citations and reference lists. Links between life-science literature and other EBI databases, including ENA, PDB or ChEMBL are also accessible. No registration or API key is required. See the vignettes for usage examples.

View Documentation

Read Data from JSTOR/DfR

Thomas Klebel
Description

Functions and helpers to import metadata, ngrams and full-texts delivered by Data for Research by JSTOR.

View Documentation

JSON for Linking Data

Jeroen Ooms
Description

JSON-LD is a light-weight syntax for expressing linked data. It is primarily intended for web-based programming environments, interoperable web services and for storing linked data in JSON-based databases. This package provides bindings to the JavaScript library for converting, expanding and compacting JSON-LD documents.

View Documentation

Interface to the Biodiversity Heritage Library

Scott Chamberlain
Description

Interface to Biodiversity Heritage Library (BHL) (https://www.biodiversitylibrary.org/) API (https://www.biodiversitylibrary.org/docs/api3.html). BHL is a repository of digitized literature on biodiversity studies, including floras, research papers, and more.

Scientific use cases
  1. Jaspers, S., De Troyer, E., & Aerts, M. (2018). Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA. EFSA Supporting Publications, 15(6), 1427E. https://doi.org/10.2903/sp.efsa.2018.EN-1427
View Documentation

Mutation Testing Framework

Scott Chamberlain
Description

Mutation testing framework.

View Documentation

Text Extraction, Rendering and Converting of PDF Documents

Jeroen Ooms
Description

Utilities based on libpoppler for extracting text, fonts, attachments and metadata from a PDF file. Also supports high quality rendering of PDF documents into PNG, JPEG, TIFF format, or into raw bitmap vectors for further processing in R.

Scientific use cases
  1. Cole, C. B., Patel, S., French, L., & Knight, J. (2016). Semi-Automated Identification of Ontological Labels in the Biomedical Literature with goldi. https://doi.org/10.1101/073460
  2. Krotov, V., & Tennyson, M. (2018). Scraping Financial Data from the Web Using R Language. Journal of Emerging Technologies in Accounting. https://doi.org/10.2308/jeta-52063
  3. Iqbal, J. (2019). Managerial Self-Attribution Bias and Banks’ Future Performance: Evidence from Emerging Economies. Journal of Risk and Financial Management, 12(2), 73. https://doi.org/10.3390/jrfm12020073
  4. Hanna, A., & Hanna, L.-A. (2019). Topic Analysis of UK Fitness to Practise Cases: What Lessons Can Be Learnt? Pharmacy, 7(3), 130. https://doi.org/10.3390/pharmacy7030130
  5. Hwang, L. J., Pauloo, R. A., & Carlen, J. (2019). Assessing Impact of Outreach through Software Citation for Community Software in Geodynamics. Computing in Science & Engineering, 1–1. https://doi.org/10.1109/mcse.2019.2940221
  6. Ulibarri, N., & Scott, T. A. (2019). Environmental hazards, rigid institutions, and transformative change: How drought affects the consideration of water and climate impacts in infrastructure management. Global Environmental Change, 59, 102005. https://doi.org/10.1016/j.gloenvcha.2019.102005
  7. Lope, D. J., & Dolgun, A. (2020). Measuring the inequality of accessible trams in Melbourne. Journal of Transport Geography, 83, 102657. https://doi.org/10.1016/j.jtrangeo.2020.102657
  8. Verde Arregoitia, L. D., Teta, P., & D’Elía, G. (2020). Patterns in research and data sharing for the study of form and function in caviomorph rodents. Journal of Mammalogy. https://doi.org/10.1093/jmammal/gyaa002
  9. Hagan, A. K., Pollet, R. M., & Libertucci, J. (2020). Suggestions for Improving Invited Speaker Diversity To Reflect Trainee Diversity. Journal of Microbiology & Biology Education, 21(1). https://doi.org/10.1128/jmbe.v21i1.2105
View Documentation

Hydrological Data Discovery Tools

Claudia Vitolo
Description

Tools to discover hydrological data, accessing catalogues and databases from various data providers.

View Documentation

Acquisition and Processing of NASA Soil Moisture Active-Passive (SMAP) Data

Maxwell Joseph
Description

Facilitates programmatic access to NASA Soil Moisture Active
Passive (SMAP) data with R. It includes functions to search for, acquire,
and extract SMAP data.

View Documentation
essurvey
CRAN

Download Data from the European Social Survey on the Fly

Jorge Cimentada
Description

Download data from the European Social Survey directly from their website http://www.europeansocialsurvey.org/. There are two families of functions that allow you to download and interactively check all countries and rounds available.

View Documentation
rusda
CRAN

Interface to USDA Databases

Franz-Sebastian Krah
Description

An interface to the web service methods provided by the United States Department of Agriculture (USDA). The Agricultural Research Service (ARS) provides a large set of databases. The current version of the package holds interfaces to the Systematic Mycology and Microbiology Laboratory (SMML), which consists of four databases: Fungus-Host Distributions, Specimens, Literature and the Nomenclature database. It provides functions for querying these databases. The main function is \code{associations}, which allows searching for fungus-host combinations.

Scientific use cases
  1. Krah, F.-S., Bässler, C., Heibl, C., Soghigian, J., Schaefer, H., & Hibbett, D. S. (2018). Evolutionary dynamics of host specialization in wood-decay fungi. BMC Evolutionary Biology, 18(1). https://doi.org/10.1186/s12862-018-1229-7
View Documentation

Extract Text from Rich Text Format (RTF) Documents

Jeroen Ooms
Description

Wraps the unrtf utility to extract text from RTF files. Supports document conversion to HTML, LaTeX or plain text. Output in HTML is recommended because unrtf has limited support for converting between character encodings.

View Documentation
internetarchive
CRAN

An API Client for the Internet Archive

Lincoln Mullen
Description

Search the Internet Archive (https://archive.org), retrieve metadata, and download files.

View Documentation
rgpdd

R Interface to the Global Population Dynamics Database

Carl Boettiger
Description

R Interface to the Global Population Dynamics Database (https://ecologicaldata.org/wiki/global-population-dynamics-database)

View Documentation

NatureServe Interface

Scott Chamberlain
Description

Interface to NatureServe (https://www.natureserve.org/). Includes methods to get data, image metadata, search taxonomic names, and make maps.

View Documentation

GeoJSON Topology Calculations and Operations

Scott Chamberlain
Description

Tools for doing calculations and manipulations on GeoJSON, a geospatial data interchange format (https://tools.ietf.org/html/rfc7946). GeoJSON is also valid JSON.

View Documentation
outcomerate
CRAN Peer-reviewed

AAPOR Survey Outcome Rates

Rafael Pilliard Hellwig
Description

Standardized survey outcome rate functions, including the response rate, contact rate, cooperation rate, and refusal rate. These outcome rates allow survey researchers to measure the quality of survey data using definitions published by the American Association of Public Opinion Research (AAPOR). For details on these standards, see AAPOR (2016) https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx.

View Documentation

Extensible Style-Sheet Language Transformations

Jeroen Ooms
Description

An extension for the xml2 package to transform XML documents by applying an xslt style-sheet.

View Documentation

Interface with the United Nations Comtrade API

Chris Muir
Description

Interface with and extract data from the United Nations Comtrade API https://comtrade.un.org/data/. Comtrade provides country level shipping data for a variety of commodities, these functions allow for easy API query and data returned as a tidy data frame.

View Documentation

Detect Text Reuse and Document Similarity

Lincoln Mullen
Description

Tools for measuring similarity among documents and detecting passages which have been reused. Implements shingled n-gram, skip n-gram, and other tokenizers; similarity/dissimilarity functions; pairwise comparisons; minhash and locality sensitive hashing algorithms; and a version of the Smith-Waterman local alignment algorithm suitable for natural language.

Scientific use cases
  1. Funk, K. R., & Mullen, L. A. (2017). The Spine of American Law: Digital Text Analysis and US Legal Practice. The American Historical Review. https://doi.org/10.1093/ahr/123.1.132
  2. A. Mullen, L., Benoit, K., Keyes, O., Selivanov, D., & Arnold, J. (2018). Fast, Consistent Tokenization of Natural Language Text. Journal of Open Source Software, 3(23), 655. https://doi.org/10.21105/joss.00655
  3. García, F. T., Villalba, L. J. G., Orozco, A. L. S., Ruiz, F. D. A., Juárez, A. A., & Kim, T. H. (2018). Locating similar names through locality sensitive hashing and graph theory. Multimedia Tools and Applications, 1-14. https://link.springer.com/article/10.1007/s11042-018-6375-9
  4. Catalano, J. (2018). Digitally Analyzing the Uneven Ground: Language Borrowing Among Indian Treaties. Current Research in Digital History, 1. https://doi.org/10.31835/crdh.2018.02
  5. Schmidt, B. (2018). Stable random projection: lightweight, general-purpose dimensionality reduction for digitized libraries. Journal of Cultural Analytics. https://doi.org/10.22148/16.025
  6. Sanger, W., & Warin, T. (2019). Dataset of Jaccard similarity indices from 1,597 European political manifestos across 27 countries (1945–2017). Data in Brief, 103907. https://doi.org/10.1016/j.dib.2019.103907
  7. Jaric, I., & Djeric, M. (2019). Curriculum and labor market: Comparative analysis of the curricular outcomes of the study program in sociology at the Faculty of Philosophy, University of Belgrade and the required competences in the labor market. Sociologija, 61(Suppl. 1), 718–741. https://doi.org/10.2298/soc19s1718j
  8. Marple, T. (2020). The social management of complex uncertainty: Central Bank similarity and crisis liquidity swaps at the Federal Reserve. The Review of International Organizations. https://doi.org/10.1007/s11558-020-09378-x
  9. Callaghan, T., Karch, A., & Kroeger, M. (2020). Model State Legislation and Intergovernmental Tensions over the Affordable Care Act, Common Core, and the Second Amendment. Publius: The Journal of Federalism. https://doi.org/10.1093/publius/pjaa012
  10. Vogler, D., Udris, L., & Eisenegger, M. (2020). Measuring Media Content Concentration at a Large Scale Using Automated Text Comparisons. Journalism Studies, 1–20. https://doi.org/10.1080/1461670x.2020.1761865
  11. Vogler, D., & Schäfer, M. S. (2020). Growing Influence of University PR on Science News Coverage? A Longitudinal Automated Content Analysis of University Media Releases and Newspaper Coverage in Switzerland, 2003‒2017. International Journal of Communication, 14, 22. https://ijoc.org/index.php/ijoc/article/download/13498/3113
  12. James, S., Pagliari, S., & Young, K. L. (2020). The internationalization of European financial networks: a quantitative text analysis of EU consultation responses. Review of International Political Economy, 1–28. https://doi.org/10.1080/09692290.2020.1779781
View Documentation
rebird
CRAN

R Client for the eBird Database of Bird Observations

Sebastian Pardo
Description

A programmatic client for the eBird database (https://ebird.org/home), including functions for searching for bird observations by geographic location (latitude, longitude), eBird hotspots, location identifiers, by notable sightings, by region, and by taxonomic name.

Scientific use cases
  1. Mittermeier, T. et al. 2019. A season for all things: Phenological imprints in Wikipedia usage and their relevance toconservation. PLoS Biology https://research.birmingham.ac.uk/portal/files/58082037/pbio.3000146_1.pdf
View Documentation
opencontext

API Client for the Open Context Archeological Database

Ben Marwick
Description

Search, browse, and download data from Open Context (https://opencontext.org)

View Documentation

An R client for HathiTrust API

Scott Chamberlain
Description

An R client for HathiTrust API (https://www.hathitrust.org). Only for the bibliographic API for now.

View Documentation
rglobi
CRAN

R Interface to Global Biotic Interactions

Jorrit Poelen
Description

A programmatic interface to the web service methods provided by Global Biotic Interactions (GloBI) (https://www.globalbioticinteractions.org/). GloBI provides access to spatial-temporal species interaction records from sources all over the world. rglobi provides methods to search species interactions by location, interaction type, and taxonomic name. In addition, it supports Cypher, a graph query language, to allow for executing custom queries on the GloBI aggregate species interaction data set.

Scientific use cases
  1. Vincent, F., & Bowler, C. (2020). Diatoms Are Selective Segregators in Global Ocean Planktonic Communities. mSystems, 5(1). https://doi.org/10.1128/msystems.00444-19
  2. Wiscovitch-Russo, R., Rivera-Perez, J., Narganes-Storde, Y. M., García-Roldán, E., Bunkley-Williams, L., Cano, R., & Toranzos, G. A. (2020). Pre-Columbian zoonotic enteric parasites: An insight into Puerto Rican indigenous culture diets and life styles. PLOS ONE, 15(1), e0227810. https://doi.org/10.1371/journal.pone.0227810
View Documentation

API Wrapper for US Energy Information Administration Open Data

Matthew Leonawicz
Description

Provides API access to data from the US Energy Information Administration (EIA) https://www.eia.gov/. Use of the API requires a free API key obtainable at https://www.eia.gov/opendata/register.php. The package includes functions for searching EIA data categories and importing time series and geoset time series datasets. Datasets returned by these functions are provided in a tidy format or alternatively in more raw form. It also offers helper functions for working with EIA date strings and time formats and for inspecting different summaries of series metadata. The package also provides control over API key storage and caching of API request results.

View Documentation
taxa
CRAN

Taxonomic Classes

Zachary Foster
Description

Provides taxonomic classes for groupings of taxonomic names without data, and those with data. Methods provided are “taxonomically aware”, in that they know about ordering of ranks, and methods that filter based on taxonomy also filter associated data. This package is described in the publication: “Taxa: An R package implementing data standards and methods for taxonomic data”, Zachary S.L. Foster, Scott Chamberlain,
Niklaus J. Grünwald (2018) doi:10.12688/f1000research.14013.2.

Scientific use cases
  1. Foster, Z. S. L., Chamberlain, S., & Grünwald, N. J. (2018). Taxa: An R package implementing data standards and methods for taxonomic data. F1000Research, 7, 272. https://doi.org/10.12688/f1000research.14013.1
View Documentation
genbankr
Bioconductor

Parsing GenBank files into semantically useful objects

Gabriel Becker
Description

Reads Genbank files.

View Documentation

Client for Turfjs for Geospatial Analysis

Scott Chamberlain
Description

Client for Turfjs (http://turfjs.org) for geospatial analysis. The package revolves around using GeoJSON data. Functions are included for creating GeoJSON data objects, measuring aspects of GeoJSON, and combining, transforming, and creating random GeoJSON data objects.

View Documentation

Client for jq, a JSON Processor

Scott Chamberlain
Description

Client for jq, a JSON processor (https://stedolan.github.io/jq/), written in C. jq allows the following with JSON data: index into, parse, do calculations, cut up and filter, change key names and values, perform conditionals and comparisons, and more.

View Documentation
photosearcher

Photo Searcher

Nathan Fox
Description

Queries the Flick API (https://www.flickr.com/services/api/) to return photograph metadata as well as the ability to download the images as jpegs.

View Documentation

R Interface to Apache Tika

Sasha Goodman
Description

Extract text or metadata from over a thousand file types, using Apache Tika https://tika.apache.org/. Get either plain text or structured XHTML content.

View Documentation
rWBclimate
CRAN

A package for accessing World Bank climate data

Edmund Hart
Description

This package will download model predictions from 15 different global circulation models in 20 year intervals from the world bank. Users can also access historical data, and create maps at 2 different spatial scales.

Scientific use cases
  1. Charalampopoulos, I. (2020). The R Language as a Tool for Biometeorological Research. Atmosphere, 11(7), 682. https://doi.org/10.3390/atmos11070682
View Documentation

General Purpose R Interface to Solr

Scott Chamberlain
Description

Provides a set of functions for querying and parsing data from Solr (https://lucene.apache.org/solr) endpoints (local and remote), including search, faceting, highlighting, stats, and more like this. In addition, some functionality is included for creating, deleting, and updating documents in a Solr database.

View Documentation
googleLanguageR
CRAN Peer-reviewed

Call Googles Natural Language API, Cloud Translation' API, Cloud Speech API and Cloud Text-to-Speech API

Mark Edmondson
Description

Call Google Cloud machine learning APIs for text and speech tasks. Call the Cloud Translation API https://cloud.google.com/translate/ for detection and translation of text, the Natural Language API https://cloud.google.com/natural-language/ to analyse text for sentiment, entities or syntax, the Cloud Speech API https://cloud.google.com/speech/ to transcribe sound files to text and the Cloud Text-to-Speech API https://cloud.google.com/text-to-speech/ to turn text into sound files.

View Documentation

HTTP Error Helpers

Scott Chamberlain
Description

HTTP error helpers. Methods included for general purpose HTTP error handling, as well as individual methods for every HTTP status code, both via status code numbers as well as their descriptive names. Supports ability to adjust behavior to stop, message or warning. Includes ability to use custom whisker template to have any configuration of status code, short description, and verbose message. Currently supports integration with crul, curl, and httr.

View Documentation

Provides some helper functions for using the GitHub V4 API

Maëlle Salmon
Description

Uses the ghql package and jqr to get some common data from Github V4 API.

View Documentation

Create Useful .gitignore Files for your Project

Philippe Massicotte
Description

Simple interface to query gitignore.io to fetch gitignore templates that can be included in the .gitignore file. More than 450 templates are currently available.

View Documentation
jsonvalidate
CRAN

Validate JSON Schema

Rich FitzJohn
Description

Uses the node library is-my-json-valid or ajv to validate JSON against a JSON schema. Drafts 04, 06 and 07 of JSON schema are supported.

View Documentation

Interact with the UK AIR Pollution Database from DEFRA

Claudia Vitolo
Description

Get data from DEFRA’s UK-AIR website https://uk-air.defra.gov.uk/. It basically scrapes the HTML content.

Scientific use cases
  1. Vitolo, C., Scutari, M., Ghalaieny, M., Tucker, A., & Russell, A. (2018). Modelling air pollution, climate and health data using Bayesian Networks: a case study of the English regions. Earth and Space Science. https://doi.org/10.1002/2017ea000326
View Documentation

Obtain and Visualize Regulome-Gene Expression Correlations in Cancer

Mahmoud Ahmed
Description

Builds a SQLite database file of pre-calculated transcription factor/microRNA-gene correlations (co-expression) in cancer from the Cistrome Cancer Liu et al. (2011) doi:10.1186/gb-2011-12-8-r83 and miRCancerdb databases (in press). Provides custom classes and functions to query, tidy and plot the correlation data.

Scientific use cases
  1. Ahmed, M., Nguyen, H., Lai, T., & Kim, D. R. (2018). miRCancerdb: a database for correlation analysis between microRNA and gene expression in cancer. BMC Research Notes, 11(1). https://doi.org/10.1186/s13104-018-3160-9
View Documentation

API Client and Dataset Management for the Demographic and Health Survey (DHS) Data

OJ Watson
Description

Provides a client for (1) querying the DHS API for survey indicators and metadata (https://api.dhsprogram.com/#/index.html), (2) identifying surveys and datasets for analysis, (3) downloading survey datasets from the DHS website, (4) loading datasets and associate metadata into R, and (5) extracting variables and combining datasets for pooled analysis.

Scientific use cases
  1. Watson, O. J., Sumner, K. M., Janko, M., Goel, V., Winskill, P., Slater, H. C., … Parr, J. B. (2019). False-negative malaria rapid diagnostic test results and their impact on community-based malaria surveys in sub-Saharan Africa. BMJ Global Health, 4(4), e001582. https://doi.org/10.1136/bmjgh-2019-001582
  2. Sánchez-Páez, D. A., & Ortega, J. A. (2019). Reported patterns of pregnancy termination from Demographic and Health Surveys. PLOS ONE, 14(8), e0221178. https://doi.org/10.1371/journal.pone.0221178
  3. Finnegan, A., Sao, S. S., & Huchko, M. J. (2019). Using a Chord Diagram to Visualize Dynamics in Contraceptive Use: Bringing Data Into Practice. Global Health: Science and Practice, 7(4), 598–605. https://doi.org/10.9745/ghsp-d-19-00205
  4. Walker, P. G. T., Whittaker, C., Watson, O. J., Baguelin, M., Winskill, P., Hamlet, A., … Ghani, A. C. (2020). The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science, eabc0035. https://doi.org/10.1126/science.abc0035
View Documentation

Interface to the Libraries.io API

Scott Chamberlain
Description

Interface to the Libraries.io API (https://libraries.io/api). Libraries.io indexes data from 36 different package managers for programming languages.

View Documentation

Read, Tidy, and Display Data from Microtiter Plates

Sean Hughes
Description

Tools for interacting with data from experiments done in microtiter plates. Easily read in plate-shaped data and convert it to tidy format, combine plate-shaped data with tidy data, and view tidy data in plate shape.

View Documentation

Manage Cached Files

Scott Chamberlain
Description

Suite of tools for managing cached files, targeting use in other R packages. Uses rappdirs for cross-platform paths. Provides utilities to manage cache directories, including targeting files by path or by key; cached directories can be compressed and uncompressed easily to save disk space.

View Documentation

R Package Client for the Netherlands Biodiversity API

Hannes Hettling
Description

Access to the digitised Natural History collection at the Naturalis Biodiversity Center. This is the official client to the Netherlands Biodiversity API (NBA, http://api.biodiversitydata.nl) for the R programming language. More information on the NBA can be found at http://docs.biodiversitydata.nl.

View Documentation

High Level Encryption Wrappers

Rich FitzJohn
Description

Encryption wrappers, using low-level support from sodium and openssl. cyphr tries to smooth over some pain points when using encryption within applications and data analysis by wrapping around differences in function names and arguments in different encryption providing packages. It also provides high-level wrappers for input/output functions for seamlessly adding encryption to existing analyses.

View Documentation
hydroscoper
CRAN Peer-reviewed

Interface to the Greek National Data Bank for Hydrometeorological Information

Konstantinos Vantas
Description

R interface to the Greek National Data Bank for Hydrological and Meteorological Information http://www.hydroscope.gr/. It covers Hydroscope’s data sources and provides functions to transliterate, translate and download them into tidy dataframes.

Scientific use cases
  1. Vantas, K. (2018). hydroscoper: R interface to the Greek National Data Bank for Hydrological and Meteorological Information. Journal of Open Source Software, 3(23), 625. https://doi.org/10.21105/joss.00625
  2. Vantas, K., Sidiropoulos, E., & Loukas, A. (2019). Robustness Spatiotemporal Clustering and Trend Detection of Rainfall Erosivity Density in Greece. Water, 11(5), 1050. https://doi.org/10.3390/w11051050
  3. Vantas, K., Sidiropoulos, E., & Loukas, A. (2020). Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests. Water, 12(3), 687. https://doi.org/10.3390/w12030687
View Documentation
PostcodesioR
CRAN Peer-reviewed

API Wrapper Around Postcodes.io

Eryk Walczak
Description

Free UK geocoding using data from Office for National Statistics. It is using several functions to get information about post codes, outward codes, reverse geocoding, nearest post codes/outward codes, validation, or randomly generate a post code. API wrapper around https://postcodes.io.

View Documentation

Client for the DataCite API

Scott Chamberlain
Description

Client for the web service methods provided by DataCite (https://www.datacite.org/), including functions to interface with their RESTful search API. The API is backed by Elasticsearch, allowing expressive queries, including faceting.

Scientific use cases
  1. Jaspers, S., De Troyer, E., & Aerts, M. (2018). Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA. EFSA Supporting Publications, 15(6), 1427E. https://doi.org/10.2903/sp.efsa.2018.EN-1427
  2. White, L., & Santy, S. (2018). DataDepsGenerators.jl: making reusing data easy by automatically generating DataDeps.jl registration code. Journal of Open Source Software, 3(31), 921. https://doi.org/10.21105/joss.00921
View Documentation
refsplitr
Peer-reviewed

author name disambiguation, author georeferencing, and mapping of coauthorship networks with Web of Science data

Emilio Bruna
Description

Tools to parse and organize reference records downloaded from the Web of Science citation database into an R-friendly format, disambiguate the names of authors, geocode their locations, and generate/visualize coauthorship networks. This package has been peer-reviewed by rOpenSci (v. 1.0).

View Documentation

Citation Style Language (CSL) Utilities

Scott Chamberlain
Description

Tools for working with the Citation Style Language (CSL) (https://citationstyles.org), an XML-based format describing the formatting of citations, notes and bibliographies. Functions are included for downloading and searching for styles and locales, and loading and parsing styles and locales. seasl aims to help users fetch and modify CSL files for work combining code and writing that requires citations.

View Documentation

Bespoke Images of OpenStreetMap Data

Mark Padgham
Description

Bespoke images of OpenStreetMap (OSM) data and data visualisation using OSM objects.

View Documentation

Programmatic Interface to the Web Service Methods Provided by UC Berkeley's Natural History Data

Karthik Ram
Description

The ecoengine (ecoengine; https://ecoengine.berkeley.edu/). provides access to more than 5 million georeferenced specimen records from the University of California, Berkeley’s Natural History Museums.

View Documentation

A Tool for Writing Cleaner, More Transparent Code

Matthew Lau
Description

To create clearer, more concise code provides this toolbox helps coders to isolate the essential parts of a script that produces a chosen result, such as an object, tables and figures written to disk.

View Documentation

Download and Aggregate Data from Public Hire Bicycle Systems

Mark Padgham
Description

Download and aggregate data from all public hire bicycle systems which provide open data, currently including Santander Cycles in London, U.K.; from the U.S.A., Ford GoBike in San Francisco CA, citibike in New York City NY, Divvy in Chicago IL, Capital Bikeshare in Washington DC, Hubway in Boston MA, Metro in Los Angeles LA, Indego in Philadelphia PA, and Nice Ride in Minnesota; Bixi from Montreal, Canada; and mibici from Guadalajara, Mexico.

Scientific use cases
  1. Hosford, K., & Winters, M. 2019. Quantifying the Bicycle Share Gender Gap. Transport Findings, November. https://doi.org/10.32866/10802
View Documentation

Access data from the NASS Quick Stats API

Nicholas Potter
Description

Interface to access data via the United States Department of Agricultures National Agricultural Statistical Service (NASS) Quick Stats’ web API https://quickstats.nass.usda.gov/api. Convenience functions facilitate building queries based on available parameters and valid parameter values. This product uses the NASS API but is not endorsed or certified by NASS.

View Documentation

Tools for Validating GeoJSON

Scott Chamberlain
Description

Tools for linting GeoJSON. Includes tools for interacting with the online tool http://geojsonlint.com, the Javascript library geojsonhint (https://www.npmjs.com/package/geojsonhint), and validating against a GeoJSON schema via the Javascript library (https://www.npmjs.com/package/is-my-json-valid). Some tools work locally while others require an internet connection.

View Documentation
chlorpromazineR
CRAN Peer-reviewed

Convert Antipsychotic Doses to Chlorpromazine Equivalents

Eric Brown
Description

As different antipsychotic medications have different potencies, the doses of different medications cannot be directly compared. Various strategies are used to convert doses into a common reference so that comparison is meaningful. Chlorpromazine (CPZ) has historically been used as a reference medication into which other antipsychotic doses can be converted, as “chlorpromazine-equivalent doses”. Using conversion keys generated from widely-cited scientific papers (Gardner et. al 2010 doi:10.1176/appi.ajp.2009.09060802, Leucht et al. 2016 doi:10.1093/schbul/sbv167), antipsychotic doses are converted to CPZ (or any specified antipsychotic) equivalents. The use of the package is described in the included vignette. Not for clinical use.

Scientific use cases
  1. Kim, J., Plitman, E., Iwata, Y., Nakajima, S., Mar, W., Patel, R., … Graff-Guerrero, A. (2020). Neuroanatomical profiles of treatment-resistance in patients with schizophrenia spectrum disorders. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 99, 109839. https://doi.org/10.1016/j.pnpbp.2019.109839
View Documentation
outsider.devtools

Build outsider Modules

Dom Bennett
Description

Developer functions and resources for building outsider modules.

View Documentation

Helper for rOpenSci Package Developpers

Maëlle Salmon
Description

Provides helpers for rOpenSci package developpers, mostly helping with metadata management (badges, DESCRIPTION) and GitHub infrastructure (GitHub issue and PR templates).

View Documentation

Download Data from the Catchment Data Explorer Website

Rob Briers
Description

Facilitates searching, download and plotting of Water Framework Directive (WFD) reporting data for all waterbodies within the UK Environment Agency area. The types of data that can be downloaded are: WFD status classification data, Reasons for Not Achieving Good (RNAG) status, objectives set for waterbodies, measures put in place to improve water quality and details of associated protected areas. The site accessed is https://environment.data.gov.uk/catchment-planning/. The data are made available under the Open Government Licence v3.0 https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/.

View Documentation

Positron Emission Tomography Time-Activity Curve Analysis

Eric Brown
Description

To facilitate the analysis of positron emission tomography (PET) time activity curve (TAC) data, and to encourage open science and replicability, this package supports data loading and analysis of multiple TAC file formats. Functions are available to analyze loaded TAC data for individual participants or in batches. Major functionality includes weighted TAC merging by region of interest (ROI), calculating models including standardized uptake value ratio (SUVR) and distribution volume ratio (DVR, Logan et al. 1996 doi:10.1097/00004647-199609000-00008), basic plotting functions and calculation of cut-off values (Aizenstein et al. 2008 doi:10.1001/archneur.65.11.1509). Please see the walkthrough vignette for a detailed overview of tacmagic functions.

Scientific use cases
  1. Brown, E. E., Rashidi‐Ranjbar, N., Caravaggio, F., Gerretsen, P., Pollock, B. G., … Mulsant, B. H. (2019). Brain Amyloid PET Tracer Delivery is Related to White Matter Integrity in Patients with Mild Cognitive Impairment. Journal of Neuroimaging. https://doi.org/10.1111/jon.12646
View Documentation
RSelenium
CRAN

R Bindings for Selenium WebDriver

Ju Yeong Kim
Description

Provides a set of R bindings for the Selenium 2.0 WebDriver (see https://selenium.dev/documentation/en/ for more information) using the JsonWireProtocol (see https://github.com/SeleniumHQ/selenium/wiki/JsonWireProtocol for more information). Selenium 2.0 WebDriver allows driving a web browser natively as a user would either locally or on a remote machine using the Selenium server it marks a leap forward in terms of web browser automation. Selenium automates web browsers (commonly referred to as browsers). Using RSelenium you can automate browsers locally or remotely.

Scientific use cases
  1. Silva, D., Meireles, F. (2015). Ciência Política na era do Big Data: automação na coleta de dados digitais. Politica Hoje, v.2, (pp. 87-102) https://github.com/meirelesff/meirelesff.github.io/raw/master/files/bigdata2016.pdf
  2. Nousiainen, K., Kanduri, K., Ricaño-Ponce, I., Wijmenga, C., Lahesmaa, R., Kumar, V., & Lähdesmäki, H. (2018). snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty460
  3. Blankers, M., van der Gouwe, D., & van Laar, M. (2019). 4-Fluoramphetamine in the Netherlands: Text-mining and sentiment analysis of internet forums. International Journal of Drug Policy, 64, 34–39. https://doi.org/10.1016/j.drugpo.2018.11.016
  4. Krah, F.-S., Bates, S., & Miller, A. (2019). rMyCoPortal - an R package to interface with the Mycology Collections Portal. Biodiversity Data Journal, 7. https://doi.org/10.3897/bdj.7.e31511
  5. Lee, A. J., Jones, B. C., & DeBruine, L. M. (2019, January 21). Investigating the association between mating-relevant self-concepts and mate preferences through a data-driven analysis of online personal descriptions. https://doi.org/10.31234/osf.io/38zef
  6. Mitchell, J. M., & Moseley, H. N. B. (2019). Deriving Accurate Lipid Classification based on Molecular Formula. https://doi.org/10.1101/572883
  7. Rybinski, K. 2019. A machine learning framework for automated analysis of central bank communication and media discourse. The case of Narodowy Bank Polski. Bank & Credit. 50(1): 1-20. http://bankikredyt.nbp.pl/content/2019/01/BIK_01_2019_01.pdf
  8. Fioravanti, G., Piervitali, E., & Desiato, F. (2019). A new homogenized daily data set for temperature variability assessment in Italy. International Journal of Climatology. https://doi.org/10.1002/joc.6177
  9. Roh, T., Jeong, Y., Jang, H., & Yoon, B. (2019). Technology opportunity discovery by structuring user needs based on natural language processing and machine learning. PLOS ONE, 14(10), e0223404. https://doi.org/10.1371/journal.pone.0223404
  10. Nüst, D., Eddelbuettel, D., Bennett, D., Cannoodt, R., Clark, D., Daroczi, G., … & Marwick, B. (2020). The Rockerverse: Packages and Applications for Containerization with R. arXiv preprint arXiv:2001.10641 https://arxiv.org/pdf/2001.10641.pdf
  11. Salgado, D., & Oancea, B. (2020). On new data sources for the production of official statistics. arXiv preprint https://arxiv.org/pdf/2003.06797.pdf
  12. Fraser, N., Momeni, F., Mayr, P., & Peters, I. (2020). The relationship between bioRxiv preprints, citations and altmetrics. Quantitative Science Studies, 1–21. https://doi.org/10.1162/qss_a_00043
  13. Hannon, B. A., Fairfield, W. D., Adams, B., Kyle, T., Crow, M., & Thomas, D. M. (2020). Use and abuse of dietary supplements in persons with diabetes. Nutrition & Diabetes, 10(1). https://doi.org/10.1038/s41387-020-0117-6
  14. Stringham, O., Toomes, A., Kanishka, A. M., Mitchell, L., Heinrich, S., Ross, J. V., & Cassey, P. (2020). A guide to using the Internet to monitor and quantify the wildlife trade. https://ecoevorxiv.org/5yzw9/download?format=pdf
  15. Bisbee, J., & Honig, D. (2020). Flight to Safety: 2020 Democratic Primary Election Results and COVID-19. Covid Economics, 3(10), 54-84. http://www.amcham-egypt.org/bic/pdf/corona1/Covid%20Economics%20by%20CEPR.pdf
View Documentation

Parse NOAA Integrated Surface Data Files

Scott Chamberlain
Description

Tools for parsing NOAA Integrated Surface Data (ISD) files, described at https://www.ncdc.noaa.gov/isd. Data includes for example, wind speed and direction, temperature, cloud data, sea level pressure, and more. Includes data from approximately 35,000 stations worldwide, though best coverage is in North America/Europe/Australia. Data is stored as variable length ASCII character strings, with most fields optional. Included are tools for parsing entire files, or individual lines of data.

View Documentation
wdman
CRAN

Webdriver/Selenium Binary Manager

Ju Yeong Kim
Description

There are a number of binary files associated with the Webdriver/Selenium project (see http://www.seleniumhq.org/download/, https://sites.google.com/a/chromium.org/chromedriver/, https://github.com/mozilla/geckodriver, http://phantomjs.org/download.html and https://github.com/SeleniumHQ/selenium/wiki/InternetExplorerDriver for more information). This package provides functions to download these binaries and to manage processes involving them.

View Documentation

Google's Compact Language Detector 3

Jeroen Ooms
Description

Google’s Compact Language Detector 3 is a neural network model for language identification and the successor of cld2 (available from CRAN). The algorithm is still experimental and takes a novel approach to language detection with different properties and outcomes. It can be useful to combine this with the Bayesian classifier results from cld2. See https://github.com/google/cld3#readme for more information.

View Documentation

Tools to Manipulate and Query Semantic Data

Carl Boettiger
Description

The Resource Description Framework, or RDF is a widely used data representation model that forms the cornerstone of the Semantic Web. RDF represents data as a graph rather than the familiar data table or rectangle of relational databases. The rdflib package provides a friendly and concise user interface for performing common tasks on RDF data, such as reading, writing and converting between the various serializations of RDF data, including rdfxml, turtle, nquads, ntriples, and json-ld; creating new RDF graphs, and performing graph queries using SPARQL. This package wraps the low level redland R package which provides direct bindings to the redland C library. Additionally, the package supports the newer and more developer friendly JSON-LD format through the jsonld package. The package interface takes inspiration from the Python rdflib library.

Scientific use cases
  1. Panayiotou, C. (2020). An Ontological Analysis and Natural Language Processing of Figures of Speech. International Journal of Artificial Intelligence & Applications, 11(1), 17–30. https://doi.org/10.5121/ijaia.2020.11102
View Documentation

Client for the Pangaea Database

Scott Chamberlain
Description

Tools to interact with the Pangaea Database (https://www.pangaea.de), including functions for searching for data, fetching datasets by dataset ID, and working with the Pangaea OAI-PMH service.

Scientific use cases
  1. Greco, M., Jonkers, L., Kretschmer, K., Bijma, J., & Kucera, M. (2019). Depth habitat of the planktonic foraminifera Neogloboquadrina pachyderma in the northern high latitudes explained by sea-ice and chlorophyll concentrations. Biogeosciences, 16(17), 3425–3437. https://doi.org/10.5194/bg-16-3425-2019
View Documentation

Create and Query a Local Copy of GenBank in R

Dom Bennett
Description

Download large sections of GenBank https://www.ncbi.nlm.nih.gov/genbank/ and generate a local SQL-based database. A user can then query this database using restez functions or through rentrez https://CRAN.R-project.org/package=rentrez wrappers.

Scientific use cases
  1. Bennett, D., Hettling, H., Silvestro, D., Vos, R., & Antonelli, A. (2018). restez: Create and Query a Local Copy of GenBank in R. Journal of Open Source Software, 3(31), 1102. https://doi.org/10.21105/joss.01102
  2. Ruiz-Sanchez, E., Maya-Lastra, C. A., Steinmann, V. W., Zamudio, S., Carranza, E., Murillo, R. M., & Rzedowski, J. (2019). Datataxa: a new script to extract metadata sequence information from GenBank, the Flora of Bajío as a case study. Botanical Sciences, 97(4), 754–760. https://doi.org/10.17129/botsci.2226
View Documentation

Collecting Twitter Data

Michael W. Kearney
Description

An implementation of calls designed to collect and organize Twitter data via Twitter’s REST and stream Application Program Interfaces (API), which can be found at the following URL: https://developer.twitter.com/en/docs. This package has been peer-reviewed by rOpenSci (v. 0.6.9).

Scientific use cases
  1. Firmansyah, F. M., & Jones, J. J. (2019). Did the Black Panther Movie Make Blacks Blacker? Examining Black Racial Identity on Twitter Before and After the Black Panther Movie Release. Social Informatics, 66–78. https://doi.org/10.1007/978-3-030-34971-4_5
  2. Sansone, A., Cignarelli, A., Ciocca, G., Pozza, C., Giorgino, F., Romanelli, F., & Jannini, E. A. (2019). The Sentiment Analysis of Tweets as a New Tool to Measure Public Perception of Male Erectile and Ejaculatory Dysfunctions. Sexual Medicine, 7(4), 464–471. https://doi.org/10.1016/j.esxm.2019.07.001
  3. Tancoigne, E. (2019). Invisible brokers: “citizen science” on Twitter. Journal of Science Communication, 18(06). https://doi.org/10.22323/2.18060205
  4. Greenhalgh, S. P., Willet, K. B. S., & Koehler, M. J. (2019). Approaches to Mormon Identity and Practice in the #ldsconf Twitter Hashtag. Journal of Media and Religion, 18(4), 122–133. https://doi.org/10.1080/15348423.2019.1696121
  5. Mingione, M., Cristofaro, M., & Mondi, D. (2020). If I give you my emotion, what do I get? Conceptualizing and measuring the co-created emotional value of the brand. Journal of Business Research, 109, 310–320. https://doi.org/10.1016/j.jbusres.2019.11.071
  6. Wunderlich, F., & Memmert, D. (2020). Innovative Approaches in Sports Science—Lexicon-Based Sentiment Analysis as a Tool to Analyze Sports-Related Twitter Communication. Applied Sciences, 10(2), 431. https://doi.org/10.3390/app10020431
  7. Fontanelli, O., & Mansilla, R. (2020). Modeling the Popularity of Twitter Hashtags with Master Equations. arXiv preprint, https://arxiv.org/pdf/2003.02672.pdf
  8. Hagen, L., Neely, S., Keller, T. E., Scharf, R., & Vasquez, F. E. (2020). Rise of the Machines? Examining the Influence of Social Bots on a Political Discussion Network. Social Science Computer Review, 089443932090819. https://doi.org/10.1177/0894439320908190
  9. Greenhalgh, S. P., Rosenberg, J. M., Staudt Willet, K. B., Koehler, M. J., & Akcaoglu, M. (2020). Identifying multiple learning spaces within a single teacher-focused Twitter hashtag. Computers & Education, 148, 103809. https://doi.org/10.1016/j.compedu.2020.103809
  10. Bramlett, B. H., & Burge, R. P. (2020). God Talk in a Digital Age: How Members of Congress Use Religious Language on Twitter. Politics and Religion, 1–23. https://doi.org/10.1017/s1755048320000231
  11. Rahman, M. M., Ali, G. G., Li, X. J., Paul, K. C., & Chong, P. H. (2020). Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment. Nawaz and Li, Xue Jun and Paul, Kamal Chandra and Chong, Peter HJ, Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment (June 30, 2020). https://arxiv.org/pdf/2007.00054.pdf
  12. Greco, F., & La Rocca, G. (2020). The Topics-scape of the Pandemic Crisis: The Italian Sentiment on Political Leaders. Culture e Studi del Sociale, 5(1, Special), 335-346. http://www.cussoc.it/index.php/journal/article/view/134
  13. Barrios‐O’Neill, D. (2020). Focus and social contagion of environmental organization advocacy on Twitter. Conservation Biology. https://doi.org/10.1111/cobi.13564
  14. Puerta, P., Laguna, L., Vidal, L., Ares, G., Fiszman, S., & Tárrega, A. (2020). Co-occurrence networks of Twitter content after manual or automatic processing. A case-study on “gluten-free.” Food Quality and Preference, 86, 103993. https://doi.org/10.1016/j.foodqual.2020.103993
  15. Stephens, M. (2020). A geospatial infodemic: Mapping Twitter conspiracy theories of COVID-19. Dialogues in Human Geography, 10(2), 276–281. https://doi.org/10.1177/2043820620935683
  16. Green, J., Edgerton, J., Naftel, D., Shoub, K., & Cranmer, S. J. (2020). Elusive consensus: Polarization in elite communication on the COVID-19 pandemic. Science Advances, 6(28), eabc2717. https://doi.org/10.1126/sciadv.abc2717
View Documentation

Conduct Co-Localization Analysis of Fluorescence Microscopy Images

Mahmoud Ahmed
Description

Automate the co-localization analysis of fluorescence microscopy images. Selecting regions of interest, extract pixel intensities from the image channels and calculate different co-localization statistics. The methods implemented in this package are based on Dunn et al. (2011) doi:10.1152/ajpcell.00462.2010.

Scientific use cases
  1. Ahmed, M., Lai, T. H., & Kim, D. R. (2019). colocr: An R package for conducting co-localization analysis on fluorescence microscopy images. https://doi.org/10.7287/peerj.preprints.27613v1
View Documentation

Interface to Phylocom

Scott Chamberlain
Description

Interface to Phylocom (http://phylodiversity.net/phylocom/), a library for analysis of phylogenetic community structure and character evolution. Includes low level methods for interacting with the three executables, as well as higher level interfaces for methods like aot, ecovolve, bladj, phylomatic, and more.

View Documentation

Preliminary Visualisation of Data

Nicholas Tierney
Description

Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using ggplot2.

Scientific use cases
  1. Tierney, N. (2017). visdat: Visualising Whole Data Frames. The Journal of Open Source Software, 2(16), 355. https://doi.org/10.21105/joss.00355
  2. Tierney, N. J., & Cook, D. H. (2018). Expanding tidy data principles to facilitate missing data exploration, visualization and assessment of imputations. arXiv preprint arXiv:1809.02264. https://arxiv.org/abs/1809.02264
View Documentation
assertr
CRAN

Assertive Programming for R Analysis Pipelines

Tony Fischetti
Description

Provides functionality to assert conditions that have to be met so that errors in data used in analysis pipelines can fail quickly. Similar to stopifnot() but more powerful, friendly, and easier for use in pipelines.

Scientific use cases
  1. Petersen, A. H., & Ekstrøm, C. T. (2019). dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R. Journal of Statistical Software, 90(6). https://doi.org/10.18637/jss.v090.i06
  2. van der Loo, M. P., & de Jonge, E. (2019). Data Validation Infrastructure for R. arXiv preprint arXiv:1912.09759. https://arxiv.org/pdf/1912.09759.pdf
  3. Brick, C., McDowell, M., & Freeman, A. L. J. (2020). Risk communication in tables versus text: a registered report randomized trial on “fact boxes.” Royal Society Open Science, 7(3), 190876. https://doi.org/10.1098/rsos.190876
View Documentation
ozflights

Get Australian Flight Data, 1985-2016

Mathew Ling
Description

A package to obtain Australian aviation data from BITRE. This incudes airport traffic data between 1985-2016 covering international freight data, and both international and domestic data on number of passengers, and flight movements - for both regional and metropolitan airports. The Package also includes distances of flight originating in or ending in Australia, and the location of all relevant airports.

View Documentation

Programmatic Interface to the openfisheries.org API

Karthik Ram
Description

A programmatic interface to openfisheries.org. This package is part of the rOpenSci suite (https://ropensci.org).

Scientific use cases
  1. Drozd, P., & Šipoš, J. (2013). R for all (I): Introduction to the new age of biological analyses. Casopis Slezskeho Zemskeho Muzea A, 62(1). https://doi.org/10.2478/cszma-2013-0004
View Documentation

Dendrograms for Evolutionary Analysis

Shaun Wilkinson
Description

Contains functions for developing phylogenetic trees as deeply-nested lists (“dendrogram” objects). Enables bi-directional conversion between dendrogram and “phylo” objects (see Paradis et al (2004) doi:10.1093/bioinformatics/btg412), and features several tools for command-line tree manipulation and import/export via Newick parenthetic text.

Scientific use cases
  1. Sawa, T., Momiyama, K., Mihara, T., Kainuma, A., Kinoshita, M., & Moriyama, K. (2020). Molecular epidemiology of clinically high‐risk Pseudomonas aeruginosa strains: Practical overview. Microbiology and Immunology. https://doi.org/10.1111/1348-0421.12776
View Documentation
helminthR
CRAN

Access London Natural History Museum Host-Helminth Record Database

Tad Dallas
Description

Access to large host-parasite data is often hampered by the availability of data and difficulty in obtaining it in a programmatic way to encourage analyses. helminthR provides a programmatic interface to the London Natural History Museum’s host-parasite database, one of the largest host-parasite databases existing currently http://www.nhm.ac.uk/research-curation/scientific-resources/taxonomy-systematics/host-parasites/. The package allows the user to query by host species, parasite species, and geographic location.

Scientific use cases
  1. Dallas, T., & Cornelius, E. (2015). Co-extinction in a host-parasite network: identifying key hosts for network stability. Scientific Reports, 5, 13185. https://doi.org/10.1038/srep13185
  2. Singh, S. K. (2017). Evaluating two freely available geocoding tools for geographical inconsistencies and geocoding errors. Open Geospatial Data, Software and Standards, 2(1). https://doi.org/10.1186/s40965-017-0026-3
  3. Mulder, C. (2017). Pathogenic helminths in the past: Much ado about nothing. F1000Research, 6, 852. https://doi.org/10.12688/f1000research.11752.1
View Documentation
DoOR.functions
Peer-reviewed

A DoOR to the Complete Olfactome

Daniel Münch
Description

This is a function package providing functions to perform data manipulations and visualizations for DoOR.data. See the URLs for the original and the DoOR 2.0 publication.

View Documentation
DoOR.data
Peer-reviewed

A DoOR to the Complete Olfactome

Daniel Münch
Description

This is a data package providing Drosophila odorant response data for DoOR.functions. See URLs for the original and the DoOR 2.0 publications.

View Documentation

The Critical Care Clinical Data Processing Tools

Sinan Shi
Description

An electronic health care record (EHR) data cleaning and processing platform. It focus on heterogeneous high resolution longitudinal data. It works with Critical Care Health Informatics Collaborative (CCHIC) dataset. It is created to address various data reliability and accessibility problems of EHRs as such.

View Documentation
wicket
CRAN

Utilities to Handle WKT Spatial Data

Oliver Keyes
Description

Utilities to generate bounding boxes from WKT (Well-Known Text) objects and R data types, validate WKT objects and convert object types from the sp package into WKT representations.

Scientific use cases
  1. Bachman, S., Walker, B., Barrios, S., Copeland, A., & Moat, J. (2020). Rapid Least Concern: towards automating Red List assessments. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e47018
View Documentation
treebase
CRAN

Discovery, Access and Manipulation of TreeBASE Phylogenies

Carl Boettiger
Description

Interface to the API for TreeBASE http://treebase.org from R. TreeBASE is a repository of user-submitted phylogenetic trees (of species, population, or genes) and the data used to create them.

View Documentation

Create Geographic and Non-Geographic Map Tiles

Matthew Leonawicz
Description

Creates geographic map tiles from geospatial map files or non-geographic map tiles from simple image files. This package provides a tile generator function for creating map tile sets for use with packages such as leaflet. In addition to generating map tiles based on a common raster layer source, it also handles the non-geographic edge case, producing map tiles from arbitrary images. These map tiles, which have a non-geographic, simple coordinate reference system (CRS), can also be used with leaflet when applying the simple CRS option. Map tiles can be created from an input file with any of the following extensions: tif, grd and nc for spatial maps and png, jpg and bmp for basic images. This package requires Python and the gdal library for Python. Windows users are recommended to install OSGeo4W (https://trac.osgeo.org/osgeo4w/) as an easy way to obtain the required gdal support for Python.

View Documentation
tif

Text Interchange Format

Taylor Arnold
Description

Provides validation functions for common interchange formats for representing text data in R. Includes formats for corpus objects, document term matrices, and tokens. Other annotations can be stored by overloading the tokens structure.

View Documentation
tidypmc
CRAN

Parse Full Text XML Documents from PubMed Central

Chris Stubben
Description

Parse XML documents from the Open Access subset of Europe PubMed Central https://europepmc.org including section paragraphs, tables, captions and references.

View Documentation
rrricanesdata
Peer-reviewed

Data for Atlantic and east Pacific tropical cyclones since 1998

Tim Trice
Description

Includes storm discussions, forecast/advisories, public advisories, wind speed probabilities, strike probabilities and more. This package can be used along with rrricanes (>= 0.2.0-6). Data is considered public domain via the National Hurricane Center.

View Documentation

Get Texts from the Perseus Digital Library

David Ranzolin
Description

The Perseus Digital Library is a collection of classical texts. This package helps you get them. The available works can also be viewed here: http://cts.perseids.org/.

View Documentation
rnaturalearthhires

High Resolution World Vector Map Data from Natural Earth used in rnaturalearth

Andy South
Description

Facilitates mapping by making natural earth map data from http:// www.naturalearthdata.com/ more easily available to R users. Focuses on vector data.

View Documentation
rnaturalearthdata
CRAN

World Vector Map Data from Natural Earth Used in rnaturalearth

Andy South
Description

Vector map data from http://www.naturalearthdata.com/. Access functions are provided in the accompanying package rnaturalearth.

Scientific use cases
  1. Rice, A., Šmarda, P., Novosolov, M., Drori, M., Glick, L., Sabath, N., … Mayrose, I. (2019). The global biogeography of polyploid plants. Nature Ecology & Evolution, 3(2), 265–273. https://doi.org/10.1038/s41559-018-0787-9
View Documentation
rfigshare
CRAN

An R Interface to figshare

Carl Boettiger
Description

An R interface to figshare.

Scientific use cases
  1. White, L., & Santy, S. (2018). DataDepsGenerators.jl: making reusing data easy by automatically generating DataDeps.jl registration code. Journal of Open Source Software, 3(31), 921. https://doi.org/10.21105/joss.00921
View Documentation
refimpact
Peer-reviewed

API Wrapper for the UK REF 2014 Impact Case Studies Database

Perry Stephenson
Description

Provides wrapper functions around the UK Research Excellence Framework 2014 Impact Case Studies Database API http://impact.ref.ac.uk/. The database contains relevant publication and research metadata about each case study as well as several paragraphs of text from the case study submissions. Case studies in the database are licenced under a CC-BY 4.0 licence http://creativecommons.org/licenses/by/4.0/legalcode.

View Documentation
rAvis
CRAN

Interface to the Bird-Watching Dataset Proyecto AVIS

Sara Varela
Description

Interface to http://proyectoavis.com database. It provides means to download data filtered by species, order, family, and several other criteria. Provides also basic functionality to plot exploratory maps of the datasets.

View Documentation

Generate Random WKT or GeoJSON

Scott Chamberlain
Description

Generate random positions (latitude/longitude), Well-known text (WKT) points or polygons, or GeoJSON points or polygons.

View Documentation

Download and Read RAM Legacy Stock Assessment Database

Kshitiz Gupta
Description

Contains functions to download, cache and read in Excel version of the RAM Legacy Stock Assessment Data Base, an online compilation of stock assessment results for commercially exploited marine populations from around the world. The database is named after Dr. Ransom A. Myers whose original stock-recruitment database, is no longer being updated. More information about the database can be found at https://ramlegacy.org/. Ricard, D., Minto, C., Jensen, O.P. and Baum, J.K. (2012) doi:10.1111/j.1467-2979.2011.00435.x.

View Documentation

Retrieves Altmerics Data for Any Published Paper from Altmetric.com

Karthik Ram
Description

Provides a programmatic interface to the citation information and alternate metrics provided by Altmetric. Data from Altmetric allows researchers to immediately track the impact of their published work, without having to wait for citations. This allows for faster engagement with the audience interested in your work. For more information, visit https://www.altmetric.com/.

Scientific use cases
  1. Madden, K., Evaniew, N., Scott, T., Domazetoska, E., Dosanjh, P., Li, C. S., … Sprague, S. (2016). Knowledge Dissemination of Intimate Partner Violence Intervention Studies Measured Using Alternative Metrics Results From a Scoping Review. Journal of Interpersonal Violence. https://doi.org/10.1177/0886260516657914
  2. Na, J.-C., & Ye, Y. E. (2017). Content Analysis of Scholarly Discussions of Psychological Academic Articles on Facebook. Online Information Review, 41(3). https://doi.org/10.1108/oir-02-2016-0058
  3. Ruano, J., Aguilar-Luque, M., Gómez-Garcia, F., Alcalde Mellado, P., Gay-Mimbrera, J., Carmona-Fernandez, P. J., … Isla-Tejera, B. (2018). The differential impact of scientific quality, bibliometric factors, and social media activity on the influence of systematic reviews and meta-analyses about psoriasis. PLOS ONE, 13(1), e0191124. https://doi.org/10.1371/journal.pone.0191124
  4. Nabout, J. C., Teresa, F. B., Machado, K. B., do Prado, V. H. M., Bini, L. M., & Diniz-Filho, J. A. F. (2018). Do traditional scientometric indicators predict social media activity on scientific knowledge? An analysis of the ecological literature. Scientometrics. https://doi.org/10.1007/s11192-018-2678-x
  5. Araujo, R. F., & Alves, M. (2018). The altmetric performance of publications authored by Brazilian researchers: analysis of CNPq productivity scholarship holders. arXiv preprint arXiv:1807.06366. https://arxiv.org/abs/1807.06366
  6. Sun, Z., Cang, J., Ruan, Y., & Zhu, D. (2019). Reporting gaps between news media and scientific papers on outdoor air pollution–related health outcomes: A content analysis. The International Journal of Health Planning and Management. https://doi.org/10.1002/hpm.2894
  7. Fu, D. Y., & Hughey, J. J. (2019). Releasing a preprint is associated with more attention and citations for the peer-reviewed article. eLife, 8. https://doi.org/10.7554/elife.52646
View Documentation

Client for Various Ocean Time Series Datasets

Scott Chamberlain
Description

Interact with various ocean time series datasets, including BATS, HOT, and more. Package focuses on data retrieval only. All functions return a data.frame for easy downstream use for plots, vizualization, analysis.

View Documentation

A Shiny Application for Automatic Measurements of Tree-Ring Widths on Digital Images

Jingning Shi
Description

Use morphological image processing and edge detection algorithms to automatically measure tree ring widths on digital images. Users can also manually mark tree rings on species with complex anatomical structures. The arcs of inner-rings and angles of successive inclined ring boundaries are used to correct ring-width series. The package provides a Shiny-based application, allowing R beginners to easily analyze tree ring images and export ring-width series in standard file formats.

View Documentation
historydata
CRAN

Datasets for Historians

Lincoln Mullen
Description

These sample data sets are intended for historians learning R. They include population, institutional, religious, military, and prosopographical data suitable for mapping, quantitative analysis, and network analysis.

View Documentation

Get Landsat 8 Data from Amazon Public Data Sets

Scott Chamberlain
Description

Get Landsat 8 Data from Amazon Web Services (AWS) public data sets (https://registry.opendata.aws/landsat-8/). Includes functions for listing images and fetching them, and handles caching to prevent unnecessary additional requests.

View Documentation

Split Geospatial Objects into Pieces

Scott Chamberlain
Description

Split geospatial objects into pieces. Includes support for some spatial object inputs, Well-Known Text, and GeoJSON.

View Documentation
genderdata

Historical Datasets for Predicting Gender from Names

Lincoln Mullen
Description

The historical datasets in this package are used in the gender package to predict gender from first names and birth years.

View Documentation
ezknitr
CRAN

Avoid the Typical Working Directory Pain When Using knitr

Dean Attali
Description

An extension of knitr that adds flexibility in several ways. One common source of frustration with knitr is that it assumes the directory where the source file lives should be the working directory, which is often not true. ezknitr addresses this problem by giving you complete control over where all the inputs and outputs are, and adds several other convenient features to make rendering markdown/HTML documents easier.

View Documentation

Client for CAMS Radiation Service

Lukas Lundstrom
Description

Copernicus Atmosphere Monitoring Service (CAMS) Radiation Service provides time series of global, direct, and diffuse irradiations on horizontal surface, and direct irradiation on normal plane for the actual weather conditions as well as for clear-sky conditions. The geographical coverage is the field-of-view of the Meteosat satellite, roughly speaking Europe, Africa, Atlantic Ocean, Middle East. The time coverage of data is from 2004-02-01 up to 2 days ago. Data are available with a time step ranging from 15 min to 1 month. For license terms and to create an account, please see http://www.soda-pro.com/web-services/radiation/cams-radiation-service.

Scientific use cases
  1. Yang, D. (2019). Making reference solar forecasts with climatology, persistence, and their optimal convex combination. Solar Energy, 193, 981–985. https://doi.org/10.1016/j.solener.2019.10.006
  2. Yagli, G. M., Yang, D., Gandhi, O., & Srinivasan, D. (2019). Can we justify producing univariate machine-learning forecasts with satellite-derived solar irradiance? Applied Energy, 114122. https://doi.org/10.1016/j.apenergy.2019.114122
  3. Yang, D. (2020). Choice of clear-sky model in solar forecasting. Journal of Renewable and Sustainable Energy, 12(2), 026101. https://doi.org/10.1063/5.0003495
  4. Yang, D., & Bright, J. M. (2020). Worldwide validation of 8 satellite-derived and reanalysis solar radiation products: A preliminary evaluation and overall metrics for hourly data over 27 years. Solar Energy. https://doi.org/10.1016/j.solener.2020.04.016
View Documentation

Client for the Bittrex Exchange

Michael Kane
Description

A client for the Bittrex crypto-currency exchange https://bittrex.com including the ability to query trade data, manage account balances, and place orders.

View Documentation
aRxiv
CRAN

Interface to the arXiv API

Karl Broman
Description

An interface to the API for arXiv (https://arxiv.org), a repository of electronic preprints for computer science, mathematics, physics, quantitative biology, quantitative finance, and statistics.

Scientific use cases
  1. Jaspers, S., De Troyer, E., & Aerts, M. (2018). Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA. EFSA Supporting Publications, 15(6), 1427E. https://doi.org/10.2903/sp.efsa.2018.EN-1427
View Documentation

programmatic interface to the AntWeb

Karthik Ram
Description

A complete programmatic interface to the AntWeb database from the California Academy of Sciences.

Scientific use cases
  1. PIE, M. R. (2016). The macroevolution of climatic niches and its role in ant diversification. Ecological Entomology, 41(3), 301–307. https://doi.org/10.1111/een.12306
View Documentation

Antarctic Geographic Place Names

Ben Raymond
Description

Antarctic geographic names from the Composite Gazetteer of Antarctica, and functions for working with those place names.

View Documentation

Interface to the National Phenology Network API

Scott Chamberlain
Description

Programmatic interface to the Web Service methods provided by the National Phenology Network (https://usanpn.org/), which includes data on various life history events that occur at specific times.

View Documentation

A GraphQL Query Parser

Jeroen Ooms
Description

Bindings to the libgraphqlparser C++ library. Parses GraphQL syntax and exports the AST in JSON format.

View Documentation

Google's Compact Language Detector 2

Jeroen Ooms
Description

Bindings to Google’s C++ library Compact Language Detector 2 (see https://github.com/cld2owners/cld2#readme for more information). Probabilistically detects over 80 languages in plain text or HTML. For mixed-language input it returns the top three detected languages and their approximate proportion of the total classified text bytes (e.g. 80% English and 20% French out of 1000 bytes). There is also a cld3 package on CRAN which uses a neural network model instead.

Scientific use cases
  1. Martín-Martín, A., Orduna-Malea, E., Thelwall, M., & López-Cózar, E. D. (2018). Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories. arXiv preprint arXiv:1808.05053 https://arxiv.org/abs/1808.05053
  2. Albrecht, U.-V., Hasenfuß, G., & von Jan, U. (2018). Description of Cardiological Apps From the German App Store: Semiautomated Retrospective App Store Analysis. JMIR mHealth and uHealth, 6(11), e11753. https://doi.org/10.2196/11753
  3. Green, E. P., Whitcomb, A., Kahumbura, C., Rosen, J. G., Goyal, S., Achieng, D., & Bellows, B. (2019). What is the best method of family planning for me?: a text mining analysis of messages between users and agents of a digital health service in Kenya. Gates Open Research, 3, 1475. https://doi.org/10.12688/gatesopenres.12999.1
  4. Jaric, I., & Djeric, M. (2019). Curriculum and labor market: Comparative analysis of the curricular outcomes of the study program in sociology at the Faculty of Philosophy, University of Belgrade and the required competences in the labor market. Sociologija, 61(Suppl. 1), 718–741. https://doi.org/10.2298/soc19s1718j
View Documentation

Supports the Analysis of RTI MicroPEM Output Files

Maëlle Salmon
Description

Supports the input and reproducible analysis of RTI MicroPEM output files.

Scientific use cases
  1. Salmon, M., Milà, C., Bhogadi, S., Addanki, S., Madhira, P., Muddepaka, N., … Tonne, C. (2018). Wearable camera-derived microenvironments in relation to personal exposure to PM 2.5. Environment International, 117, 300–307. https://doi.org/10.1016/j.envint.2018.05.021
  2. Milà, C., Curto, A., Dimitrova, A., Sreekanth, V., Kinra, S., Marshall, J. D., & Tonne, C. (2020). Identifying predictors of personal exposure to air temperature in peri-urban India. Science of The Total Environment, 707, 136114. https://doi.org/10.1016/j.scitotenv.2019.136114
  3. Upadhya, A., Agrawal, P., Vakacherla, S., & Kushwaha, M. (2020). mmaqshiny v1.0: R-Shiny package to explore Air-Quality Mobile-Monitoring data. Journal of Open Source Software, 5(50), 2250. https://doi.org/10.21105/joss.02250
View Documentation

Accesses the Monkeylearn API for Text Classifiers and Extractors

Maëlle Salmon
Description

Allows using some services of Monkeylearn http://monkeylearn.com/ which is a Machine Learning platform on the cloud for text analysis (classification and extraction).

View Documentation

OpenBIS API Access to the InfectX Data Repository

Nicolas Bennett
Description

The Open Source Biology Information System (openBIS) is a general purpose framework for management, annotation and publication of large data sets that arise from biological experiments. By making the JSON-RPC based openBIS API available to R, image-based high throughput screening data as generated by the InfectX/TargetInfectX projects can be browsed, searched and downloaded directly from R. Currently, several kinome-wide RNA interference screens performed on HeLa cells in presence of a selection of bacterial and viral pathogens and using oligo libraries form multiple vendors are available. Further genome-wide screens are forthcoming. The full data obtained from these experiments is accessible, including raw microscopy images, object segmentation masks, single cell feature data generated by CellProfiler and infection scoring data, alongside rich meta data and quality control data.

View Documentation

Working with GTFS (General Transit Feed Specification) feeds in R

Danton Noriega-Goodwin
Description

Provides API wrappers for popular public GTFS feed sharing sites, reads feed data into a gtfs data object, validates data quality, provides convenience functions for common tasks.

View Documentation

Generate Starting Trees For Combined Molecular, Morphological and Stratigraphic Data

April Wright
Description

Combine a list of taxa with a phylogeny to generate a starting tree for use in total evidence dating analyses.

View Documentation
rnaturalearth
CRAN Peer-reviewed

World Map Data from Natural Earth

Andy South
Description

Facilitates mapping by making natural earth map data from http://www.naturalearthdata.com/ more easily available to R users.

Scientific use cases
  1. Chapman, C. A., Omeja, P. A., Kalbitzer, U., Fan, P., & Lawes, M. J. (2018). Restoration Provides Hope for Faunal Recovery: Changes in Primate Abundance Over 45 Years in Kibale National Park, Uganda. Tropical Conservation Science, 11, 194008291878737. https://doi.org/10.1177/1940082918787376
  2. Farache, F. H. A., Pereira, C. B., Koschnitzke, C., Barros, L. O., Souza, E. M. de C., Felício, D. T., … Pereira, R. A. S. (2018). The unknown followers: Discovery of a new species of Sycobia Walker (Hymenoptera: Epichrysomallinae) associated with Ficus benjamina L (Moraceae) in the Neotropical region. Journal of Hymenoptera Research. 67, 85–102. https://doi.org/10.3897/jhr.67.29733
  3. Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C., Edler, D., … Antonelli, A. (2019). CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13152
  4. Atickem, A., Stenseth, N. C., Fashing, P. J., Nguyen, N., Chapman, C. A., Bekele, A., … Kalbitzer, U. (2019). Build science in Africa. Nature, 570(7761), 297–300. https://doi.org/10.1038/d41586-019-01885-1
  5. Umlauf, N., Klein, N., Simon, T., & Zeileis, A. (2019). bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond). arXiv preprint arXiv:1909.11784. https://arxiv.org/abs/1909.11784
  6. Rodewald, A. D., Strimas-Mackey, M., Schuster, R., & Arcese, P. (2019). Tradeoffs in the value of biodiversity feature and cost data in conservation prioritization. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-52241-2
  7. Næss, M. W. (2019). From hunter-gatherers to nomadic pastoralists: forager bands do not tell the whole story of the evolution of human cooperation. https://doi.org/10.31235/osf.io/9c8bm
  8. Marshall, B. M., & Strine, C. T. (2019). Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media. PeerJ, 7, e8059. https://doi.org/10.7717/peerj.8059
  9. Czernecki, B., Głogowski, A., & Nowosad, J. (2020). Climate: An R Package to Access Free In-Situ Meteorological and Hydrological Datasets For Environmental Assessment. Sustainability, 12(1), 394. https://doi.org/10.3390/su12010394
  10. Rego, A., Sousa, A. G. G., Santos, J. P., Pascoal, F., Canário, J., Leão, P. N., & Magalhães, C. (2020). Diversity of Bacterial Biosynthetic Genes in Maritime Antarctica. Microorganisms, 8(2), 279. https://doi.org/10.3390/microorganisms8020279
  11. Eastman, R. T., Roth, J. S., Brimacombe, K. R., Simeonov, A., Shen, M., Patnaik, S., & Hall, M. D. (2020). Remdesivir: A Review of Its Discovery and Development Leading to Emergency Use Authorization for Treatment of COVID-19. ACS Central Science, 6(5), 672–683. https://doi.org/10.1021/acscentsci.0c00489
  12. Ozturk, R. C., & Altinok, I. (2020). Interaction of Plastics with Marine Species. Turkish Journal of Fisheries and Aquatic Sciences, 20(8). https://doi.org/10.4194/1303-2712-v20_8_07
  13. Deconinck, D., Volckaert, F. A. M., Hostens, K., Panicz, R., Eljasik, P., Faria, M., … Derycke, S. (2020). A high-quality genetic reference database for European commercial fishes reveals substitution fraud of processed Atlantic cod (Gadus morhua) and common sole (Solea solea) at different steps in the Belgian supply chain. Food and Chemical Toxicology, 141, 111417. https://doi.org/10.1016/j.fct.2020.111417
  14. Connors, B., Malick, M. J., Ruggerone, G. T., Rand, P., Adkison, M., Irvine, J. R., … Gorman, K. (2020). Climate and competition influence sockeye salmon population dynamics across the Northeast Pacific Ocean. Canadian Journal of Fisheries and Aquatic Sciences, 77(6), 943–949. https://doi.org/10.1139/cjfas-2019-0422
  15. Runge, C. A., Hausner, V. H., Daigle, R. M., & Monz, C. A. (2020). Pan-Arctic analysis of cultural ecosystem services using social media and automated content analysis. Environmental Research Communications, 2(7), 075001. https://doi.org/10.1088/2515-7620/ab9c33
  16. Swetnam, D. M., Stuart, J. B., Young, K., Maharaj, P. D., Fang, Y., Garcia, S., … Coffey, L. L. (2020). Movement of St. Louis encephalitis virus in the Western United States, 2014- 2018. PLOS Neglected Tropical Diseases, 14(6), e0008343. https://doi.org/10.1371/journal.pntd.0008343
  17. Kurose, D., Pollard, K. M., & Ellison, C. A. (2020). Chloroplast DNA analysis of the invasive weed, Himalayan balsam (Impatiens glandulifera), in the British Isles. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-67871-0
View Documentation
simpletextr

Simple Text Wrappers

Matt Johnson
Description

Simple functions for common repeatable tasks in NLP and text mining.

View Documentation

Simulating Neutral Landscape Models

Marco Sciaini
Description

Provides neutral landscape models (doi:10.1007/BF02275262, http://sci-hub.tw/10.1007/bf02275262).
Neutral landscape models range from “hard” neutral models (completely random distributed), to “soft” neutral models (definable spatial characteristics) and generate landscape patterns that are independent of ecological processes. Thus, these patterns can be used as null models in landscape ecology. nlmr combines a large number of algorithms from other published software for simulating neutral landscapes. The simulation results are obtained in a geospatial data format (raster* objects from the raster package) and can, therefore, be used in any sort of raster data operation that is performed with standard observation data.

Scientific use cases
  1. Langhammer, M., Thober, J., Lange, M., Frank, K., & Grimm, V. (2019). Agricultural landscape generators for simulation models: A review of existing solutions and an outline of future directions. Ecological Modelling, 393, 135–151. https://doi.org/10.1016/j.ecolmodel.2018.12.010
  2. Fletcher, R., & Fortin, M.-J. (2018). Land-Cover Pattern and Change. Spatial Ecology and Conservation Modeling, 55–100. https://doi.org/10.1007/978-3-030-01989-1_3
  3. Harris, M. (2019). KLRfome - Kernel Logistic Regression on Focal Mean Embeddings. Journal of Open Source Software, 4(35), 722. https://doi.org/10.21105/joss.00722
  4. Etherington, T., & Omondiagbe, O. (2019). virtualNicheR: generating virtual fundamental and realised niches for use in virtual ecology experiments. Journal of Open Source Software, 4(41), 1661. https://doi.org/10.21105/joss.01661
  5. Betts, M. G., Wolf, C., Pfeifer, M., Banks-Leite, C., Arroyo-Rodríguez, V., Ribeiro, D. B., … Ewers, R. M. (2019). Extinction filters mediate the global effects of habitat fragmentation on animals. Science, 366(6470), 1236–1239. https://doi.org/10.1126/science.aax9387
  6. Scherer, C., Radchuk, V., Franz, M., Thulke, H., Lange, M., Grimm, V., & Kramer‐Schadt, S. (2020). Moving infections: individual movement decisions drive disease persistence in spatially structured landscapes. Oikos. https://doi.org/10.1111/oik.07002
  7. Silva, I., Crane, M., Marshall, B. M., & Strine, C. T. (2020). Revisiting reptile home ranges: moving beyond traditional estimators with dynamic Brownian Bridge Movement Models. https://doi.org/10.1101/2020.02.10.941278
View Documentation
chromer
CRAN

Interface to Chromosome Counts Database API

Paula Andrea Martinez
Description

A programmatic interface to the Chromosome Counts Database (http://ccdb.tau.ac.il/). This package is part of the rOpenSci suite (https://ropensci.org).

Scientific use cases
  1. Zenil-Ferguson, R., Ponciano, J. M., & Burleigh, J. G. (2017). Testing the association of phenotypes with polyploidy: An example using herbaceous and woody eudicots. Evolution. https://doi.org/10.1111/evo.13226
  2. Rivero, R., Sessa, E. B., & Zenil-Ferguson, R. (2019). EyeChrom and CCDBcurator: Visualizing chromosome count data from plants. Applications in Plant Sciences, e01207. https://doi.org/10.1002/aps3.1207
  3. Han, T., Zheng, Q., Onstein, R. E., Rojas‐Andrés, B. M., Hauenschild, F., Muellner‐Riehl, A. N., & Xing, Y. (2019). Polyploidy promotes species diversification of Allium through ecological shifts. New Phytologist. https://doi.org/10.1111/nph.16098
  4. Carta, A., Bedini, G., & Peruzzi, L. (2020). A deep dive into the ancestral chromosome number of flowering plants. bioRxiv preprint. https://doi.org/10.1101/2020.01.05.893859
View Documentation

Read EPUB File Metadata and Text

Matthew Leonawicz
Description

Provides functions supporting the reading and parsing of internal e-book content from EPUB files. The epubr package provides functions supporting the reading and parsing of internal e-book content from EPUB files. E-book metadata and text content are parsed separately and joined together in a tidy, nested tibble data frame. E-book formatting is not completely standardized across all literature. It can be challenging to curate parsed e-book content across an arbitrary collection of e-books perfectly and in completely general form, to yield a singular, consistently formatted output. Many EPUB files do not even contain all the same pieces of information in their respective metadata. EPUB file parsing functionality in this package is intended for relatively general application to arbitrary EPUB e-books. However, poorly formatted e-books or e-books with highly uncommon formatting may not work with this package. There may even be cases where an EPUB file has DRM or some other property that makes it impossible to read with epubr. Text is read as is for the most part. The only nominal changes are minor substitutions, for example curly quotes changed to straight quotes. Substantive changes are expected to be performed subsequently by the user as part of their text analysis. Additional text cleaning can be performed at the users discretion, such as with functions from packages like tm or qdap’.

View Documentation

Split, Combine and Compress PDF Files

Jeroen Ooms
Description

Content-preserving transformations transformations of PDF files such as split, combine, and compress. This package interfaces directly to the qpdf C++ API and does not require any command line utilities. Note that qpdf does not read actual content from PDF files: to extract text and data you need the pdftools package.

View Documentation

Popler R Package

Compagnoni Aldo
Description

Browse and query the popler database.

View Documentation

Extract Text from Microsoft Word Documents

Jeroen Ooms
Description

Wraps the AntiWord utility to extract text from Microsoft Word documents. The utility only supports the old doc format, not the new xml based docx format. Use the xml2 package to read the latter.

View Documentation
rentrez
CRAN

Entrez in R

David Winter
Description

Provides an R interface to the NCBIs EUtils’ API, allowing users to search databases like GenBank https://www.ncbi.nlm.nih.gov/genbank/ and PubMed https://www.ncbi.nlm.nih.gov/pubmed/, process the results of those searches and pull data into their R sessions.

Scientific use cases
  1. Drozd, P., & Šipoš, J. (2013). R for all (I): Introduction to the new age of biological analyses. Casopis Slezskeho Zemskeho Muzea A, 62(1). https://doi.org/10.2478/cszma-2013-0004
  2. Hampton, S. E., Anderson, S. S., Bagby, S. C., Gries, C., Han, X., Hart, E. M., et al. (2015). The Tao of open science for ecology. Ecosphere, 6(7), art120. https://doi.org/10.1890/es14-00402.1
  3. Nguyen, N. T., Zhang, X., Wu, C., Lange, R. A., Chilton, R. J., Lindsey, M. L., & Jin, Y.-F. (2014). Integrative Computational and Experimental Approaches to Establish a Post-Myocardial Infarction Knowledge Map. PLoS Computational Biology, 10(3), e1003472. https://doi.org/10.1371/journal.pcbi.1003472
  4. Lee, Y. Y., Foster, E. D., Polley, D. E., & Odell, J. Using the ‘rentrez’ R Package to Identify Repository Records for NCBI LinkOut. Code4lib Journal. http://journal.code4lib.org/articles/12792
  5. Winter, D. J. (2017). rentrez: An R package for the NCBI eUtils API (Version 1). PeerJ Preprints. https://doi.org/10.7287/peerj.preprints.3179v1
  6. Krawczyk, P. S., Lipinski, L., & Dziembowski, A. (2018). PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Research. https://doi.org/10.1093/nar/gkx1321
  7. Claypool, K., & Patel, C. J. (2018). A transcript-wide association study in physical activity intervention implicates molecular pathways in chronic disease. https://doi.org/10.1101/260398
  8. Chen, L., Heikkinen, L., Wang, C., Yang, Y., Knott, K. E., & Wong, G. (2018). miRToolsGallery: a tag-based and rankable microRNA bioinformatics resources database portal. Database, 2018. https://doi.org/10.1093/database/bay004
  9. Lakiotaki, K., Vorniotakis, N., Tsagris, M., Georgakopoulos, G., & Tsamardinos, I. (2018). BioDataome: a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology. Database, 2018. https://doi.org/10.1093/database/bay011
  10. Reibe, S., Hjorth, M., Febbraio, M. A., & Whitham, M. (2018). GeneXX: An online tool for the exploration of transcript changes in skeletal muscle associated with exercise. Physiological genomics. https://doi.org/10.1152/physiolgenomics.00127.2017
  11. Barnett, A. (2018). Missing the point: are journals using the ideal number of decimal places? F1000Research, 7, 450. https://doi.org/10.12688/f1000research.14488.1
  12. Spalink, D., Stoffel, K., Walden, G. K., Hulse-Kemp, A. M., Hill, T. A., Van Deynze, A., & Bohs, L. (2018). Comparative transcriptomics and genomic patterns of discordance in Capsiceae (Solanaceae). Molecular Phylogenetics and Evolution, 126, 293–302. https://doi.org/10.1016/j.ympev.2018.04.030
  13. Han, X., Williams, S. R., & Zuckerman, B. L. (2018). A snapshot of translational research funded by the National Institutes of Health (NIH): A case study using behavioral and social science research awards and Clinical and Translational Science Awards funded publications. PLOS ONE, 13(5), e0196545. https://doi.org/10.1371/journal.pone.0196545
  14. Machado, V. N., Collins, R. A., Ota, R. P., Andrade, M. C., Farias, I. P., & Hrbek, T. (2018). One thousand DNA barcodes of piranhas and pacus reveal geographic structure and unrecognised diversity in the Amazon. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-26550-x
  15. Sun, B. B., Maranville, J. C., Peters, J. E., Stacey, D., Staley, J. R., Blackshaw, J., … Butterworth, A. S. (2018). Genomic atlas of the human plasma proteome. Nature, 558(7708), 73–79. https://doi.org/10.1038/s41586-018-0175-2
  16. Mioduchowska, M., Czyż, M. J., Gołdyn, B., Kur, J., & Sell, J. (2018). Instances of erroneous DNA barcoding of metazoan invertebrates: Are universal cox1 gene primers too “universal”? PLOS ONE, 13(6), e0199609. https://doi.org/10.1371/journal.pone.0199609
  17. Magoga, G., Sahin, D. C., Fontaneto, D., & Montagna, M. (2018). Barcoding of Chrysomelidae of Euro-Mediterranean area: efficiency and problematic species. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-31545-9
  18. Otten, C., Knox, J., Boulday, G., Eymery, M., Haniszewski, M., Neuenschwander, M., … Abdelilah‐Seyfried, S. (2018). Systematic pharmacological screens uncover novel pathways involved in cerebral cavernous malformations. EMBO Molecular Medicine, e9155. https://doi.org/10.15252/emmm.201809155
  19. Yángüez, E., Hunziker, A., Dobay, M. P., Yildiz, S., Schading, S., Elshina, E., … Stertz, S. (2018). Phosphoproteomic-based kinase profiling early in influenza virus infection identifies GRK2 as antiviral drug target. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-06119-y
  20. Collins, R. A., Wangensteen, O. S., O’Gorman, E. J., Mariani, S., Sims, D. W., & Genner, M. J. (2018). Persistence of environmental DNA in marine systems. Communications Biology, 1(1). https://doi.org/10.1038/s42003-018-0192-6
  21. Cholet, F., Ijaz, U. Z., & Smith, C. J. (2018). Differential ratio amplicons (Ramp) for the evaluation of RNA integrity extracted from complex environmental samples. Environmental Microbiology. https://doi.org/10.1111/1462-2920.14516
  22. Die, J. V., Elmassry, M. M., Leblanc, K. H., Awe, O. I., Dillman, A., & Busby, B. (2018). GeneHummus: A pipeline to define gene families and their expression in legumes and beyond. https://doi.org/10.1101/436659
  23. Mioduchowska, M., Czyż, M. J., Gołdyn, B., Kilikowska, A., Namiotko, T., Pinceel, T., … Sell, J. (2018). Detection of bacterial endosymbionts in freshwater crustaceans: the applicability of non-degenerate primers to amplify the bacterial 16S rRNA gene. PeerJ, 6, e6039. https://doi.org/10.7717/peerj.603
  24. Bennett, D., Hettling, H., Silvestro, D., Vos, R., & Antonelli, A. (2018). restez: Create and Query a Local Copy of GenBank in R. Journal of Open Source Software, 3(31), 1102. https://doi.org/10.21105/joss.01102
  25. Brooks, L., Kaze, M., & Sistrom, M. (2019). A Curated, Comprehensive Database of Plasmid Sequences. Microbiology Resource Announcements, 8(1). https://doi.org/10.1128/mra.01325-18
  26. Poulin, R., Hay, E., & Jorge, F. (2019). Taxonomic and geographic bias in the genetic study of helminth parasites. International Journal for Parasitology. https://doi.org/10.1016/j.ijpara.2018.12.005
  27. Phelps, K., Hamel, L., Alhmoud, N., Ali, S., Bilgin, R., Sidamonidze, K., … Olival, K. (2019). Bat Research Networks and Viral Surveillance: Gaps and Opportunities in Western Asia. Viruses, 11(3), 240. https://doi.org/10.3390/v11030240
  28. Barnett, A. G., & Moher, D. (2019). Turning the tables: A university league-table based on quality not quantity. F1000Research, 8, 583. https://doi.org/10.12688/f1000research.18453.1
  29. Mann, C. M., Martínez-Gálvez, G., Welker, J. M., Wierson, W. A., Ata, H., Almeida, M. P., … Dobbs, D. (2019). The Gene Sculpt Suite: a set of tools for genome editing. Nucleic Acids Research. https://doi.org/10.1093/nar/gkz405
  30. Al-Mustanjid, A. (2019). Design of a common pathway drug for all types of cardiovascular diseases: A network biology approach. Network Biology, 9(2), 28. http://www.iaees.org/publications/journals/nb/articles/2019-9(2)/design-of-a-common-pathway-drug-for-cardiovascular-diseases.pdf
  31. Shackleton, M. E., Rees, G. N., Watson, G., Campbell, C., & Nielsen, D. (2019). Environmental DNA reveals landscape mosaic of wetland plant communities. Global Ecology and Conservation, 19, e00689. https://doi.org/10.1016/j.gecco.2019.e00689
  32. Koppelstaetter, C., Leierer, J., Rudnicki, M., Kerschbaum, J., Kronbichler, A., Melk, A., … Perco, P. (2019). Computational Drug Screening Identifies Compounds Targeting Renal Age-associated Molecular Profiles. Computational and Structural Biotechnology Journal, 17, 843–853. https://doi.org/10.1016/j.csbj.2019.06.019
  33. Ferraz, M. de A. M. M., Carothers, A., Dahal, R., Noonan, M. J., & Songsasen, N. (2019). Oviductal extracellular vesicles interact with the spermatozoon’s head and mid-piece and improves its motility and fertilizing ability in the domestic cat. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-45857-x
  34. Collins, R. A., Bakker, J., Wangensteen, O. S., Soto, A. Z., Corrigan, L., Sims, D. W., … Mariani, S. (2019). Non‐specific amplification compromises environmental DNA metabarcoding with COI. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13276
  35. Die, J. V., Elmassry, M. M., LeBlanc, K. H., Awe, O. I., Dillman, A., & Busby, B. (2019). geneHummus: an R package to define gene families and their expression in legumes and beyond. BMC Genomics, 20(1). https://doi.org/10.1186/s12864-019-5952-2
  36. Piper, A. M., Batovska, J., Cogan, N. O. I., Weiss, J., Cunningham, J. P., Rodoni, B. C., & Blacket, M. J. (2019). Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance. GigaScience, 8(8). https://doi.org/10.1093/gigascience/giz092
  37. Neugebauer, K., El‐Serehy, H. A., George, T. S., McNicol, J. W., Moraes, M. F., Sorreano, M. C. M., & White, P. J. (2019). The influence of phylogeny and ecology on root, shoot and plant ionomes of fourteen native Brazilian species. Physiologia Plantarum. https://doi.org/10.1111/ppl.13018
  38. Wittouck, S., Wuyts, S., Meehan, C. J., van Noort, V., & Lebeer, S. (2019). A Genome-Based Species Taxonomy of the Lactobacillus Genus Complex. mSystems, 4(5). https://doi.org/10.1128/msystems.00264-19
  39. Alex Dornburg, Dustin J. Wcisel, J. Thomas Howard et al. Transcriptome Ortholog Alignment Sequence Tools (TOAST) for Phylogenomic Dataset Assembly, 21 October 2019, PREPRINT (Version 1) available at Research Square https://doi.org/10.21203/rs.2.16269/v1
  40. Fu, D. Y., & Hughey, J. J. (2019). Releasing a preprint is associated with more attention and citations for the peer-reviewed article. eLife, 8. https://doi.org/10.7554/elife.52646
  41. Vitale, O., Preste, R., Palmisano, D., & Attimonelli, M. (2019). A data and text mining pipeline to annotate human mitochondrial variants with functional and clinical information. Molecular Genetics & Genomic Medicine, 8(2). https://doi.org/10.1002/mgg3.1085
  42. Die, J. V., Elmassry, M. M., LeBlanc, K. H., Awe, O. I., Dillman, A., & Busby, B. (2018). GeneHummus: A pipeline to define gene families and their expression in legumes and beyond. https://doi.org/10.1101/436659
  43. Oliphant, K., Cochrane, K., Schroeter, K., Daigneault, M. C., Yen, S., Verdu, E. F., & Allen-Vercoe, E. (2020). Effects of Antibiotic Pretreatment of an Ulcerative Colitis-Derived Fecal Microbial Community on the Integration of Therapeutic Bacteria In Vitro. mSystems, 5(1). https://doi.org/10.1128/msystems.00404-19
  44. Thompson, K. A. (2020). Experimental hybridization studies suggest that pleiotropic alleles commonly underlie adaptive divergence between natural populations. The American Naturalist. https://doi.org/10.1086/708722
  45. Pavlovich, S. S., Darling, T., Hume, A. J., Davey, R. A., Feng, F., Mühlberger, E., & Kepler, T. B. (2020). Egyptian Rousette IFN-ω Subtypes Elicit Distinct Antiviral Effects and Transcriptional Responses in Conspecific Cells. Frontiers in Immunology, 11. https://doi.org/10.3389/fimmu.2020.00435
  46. Bärenstrauch, M., Mann, S., Jacquemin, C., Bibi, S., Sylla, O.-K., Baudouin, E., … Kunz, C. (2020). Molecular crosstalk between the endophyte Paraconiothyrium variabile and the phytopathogen Fusarium oxysporum – Modulation of lipoxygenase activity and beauvericin production during the interaction. Fungal Genetics and Biology, 139, 103383. https://doi.org/10.1016/j.fgb.2020.103383
  47. Martínez, A., Eckert, E. M., Artois, T., Careddu, G., Casu, M., Curini-Galletti, M., … Fontaneto, D. (2020). Human access impacts biodiversity of microscopic animals in sandy beaches. Communications Biology, 3(1). https://doi.org/10.1038/s42003-020-0912-6
  48. De Almeida Monteiro Melo Ferraz, M., Fujihara, M., Nagashima, J. B., Noonan, M. J., Inoue-Murayama, M., & Songsasen, N. (2020). Follicular extracellular vesicles enhance meiotic resumption of domestic cat vitrified oocytes. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-65497-w
  49. Oh, S., Yeom, J., Cho, H. J., Kim, J.-H., Yoon, S.-J., Kim, H., … Kim, H. S. (2020). Integrated pharmaco-proteogenomics defines two subgroups in isocitrate dehydrogenase wild-type glioblastoma with prognostic and therapeutic opportunities. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17139-y
View Documentation
workloopR
Peer-reviewed

Analysis of Work Loops and Other Data from Muscle Physiology Experiments

Vikram B. Baliga
Description

Functions for the import, transformation, and analysis of data from muscle physiology experiments. The work loop technique is used to evaluate the mechanical work and power output of muscle. Josephson (1985) https://jeb.biologists.org/content/114/1/493 modernized the technique for application in comparative biomechanics. Although our initial motivation was to provide functions to analyze work loop experiment data, as we developed the package we incorporated the ability to analyze data from experiments that are often complementary to work loops. There are currently three supported experiment types: work loops, simple twitches, and tetanus trials. Data can be imported directly from .ddf files or via an object constructor function. Through either method, data can then be cleaned or transformed via methods typically used in studies of muscle physiology. Data can then be analyzed to determine the timing and magnitude of force development and relaxation (for isometric trials) or the magnitude of work, net power, and instantaneous power among other things (for work loops). Although we do not provide plotting functions, all resultant objects are designed to be friendly to visualization via either base-R plotting or tidyverse functions. This package has been peer-reviewed by rOpenSci (v. 1.1.0).

View Documentation
pkgreviewr

rOpenSci package review project template

Anna Krystalli
Description

Creates files and collects materials necessary to complete an rOpenSci package review. Review files are prepopulated with review package specific metadata. Review package source code is also cloned for local testing and inspection.

View Documentation
plotdap
CRAN

Easily Visualize Data from ERDDAP Servers via the rerddap Package

Roy Mendelssohn
Description

Easily visualize and animate tabledap and griddap objects obtained via the rerddap package in a simple one-line command, using either base graphics or ggplot2 graphics. plotdap handles extracting and reshaping the data, map projections and continental outlines. Optionally the data can be animated through time using the gganmiate package.

View Documentation
defender

Check Package for Potential Security Violations

Ildiko Czeller
Description

Check an R package for potential security risks and violations via static code analysis.

View Documentation
ropsec

Personal Workstation Safety Check

Ildiko Czeller
Description

Sobriety checkpoints are designed to help ensure personal and public safety. Methods are provided to run “sobriety” checks on your system to ensure your computing environment is as safe as possible from the perspectives of confidentiality and integrity.

View Documentation

Bindings for Tabula PDF Table Extractor Library

Tom Paskhalis
Description

Bindings for the Tabula http://tabula.technology/ Java library, which can extract tables from PDF documents. The tabulizerjars package https://github.com/ropensci/tabulizerjars provides versioned Java .jar files, including all dependencies, aligned to releases of Tabula.

Scientific use cases
  1. Baquero, O. S., & Machado, G. (2018). Spatiotemporal dynamics and risk factors for human Leptospirosis in Brazil. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-33381-3
  2. Prats, J., & Danis, P.-A. (2019). An epilimnion and hypolimnion temperature model based on air temperature and lake characteristics. Knowledge & Management of Aquatic Ecosystems, (420), 8. https://doi.org/10.1051/kmae/2019001
View Documentation

DBHYDRO Hydrologic and Water Quality Data

Joseph Stachelek
Description

Client for programmatic access to the South Florida Water Management Districts DBHYDRO’ database at https://www.sfwmd.gov/science-data/dbhydro, with functions for accessing hydrologic and water quality data.

View Documentation
gendercodeR

Recodes Sex/Gender Descriptions Into A Standard Set

Emily Kothe
Description

gendercodeR allows for simple recoding of freetext gender responses.

View Documentation
USAboundaries
CRAN

Historical and Contemporary Boundaries of the United States of America

Lincoln Mullen
Description

The boundaries for geographical units in the United States of America contained in this package include state, county, congressional district, and zip code tabulation area. Contemporary boundaries are provided by the U.S. Census Bureau (public domain). Historical boundaries for the years from 1629 to 2000 are provided form the Newberry Librarys Atlas of Historical County Boundaries’ (licensed CC BY-NC-SA). Additional data is provided in the USAboundariesData package; this package provides an interface to access that data.

View Documentation

checkers

Noam Ross
Description

Package to assess analysis + review guide for analysis best practice

View Documentation
datastorr

Simple Data Versioning

Rich FitzJohn
Description

Simple dataversioning using GitHub to store data.

Scientific use cases
  1. Falster, D. S., FitzJohn, R. G., Pennell, M. W., & Cornwell, W. K. (2019). Datastorr: a workflow and package for delivering successive versions of “evolving data” directly into R. GigaScience, 8(5). https://doi.org/10.1093/gigascience/giz035
View Documentation
geonames
CRAN

Interface to the "Geonames" Spatial Query Web Service

Barry Rowlingson
Description

The web service at https://www.geonames.org/ provides a number of spatial data queries, including administrative area hierarchies, city locations and some country postal code queries. A (free) username is required and rate limits exist.

Scientific use cases
  1. Harsch, M. A., & HilleRisLambers, J. (2016). Climate Warming and Seasonal Precipitation Change Interact to Limit Species Distribution Shifts across Western North America. PLOS ONE, 11(7), e0159184. https://doi.org/10.1371/journal.pone.0159184
  2. Ummel, K. (2012). CARMA revisited: an updated database of carbon dioxide emissions from power plants worldwide. Center for Global Development Working Paper, (304). http://www.cgdev.org/publication/carma-revisited-updated-database-carbon-dioxide-emissions-power-plants-worldwide-working
  3. Kolb, J.-P. (2016). Visualizing GeoData with R. Austrian Journal of Statistics, 45(1), 45. https://doi.org/10.17713/ajs.v45i1.88
  4. Kevin Ummel. 2012. “CARMA Revisited: An Updated Database of Carbon Dioxide Emissions from Power Plants Worldwide.” CGD Working Paper 304. Washington, D.C.: Center for Global Development. http://www.cgdev.org/content/publications/detail/1426429
  5. Holzmeyer, L., Hartig, A.-K., Franke, K., Brandt, W., Muellner-Riehl, A. N., Wessjohann, L. A., & Schnitzler, J. (2020). Evaluation of plant sources for antiinfective lead compound discovery by correlating phylogenetic, spatial, and bioactivity data. Proceedings of the National Academy of Sciences, 117(22), 12444–12451. https://doi.org/10.1073/pnas.1915277117
View Documentation

SPARQL DSL Client

Scott Chamberlain
Description

SPARQL DSL Client.

View Documentation
tl

Quick Ref. Guides For R Functions

Dan Wilson
Description

An R equivalent for the command line tool “tldr”, which provides quick guides to functions. Contributions from the community are welcome!

View Documentation
bindertools

Create requisite files and launch binder with mybinder.org

Saras Windecker
Description

Computational reproducibility is a critical component of modern open science. Methods such as docker exist to containerise analyses, ensuring that operating systems and package versions are recorded and can be recreated in order to rerun analyses. Setting up dockerfiles, however, is a nontrivial task on top of a growing technical barrier to reproducible research. Binder is a easy interface to produce a virtual machine within which to rerun analyses without requiring installation or understanding of underlying containerisation principles. It does however still require researchers to search through their code to find packages and version of packages used in the project. This package seeks to make the bridge to using binder for analyses in R even simpler, by setting up the install.R file with all packages and version (both on CRAN and github) in one step. The binder can also be launched right from R, without needing to manually input repository information into the mybinder.org interface.

View Documentation
ozbabynames

Australian Popular Baby Names

Rob Hyndman
Description

Data on the most popular baby names in Australia.

View Documentation
reviewer

Improving the Track Changes and Reviewing Experience in R Markdown

Amy Stringer
Description

Provides functionality to compare two versions of an rmarkdown document and display their differences in a nicely-formatted manner, along with an RStudio addin that adds the required JavaScript code to an rmarkdown document, so that when rendered to HTML it can be annotated using the Hypothes.is service.

View Documentation
rdopa

R client to Joint Research Centre's DOPA REST API

Joona Lehtomaki
Description

R client for REST web services of DOPA (Digital Observatory for protected Areas) by the European Union Joint Research Centre.

View Documentation
pkginspector

Package review tools

Sam Albers
Description

Provides tools to facilitate an R package review.

View Documentation
MonetDBLite

In-Process Version of MonetDB

Hannes Mühleisen
Description

An in-process version of MonetDB, a SQL database designed for analytical tasks. Similar to SQLite, the database runs entirely inside the R shell.

View Documentation
dirdf

Extracts Metadata from Directory and File Names

Henrik Bengtsson
Description

Extract metadata from directory and file names based on a template into data frame.

View Documentation
roomba

Tidy up nested list hairballs

Jim Hester
Description

This is a package to transform large, multi-nested lists into a more user-friendly format. The initial focus is on making processing of return values from jsonlite::fromJSON() queries more seamless, but ideally this package should be useful for deeply-nested lists from an array of sources.

View Documentation
middlechild

Tools to Intercept, Validate and Consume Web/Network Traffic

Ildiko Czeller
Description

The mitmproxy https://mitmproxy.org/ project provides tools to intercept, modify and/or introspect network traffic. Methods are provided to download, install, configure and launch mitmproxy plus introspect and validate network captures.

View Documentation
USAboundariesData

Datasets for the USAboundaries package

Lincoln Mullen
Description

Contains datasets, including higher resolution boundary data, for use in the USAboundaries package. These datasets come from the U.S. Census Bureau, the Newberry Librarys Historical Atlas of U.S. County Boundaries, and Erik Steiners ‘United States Historical City Populations, 1790-2010’.

View Documentation
testevil

Intuit Package Harm

Ildiko Czeller
Description

Intuit package harm.

View Documentation
decapitated

Headless Chrome Orchestration

Bob Rudis
Description

The Chrome browser https://www.google.com/chrome/ has a headless mode which can be instrumented programmatically. Tools are provided to perform headless Chrome instrumentation on the command-line and will eventually provide support for the DevTools instrumentation API or the forthcoming phantomjs-like higher-level API being promised by the development team.

View Documentation

Read and Write Data Packages

Jeroen Ooms
Description

Convenience functions for reading and writing datasets following the data packagist format.

View Documentation
arresteddev

Arrested Development

Lucy D'Agostino McGowan
Description

Here to help you when your development is, shall we say, arrested.

View Documentation
mchtoolbox

What the Package Does (Title Case)

Monica Gerber
Description

More about what it does (maybe more than one line) Use four spaces when indenting paragraphs within the Description.

View Documentation
jobstatus

Send Live Status, Progress and Other Information Between Functions and Processes

Nick Golding
Description

jobstatus lets you pass live progress, status, and other information between functions and processes in R, so that you can keep an eye on how complex and long-running jobs are progressing. jobstatus uses the future package so you can even get live progress information back from jobs running in parallel.

View Documentation
trackmd

RStudio Addin for Tracking Document Changes

Sam Tyner
Description

More about what it does (maybe more than one line) Use four spaces when indenting paragraphs within the Description.

View Documentation
keybase

Tools to Work with the Keybase API

Ildiko Czeller
Description

Keybase <keybase.io> is a directory of people and public keys and provides methods for obtaining public keys, validating users and exchanging files and/or messages in a secure fashion. Tools are provided to search for and retrieve information about Keybase users, retrieve and import user public keys and list and/or download files. There’s also a thin but useful R wrapper around many of they keybase command-line utility functions.

View Documentation
datasauce

Create and manipulate Schema.org Dataset metadata

Carl Boettiger
Description

What the package does (one paragraph).

View Documentation
IEEER

Interface to the IEEE Xplore Gateway

Saul Wiggin
Description

An interface to the IEEE Xplore Gateway, for searching IEEE publications.

View Documentation

R Bindings to rlite

Rich FitzJohn
Description

R bindings to rlite. rlite is a “self-contained, serverless, zero-configuration, transactional redis-compatible database engine. rlite is to Redis what SQLite is to SQL.”.

View Documentation
changes

A simple interface and workflow for version control in R (implemented using git as backend but without the confusion)

Nick Golding
Description

This package provides a set of easy-to-use tools for beginners wanting to implement version control via git for their projects.

View Documentation
ochRe

Australia-Themed Color Palettes

Holly Kirk
Description

Provide Australia-themed color palettes.

View Documentation
notary

Signing and Verification of R Packages

Bob Rudis
Description

Signing and verification of R packages.

View Documentation
testrmd

Testing for R Markdown Chunks

Robert M Flight
Description

Provides facilities for adding test chunks to RMarkdown documents, as well as CSS and javascript for nice styling of the output. This enables testing of data without completely stopping the knitting of a document, while seeing possible problems in the final HTML output.

View Documentation
ponyexpress
Staff maintained

Automate sending email with Gmail

Karthik Ram
Description

What the package does (one paragraph).

View Documentation
convertr
CRAN

Convert Between Units

Gordon Shotwell
Description

Provides conversion functionality between a broad range of scientific, historical, and industrial unit types.

View Documentation

Client for the Index Database of Remote Sensing Indices

Scott Chamberlain
Description

Index Database (http://www.indexdatabase.de/) of remote sensing indices.

View Documentation
snowball

Spin up a managed cluster and perform parallel calculations

#auunconf hackathoners
Description

Spin up a head node, which spins up worker nodes, and performs parallel calculations

View Documentation
colorpiler

Provides community-driven color palettes

Mika Braginsky
Description

Provides community-driven color palettes.

View Documentation
riodata

Get data related to transportation and cultural places from Rio de Janeiro, Brazil.

Gabriela de Queiroz
Description

Get data related to transportation and cultural places from Rio de Janeiro, Brazil.

View Documentation