rOpenSci Packages

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.

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
2. Ashby, M. P. J. (2020). The Open-Access Availability of Criminological Research to Practitioners and Policy Makers. Journal of Criminal Justice Education, 1–21. https://doi.org/10.1080/10511253.2020.1838588
3. Robinson-Garcia, N., van Leeuwen, T. N., & Torres-Salinas, D. (2020). Measuring Open Access Uptake: Data Sources, Expectations, and Misconceptions. Scholarly Assessment Reports, 2(1). https://doi.org/10.29024/sar.23
4. Clayson, P. E., Baldwin, S., & Larson, M. J. (2020). The Open Access Advantage for Studies of Human Electrophysiology: Impact on Citations and Altmetrics. https://doi.org/10.31234/osf.io/5xagd

Simplifies the creation of reproducible data science environments using the Nix package manager, as described in Dolstra (2006) <ISBN 90-393-4130-3>. The included rix() function generates a complete description of the environment as a default.nix file, which can then be built using Nix. This results in project specific software environments with pinned versions of R, packages, linked system dependencies, and other tools. Additional helpers make it easy to run R code in Nix software environments for testing and production.

Function-oriented Make-like declarative pipelines for Statistics and data science are supported in the targets R package. As an extension to targets, the tarchetypes package provides convenient user-side functions to make targets easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the drake R package by Will Landau (2018) doi:10.21105/joss.00550.

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

1. Çetinkaya-Rundel, M., & Ellison, V. (2020). A Fresh Look at Introductory Data Science. Journal of Statistics Education, 1–11. https://doi.org/10.1080/10691898.2020.1804497

A set of tools to extract bibliographic content from OpenAlex database using API https://docs.openalex.org.

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.

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
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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
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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
131. Schertler, A., Rabitsch, W., Moser, D., Wessely, J., & Essl, F. (2020). The potential current distribution of the coypu (Myocastor coypus) in Europe and climate change induced shifts in the near future. NeoBiota, 58, 129–160. https://doi.org/10.3897/neobiota.58.33118
132. Weyna, A., & Romiguier, J. (2020). Relaxation of purifying selection suggests low effective population size in eusocial Hymenoptera and solitary pollinating bees. https://doi.org/10.1101/2020.04.14.038893
133. Sanchez‐Martinez, P., Martínez‐Vilalta, J., Dexter, K. G., Segovia, R. A., & Mencuccini, M. (2020). Adaptation and coordinated evolution of plant hydraulic traits. Ecology Letters. https://doi.org/10.1111/ele.13584
134. Koch, L. K., Cunze, S., Kochmann, J., & Klimpel, S. (2020). Bats as putative Zaire ebolavirus reservoir hosts and their habitat suitability in Africa. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-71226-0
135. De Sousa, K., & Solberg, S. Ø. (2020). Conservation Gaps in Traditional Vegetables Native to Europe and Fennoscandia. Agriculture, 10(8), 340. https://doi.org/10.3390/agriculture10080340
136. Polaina, E., Pärt, T., & Recio, M. R. (2020). Identifying hotspots of invasive alien terrestrial vertebrates in Europe to assist transboundary prevention and control. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-68387-3
137. Falconi, N., Fuller, T. K., DeStefano, S., & Organ, J. F. (2020). An open-access occurrence database for Andean bears in Peru. Ursus, 2020(31e11), 1. https://doi.org/10.2192/ursus-d-19-00012.1
138. Brightly, W. H., Hartley, S. E., Osborne, C. P., Simpson, K. J., & Strömberg, C. A. E. (2020). High silicon concentrations in grasses are linked to environmental conditions and not associated with C4 photosynthesis. Global Change Biology. https://doi.org/10.1111/gcb.15343
139. Dianzinga, N. T., Moutoussamy, M., Sadeyen, J., Ravaomanarivo, L. H. R., & Frago, E. (2020). The interacting effect of habitat amount, habitat diversity and fragmentation on insect diversity along elevational gradients. Journal of Biogeography, 47(11), 2377–2391. https://doi.org/10.1111/jbi.13959
140. Grattarola, F., González, A., Mai, P., Cappuccio, L., Fagúndez-Pachón, C., Rossi, F., … Pincheira-Donoso, D. (2020). Biodiversidata: A novel dataset for the vascular plant species diversity in Uruguay. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e56850
141. Romain Courault, Marianne Cohen, Mathilde Pottier. Disentangling the influence of geographical laws and sampling biais to model distribution of Birch tree from Open-acess Biodiversity Dataset (OBDs) in Swedish Lapland. 2020. ird-02973852 https://hal.ird.fr/ird-02973852/file/151020_Courault_Cohen_et_al_AGIS_SI_GPAF.pdf
142. Higgins, S. I., Larcombe, M. J., Beeton, N. J., Conradi, T., & Nottebrock, H. (2020). Predictive ability of a process‐based versus a correlative species distribution model. Ecology and Evolution, 10(20), 11043–11054. https://doi.org/10.1002/ece3.6712
143. Nelsen, M. P., & Lumbsch, H. T. (2020). A data-driven evaluation of lichen climate change indicators in Central Europe. Biodiversity and Conservation, 29(14), 3959–3971. https://doi.org/10.1007/s10531-020-02057-8
144. Alroy, J. (2020). A simple graph theoretic method provides accurate range area estimates. Authorea Preprints. https://d197for5662m48.cloudfront.net/documents/publicationstatus/49087/preprint_pdf/f0cf787b22dacb984cfd2771d532870b.pdf
145. Gadelha, L. M. R., Siracusa, P. C., Dalcin, E. C., Silva, L. A. E., Augusto, D. A., Krempser, E., … Siqueira, M. F. (2020). A survey of biodiversity informatics: Concepts, practices, and challenges. WIREs Data Mining and Knowledge Discovery, 11(1). https://doi.org/10.1002/widm.1394
146. Brandt, A. J., Bellingham, P. J., Duncan, R. P., Etherington, T. R., Fridley, J. D., Howell, C. J., … Peltzer, D. A. (2020). Naturalised plants transform the composition and function of the New Zealand flora. Biological Invasions. https://doi.org/10.1007/s10530-020-02393-4
147. Alò, D., Lacy, S. N., Castillo, A., Samaniego, H. A., & Marquet, P. A. (2020). The macroecology of fish migration. Global Ecology and Biogeography, 30(1), 99–116. https://doi.org/10.1111/geb.13199
148. Simpson, K. J., Jardine, E. C., Archibald, S., Forrestel, E. J., Lehmann, C. E. R., Thomas, G. H., & Osborne, C. P. (2020). Resprouting grasses are associated with less frequent fire than seeders. New Phytologist. https://doi.org/10.1111/nph.17069
149. Basooma, A., Nakiyende, H., Olokotum, M., Nkalubo, W., Musinguzi, L., & Natugonza, V. (2020). Using the novel priority index in prioritizing the selection of inland water bodies for site-based fish species conservation. bioRxiv. https://www.biorxiv.org/content/biorxiv/early/2020/11/14/2020.11.12.380618.full.pdf
150. Bombi, P. (2020). Potential conflict extent between two invasive alien pests, Rhynchophorus ferrugineus and Paysandisia archon, and the native populations of the Mediterranean fan palm. Journal for Nature Conservation, 58, 125927. https://doi.org/10.1016/j.jnc.2020.125927
151. Capurucho, J. M. G., Ashley, M. V., Tsuru, B. R., Cooper, J. C., & Bates, J. M. (2020). Dispersal ability correlates with range size in Amazonian habitat-restricted birds. Proceedings of the Royal Society B: Biological Sciences, 287(1939), 20201450. https://doi.org/10.1098/rspb.2020.1450
152. Panter, C. T., Clegg, R. L., Moat, J., Bachman, S. P., Klitgård, B. B., & White, R. L. (2020). To clean or not to clean: Cleaning open‐source data improves extinction risk assessments for threatened plant species. Conservation Science and Practice, 2(12). https://doi.org/10.1111/csp2.311
153. Pons, J., Campión, D., Chiozzi, G., Ettwein, A., Grangé, J., Kajtoch, Ł., … Fuchs, J. (2020). Phylogeography of a widespread Palaearctic forest bird species: The White‐backed Woodpecker (Aves, Picidae). Zoologica Scripta, 50(2), 155–172. https://doi.org/10.1111/zsc.12466
154. Ludt, W. B., & Myers, C. E. (2020). Distinguishing between dispersal and vicariance: A novel approach using anti‐tropical taxa across the fish Tree of Life. Journal of Biogeography, 48(3), 577–589. https://doi.org/10.1111/jbi.14021
155. Mayani-Parás, F., Botello, F., Castañeda, S., Munguía-Carrara, M., & Sánchez-Cordero, V. (2021). Cumulative habitat loss increases conservation threats on endemic species of terrestrial vertebrates in Mexico. Biological Conservation, 253, 108864. https://doi.org/10.1016/j.biocon.2020.108864
156. Zonneveld, M., Kindt, R., Solberg, S. Ø., N’Danikou, S., & Dawson, I. K. (2020). Diversity and conservation of traditional African vegetables: Priorities for action. Diversity and Distributions, 27(2), 216–232. https://doi.org/10.1111/ddi.13188
157. Santos, G. N. dos, Silva, A. C. da, & Higuchi, P. (2020). IMPACT OF CLIMATE CHANGE ON THE GEOGRAPHICAL DISTRIBUTION OF A CLOUD FOREST INDICATOR TREE SPECIES. Revista Árvore, 44. https://doi.org/10.1590/1806-908820200000032
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160. Freimuth, J., Bossdorf, O., Scheepens, J. F., & Willems, F. M. (2021). Climate warming changes synchrony of plants and pollinators. https://doi.org/10.1101/2021.01.10.425984
161. Ascensão, F., D’Amico, M., Martins, R. C., Rebelo, R., Barbosa, A. M., Bencatel, J., … Capinha, C. (2021). Distribution of alien tetrapods in the Iberian Peninsula. NeoBiota, 64, 1–21. https://doi.org/10.3897/neobiota.64.55597
162. Schwery, O., & O’Meara, B. C. (2021). Age, Origin, and Biogeography: Unveiling the Factors Behind the Diversification of Dung Beetles. doi:10.1101/2021.01.26.428346

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

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
7. Baddeley, A., Nair, G., Rakshit, S., McSwiggan, G., & Davies, T. M. (2020). Analysing point patterns on networks — A review. Spatial Statistics, 100435. https://doi.org/10.1016/j.spasta.2020.100435
8. Cervigni, E., Renton, M., Haslam McKenzie, F., Hickling, S., & Olaru, D. (2020). Describing and mapping diversity and accessibility of the urban food environment with open data and tools. Applied Geography, 125, 102352. doi:10.1016/j.apgeog.2020.102352
9. Padgham, M., Lovelace, R., Salmon, M., & Rudis, B. (2017). osmdata. The Journal of Open Source Software, 2(14), 305. https://doi.org/10.21105/joss.00305
10. Moradi, M. (2020). Evaluating the quality of OSM roads and buildings in the Québec Province. https://corpus.ulaval.ca/jspui/bitstream/20.500.11794/67232/1/36576.pdf
11. Wilkins, E.J. 2020. Using Social Media to Assess the Impact of Weather and Climate on Visitation to Outdoor Recreation Settings. https://digitalcommons.usu.edu/etd/7986

Companion package to rOpenSci statistical software review project.

Pipeline tools coordinate the pieces of computationally demanding analysis projects. The targets package is a Make-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU Make (2015, ISBN:978-9881443519) and drake (2018, doi:10.21105/joss.00550).

Simple git client for R based on libgit2 https://libgit2.org 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.

Read and write Frictionless Data Packages. A Data Package (https://specs.frictionlessdata.io/data-package/) is a simple container format and standard to describe and package a collection of (tabular) data. It is typically used to publish FAIR (https://www.go-fair.org/fair-principles/) and open datasets.

Interface with and extract data from the United Nations Comtrade API https://comtradeplus.un.org/. 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.

1. Chini, C. M., & Peer, R. A. M. (2021). The traded water footprint of global energy from 2010 to 2018. Scientific Data, 8(1). https://doi.org/10.1038/s41597-020-00795-6

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

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.

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

Detects spatial and temporal groups in GPS relocations (Robitaille et al. (2019) 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.

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
5. Robitaille, A. L., Webber, Q. M. R., Turner, J. W., & Wal Eric, V. (2020). The problem and promise of scale in multilayer animal social networks. Current Zoology. https://doi.org/10.1093/cz/zoaa052

Automate rendering and cross-linking of Quarto books following a prescribed structure.

Utilities based on libpoppler https://poppler.freedesktop.org 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.

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
10. Berkel, C., & Cacan, E. (2020). GAB2 and GAB3 are expressed in a tumor stage-, grade- and histotype-dependent manner and are associated with shorter progression-free survival in ovarian cancer. Journal of Cell Communication and Signaling. https://doi.org/10.1007/s12079-020-00582-3
11. Scott, T. A., Ulibarri, N., & Perez Figueroa, O. (2020). NEPA and National Trends in Federal Infrastructure Siting in the United States. Review of Policy Research. https://doi.org/10.1111/ropr.12399
12. Roa-Ureta, R. H., Henríquez, J., & Molinet, C. (2020). Achieving sustainable exploitation through co-management in three Chilean small-scale fisheries. Fisheries Research, 230, 105674. https://doi.org/10.1016/j.fishres.2020.105674
13. Westgate, M. J., Barton, P. S., Lindenmayer, D. B., & Andrew, N. R. (2020). Quantifying shifts in topic popularity over 44 years of Austral Ecology. Austral Ecology, 45(6), 663–671. https://doi.org/10.1111/aec.12938
14. Marshall, B. M., Strine, C., & Hughes, A. C. (2020). Thousands of reptile species threatened by under-regulated global trade. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-18523-4
15. Li, B., Trueman, B. F., Rahman, M. S., & Gagnon, G. A. (2021). Controlling lead release due to uniform and galvanic corrosion — An evaluation of silicate-based inhibitors. Journal of Hazardous Materials, 407, 124707. https://doi.org/10.1016/j.jhazmat.2020.124707
16. Hines, R. E., Grandage, A. J., & Willoughby, K. G. (2020). Staying Afloat: Planning and Managing Climate Change and Sea Level Rise Risk in Florida’s Coastal Counties. Urban Affairs Review, 107808742098052. https://doi.org/10.1177/1078087420980526

Bindings for the Tabula https://tabula.technology/ Java library, which can extract tables from PDF files. This tool can reduce time and effort in data extraction processes in fields like investigative journalism. It allows for automatic and manual table extraction, the latter facilitated through a Shiny interface, enabling manual areas selection\ with a computer mouse for data retrieval.

Renders vector-based svg images into high-quality custom-size bitmap arrays using librsvg2. The resulting bitmap can be written to e.g. png, jpeg or webp format. In addition, the package can convert images directly to various formats such as pdf or postscript.

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.

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
14. Berkel, C., & Cacan, E. (2020). GAB2 and GAB3 are expressed in a tumor stage-, grade- and histotype-dependent manner and are associated with shorter progression-free survival in ovarian cancer. Journal of Cell Communication and Signaling. https://doi.org/10.1007/s12079-020-00582-3
15. Sodhi, K., Wang, X., Chaudhry, M. A., Lakhani, H. V., Zehra, M., Pratt, R., … Shapiro, J. I. (2020). Central Role for Adipocyte Na,K-ATPase Oxidant Amplification Loop in the Pathogenesis of Experimental Uremic Cardiomyopathy. Journal of the American Society of Nephrology, 31(8), 1746–1760. https://doi.org/10.1681/asn.2019101070
16. Pregla, D., Lissón, P., Vasishth, S., Burchert, F., & Stadie, N. (2020, September 18). Variability in sentence comprehension in aphasia in German. https://doi.org/10.31234/osf.io/7hfpx
17. Ostrop, J., Zwiggelaar, R., Pedersen, M. T., Gerbe, F., Bösl, K., Lindholm, H. T., … Oudhoff, M. J. (2020). A semi-automated organoid screening method demonstrates epigenetic control of intestinal epithelial differentiation. https://doi.org/10.1101/2020.07.23.217414
18. Ingenloff, K., & Peterson, A. T. (2020). Incorporating time into the traditional correlational distributional modelling framework: A proof‐of‐concept using the Wood Thrush Hylocichla mustelina. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13523
19. Liang, X., Hu, Y., Yan, C., & Xu, K. (2020). i2d: an R package for simulating data from images and the implications in biomedical research. Bioinformatics. https://doi.org/10.1093/bioinformatics/btaa991
20. Wang, J., Wang, X., Gao, Y., Lin, Z., Chen, J., Gigantelli, J., … Pierre, S. V. (2020). Stress Signal Regulation by Na/K-ATPase As a New Approach to Promote Physiological Revascularization in a Mouse Model of Ischemic Retinopathy. Investigative Opthalmology & Visual Science, 61(14), 9. https://doi.org/10.1167/iovs.61.14.9
21. Loser, D., Schaefer, J., Danker, T., Möller, C., Brüll, M., Suciu, I., … Kraushaar, U. (2020). Human neuronal signaling and communication assays to assess functional neurotoxicity. Archives of Toxicology, 95(1), 229–252. https://doi.org/10.1007/s00204-020-02956-3
22. Ball, J. (2020). Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment Analysis. arXiv preprint arXiv:2012.03170. https://arxiv.org/pdf/2012.03170
23. Hillary, R. F., & Marioni, R. E. (2020). MethylDetectR: a software for methylation-based health profiling. Wellcome Open Research, 5, 283. https://doi.org/10.12688/wellcomeopenres.16458.1
24. Sellés Vidal, L., Ayala, R., Stan, G.-B., & Ledesma-Amaro, R. (2021). rfaRm: An R client-side interface to facilitate the analysis of the Rfam database of RNA families. PLOS ONE, 16(1), e0245280. doi:10.1371/journal.pone.0245280
25. Mann, D. C., Fitch, W. T., Tu, H.-W., & Hoeschele, M. (2021). Universal principles underlying segmental structures in parrot song and human speech. Scientific Reports, 11(1). doi:10.1038/s41598-020-80340-y

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.

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

Provee un acceso conveniente a mas de 17 millones de registros de la base de datos del Censo 2017. Los datos fueron importados desde el DVD oficial del INE usando el Convertidor REDATAM creado por Pablo De Grande. Esta paquete esta documentado intencionalmente en castellano asciificado para que funcione sin problema en diferentes plataformas. (Provides convenient access to more than 17 million records from the Chilean Census 2017 database. The datasets were imported from the official DVD provided by the Chilean National Bureau of Statistics by using the REDATAM converter created by Pablo De Grande and in addition it includes the maps accompanying these datasets.)

Match, download, convert and import Open Street Map data extracts obtained from several providers.

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 on GitHub, and used to share data for manuscripts, collaboration and reproducible research.

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

Plumber API to report package structure and function.

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.

Provides automated downloading, parsing and formatting of weather data for Australia through API endpoints provided by the Department of Primary Industries and Regional Development (DPIRD) of Western Australia and by the Science and Technology Division of the Queensland Governments Department of Environment and Science (DES). As well as the Bureau of Meteorology (BOM) of the Australian government precis and coastal forecasts, agriculture bulletin data, and downloading and importing radar and satellite imagery files. DPIRD weather data are accessed through public APIs provided by DPIRD, https://www.agric.wa.gov.au/weather-api-20, providing access to weather station data from the DPIRD weather station network. Australia-wide weather data are based on data from the Australian Bureau of Meteorology (BOM) data and accessed through SILO (Scientific Information for Land Owners) Jeffrey et al. (2001) doi:10.1016/S1364-8152(01)00008-1. DPIRD data are made available under a Creative Commons Attribution 3.0 Licence (CC BY 3.0 AU) license https://creativecommons.org/licenses/by/3.0/au/deed.en. SILO data are released under a Creative Commons Attribution 4.0 International licence (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/. BOM data are (c) Australian Government Bureau of Meteorology and released under a 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.

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.

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
2. Halimubieke, N., Kupán, K., Valdebenito, J. O., Kubelka, V., Carmona-Isunza, M. C., Burgas, D., … Székely, T. (2020). Successful breeding predicts divorce in plovers. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-72521-6

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?Z1qr_VQ=E&Z1qr_Qr5p=N8vn6v10&f7owrp6_VQF=D.

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
2. Teixeira, F. M., & Derudder, B. (2021). Spatio-temporal dynamics in airport catchment areas: The case of the New York Multi Airport Region. Journal of Transport Geography, 90, 102916. https://doi.org/10.1016/j.jtrangeo.2020.102916

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'.

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.

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.

There are many different formats dates are commonly represented with: the order of day, month, or year can differ, different separators ("-", “/”, or whitespace) can be used, months can be numerical, names, or abbreviations and year given as two digits or four. datefixR takes dates in all these different formats and converts them to Rs built-in date class. If datefixR cannot standardize a date, such as because it is too malformed, then the user is told which date cannot be standardized and the corresponding ID for the row. datefixR’ also allows the imputation of missing days and months with user-controlled behavior.

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.

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.

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

Bindings to Googles 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.

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

Convert latex math expressions to HTML and MathML for use in markdown documents or package manual pages. The rendering is done in R using the V8 engine (i.e. server-side), which eliminates the need for embedding the MathJax library into your web pages. In addition a math-to-rd wrapper is provided to automatically render beautiful math in R documentation files.

Utilities to interact with the R-universe platform. Includes functions to manage local package repositories, as well as API wrappers for retrieving data and metadata about packages in r-universe.

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.

Googles 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.

Wraps the unrtf utility https://www.gnu.org/software/unrtf/ 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.

The CommonMark specification defines a rationalized version of markdown syntax. This package uses the cmark reference implementation for converting markdown text into various formats including html, latex and groff man. In addition it exposes the markdown parse tree in xml format. Also includes opt-in support for GFM extensions including tables, autolinks, and strikethrough text.

Zero-dependency data frame to xlsx exporter based on libxlsxwriter https://libxlsxwriter.github.io. Fast and no Java or Excel required.

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
7. Wiley, M., & Wiley, J. F. (2020). Data Input and Output. Beginning R 4, 33–46. https://doi.org/10.1007/978-1-4842-6053-1_3
8. Yan, T., Wang, Q., Maodzeka, A., Wu, D., & Jiang, L. (2020). BnaSNPDB: An interactive web portal for the efficient retrieval and analysis of SNPs among 1,007 rapeseed accessions. Computational and Structural Biotechnology Journal, 18, 2766–2773. https://doi.org/10.1016/j.csbj.2020.09.031
9. Munzert, S., & Ramirez-Ruiz, S. (2020, October 10). Meta-Analysis of the Effects of Voting Advice Applications. https://doi.org/10.31219/osf.io/utdn4

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.

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

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.

Exposes some of the available OpenCV https://opencv.org/ algorithms, such as a QR code scanner, and edge, body or face detection. These can either be applied to analyze static images, or to filter live video footage from a camera device.

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

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.

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

Bindings to libsodium https://doc.libsodium.org/: a modern, easy-to-use software library for encryption, decryption, signatures, password hashing and more. Sodium uses curve25519, a state-of-the-art Diffie-Hellman function by Daniel Bernstein, which has become very popular after it was discovered that the NSA had backdoored Dual EC DRBG.

Generates simple and beautiful one-page HTML reference manuals with package documentation. Math rendering and syntax highlighting are done server-side in R such that no JavaScript libraries are needed in the browser, which makes the documentation portable and fast to load.

Prism https://prismjs.com/ is a lightweight, extensible syntax highlighter, built with modern web standards in mind. This package provides server-side rendering in R using V8 such that no JavaScript library is required in the resulting HTML documents. Over 400 languages are supported.

Bindings to 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.

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
4. Candarli, D. (2020). A longitudinal study of multi-word constructions in L2 academic writing: the effects of frequency and dispersion. Reading and Writing. https://doi.org/10.1007/s11145-020-10108-3
5. Hines, R. E., Grandage, A. J., & Willoughby, K. G. (2020). Staying Afloat: Planning and Managing Climate Change and Sea Level Rise Risk in Florida’s Coastal Counties. Urban Affairs Review, 107808742098052. https://doi.org/10.1177/1078087420980526

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.

Client for jq, a JSON processor (https://jqlang.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.

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.

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
24. Garvey, M. D., Samuel, J., & Pelaez, A. (2021). Would you please like my tweet?! An artificially intelligent, generative probabilistic, and econometric based system design for popularity-driven tweet content generation. Decision Support Systems, 144, 113497. doi:10.1016/j.dss.2021.113497

Bindings to libfluidsynth to parse and synthesize MIDI files. It can read MIDI into a data frame, play it on the local audio device, or convert into an audio file.

An API client for NASA POWER global meteorology, surface solar energy and climatology data API. POWER (Prediction Of Worldwide Energy Resources) data are freely available for download with varying spatial resolutions dependent on the original data and with several temporal resolutions depending on the POWER parameter and community. This work is funded through the NASA Earth Science Directorate Applied Science Program. For more on the data themselves, the methodologies used in creating, a web- based data viewer and web access, please see https://power.larc.nasa.gov/.

1. Charalampopoulos, I. (2020). The R Language as a Tool for Biometeorological Research. Atmosphere, 11(7), 682. https://doi.org/10.3390/atmos11070682
2. Costa-Neto, G., Fritsche-Neto, R., & Crossa, J. (2020). Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity. https://doi.org/10.1038/s41437-020-00353-1

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the jagstargets R package is leverages targets and R2jags to ease this burden. jagstargets makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than targets alone. For the underlying methodology, please refer to the documentation of targets doi:10.21105/joss.02959 and JAGS (Plummer 2003) https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf.

The git2rdata package is an R package for writing and reading dataframes as plain text files. A metadata file stores important information. 1) Storing metadata allows to maintain the classes of variables. By default, git2rdata optimizes the data for file storage. The optimization is most effective on data containing factors. The optimization makes the data less human readable. The user can turn this off when they prefer a human readable format over smaller files. Details on the implementation are available in vignette(“plain_text”, package = “git2rdata”). 2) Storing metadata also allows smaller row based diffs between two consecutive commits. This is a useful feature when storing data as plain text files under version control. Details on this part of the implementation are available in vignette(“version_control”, package = “git2rdata”). Although we envisioned git2rdata with a git workflow in mind, you can use it in combination with other version control systems like subversion or mercurial. 3) git2rdata is a useful tool in a reproducible and traceable workflow. vignette(“workflow”, package = “git2rdata”) gives a toy example. 4) vignette(“efficiency”, package = “git2rdata”) provides some insight into the efficiency of file storage, git repository size and speed for writing and reading.

Interface to OpenStreetMap API for fetching and saving data from/to the OpenStreetMap database (https://wiki.openstreetmap.org/wiki/API_v0.6).

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 work with BEAST2 packages from R.

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.

A universal client for depositing and accessing research data anywhere. Currently supported services are zenodo and figshare.

Facilitates the gathering of biodiversity occurrence data from disparate sources. Metadata is managed throughout the process to facilitate reporting and enhanced ability to repeat analyses.

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.

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
5. Kopp, T., & Salecker, J. (2020). How traders influence their neighbours: Modelling social evolutionary processes and peer effects in agricultural trade networks. Journal of Economic Dynamics and Control, 117, 103944. https://doi.org/10.1016/j.jedc.2020.103944
6. Azizi, A., Mubayi, A., & Mubayi, A. (2020). The Impact of Individual’s Ecological Factors on the Dynamics of Alcohol Drinking among Arizona State University Students: An Application of the Survey Data-driven Agent-based Model. arXiv preprint arXiv:2011.01876 https://arxiv.org/abs/2011.01876.
7. Widyastuti, K., Imron, M. A., Pradopo, S. T., Suryatmojo, H., Sopha, B. M., Spessa, A., & Berger, U. (2020). PeatFire: an agent-based model to simulate fire ignition and spreading in a tropical peatland ecosystem. International Journal of Wildland Fire. https://doi.org/10.1071/wf19213
8. Dahirel, M., Bertin, A., Haond, M., Blin, A., Lombaert, E., Calcagno, V., … Vercken, E. (2020). Shifts from pulled to pushed range expansions caused by reduction of landscape connectivity. https://doi.org/10.1101/2020.05.13.092775
9. Ghoreishi, M., Razavi, S., & Elshorbagy, A. (2021). Understanding human adaptation to drought: agent-based agricultural water demand modeling in the Bow River Basin, Canada. Hydrological Sciences Journal, 66(3), 389–407. doi:10.1080/02626667.2021.1873344

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.

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
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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
145. Carraro, L., Mächler, E., Wüthrich, R., & Altermatt, F. (2020). Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17337-8
146. Grattarola, F., & Rodríguez-Tricot, L. (2020). Mammals of Paso Centurión, an area with relicts of Atlantic Forest in Uruguay. Neotropical Biology and Conservation, 15(3), 267–283. https://doi.org/10.3897/neotropical.15.e53062
147. Calderón Franco, D. (2020). Hi-C pipeline development for linking resistome and mobilome to the microbiome of wastewater samples http://openaccess.uoc.edu/webapps/o2/bitstream/10609/120166/1/MEP_BioinformaticsBiostatistics_DavidCalderonFranco_UB_UOC_good.pdf
148. Walton, S., Livermore, L., Bánki, O., Cubey, R., Drinkwater, R., Englund, M., … Wu, Z. (2020). Landscape Analysis for the Specimen Data Refinery. Research Ideas and Outcomes, 6. https://doi.org/10.3897/rio.6.e57602
149. Ossola, A., Hoeppner, M. J., Burley, H. M., Gallagher, R. V., Beaumont, L. J., & Leishman, M. R. (2020). The Global Urban Tree Inventory: A database of the diverse tree flora that inhabits the world’s cities. Global Ecology and Biogeography, 29(11), 1907–1914. https://doi.org/10.1111/geb.13169
150. Atwood, T. B., Valentine, S. A., Hammill, E., McCauley, D. J., Madin, E. M. P., Beard, K. H., & Pearse, W. D. (2020). Herbivores at the highest risk of extinction among mammals, birds, and reptiles. Science Advances, 6(32), eabb8458. https://doi.org/10.1126/sciadv.abb8458
151. Etard, A., Morrill, S., & Newbold, T. (2020). Global gaps in trait data for terrestrial vertebrates. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13184
152. Oegelund Nielsen, R., da Silva, R., Juergens, J., Staerk, J., Lindholm Sørensen, L., Jackson, J., … Conde, D. A. (2020). Standardized data to support conservation prioritization for sharks and batoids (Elasmobranchii). Data in Brief, 33, 106337. https://doi.org/10.1016/j.dib.2020.106337
153. Grattarola, F., González, A., Mai, P., Cappuccio, L., Fagúndez-Pachón, C., Rossi, F., … Pincheira-Donoso, D. (2020). Biodiversidata: A novel dataset for the vascular plant species diversity in Uruguay. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e56850
154. Fredston, A., Pinsky, M., Selden, R., Szuwalski, C., Thorson, J., Halpern, B., & Gaines, S. (2020). Range edges of North American marine species are tracking temperature over decades. Authorea Preprints. https://www.authorea.com/doi/pdf/10.22541/au.160331933.33155622
155. Benesh, D. P., Parker, G., Chubb, J. C., & Lafferty, K. D. (2020). Tradeoffs with growth limit host range in complex life cycle helminths. The American Naturalist. https://doi.org/10.1086/712249
156. Loiseau, N., Mouquet, N., Casajus, N., Grenié, M., Guéguen, M., Maitner, B., … Violle, C. (2020). Global distribution and conservation status of ecologically rare mammal and bird species. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-18779-w
157. Esenkulova, S., Sutherland, B. J. G., Tabata, A., Haigh, N., Pearce, C. M., & Miller, K. M. (2020). Comparing metabarcoding and morphological approaches to identify phytoplankton taxa associated with harmful algal blooms. FACETS, 5(1), 784–811. https://doi.org/10.1139/facets-2020-0025
158. Guerrero‐Ramírez, N. R., Mommer, L., Freschet, G. T., Iversen, C. M., McCormack, M. L., Kattge, J., … Weigelt, A. (2020). Global root traits (GRooT) database. Global Ecology and Biogeography, 30(1), 25–37. https://doi.org/10.1111/geb.13179
159. Niittynen, P., Heikkinen, R. K., Aalto, J., Guisan, A., Kemppinen, J., & Luoto, M. (2020). Fine-scale tundra vegetation patterns are strongly related to winter thermal conditions. Nature Climate Change, 10(12), 1143–1148. https://doi.org/10.1038/s41558-020-00916-4
160. Sigsgaard, E. E., Olsen, K., Hansen, M. D. D., Hansen, O. L. P., Høye, T. T., Svenning, J., & Thomsen, P. F. (2020). Environmental DNA metabarcoding of cow dung reveals taxonomic and functional diversity of invertebrate assemblages. Molecular Ecology. https://doi.org/10.1111/mec.15734
161. Freiberg, M., Winter, M., Gentile, A., Zizka, A., Muellner-Riehl, A. N., Weigelt, A., & Wirth, C. (2020). LCVP, The Leipzig catalogue of vascular plants, a new taxonomic reference list for all known vascular plants. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-00702-z
162. Meireles, J. E., Cavender‐Bares, J., Townsend, P. A., Ustin, S., Gamon, J. A., Schweiger, A. K., … O’Meara, B. C. (2020). Leaf reflectance spectra capture the evolutionary history of seed plants. New Phytologist, 228(2), 485–493. https://doi.org/10.1111/nph.16771
163. Janssen, J., & Gomez, L. (2021). An examination of the import of live reptiles from Indonesia by the United States from 2000 to 2015. Journal for Nature Conservation, 59, 125949. https://doi.org/10.1016/j.jnc.2020.125949
164. Tamme, R., Pärtel, M., Kõljalg, U., Laanisto, L., Liira, J., Mander, Ü., … Zobel, M. (2020). Global macroecology of nitrogen‐fixing plants. Global Ecology and Biogeography, 30(2), 514–526. https://doi.org/10.1111/geb.13236
165. Albery, G. F., Carlson, C. J., Cohen, L. E., Eskew, E. A., Gibb, R., Ryan, S. J., … Becker, D. J. (2021). Urban-adapted mammal species have more known pathogens. https://doi.org/10.1101/2021.01.02.425084
166. Freimuth, J., Bossdorf, O., Scheepens, J. F., & Willems, F. M. (2021). Climate warming changes synchrony of plants and pollinators. https://doi.org/10.1101/2021.01.10.425984
167. Vizentin-Bugoni, J., Sperry, J. H., Kelley, J. P., Gleditsch, J. M., Foster, J. T., Drake, D. R., … Tarwater, C. E. (2021). Ecological correlates of species’ roles in highly invaded seed dispersal networks. Proceedings of the National Academy of Sciences, 118(4), e2009532118. https://doi.org/10.1073/pnas.2009532118
168. Durso, A. M., Bolon, I., Kleinhesselink, A. R., Mondardini, M. R., Fernandez-Marquez, J. L., Gutsche-Jones, F., … Ruiz de Castañeda, R. (2021). Crowdsourcing snake identification with online communities of professional herpetologists and avocational snake enthusiasts. Royal Society Open Science, 8(1), 201273. https://doi.org/10.1098/rsos.201273
169. Gibb, R., Albery, G. F., Becker, D. J., Brierley, L., Connor, R., Dallas, T. A., … Poisot, T. (2021). Data proliferation, reconciliation, and synthesis in viral ecology. doi:10.1101/2021.01.14.426572
170. Schwery, O., & O’Meara, B. C. (2021). Age, Origin, and Biogeography: Unveiling the Factors Behind the Diversification of Dung Beetles. doi:10.1101/2021.01.26.428346
171. Herzog, S. A., & Latvis, M. (2021). Examining the utility of DNA barcodes for the identification of tallgrass prairie flora. Applications in Plant Sciences, 9(1). doi:10.1002/aps3.11405

Explore and retrieve marine geospatial data from the Marine Regions Gazetteer https://marineregions.org/gazetteer.php?p=webservices and the Marine Regions Data Products https://marineregions.org/webservices.php.

A package to download information from CETESB QUALAR https://cetesb.sp.gov.br/ar/qualar/ and MonitorAr http://jeap.rio.rj.gov.br/je-metinfosmac/institucional/index.html systems. It contains function to download different parameters, a set of criteria pollutants and the most frequent meteorological parameters used in air quality data analysis and air quality model evaluation.

Intended to facilitate acoustic analysis of (animal) sound transmission experiments, which typically aim to quantify changes in signal structure when transmitted in a given habitat by broadcasting and re-recording animal sounds at increasing distances. The package offers a workflow with functions to prepare the data set for analysis as well as to calculate and visualize several degradation metrics, including blur ratio, signal-to-noise ratio, excess attenuation and envelope correlation among others (Dabelsteen et al 1993 doi:10.1121/1.406682).

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.

1. Dogucu, M., & Çetinkaya-Rundel, M. (2020). Web Scraping in the Statistics and Data Science Curriculum: Challenges and Opportunities. Journal of Statistics Education, 1–11. https://doi.org/10.1080/10691898.2020.1787116

Functions and example data to support research into the slope (also known as longitudinal gradient or steepness) of linear geographic entities such as roads doi:10.1038/s41597-019-0147-x and rivers doi:10.1016/j.jhydrol.2018.06.066. The package was initially developed to calculate the steepness of street segments but can be used to calculate steepness of any linear feature that can be represented as LINESTRING geometries in the sf class system. The package takes two main types of input data for slope calculation: vector geographic objects representing linear features, and raster geographic objects with elevation values (which can be downloaded using functionality in the package) representing a continuous terrain surface. Where no raster object is provided the package attempts to download elevation data using the ceramic package.

Generate reports that enable quick visual review of temporal shifts in record-level data. Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single “event”, one column contains the “event date”, and other columns contain any associated values for the event).

Tools to discover hydrological data, accessing catalogues and databases from various data providers. The package is described in Vitolo (2017) “hddtools: Hydrological Data Discovery Tools” doi:10.21105/joss.00056.

1. Zheng, X., Kottas, A., & Sansó, B. (2020). On Construction and Estimation of Stationary Mixture Transition Distribution Models. arXiv preprint arXiv:2010.12696 https://arxiv.org/abs/2010.12696.

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.

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.

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
5. Li, Z. R., Martin, B. D., Dong, T. Q., Fuglstad, G. A., Paige, J., Riebler, A., … & Wakefield, J. (2020). Space-Time Smoothing of Demographic and Health Indicators using the R Package SUMMER. arXiv preprint arXiv:2007.05117 https://arxiv.org/pdf/2007.05117.
6. Stresman, G., Whittaker, C., Slater, H. C., Bousema, T., & Cook, J. (2020). Quantifying Plasmodium falciparum infections clustering within households to inform household-based intervention strategies for malaria control programs: An observational study and meta-analysis from 41 malaria-endemic countries. PLOS Medicine, 17(10), e1003370. https://doi.org/10.1371/journal.pmed.1003370
7. Fu, H., Lewnard, J. A., Frost, I., Laxminarayan, R., & Arinaminpathy, N. (2021). Modelling the global burden of drug-resistant tuberculosis avertable by a post-exposure vaccine. Nature Communications, 12(1). doi:10.1038/s41467-020-20731-x

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 spatial 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.

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
8. Dupont, G., Royle, J. A., Nawaz, M. A., & Sutherland, C. (2020). Optimal sampling design for spatial capture-recapture. https://doi.org/10.1101/2020.04.16.045740
9. Kuempel, C. D., Frazier, M., Nash, K. L., Jacobsen, N. S., Williams, D. R., Blanchard, J. L., … Halpern, B. S. (2020). Integrating Life Cycle and Impact Assessments to Map Food’s Cumulative Environmental Footprint. One Earth, 3(1), 65–78. https://doi.org/10.1016/j.oneear.2020.06.014
10. Silva, I., Crane, M., Marshall, B. M., & Strine, C. T. (2020). Reptiles on the wrong track? Moving beyond traditional estimators with dynamic Brownian Bridge Movement Models. Movement Ecology, 8(1). https://doi.org/10.1186/s40462-020-00229-3
11. Braziunas, K. H., Seidl, R., Rammer, W., & Turner, M. G. (2020). Can we manage a future with more fire? Effectiveness of defensible space treatment depends on housing amount and configuration. Landscape Ecology, 36(2), 309–330. https://doi.org/10.1007/s10980-020-01162-x
12. Thompson, P. R., Derocher, A. E., Edwards, M. A., & Lewis, M. A. (2021). Describing spatiotemporal memory patterns using animal movement modelling. arXiv preprint arXiv:2101.04183. https://arxiv.org/pdf/2101.04183
13. Savary, P., Foltête, J., Moal, H., Vuidel, G., & Garnier, S. (2021). Analysing landscape effects on dispersal networks and gene flow with genetic graphs. Molecular Ecology Resources, 21(4), 1167–1185. doi:10.1111/1755-0998.13333

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.

A programmatic interface to FishBase, 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 SeaLifeBase data, which contains nearly 200,000 species records for all types of aquatic life not covered by FishBase.

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
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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
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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
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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
56. Anderson, D. M., & Gillooly, J. F. (2020). Predicting egg size across temperatures in marine teleost fishes. Fish and Fisheries, 21(5), 1027–1033. https://doi.org/10.1111/faf.12486
57. Larouche, O., Hodge, J. R., Alencar, L. R. V., Camper, B., Adams, D. S., Zapfe, K., … Price, S. A. (2020). Do key innovations unlock diversification? A case-study on the morphological and ecological impact of pharyngognathy in acanthomorph fishes. Current Zoology. https://doi.org/10.1093/cz/zoaa048
58. Bayley, D. T. I., Purvis, A., Nellas, A. C., Arias, M., & Koldewey, H. J. (2020). Measuring the long-term success of small-scale marine protected areas in a Philippine reef fishery. Coral Reefs. https://doi.org/10.1007/s00338-020-01987-7
59. Larouche, O., Benton, B., Corn, K. A., Friedman, S. T., Gross, D., Iwan, M., … Price, S. A. (2020). Reef-associated fishes have more maneuverable body shapes at a macroevolutionary scale. Coral Reefs, 39(5), 1427–1439. https://doi.org/10.1007/s00338-020-01976-w
60. Keppeler, F. W., Montaña, C. G., & Winemiller, K. O. (2020). The relationship between trophic level and body size in fishes depends on functional traits. Ecological Monographs. https://doi.org/10.1002/ecm.1415
61. Huang, M., Ding, L., Wang, J., Ding, C., & Tao, J. (2021). The impacts of climate change on fish growth: A summary of conducted studies and current knowledge. Ecological Indicators, 121, 106976. https://doi.org/10.1016/j.ecolind.2020.10697
62. Borstein, S. R. (2020). dietr: an R package for calculating fractional trophic levels from quantitative and qualitative diet data. Hydrobiologia, 847(20), 4285–4294. https://doi.org/10.1007/s10750-020-04417-5
63. Denderen, D., Gislason, H., Heuvel, J., & Andersen, K. H. (2020). Global analysis of fish growth rates shows weaker responses to temperature than metabolic predictions. Global Ecology and Biogeography, 29(12), 2203–2213. https://doi.org/10.1111/geb.13189
64. Oegelund Nielsen, R., da Silva, R., Juergens, J., Staerk, J., Lindholm Sørensen, L., Jackson, J., … Conde, D. A. (2020). Standardized data to support conservation prioritization for sharks and batoids (Elasmobranchii). Data in Brief, 33, 106337. https://doi.org/10.1016/j.dib.2020.106337
65. Guerra, A. S., Kao, A. B., McCauley, D. J., & Berdahl, A. M. (2020). Fisheries-induced selection against schooling behaviour in marine fishes. Proceedings of the Royal Society B: Biological Sciences, 287(1935), 20201752. https://doi.org/10.1098/rspb.2020.1752
66. Webb, T. J., & Vanhoorne, B. (2020). Linking dimensions of data on global marine animal diversity. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1814), 20190445. https://doi.org/10.1098/rstb.2019.0445
67. Morat, F., Wicquart, J., Schiettekatte, N. M. D., de Sinéty, G., Bienvenu, J., Casey, J. M., … Parravicini, V. (2020). Individual back-calculated size-at-age based on otoliths from Pacific coral reef fish species. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-00711-y
68. Whalen, M. A., Whippo, R. D. B., Stachowicz, J. J., York, P. H., Aiello, E., Alcoverro, T., … Bresch, M. (2020). Climate drives the geography of marine consumption by changing predator communities. Proceedings of the National Academy of Sciences, 117(45), 28160–28166. https://doi.org/10.1073/pnas.2005255117
69. Palacios-Abrantes, J., Reygondeau, G., Wabnitz, C. C. C., & Cheung, W. W. L. (2020). The transboundary nature of the world’s exploited marine species. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-74644-2
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71. Huang, M., Ding, L., Wang, J., Ding, C., & Tao, J. (2021). The impacts of climate change on fish growth: A summary of conducted studies and current knowledge. Ecological Indicators, 121, 106976. doi:10.1016/j.ecolind.2020.106976
72. Comte, L., Carvajal‐Quintero, J., Tedesco, P. A., Giam, X., Brose, U., Erős, T., … Olden, J. D. (2020). RivFishTIME: A global database of fish time‐series to study global change ecology in riverine systems. Global Ecology and Biogeography, 30(1), 38–50. https://doi.org/10.1111/geb.13210
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74. Kopf, R. K., Yen, J. D. L., Nimmo, D. G., Brosse, S., & Villéger, S. (2020). Global patterns and predictors of trophic position, body size and jaw size in fishes. Global Ecology and Biogeography, 30(2), 414–428. https://doi.org/10.1111/geb.13227
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76. Palacios-Abrantes, J., Reygondeau, G., Wabnitz, C. C. C., & Cheung, W. W. L. (2020). The transboundary nature of the world’s exploited marine species. Scientific Reports, 10(1). doi:10.1038/s41598-020-74644-2
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This is a private template for use by rOpenSci packages. Please don’t use it for your own non-rOpenSci package.

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.

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.

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
5. Wang, S., Xiong, Y., Gu, K., Zhao, L., Li, Y., Zhao, F., … Liu, X.-S. (2020). UCSCXenaShiny: An R Package for Exploring and Analyzing UCSC Xena Public Datasets in Web Browser. https://doi.org/10.20944/preprints202007.0179.v1
6. Gvaldin, D. Y., Pushkin, A. A., Timoshkina, N. N., Rostorguev, E. E., Nalgiev, A. M., & Kit, O. I. (2020). Integrative analysis of mRNA and miRNA sequencing data for gliomas of various grades. Egyptian Journal of Medical Human Genetics, 21(1). https://doi.org/10.1186/s43042-020-00119-8
7. Cui, Y., Hunt, A., Li, Z., Birkin, E., Lane, J., Ruge, F., & Jiang, W. G. (2021). Lead DEAD/H box helicase biomarkers with the therapeutic potential identified by integrated bioinformatic approaches in lung cancer. Computational and Structural Biotechnology Journal, 19, 261–278. https://doi.org/10.1016/j.csbj.2020.12.007

The Citation File Format version 1.2.0 doi:10.5281/zenodo.5171937 is a human and machine readable file format which provides citation metadata for software. This package provides core utilities to generate and validate this metadata.

Check whether a package is ready for submission to rOpenSci’s peer review system.

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

The OPtical TRapezoid Model (OPTRAM) derives soil moisture based on the linear relation between a vegetation index and Land Surface Temperature (LST). The Short Wave Infra-red (SWIR) band is used as a proxy for LST. See: Sadeghi, M. et al., 2017. https://doi.org/10.1016/j.rse.2017.05.041 .

Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. The package was originally developed to support the Propensity to Cycle Tool, a publicly available strategic cycle network planning tool (Lovelace et al. 2017) doi:10.5198/jtlu.2016.862, but has since been extended to support public transport routing and accessibility analysis (Moreno-Monroy et al. 2017) doi:10.1016/j.jtrangeo.2017.08.012 and routing with locally hosted routing engines such as OSRM (Lowans et al. 2023) doi:10.1016/j.enconman.2023.117337. The main functions are for creating and manipulating geographic “desire lines” from origin-destination (OD) data (building on the od package); calculating routes on the transport network locally and via interfaces to routing services such as https://cyclestreets.net/ (Desjardins et al. 2021) doi:10.1007/s11116-021-10197-1; and calculating route segment attributes such as bearing. The package implements the travel flow aggregration method described in Morgan and Lovelace (2020) doi:10.1177/2399808320942779 and the OD jittering method described in Lovelace et al. (2022) doi:10.32866/001c.33873. Further information on the package’s aim and scope can be found in the vignettes and in a paper in the R Journal (Lovelace and Ellison 2018) doi:10.32614/RJ-2018-053, and in a paper outlining the landscape of open source software for geographic methods in transport planning (Lovelace, 2021) doi:10.1007/s10109-020-00342-2.

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
5. Morgan, M., & Lovelace, R. (2020). Travel flow aggregation: Nationally scalable methods for interactive and online visualisation of transport behaviour at the road network level. Environment and Planning B: Urban Analytics and City Science, 239980832094277. https://doi.org/10.1177/2399808320942779
6. Baddeley, A., Nair, G., Rakshit, S., McSwiggan, G., & Davies, T. M. (2020). Analysing point patterns on networks — A review. Spatial Statistics, 100435. https://doi.org/10.1016/j.spasta.2020.100435
7. Bivand, R. S. (2020). Progress in the R ecosystem for representing and handling spatial data. Journal of Geographical Systems. https://doi.org/10.1007/s10109-020-00336-0
8. Fitzgerald, D. B., Henderson, A. R., Maloney, K. O., Freeman, M. C., Young, J. A., Rosenberger, A. E., … Smith, D. R. (2021). A Bayesian framework for assessing extinction risk based on ordinal categories of population condition and projected landscape change. Biological Conservation, 253, 108866. https://doi.org/10.1016/j.biocon.2020.108866
9. Lovelace, R. (2021). Open source tools for geographic analysis in transport planning. Journal of Geographical Systems. doi:10.1007/s10109-020-00342-2

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.

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
10. Carroll, L. M., Huisman, J. S., & Wiedmann, M. (2020). Twentieth-century emergence of antimicrobial resistant human- and bovine-associated Salmonella enterica serotype Typhimurium lineages in New York State. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-71344-9
11. Whitmer, S. L. M., Lo, M. K., Sazzad, H. M. S., Zufan, S., Gurley, E. S., Sultana, S., … Klena, J. D. (2020). Inference of Nipah virus Evolution, 1999-2015. Virus Evolution. https://doi.org/10.1093/ve/veaa062
12. Ettinger, C. L., & Eisen, J. A. (2020). Fungi, bacteria and oomycota opportunistically isolated from the seagrass, Zostera marina. PLOS ONE, 15(7), e0236135. https://doi.org/10.1371/journal.pone.0236135
13. Huang, R., Soneson, C., Ernst, F. G. M., Rue-Albrecht, K. C., Yu, G., Hicks, S. C., & Robinson, M. D. (2020). TreeSummarizedExperiment: a S4 class for data with hierarchical structure. F1000Research, 9, 1246. https://doi.org/10.12688/f1000research.26669.1
14. Figueroa, H., & Smith, S. A. (2020). A targeted phylogenetic approach helps explain New World functional diversity patterns of two eudicot lineages. Journal of Biogeography. https://doi.org/10.1111/jbi.13993
15. Alvarado-Ortega, J., & Díaz-Cruz, J. A. (2021). Hastichthys totonacus sp. nov., a North American Turonian dercetid fish (Teleostei, Aulopiformes) from the Huehuetla quarry, Puebla, Mexico. Journal of South American Earth Sciences, 105, 102900. https://doi.org/10.1016/j.jsames.2020.102900
16. Chak, S. T. C., Baeza, J. A., & Barden, P. (2020). Eusociality Shapes Convergent Patterns of Molecular Evolution across Mitochondrial Genomes of Snapping Shrimps. Molecular Biology and Evolution. https://doi.org/10.1093/molbev/msaa297
17. Wagner, E., Zaiser, A., Leitner, R., Quijada, N. M., Pracser, N., Pietzka, A., … Rychli, K. (2020). Virulence characterization and comparative genomics of Listeria monocytogenes sequence type 155 strains. BMC Genomics, 21(1). https://doi.org/10.1186/s12864-020-07263-w
18. Toparslan, E., Karabag, K., & Bilge, U. (2020). A workflow with R: Phylogenetic analyses and visualizations using mitochondrial cytochrome b gene sequences. PLOS ONE, 15(12), e0243927. https://doi.org/10.1371/journal.pone.0243927
19. Oswald, K. N., Lee, A. T. K., & Smit, B. (2021). Seasonal metabolic adjustments in an avian evolutionary relict restricted to mountain habitat. Journal of Thermal Biology, 95, 102815. https://doi.org/10.1016/j.jtherbio.2020.102815
20. Maruyama, H., Masago, A., Nambu, T., Mashimo, C., Takahashi, K., & Okinaga, T. (2020). Inter-site and interpersonal diversity of salivary and tongue microbiomes, and the effect of oral care tablets. F1000Research, 9, 1477. https://doi.org/10.12688/f1000research.27502.1
21. Gates, M. W., Zhang, Y. M., & Buffington, M. L. (2020). The great greenbriers gall mystery resolved? New species of Aprostocetus Westwood (Hymenoptera, Eulophidae) gall inducer and two new parasitoids (Hymenoptera, Eurytomidae) associated with Smilax L. in southern Florida, USA. Journal of Hymenoptera Research, 80, 71–98. https://doi.org/10.3897/jhr.80.59466
22. Sellés Vidal, L., Ayala, R., Stan, G.-B., & Ledesma-Amaro, R. (2021). rfaRm: An R client-side interface to facilitate the analysis of the Rfam database of RNA families. PLOS ONE, 16(1), e0245280. doi:10.1371/journal.pone.0245280
23. Vozdova, M., Kubickova, S., Martínková, N., Galindo, D. J., Bernegossi, A. M., Cernohorska, H., … Rubes, J. (2021). Satellite DNA in Neotropical Deer Species. Genes, 12(1), 123. doi:10.3390/genes12010123

Allows users to fit a cosinor model using the glmmTMB framework. This extends on existing cosinor modelling packages, including cosinor and circacompare, by including a wide range of available link functions and the capability to fit mixed models. The cosinor model is described by Cornelissen (2014) doi:10.1186/1742-4682-11-16.

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().

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.

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).

1. Hazlett, M. A., Henderson, K. M., Zeitzer, I. F., & Drew, J. A. (2020). The geography of publishing in the Anthropocene. Conservation Science and Practice, 2(10). https://doi.org/10.1111/csp2.270
2. Smith, T. B., Vacca, R., Krenz, T., & McCarty, C. (2021). Great minds think alike, or do they often differ? Research topic overlap and the formation of scientific teams. Journal of Informetrics, 15(1), 101104. https://doi.org/10.1016/j.joi.2020.101104

Easy-to-use and efficient interface for Bayesian inference of complex panel (time series) data using dynamic multivariate panel models by Helske and Tikka (2024) doi:10.1016/j.alcr.2024.100617. The package supports joint modeling of multiple measurements per individual, time-varying and time-invariant effects, and a wide range of discrete and continuous distributions. Estimation of these dynamic multivariate panel models is carried out via Stan. For an in-depth tutorial of the package, see (Tikka and Helske, 2024) doi:10.48550/arXiv.2302.01607.

Static code analyses for R packages using the external code-tagging libraries ctags and gtags. Static analyses enable packages to be analysed very quickly, generally a couple of seconds at most. The package also provides access to a database generating by applying the main function to the full CRAN archive, enabling the statistical properties of any package to be compared with all other CRAN packages.

Download and parse public files released by B3 and convert them into useful formats and data structures common to data analysis practitioners.

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.

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

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.

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

A complete toolkit for processing the Munich ChronoType Questionnaire (MCTQ) in its three versions: standard, micro, and shift. The MCTQ is a quantitative and validated tool used to assess chronotypes based on individuals’ sleep behavior. It was originally presented by Till Roenneberg, Anna Wirz-Justice, and Martha Merrow in 2003 (2003, doi:10.1177/0748730402239679).

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

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
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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
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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
18. Lawlor, J. A., & Arellano, S. M. (2020). Temperature and salinity, not acidification, predict near-future larval growth and larval habitat suitability of Olympia oysters in the Salish Sea. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-69568-w
19. Cahill, C. L., Anderson, S. C., Paul, A. J., MacPherson, L., Sullivan, M. G., van Poorten, B. T., … & Post, J. R. (2020). A spatial-temporal approach to modeling somatic growth across inland recreational fisheries landscapes. Canadian Journal of Fisheries and Aquatic Sciences, (ja). https://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2019-0434
20. Chugunkova, A. V., & Pyzhev, A. I. (2020). Impacts of Global Climate Change on Duration of Logging Season in Siberian Boreal Forests. Forests, 11(7), 756. https://doi.org/10.3390/f11070756
21. Kakioka, R., Mori, S., Kokita, T., Hosoki, T. K., Nagano, A. J., Ishikawa, A., … Kitano, J. (2020). Multiple waves of freshwater colonization of the three-spined stickleback in the Japanese Archipelago. https://doi.org/10.21203/rs.3.rs-59443/v1
22. Yeşilkanat, C. M. (2020). Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solitons & Fractals, 140, 110210. https://doi.org/10.1016/j.chaos.2020.110210
23. Obradovich, N., Özak, Ö., Martín, I., Ortuño-Ortín, I., Awad, E., Cebrián, M., … Cuevas, Á. (2020). Expanding the Measurement of Culture with a Sample of Two Billion Humans. National Bureau of Economic Research. https://doi.org/10.3386/w27827
24. Rycyk, A. M., Tyson Moore, R. B., Wells, R. S., McHugh, K. A., Berens McCabe, E. J., & Mann, D. A. (2020). Passive acoustic listening stations (PALS) show rapid onset of ecological effects of harmful algal blooms in real time. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-74647-z
25. Wenndt, A., Sudini, H. K., Pingali, P., & Nelson, R. (2020). Exploring aflatoxin contamination and household-level exposure risk in diverse Indian food systems. PLOS ONE, 15(10), e0240565. https://doi.org/10.1371/journal.pone.0240565
26. Abbas, H. K., Zablotowicz, R. M., Bruns, H. A., & Abel, C. A. (2006). Biocontrol of aflatoxin in corn by inoculation with non-aflatoxigenicAspergillus flavusisolates. Biocontrol Science and Technology, 16(5), 437–449. https://doi.org/10.1080/09583150500532477
27. Jacobs, E., Bittig, H. C., Gräwe, U., Graves, C. A., Glockzin, M., Müller, J. D., … Rehder, G. (2020). Upwelling-induced trace gas dynamics in the Baltic Sea inferred from 8 years of autonomous measurements on a ship of opportunity. https://doi.org/10.5194/bg-2020-365
28. Hotez, P., Bottazzi, M. E., Strub-Wourgaft, N., Sosa-Estani, S., Torrico, F., Pajín, L., … Sancho, J. (2020). A new patient registry for Chagas disease. PLOS Neglected Tropical Diseases, 14(10), e0008418. https://doi.org/10.1371/journal.pntd.0008418
29. Raut, S. (2020). A computer vision approach to assess wood variability from whole-disk images of longleaf pine (Order No. 28023614). https://search.proquest.com/docview/2446699035
30. Cramer, M. T., Fidler, R. Y., Penrod, L. M., Carroll, J., & Turingan, R. G. (2020). A spatiotemporal comparison of length-at-age in the coral reef fish Acanthurus nigrofuscus between marine reserves and fished reefs. PLOS ONE, 15(9), e0239842. https://doi.org/10.1371/journal.pone.0239842
31. Winter-Billington, A., Moore, R. D., & Dadic, R. (2020). Evaluating the transferability of empirical models of debris-covered glacier melt. Journal of Glaciology, 1–18. https://doi.org/10.1017/jog.2020.57
32. Pellowe, K. E., & Leslie, H. M. (2020). Ecosystem service lens reveals diverse community values of small-scale fisheries. Ambio. https://doi.org/10.1007/s13280-020-01405-w
33. Stresman, G., Whittaker, C., Slater, H. C., Bousema, T., & Cook, J. (2020). Quantifying Plasmodium falciparum infections clustering within households to inform household-based intervention strategies for malaria control programs: An observational study and meta-analysis from 41 malaria-endemic countries. PLOS Medicine, 17(10), e1003370. https://doi.org/10.1371/journal.pmed.1003370
34. Lockley, E. C., Fouda, L., Correia, S. M., Taxonera, A., Nash, L. N., Fairweather, K., … Eizaguirre, C. (2020). Long-term survey of sea turtles (Caretta caretta) reveals correlations between parasite infection, feeding ecology, reproductive success and population dynamics. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-75498-4
35. Hirons, A. D., Watkins, J. H. R., Baxter, T. J., Miesbauer, J. W., Male‐Muñoz, A., Martin, K. W. E., … Sjöman, H. (2020). Using botanic gardens and arboreta to help identify urban trees for the future. PLANTS, PEOPLE, PLANET. https://doi.org/10.1002/ppp3.10162
36. Alhajeri, B. H. (2020). A Geometric Morphometric Analysis of Geographic Mandibular Variation in the Dwarf Gerbil Gerbillus nanus (Gerbillinae, Rodentia). Journal of Mammalian Evolution. https://doi.org/10.1007/s10914-020-09530-9
37. Tchokponhoué, D. A., Achigan-Dako, E. G., N’Danikou, S., Nyadanu, D., Kahane, R., Houéto, J., … Sibiya, J. (2020). Phenotypic variation, functional traits repeatability and core collection inference in Synsepalum dulcificum (Schumach & Thonn.) Daniell reveals the Dahomey Gap as a centre of diversity. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-76103-4
38. Chung, M., Jørgensen, K. M., Trueman, C. N., Knutsen, H., Jorde, P. E., & Grønkjær, P. (2020). First measurements of field metabolic rate in wild juvenile fishes show strong thermal sensitivity but variations between sympatric ecotypes. Oikos. https://doi.org/10.1111/oik.07647
39. Evdokimova, E. V., Gladkov, G. V., Kuzina, N. I., Ivanova, E. A., Kimeklis, A. K., Zverev, A. O., … Andronov, E. E. (2020). The difference between cellulolytic “culturomes” and microbiomes inhabiting two contrasting soil types. PLOS ONE, 15(11), e0242060. https://doi.org/10.1371/journal.pone.0242060
40. Hobson, K. A., Jinguji, H., Ichikawa, Y., Kusack, J. W., & Anderson, R. C. (2020). Long-Distance Migration of the Globe Skimmer Dragonfly to Japan Revealed Using Stable Hydrogen (δ 2H) Isotopes. Environmental Entomology. https://doi.org/10.1093/ee/nvaa147
41. Bennie, J. A., De Cocker, K., Smith, J. J., & Wiesner, G. H. (2020). The epidemiology of muscle-strengthening exercise in Europe: A 28-country comparison including 280,605 adults. PLOS ONE, 15(11), e0242220. https://doi.org/10.1371/journal.pone.0242220
42. Rose Vineer, H., Morgan, E. R., Hertzberg, H., Bartley, D. J., Bosco, A., Charlier, J., … Rinaldi, L. (2020). Increasing importance of anthelmintic resistance in European livestock: creation and meta-analysis of an open database. Parasite, 27, 69. https://doi.org/10.1051/parasite/2020062
43. Farooq, H., Azevedo, J. A. R., Soares, A., Antonelli, A., & Faurby, S. (2020). Mapping Africa’s Biodiversity: More of the Same Is Just Not Good Enough. Systematic Biology. https://doi.org/10.1093/sysbio/syaa090
44. Shaw, E. C., Fowler, R., Ohadi, S., Bayly, M. J., Barrett, R. A., Tibbits, J., … Cousens, R. D. (2020). Explaining the worldwide distributions of two highly mobile species: Cakile edentula and Cakile maritima. Journal of Biogeography, 48(3), 603–615. https://doi.org/10.1111/jbi.14024
45. Pogorevc, N., Simčič, M., Khayatzadeh, N., Soelkner, J., Berger, B., Bojkovski, D., … Horvat, S. (2020). Post-Genotyping Optimization of Dataset Formation Could Affect Genetic Diversity Parameters: An Example of Analyses with Alpine Goat Breeds. https://doi.org/10.21203/rs.3.rs-133590/v1
46. Ionela-Andreea, P., & Marian, N. (2020). Cluster Analysis of Regional Research and Development Disparities in Europe. Studies in Business and Economics, 15(3), 303–312. https://doi.org/10.2478/sbe-2020-0060
47. Mari, A., Roloff, T.-C., Stange, M., Soegaard, K. K., Asllanaj, E., Tauriello, G., … Egli, A. (2021). Global surveillance of potential antiviral drug resistance in SARS-CoV-2: proof of concept focussing on the RNA-dependent RNA polymerase. https://doi.org/10.1101/2020.12.28.20248663
48. Changmai, P., Jaisamut, K., Kampuansai, J., Kutanan, W., Altınışık, N. E., Flegontova, O., … Flegontov, P. (2021). Indian genetic heritage in Southeast Asian populations. doi:10.1101/2021.01.21.427591
49. Thomas, C. L., Jansen, B., van Loon, E. E., & Wiesenberg, G. L. B. (2021). Transformation of <i>n</i>-alkanes from plant to soil: a review. doi:10.5194/soil-2020-107
50. Meca, M. A., Zhadan, A., & Struck, T. H. (2021). The Early Branching Group of Orbiniida Sensu Struck et al., 2015: Parergodrilidae and Orbiniidae. Diversity, 13(1), 29. doi:10.3390/d13010029
51. Lovell, J. T., MacQueen, A. H., Mamidi, S., Bonnette, J., Jenkins, J., Napier, J. D., … Shu, S. (2021). Genomic mechanisms of climate adaptation in polyploid bioenergy switchgrass. Nature, 590(7846), 438–444. doi:10.1038/s41586-020-03127-1

Species range dynamics simulation toolset.

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

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
3. Benjamens, S., Banning, L. B., van den Berg, T. A., & Pol, R. A. (2020). Gender Disparities in Authorships and Citations in Transplantation Research. Transplantation direct, 6(11). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575186/

Tools to download and manipulate the Permanent Household Survey from Argentina (EPH is the Spanish acronym for Permanent Household Survey). e.g: get_microdata() for downloading the datasets, get_poverty_lines() for downloading the official poverty baskets, calculate_poverty() for the calculation of stating if a household is in poverty or not, following the official methodology. organize_panels() is used to concatenate observations from different periods, and organize_labels() adds the official labels to the data. The implemented methods are based on INDEC (2016) http://www.estadistica.ec.gba.gov.ar/dpe/images/SOCIEDAD/EPH_metodologia_22_pobreza.pdf. As this package works with the argentinian Permanent Household Survey and its main audience is from this country, the documentation was written in Spanish.

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.

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

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.

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
13. Hansen, E. R., & Jansa, J. M. (2020). Complexity, Resources, and Text Borrowing in State Legislatures. http://ehansen4.sites.luc.edu/documents/Hansen_Jansa_Complexity.pdf

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 data frame or a list of terra rast 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 arc minutes (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.

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 to find walking, cycling, driving, or transit routes. This package has been peer-reviewed by rOpenSci (v. 0.2.0.0).

1. Lovelace, R. (2021). Open source tools for geographic analysis in transport planning. Journal of Geographical Systems. doi:10.1007/s10109-020-00342-2

Tools to help download, process and analyse the UK road collision data collected using the STATS19 form. The datasets are provided as CSV files with detailed road safety information about the circumstances of car crashes and other incidents on the roads resulting in casualties in Great Britain from 1979 to present. Tables are available on colissions with the circumstances (e.g. speed limit of road), information about vehicles involved (e.g. type of vehicle), and casualties (e.g. age). The statistics relate only to events on public roads that were reported to the police, and subsequently recorded, using the STATS19 collision reporting form. See the Department for Transport website https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data for more information on these datasets. The package is described in a paper in the Journal of Open Source Software (Lovelace et al. 2019) doi:10.21105/joss.01181. See Gilardi et al. (2022) doi:10.1111/rssa.12823, Vidal-Tortosa et al. (2021) doi:10.1016/j.jth.2021.101291, and Tait et al. (2023) doi:10.1016/j.aap.2022.106895 for examples of how the data can be used for methodological and empirical road safety research.

Tools for interacting with the Circle CI API (https://circleci.com/docs/api/v2/). Besides executing common tasks such as querying build logs and restarting builds, this package also helps setting up permissions to deploy from builds.

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://github.com/ropensci/babette/).

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 2020 https://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.

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.

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.

This is the R implementation of Karel the robot, a programming language created by Dr. R. E. Pattis at Stanford University in 1981. Karel is an useful tool to teach introductory concepts about general programming, such as algorithmic decomposition, conditional statements, loops, etc., in an interactive and fun way, by writing programs to make Karel the robot achieve certain tasks in the world she lives in. Originally based on Pascal, Karel was implemented in many languages through these decades, including Java, C++, Ruby and Python. This is the first package implementing Karel in R.

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

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

Simplified JSON document database access and manipulation, providing a common API across supported NoSQL databases Elasticsearch, CouchDB, MongoDB as well as SQLite/JSON1, PostgreSQL, and DuckDB.

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.

Facilitates the automatic detection of acoustic signals, providing functions to diagnose and optimize the performance of detection routines. Detections from other software can also be explored and optimized. This package has been peer-reviewed by rOpenSci. Araya-Salas et al. (2022) doi:10.1101/2022.12.13.520253.

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.se/libcurl/).

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.

Access and tidy up data from the ODK Central API. ODK Central is a clearinghouse for digitally captured data https://docs.getodk.org/central-intro/. ODK Central and its API are documented at https://docs.getodk.org/.

Estimates when and where a model-guided treatment strategy may outperform a treat-all or treat-none approach by Monte Carlo simulation and evaluation of the Net Monetary Benefit. Details can be viewed in Parsons et al. (2023) doi:10.21105/joss.05328.

Provides functions to simplify the PatentsView API (https://patentsview.org/apis/purpose) query language, send GET and POST requests to the API’s seven endpoints, and parse the data that comes back.

Provides API access to data from the U.S. Energy Information Administration (EIA) https://www.eia.gov/. Use of the EIA’s API and this package requires a free API key obtainable at https://www.eia.gov/opendata/register.php. This package includes functions for searching the EIA data directory and returning time series and geoset time series datasets. Datasets returned by these functions are provided by default in a tidy format, or alternatively, in more raw formats. 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.

Enables preparation of maps to be printed and drawn on. Modified maps can then be scanned back in, and hand-drawn marks converted to spatial objects.

Edit and validate taxonomic data in compliance with Darwin Core standards (Darwin Core Taxon class https://dwc.tdwg.org/terms/#taxon).

Motivated by changing administrative boundaries over time, the nuts package can convert European regional data with NUTS codes between versions (2006, 2010, 2013, 2016 and 2021) and levels (NUTS 1, NUTS 2 and NUTS 3). The package uses spatial interpolation as in Lam (1983) doi:10.1559/152304083783914958 based on granular (100m x 100m) area, population and land use data provided by the European Commission’s Joint Research Center.

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.

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
3. Crespo, L. C., Silva, I., Enguídanos, A., Cardoso, P., & Arnedo, M. A. (2020). Integrative taxonomic revision of the woodlouse-hunter spider genus Dysdera (Araneae: Dysderidae) in the Madeira archipelago with notes on its conservation status. Zoological Journal of the Linnean Society. https://doi.org./10.1093/zoolinnean/zlaa089

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

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
4. Vantas, K., Sidiropoulos, E., & Evangelides, C. (2020). Estimating Rainfall Erosivity from Daily Precipitation Using Generalized Additive Models. Environmental Sciences Proceedings, 2(1), 21. doi:10.3390/environsciproc2020002021

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/.

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.

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, reprojecting and resizing 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 users to automatically update time series related to a MODIS product whenever a new image is available. For additional documentation refer to the following article: Busetto and Ranghetti (2016) doi:10.1016/j.cageo.2016.08.020.

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
36. Da Silva Abel, E. L., Delgado, R. C., Vilanova, R. S., Teodoro, P. E., da Silva Junior, C. A., Abreu, M. C., & Silva, G. F. C. (2020). Environmental dynamics of the Juruá watershed in the Amazon. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-020-00890-z
37. Gutiérrez-Hernández, O. (2020). Fenología de los ecosistemas de alta montaña en Andalucía: Análisis de la tendencia estacional del SAVI (2000-2019). Pirineos, 175, 055. https://doi.org/10.3989/pirineos.2020.175005
38. Estrada-Peña, A., D’Amico, G., & Fernández-Ruiz, N. (2020). Modelling the potential spread of Hyalomma marginatum ticks in Europe by migratory birds. International Journal for Parasitology. doi:10.1016/j.ijpara.2020.08.004
39. De Andrade, C. F., Delgado, R. C., Barbosa, M. L. F., Teodoro, P. E., Junior, C. A. da S., Wanderley, H. S., & Capristo-Silva, G. F. (2020). Fire regime in Southern Brazil driven by atmospheric variation and vegetation cover. Agricultural and Forest Meteorology, 295, 108194. doi:10.1016/j.agrformet.2020.108194
40. Sao, D., Kato, T., Tu, L. H., Thouk, P., Fitriyah, A., & Oeurng, C. (2020). Evaluation of Different Objective Functions Used in the SUFI-2 Calibration Process of SWAT-CUP on Water Balance Analysis: A Case Study of the Pursat River Basin, Cambodia. Water, 12(10), 2901. https://doi.org/10.3390/w12102901
41. Chaves, L. F., & Friberg, M. D. (2021). Aedes albopictus and Aedes flavopictus (Diptera: Culicidae) pre-imaginal abundance patterns are associated with different environmental factors along an altitudinal gradient. Current Research in Insect Science, 1, 100001. https://doi.org/10.1016/j.cris.2020.100001
42. De Andrade, M. D., Delgado, R. C., da Costa de Menezes, S. J. M., de Ávila Rodrigues, R., Teodoro, P. E., da Silva Junior, C. A., & Pereira, M. G. (2021). Evaluation of the MOD11A2 product for canopy temperature monitoring in the Brazilian Atlantic Forest. Environmental Monitoring and Assessment, 193(1). doi:10.1007/s10661-020-08788-z
43. Akpoti, K., Dossou-Yovo, E. R., Zwart, S. J., & Kiepe, P. (2021). The potential for expansion of irrigated rice under alternate wetting and drying in Burkina Faso. Agricultural Water Management, 247, 106758. doi:10.1016/j.agwat.2021.106758

Read ODS (OpenDocument Spreadsheet) into R as data frame. Also support writing data frame into ODS file.

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

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
3. Stuijfzand, S., Garthus-Niegel, S., & Horsch, A. (2020). Parental Birth-Related PTSD Symptoms and Bonding in the Early Postpartum Period: A Prospective Population-Based Cohort Study. Frontiers in Psychiatry, 11. https://doi.org/10.3389/fpsyt.2020.570727
4. Borg, D. N., Nguyen, R., & Tierney, N. J. (2020). Missing Data: Current Practice in Football Research and Recommendations for Improvement. https://doi.org/10.31236/osf.io/fhwcu

This tool is for parsing public drug databases such as DrugBank XML database https://go.drugbank.com/. The parsed data are then returned in a proper R object called dvobject.

This packages allows to retrieve air pollution data from the Air Information Resource (UK-AIR, https://uk-air.defra.gov.uk/) of the Department for Environment, Food and Rural Affairs (DEFRA) in the United Kingdom. UK-AIR does not provide a public API for programmatic access to data, therefore this package scrapes the HTML pages to get relevant information. The package is described in Vitolo et al. (2016) “rdefra: Interact with the UK AIR Pollution Database from DEFRA” doi:10.21105/joss.00051.

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

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

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the stantargets R package leverages targets and cmdstanr to ease these burdens. stantargets makes it super easy to set up scalable Stan pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than targets alone. stantargets can access all of cmdstanrs major algorithms (MCMC, variational Bayes, and optimization) and it supports both single-fit workflows and multi-rep simulation studies. For the statistical methodology, please refer to Stan’ documentation (Stan Development Team 2020) https://mc-stan.org/.

In computationally demanding data analysis pipelines, the targets R package (2021, doi:10.21105/joss.02959) maintains an up-to-date set of results while skipping tasks that do not need to rerun. This process increases speed and increases trust in the final end product. However, it also overwrites old output with new output, and past results disappear by default. To preserve historical output, the gittargets package captures version-controlled snapshots of the data store, and each snapshot links to the underlying commit of the source code. That way, when the user rolls back the code to a previous branch or commit, gittargets can recover the data contemporaneous with that commit so that all targets remain up to date.

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.

Time series toolkit with identical behavior for all time series classes: ts,xts, data.frame, data.table, tibble, zoo, timeSeries, tsibble, tis or irts. Also converts reliably between these classes.

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) doi:10.1242/jeb.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).

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. GitHub Actions or Circle CI). This package has been peer-reviewed by rOpenSci (v0.3.0.9004).

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

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.

1. Guiterman, C. H., Lynch, A. M., & Axelson, J. N. (2020). dfoliatR: An R package for detection and analysis of insect defoliation signals in tree rings. Dendrochronologia, 63, 125750. https://doi.org/10.1016/j.dendro.2020.125750
2. Makela, K., Ophelders, T., Quigley, M., Munch, E., Chitwood, D., & Dowtin, A. (2020). Automatic Tree Ring Detection using Jacobi Sets. arXiv preprint arXiv:2010.08691 https://arxiv.org/abs/2010.08691.

Acknowledge all contributors to a project via a single function call. The function appends to a README or other specified file(s) a table with names of all individuals who contributed via code or repository issues. The package also includes several additional functions to extract and quantify contributions to any repository.

Import, process, summarize and visualize raw data from metabolic carts. See Robergs, Dwyer, and Astorino (2010) doi:10.2165/11319670-000000000-00000 for more details on data processing.

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 https://www.microspectra.com/, and OceanInsight (formerly OceanOptics) https://www.oceaninsight.com/ brands.

Automatic testing of R packages via a simple YAML schema.

Assessing predictive models of spatial data can be challenging, both because these models are typically built for extrapolating outside the original region represented by training data and due to potential spatially structured errors, with “hot spots” of higher than expected error clustered geographically due to spatial structure in the underlying data. Methods are provided for assessing models fit to spatial data, including approaches for measuring the spatial structure of model errors, assessing model predictions at multiple spatial scales, and evaluating where predictions can be made safely. Methods are particularly useful for models fit using the tidymodels framework. Methods include Morans I (Moran (1950) doi:10.2307/2332142), Gearys C (Geary (1954) doi:10.2307/2986645), Getis-Ords G (Ord and Getis (1995) doi:10.1111/j.1538-4632.1995.tb00912.x), agreement coefficients from Ji and Gallo (2006) ([doi: 10.14358/PERS.72.7.823](https://doi.org/ 10.14358/PERS.72.7.823)), agreement metrics from Willmott (1981) ([doi: 10.1080/02723646.1981.10642213](https://doi.org/ 10.1080/02723646.1981.10642213)) and Willmott et al. (2012) ([doi: 10.1002/joc.2419](https://doi.org/ 10.1002/joc.2419)), an implementation of the area of applicability methodology from Meyer and Pebesma (2021) (doi:10.1111/2041-210X.13650), and an implementation of multi-scale assessment as described in Riemann et al’. (2010) (doi:10.1016/j.rse.2010.05.010).

rfema allows users to access The Federal Emergency Management Agencys (FEMA) publicly available data through their API. The package provides a set of functions to easily navigate and access data from the National Flood Insurance Program along with FEMAs various disaster aid programs, including the Hazard Mitigation Grant Program, the Public Assistance Grant Program, and the Individual Assistance Grant Program.

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.

Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) DOI:10.1080/10618600.2023.2231048.

Functions for the retrieval, manipulation, and visualization of geospatial data, with an aim towards producing 3D landscape visualizations in the Unity 3D rendering engine. Functions are also provided for retrieving elevation data and base map tiles from the USGS National Map https://apps.nationalmap.gov/services/.

The phruta R package is designed to simplify the basic phylogenetic pipeline. Specifically, all code is run within the same program and data from intermediate steps are saved in independent folders. Furthermore, all code is run within the same environment which increases the reproducibility of your analysis. phruta retrieves gene sequences, combines newly downloaded and local gene sequences, and performs sequence alignments.

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.

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.

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

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.

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
10. Bailey, T. W., Santos, A., Nascimento, N. C. de, Sivasankar, M. P., & Cox, A. (2020). RNA sequencing identifies transcriptional changes in the rabbit larynx in response to low humidity challenge. https://doi.org/10.21203/rs.3.rs-45442/v1
11. Pimsler, M. L., Oyen, K. J., Herndon, J. D., Jackson, J. M., Strange, J. P., Dillon, M. E., & Lozier, J. D. (2020). Biogeographic parallels in thermal tolerance and gene expression variation under temperature stress in a widespread bumble bee. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-73391-8
12. Manjang, K., Tripathi, S., Yli-Harja, O., Dehmer, M., & Emmert-Streib, F. (2020). Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-73326-3
13. Thrupp, N., Sala Frigerio, C., Wolfs, L., Skene, N. G., Fattorelli, N., Poovathingal, S., … Fiers, M. (2020). Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans. Cell Reports, 32(13), 108189. https://doi.org/10.1016/j.celrep.2020.108189
14. Sarmah, D. T., Bairagi, N., & Chatterjee, S. (2020). Tracing the footsteps of autophagy in computational biology. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbaa286
15. Henkel, L., Rauscher, B., Schmitt, B., Winter, J., & Boutros, M. (2020). Genome-scale CRISPR screening at high sensitivity with an empirically designed sgRNA library. BMC Biology, 18(1). https://doi.org/10.1186/s12915-020-00905-1
16. Böttcher, A., Büttner, M., Tritschler, S., Sterr, M., Aliluev, A., Oppenländer, L., … Lickert, H. (2021). Non-canonical Wnt/PCP signalling regulates intestinal stem cell lineage priming towards enteroendocrine and Paneth cell fates. Nature Cell Biology, 23(1), 23–31. https://doi.org/10.1038/s41556-020-00617-2
17. Tjeldnes, H., Labun, K., Cleuren, Y. T., Chyżyńska, K., Świrski, M., & Valen, E. (2021). ORFik: a comprehensive R toolkit for the analysis of translation. doi:10.1101/2021.01.16.426936

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.

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
8. Wilkes, M. A., Edwards, F., Jones, J. I., Murphy, J. F., England, J., Friberg, N., … Brown, L. E. (2020). Trait‐based ecology at large scales: Assessing functional trait correlations, phylogenetic constraints and spatial variability using open data. Global Change Biology. https://doi.org/10.1111/gcb.15344
9. Sigsgaard, E. E., Olsen, K., Hansen, M. D. D., Hansen, O. L. P., Høye, T. T., Svenning, J., & Thomsen, P. F. (2020). Environmental DNA metabarcoding of cow dung reveals taxonomic and functional diversity of invertebrate assemblages. Molecular Ecology. https://doi.org/10.1111/mec.15734
10. Batovska, J., Piper, A., Valenzuela, I., Cunningham, J., & Blacket, M. (2020). Developing a Non-destructive Metabarcoding Protocol for Detection of Pest Insects in Bulk Trap Catches. https://doi.org/10.21203/rs.3.rs-125070/v1

Parsing (R)Markdown files with numerous regular expressions can be fraught with peril, but it does not have to be this way. Converting (R)Markdown files to XML using the commonmark package allows in-memory editing via of markdown elements via XPath through the extensible R6 class called yarn. These modified XML representations can be written to (R)Markdown documents via an xslt stylesheet which implements an extended version of GitHub-flavoured markdown so that you can tinker to your hearts content.

Facilitates download of financial data from Yahoo Finance https://finance.yahoo.com/, a vast repository of stock price data across multiple financial exchanges. The package offers a local caching system and support for parallel computation.

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.

Convert R package documentation to a readthedocs website.

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.

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

Helps store files as GitHub release assets, which is a convenient way for large/binary data files to piggyback onto public and private GitHub repositories. Includes functions for file downloads, uploads, and managing releases via the GitHub API.

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

Give advice about good practices when building R packages. Advice includes functions and syntax to avoid, package structure, code complexity, code formatting, etc.

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.

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

Query different C14 date databases and apply basic data cleaning, merging and calibration steps. Currently available databases: 14cpalaeolithic, 14sea, adrac, agrichange, aida, austarch, bda, calpal, caribbean, eubar, euroevol, irdd, jomon, katsianis, kiteeastafrica, medafricarbon, mesorad, neonet, neonetatl, nerd, p3k14c, pacea, palmisano, rado.nb, rxpand, sard.

1. Crema, E. R., & Shoda, S. (2021). A Bayesian approach for fitting and comparing demographic growth models of radiocarbon dates: A case study on the Jomon-Yayoi transition in Kyushu (Japan). PLOS ONE, 16(5), e0251695. doi:10.1371/journal.pone.0251695

Provide workflows and guidance for automatic translation of Markdown-based R content using DeepL API.

Provide functionality to download archives (backups) for all repositories in a GitHub organization (useful for backups!).

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.

fellingdater is an R package that aims to facilitate the analysis and interpretation of tree-ring data from wooden cultural heritage objects and structures. The package standardizes the process of computing and combining felling date estimates, both for individual and groups of related tree-ring series.

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.

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

Performs multiple empirical likelihood tests. It offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. The core computational routines are implemented using the Eigen C++ library and RcppEigen interface, with OpenMP for parallel computation. Details of the testing procedures are provided in Kim, MacEachern, and Peruggia (2023) doi:10.1080/10485252.2023.2206919. A companion paper by Kim, MacEachern, and Peruggia (2024) doi:10.18637/jss.v108.i05 is available for further information. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

Creating dendrochronological networks based on the similarity between tree-ring series or chronologies. The package includes various functions to compare tree-ring curves building upon the dplR package. The networks can be used to visualise and understand the relations between tree-ring curves. These networks are also very useful to estimate the provenance of wood as described in Visser (2021) DOI:10.5334/jcaa.79 or wood-use within a structure/context/site as described in Visser and Vorst (2022) DOI:10.1163/27723194-bja10014.

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.

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

Make fake data that looks realistic, 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.

For reproducible quantitative PCR (qPCR) analysis building on packages from the ’tidyverse’, notably ’dplyr’ and ’ggplot2’. It normalizes (by ddCq), summarizes, and plots pre-calculated Cq data, and plots raw amplification and melt curves from Roche Lightcycler (tm) machines. It does NOT (yet) calculate Cq data from amplification curves.

API Client for the Climate Hazards Center CHIRPS and CHIRTS. The CHIRPS data is a quasi-global (50°S – 50°N) high-resolution (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. CHIRTS is a quasi-global (60°S – 70°N), high-resolution data set of daily maximum and minimum temperatures. For more details on CHIRPS and CHIRTS data please visit its official home page https://www.chc.ucsb.edu/data.

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.

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.

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

Downloading, customizing, and processing time series of satellite images for a region of interest. rsat functions allow a unified access to multispectral images from Landsat, MODIS and Sentinel repositories. rsat also offers capabilities for customizing satellite images, such as tile mosaicking, image cropping and new variables computation. Finally, rsat covers the processing, including cloud masking, compositing and gap-filling/smoothing time series of images (Militino et al., 2018 doi:10.3390/rs10030398 and Militino et al., 2019 doi:10.1109/TGRS.2019.2904193).

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.

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
17. Dogucu, M., & Çetinkaya-Rundel, M. (2020). Web Scraping in the Statistics and Data Science Curriculum: Challenges and Opportunities. Journal of Statistics Education, 1–11. https://doi.org/10.1080/10691898.2020.1787116
18. Xaudiera, S., & Cardenal, A. S. (2020). Ibuprofen Narratives in Five European Countries During the COVID-19 Pandemic. Harvard Kennedy School Misinformation Review. https://doi.org/10.37016/mr-2020-029
19. Ferster, C., Laberee, K., Nelson, T., Thigpen, C., Simeone, M., & Winters, M. (2020). From advocacy to acceptance: Social media discussions of protected bike lane installations. Urban Studies, 004209802093825. https://doi.org/10.1177/0042098020938252
20. Laguna, L., Fiszman, S., Puerta, P., Chaya, C., & Tárrega, A. (2020). The impact of COVID-19 lockdown on food priorities. Results from a preliminary study using social media and an online survey with Spanish consumers. Food Quality and Preference, 86, 104028. https://doi.org/10.1016/j.foodqual.2020.104028
21. Sass, C. A. B., Pimentel, T. C., Aleixo, M. G. B., Dantas, T. M., Cyrino Oliveira, F. L., Freitas, M. Q., … Esmerino, E. A. (2020). Exploring social media data to understand consumers’ perception of eggs: A multilingual study using Twitter. Journal of Sensory Studies. https://doi.org/10.1111/joss.12607
22. Kamiński, M., Szymańska, C., & Nowak, J. K. (2020). Whose Tweets on COVID-19 Gain the Most Attention: Celebrities, Political, or Scientific Authorities? Cyberpsychology, Behavior, and Social Networking. https://doi.org/10.1089/cyber.2020.0336
23. Xu, S., & Xiong, Y. (2020). Setting socially mediated engagement parameters: A topic modeling and text analytic approach to examining polarized discourses on Gillette’s campaign. Public Relations Review, 46(5), 101959. doi:10.1016/j.pubrev.2020.101959
24. Sältzer, M. (2020). Finding the bird’s wings: Dimensions of factional conflict on Twitter. Party Politics, 135406882095796. https://doi.org/10.1177/1354068820957960
25. Sutton, J., Renshaw, S. L., & Butts, C. T. (2020). The First 60 Days: American Public Health Agencies’ Social Media Strategies in the Emerging COVID-19 Pandemic. Health Security, 18(6), 454–460. https://doi.org/10.1089/hs.2020.0105
26. Lemay, D. J., & Doleck, T. (2020). Online Learning Communities in the COVID-19 Pandemic: Social Learning Network Analysis of Twitter during the Shutdown. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI), 2(1) https://onlinejour.journals.publicknowledgeproject.org/index.php/i-jai/article/view/15427
27. Hu, L., & Kearney, M. W. (2020). Gendered Tweets: Computational Text Analysis of Gender Differences in Political Discussion on Twitter. Journal of Language and Social Psychology, 0261927X2096975. https://doi.org/10.1177/0261927x20969752
28. Fuoli, M., Clarke, I., Wiegand, V., Ziezold, H., & Mahlberg, M. (2020). Responding Effectively to Customer Feedback on Twitter: A Mixed Methods Study of Webcare Styles. Applied Linguistics. https://doi.org/10.1093/applin/amaa046
29. Johnson, T., & Greenwell, M. P. (2020, November 12). Is sustainability advertising just a public relations stunt?. https://doi.org/10.31235/osf.io/avy4d
30. Koh, J. X., & Liew, T. M. (2020). How loneliness is talked about in social media during COVID-19 pandemic: Text mining of 4,492 Twitter feeds. Journal of Psychiatric Research. https://doi.org/10.1016/j.jpsychires.2020.11.015
31. Bittermann, A., Batzdorfer, V., Müller, S. M., & Steinmetz, H. (2021). Mining Twitter to detect hotspots in psychology. Zeitschrift für Psychologie. https://www.psycharchives.org/bitstream/20.500.12034/3956/1/ESM%201_methods.pdf
32. Lucas, B., & Landman, T. (2020). Social listening, modern slavery, and COVID-19. Journal of Risk Research, 1–21. https://doi.org/10.1080/13669877.2020.1864009
33. Boehm, F. J., & Hanlon, B. M. (2021). What Is Happening on Twitter? A Framework for Student Research Projects With Tweets. Journal of Statistics and Data Science Education, 29(sup1), S95–S102. https://doi.org/10.1080/10691898.2020.1848486
34. Gutiérrez García-Pardo, I., Guevara Gil, J. A., Gómez González, D., Castro Cantalejo, J., & Espínola Vílchez, R. (2021). Community Detection Problem Based on Polarization Measures. An application to Twitter: the COVID-19 case in Spain. https://doi.org/10.20944/preprints202101.0080.v1
35. Nkonde, M., Rodriguez, M. Y., Cortana, L., Mukogosi, J. K., King, S., Serrato, R., … Malik, M. M. (2021). Disinformation creep: ADOS and the strategic weapon-ization of breaking news. Harvard Kennedy School Misinformation Review. doi:10.37016/mr-2020-52
36. Heyerdahl, L. W., Vray, M., Leger, V., Le Fouler, L., Antouly, J., Troit, V., & Giles-Vernick, T. (2021). Evaluating the motivation of Red Cross Health volunteers in the COVID-19 pandemic: a mixed-methods study protocol. BMJ Open, 11(1), e042579. doi:10.1136/bmjopen-2020-042579
37. Adepeju, M., & Jimoh, F. (2021). An Analytical Framework for Measuring Inequality in the Public Opinion on Policing—Assessing the Impacts of COVID-19 Pandemic Using Twitter Data. Journal of Geographic Information System, 13(02), 122–147. doi:10.4236/jgis.2021.132008

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

Retrieve, map and summarize data from the VertNet.org archives (https://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.

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

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.

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
4. Goel, A., & Vitek, J. (2019). On the design, implementation, and use of laziness in R. Proceedings of the ACM on Programming Languages, 3(OOPSLA), 1–27. doi:10.1145/3360579

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.

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 nine 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 4, available from the Oak Ridge National Laboratory’s Distributed Active Archive Center (DAAC); the International Tree Ring Data Bank; the National Land Cover Database (NLCD); the Cropland Data Layer from the National Agricultural Statistics Service; and the PAD-US dataset of protected area boundaries from the USGS.

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
6. Saadi, M., Oudin, L., & Ribstein, P. (2020). Beyond Imperviousness: The Role of Antecedent Wetness in Runoff Generation in Urbanized Catchments. Water Resources Research, 56(11). https://doi.org/10.1029/2020wr028060
7. Porter, W. T., Barrand, Z. A., Wachara, J., DaVall, K., Mihaljevic, J. R., Pearson, T., … Nieto, N. C. (2021). Predicting the current and future distribution of the western black-legged tick, Ixodes pacificus, across the Western US using citizen science collections. PLOS ONE, 16(1), e0244754. https://doi.org/10.1371/journal.pone.0244754

A programmatic interface to the Chromosome Counts Database (https://taux.evolseq.net/CCDB_web/), Rice et al. (2014) doi:10.1111/nph.13191. This package is part of the ROpenSci suite (https://ropensci.org).

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

Convert natural language text into tokens. Includes tokenizers for shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs, characters, shingled characters, lines, 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.

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
9. Scott, T. A., Ulibarri, N., & Perez Figueroa, O. (2020). NEPA and National Trends in Federal Infrastructure Siting in the United States. Review of Policy Research. https://doi.org/10.1111/ropr.12399
10. Grassl, P., Schraffenberger, H., Zuiderveen Borgesius, F., & Buijzen, M. (2020, July 21). Dark and bright patterns in cookie consent requests. https://doi.org/10.31234/osf.io/gqs5h
11. López Galán, A., Chung, W.-S., & Marshall, N. J. (2020). Dynamic Courtship Signals and Mate Preferences in Sepia plangon. Frontiers in Physiology, 11. https://doi.org/10.3389/fphys.2020.00845
12. Brandão, L. A. C., Agrelli, A., Bernardo, L., Paparella, F., Moura, R., & Crovella, S. (2020). PlatCOVID: A Novel Web Tool to Analyze, Curate and Share COVID-19 Literature. doi:10.21203/rs.3.rs-42169/v1

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.

Queries a number of scientific awards databases. Collects relevant results based on keyword and date parameters, returns list of projects that fit those criteria as a data frame. Sources include: Arnold Ventures, Carnegie Corp, Federal RePORTER, Gates Foundation, MacArthur Foundation, Mellon Foundation, NEH, NIH, NSF, Open Philanthropy, Open Society Foundations, Rockefeller Foundation, Russell Sage Foundation, Robert Wood Johnson Foundation, Sloan Foundation, Social Science Research Council, John Templeton Foundation, and USASpending.gov.

Quadkeyr functions generate raster images based on QuadKey-identified data, facilitating efficient integration of Tile Maps data into R workflows. In particular, Quadkeyr provides support to process and analyze Facebook mobility datasets within the R environment.

Based on individual market shares of all participants in a market or space, the package offers a set of different structural and concentration measures frequently - and not so frequently - used in research and in practice. Measures can be calculated in groups or individually. The calculated measure or the resulting vector in table format should help practitioners make more informed decisions. Methods used in this package are from: 1. Chang, E. J., Guerra, S. M., de Souza Penaloza, R. A. & Tabak, B. M. (2005) “Banking concentration: the Brazilian case”. 2. Cobham, A. and A. Summer (2013). “Is It All About the Tails? The Palma Measure of Income Inequality”. 3. Garcia Alba Idunate, P. (1994). “Un Indice de dominancia para el analisis de la estructura de los mercados”. 4. Ginevicius, R. and S. Cirba (2009). “Additive measurement of market concentration” doi:10.3846/1611-1699.2009.10.191-198. 5. Herfindahl, O. C. (1950), “Concentration in the steel industry” (PhD thesis). 6. Hirschmann, A. O. (1945), “National power and structure of foreign trade”. 7. Melnik, A., O. Shy, and R. Stenbacka (2008), “Assessing market dominance” doi:10.1016/j.jebo.2008.03.010. 8. Palma, J. G. (2006). “Globalizing Inequality: Centrifugal and Centripetal Forces at Work”. 9. Shannon, C. E. (1948). “A Mathematical Theory of Communication”. 10. Simpson, E. H. (1949). “Measurement of Diversity” doi:10.1038/163688a0.

A programmatic interface to many species occurrence data sources, including Global Biodiversity Information Facility (GBIF), iNaturalist, 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.

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
18. Bonello, G., Grillo, M., Cecchetto, M., Giallain, M., Granata, A., Guglielmo, L., … Schiaparelli, S. (2020). Distributional records of Ross Sea (Antarctica) planktic Copepoda from bibliographic data and samples curated at the Italian National Antarctic Museum (MNA): checklist of species collected in the Ross Sea sector from 1987 to 1995. ZooKeys, 969, 1–22. https://doi.org/10.3897/zookeys.969.52334
19. Milanesi, P., Mori, E., & Menchetti, M. (2020). Observer‐oriented approach improves species distribution models from citizen science data. Ecology and Evolution, 10(21), 12104–12114. https://doi.org/10.1002/ece3.6832
20. Fassio, G., Russini, V., Buge, B., Schiaparelli, S., Modica, M. V., Bouchet, P., & Oliverio, M. (2020). High cryptic diversity in the kleptoparasitic genus Hyalorisia Dall, 1889 (Littorinimorpha: Capulidae) with the description of nine new species from the Indo-West Pacific. Journal of Molluscan Studies, 86(4), 401–421. https://doi.org/10.1093/mollus/eyaa028
21. Lozano, V. (2021). Distribution of Five Aquatic Plants Native to South America and Invasive Elsewhere under Current Climate. Ecologies, 2(1), 27–42. doi:10.3390/ecologies2010003
22. Escobar, S., Helmstetter, A. J., Jarvie, S., Montúfar, R., Balslev, H., & Couvreur, T. L. P. (2021). Pleistocene climatic fluctuations promoted alternative evolutionary histories in Phytelephas aequatorialis, an endemic palm from western Ecuador. Journal of Biogeography, 48(5), 1023–1037. doi:10.1111/jbi.14055

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.

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
25. Waldock, C. A., De Palma, A., Borges, P. A. V., & Purvis, A. (2020). Insect occurrence in agricultural land‐uses depends on realized niche and geographic range properties. Ecography. https://doi.org/10.1111/ecog.05162
26. Colli‐Silva, M., Reginato, M., Cabral, A., Forzza, R. C., Pirani, J. R., & Vasconcelos, T. N. da C. (2020). Evaluating shortfalls and spatial accuracy of biodiversity documentation in the Atlantic Forest, the most diverse and threatened Brazilian phytogeographic domain. TAXON, 69(3), 567–577. https://doi.org/10.1002/tax.12239
27. Sanchez‐Martinez, P., Martínez‐Vilalta, J., Dexter, K. G., Segovia, R. A., & Mencuccini, M. (2020). Adaptation and coordinated evolution of plant hydraulic traits. Ecology Letters. https://doi.org/10.1111/ele.13584
28. Reimuth, J., & Zotz, G. (2020). The biogeography of the megadiverse genus Anthurium (Araceae). Botanical Journal of the Linnean Society, 194(2), 164-176. https://doi.org/10.1093/botlinnean/boaa044
29. Polaina, E., Pärt, T., & Recio, M. R. (2020). Identifying hotspots of invasive alien terrestrial vertebrates in Europe to assist transboundary prevention and control. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-68387-3
30. Mothes, C. C., Howell, H. J., & Searcy, C. A. (2020). Habitat suitability models for the imperiled wood turtle (Glyptemys insculpta) raise concerns for the species’ persistence under future climate change. Global Ecology and Conservation, 24, e01247. https://doi.org/10.1016/j.gecco.2020.e01247
31. Nania, D., Flecks, M., & Rödder, D. (2020). Continuous expansion of the geographic range linked to realized niche expansion in the invasive Mourning gecko Lepidodactylus lugubris (Duméril & Bibron, 1836). PLOS ONE, 15(7), e0235060. https://doi.org/10.1371/journal.pone.0235060
32. Brightly, W. H., Hartley, S. E., Osborne, C. P., Simpson, K. J., & Strömberg, C. A. E. (2020). High silicon concentrations in grasses are linked to environmental conditions and not associated with C4 photosynthesis. Global Change Biology. https://doi.org/10.1111/gcb.15343
33. Paton, A., Antonelli, A., Carine, M., Forzza, R. C., Davies, N., Demissew, S., … Dickie, J. (2020). Plant and fungal collections: Current status, future perspectives. PLANTS, PEOPLE, PLANET, 2(5), 499–514. https://doi.org/10.1002/ppp3.10141
34. Carrillo, J. D., Faurby, S., Silvestro, D., Zizka, A., Jaramillo, C., Bacon, C. D., & Antonelli, A. (2020). Disproportionate extinction of South American mammals drove the asymmetry of the Great American Biotic Interchange. Proceedings of the National Academy of Sciences, 117(42), 26281–26287. https://doi.org/10.1073/pnas.2009397117
35. Figueroa, H., & Smith, S. A. (2020). A targeted phylogenetic approach helps explain New World functional diversity patterns of two eudicot lineages. Journal of Biogeography. https://doi.org/10.1111/jbi.13993
36. Simpson, K. J., Jardine, E. C., Archibald, S., Forrestel, E. J., Lehmann, C. E. R., Thomas, G. H., & Osborne, C. P. (2020). Resprouting grasses are associated with less frequent fire than seeders. New Phytologist. https://doi.org/10.1111/nph.17069
37. Bello, C., Cintra, A. L. P., Barreto, E., Vancine, M. H., Sobral-Souza, T., Graham, C. H., & Galetti, M. (2020). Environmental niche and functional role similarity between invasive and native palms in the Atlantic Forest. Biological Invasions. https://doi.org/10.1007/s10530-020-02400-8
38. Roigé, M., & Phillips, C. B. (2021). Validation and uncertainty analysis of the match climates regional algorithm (CLIMEX) for Pest risk analysis. Ecological Informatics, 61, 101196. https://doi.org/10.1016/j.ecoinf.2020.101196
39. Panter, C. T., Clegg, R. L., Moat, J., Bachman, S. P., Klitgård, B. B., & White, R. L. (2020). To clean or not to clean: Cleaning open‐source data improves extinction risk assessments for threatened plant species. Conservation Science and Practice, 2(12). https://doi.org/10.1111/csp2.311
40. Chowdhury, S., Braby, M. F., Fuller, R. A., & Zalucki, M. P. (2020). Coasting along to a wider range: niche conservatism in the recent range expansion of the Tawny Coster, Acraea terpsicore (Lepidoptera: Nymphalidae). Diversity and Distributions. https://doi.org/10.1111/ddi.13200
41. Farooq, H., Azevedo, J. A. R., Soares, A., Antonelli, A., & Faurby, S. (2020). Mapping Africa’s Biodiversity: More of the Same Is Just Not Good Enough. Systematic Biology. https://doi.org/10.1093/sysbio/syaa090
42. Tamme, R., Pärtel, M., Kõljalg, U., Laanisto, L., Liira, J., Mander, Ü., … Zobel, M. (2020). Global macroecology of nitrogen‐fixing plants. Global Ecology and Biogeography, 30(2), 514–526. https://doi.org/10.1111/geb.13236
43. Esquivel, D. A., Aya-Cuero, C., Penagos, A. P., Chacón-Pacheco, J., Agámez-López, C. J., Ochoa, A. V., … Bennett, D. (2020). Updating the distribution of Vampyrum spectrum (Chiroptera, Phyllostomidae) in Colombia: new localities, potential distribution and notes on its conservation. Neotropical Biology and Conservation, 15(4), 689–709. https://doi.org/10.3897/neotropical.15.e58383
44. Suissa, J. S., & Sundue, M. A. (2020). Diversity Patterns of Neotropical Ferns: Revisiting Tryon’s Centers of Richness and Endemism. American Fern Journal, 110(4). https://doi.org/10.1640/0002-8444-110.4.211
45. BELLO, A., MUKHTAR, F. B., & MUELLNER-RIEHL, A. N. (2021). DIVERSITY AND DISTRIBUTION OF NIGERIAN LEGUMES (FABACEAE). Phytotaxa, 480(2), 103–124. doi:10.11646/phytotaxa.480.2.1
46. Delso, A., Muñoz, J., & Fajardo, J. (2021). Protected Area Networks Do Not Represent Unseen Diversity. doi:10.21203/rs.3.rs-145219/v1
47. Escobar, S., Helmstetter, A. J., Jarvie, S., Montúfar, R., Balslev, H., & Couvreur, T. L. P. (2021). Pleistocene climatic fluctuations promoted alternative evolutionary histories in Phytelephas aequatorialis, an endemic palm from western Ecuador. Journal of Biogeography, 48(5), 1023–1037. doi:10.1111/jbi.14055
48. Ryeland, J., Derham, T. T., & Spencer, R. J. (2021). Past and future potential range changes in one of the last large vertebrates of the Australian continent, the emu Dromaius novaehollandiae. Scientific Reports, 11(1). doi:10.1038/s41598-020-79551-0

An interface to the API for arXiv, a repository of electronic preprints for computer science, mathematics, physics, quantitative biology, quantitative finance, and statistics.

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

Includes functions to wrap most endpoints of the PaleobioDB API and functions to visualize and process the fossil data. The API documentation for the Paleobiology Database can be found at https://paleobiodb.org/data1.2/.

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
5. Carrillo, J. D., Faurby, S., Silvestro, D., Zizka, A., Jaramillo, C., Bacon, C. D., & Antonelli, A. (2020). Disproportionate extinction of South American mammals drove the asymmetry of the Great American Biotic Interchange. Proceedings of the National Academy of Sciences, 117(42), 26281–26287. https://doi.org/10.1073/pnas.2009397117

Provides functions and dictionaries for recoding of freetext gender responses into more consistent categories.

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.

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
5. Stevens, M. C., Chen, Y., Stringer, A., Clemmow, C., & Jones, L. A. (2020). Key factors driving obesity in the UK. http://london.gisruk.org/gisruk2020_proceedings/GISRUK2020_paper_17.pdf

Provides classes for storing and manipulating taxonomic data. Most of the classes can be treated like base R vectors (e.g. can be used in tables as columns and can be named). Vectorized classes can store taxon names and authorities, taxon IDs from databases, taxon ranks, and other types of information. More complex classes are provided to store taxonomic trees and user-defined data associated with them.

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
2. Harvey, B. P., Kerfahi, D., Jung, Y., Shin, J.-H., Adams, J. M., & Hall-Spencer, J. M. (2020). Ocean acidification alters bacterial communities on marine plastic debris. Marine Pollution Bulletin, 161, 111749. https://doi.org/10.1016/j.marpolbul.2020.111749

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.

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, ChemSpider, ETOX, Flavornet, NIST Chemistry WebBook, OPSIN, PubChem, SRS, Wikidata.

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
9. Costanzi, S., Slavick, C. K., Hutcheson, B. O., Koblentz, G. D., & Cupitt, R. T. (2020). Lists of Chemical Warfare Agents and Precursors from International Nonproliferation Frameworks: Structural Annotation and Chemical Fingerprint Analysis. Journal of Chemical Information and Modeling, 60(10), 4804–4816. https://doi.org/10.1021/acs.jcim.0c00896
10. Islam, S. M., Hossain, S. M. M., & Ray, S. (2020). DTI-SNNFRA: Drug-Target interaction prediction by shared nearest neighbors and fuzzy-rough approximation. arXiv preprint arXiv:2009.10766 https://arxiv.org/abs/2009.10766.
11. Hammoud, Z., & Kramer, F. (2020). Multipath: An R Package to Generate Integrated Reproducible Pathway Models. Biology, 9(12), 483. https://doi.org/10.3390/biology9120483
12. Su, Q.-Z., Vera, P., Nerín, C., Lin, Q.-B., & Zhong, H.-N. (2021). Safety concerns of recycling postconsumer polyolefins for food contact uses: Regarding (semi-)volatile migrants untargetedly screened. Resources, Conservation and Recycling, 167, 105365. https://doi.org/10.1016/j.resconrec.2020.105365

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.

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

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
2. Bennie, J. A., De Cocker, K., Smith, J. J., & Wiesner, G. H. (2020). The epidemiology of muscle-strengthening exercise in Europe: A 28-country comparison including 280,605 adults. PLOS ONE, 15(11), e0242220. https://doi.org/10.1371/journal.pone.0242220

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.

1. Hosford, K., & Winters, M. 2019. Quantifying the Bicycle Share Gender Gap. Transport Findings, November. https://doi.org/10.32866/10802
2. Morton, C. (2020). The demand for cycle sharing: Examining the links between weather conditions, air quality levels, and cycling demand for regular and casual users. Journal of Transport Geography, 88, 102854. doi:10.1016/j.jtrangeo.2020.102854

Functions for the creation and manipulation of scenes and objects within the Unity 3D video game engine (https://unity.com/). Specific focuses include the creation and import of terrain data and GameObjects as well as scene management.

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.

1. Walker, V. K., Das, P., Li, P., Lougheed, S. C., Moniz, K., Schott, S., … Koch, I. (2020). Identification of Arctic Food Fish Species for Anthropogenic Contaminant Testing Using Geography and Genetics. Foods, 9(12), 1824. https://doi.org/10.3390/foods9121824

Data that are collected through online sources such as Mechanical Turk may require excluding rows because of IP address duplication, geolocation, or completion duration. This package facilitates exclusion of these data for Qualtrics datasets.

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.

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.

Client for World Register of Marine Species (https://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.

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
4. Webb, T. J., & Vanhoorne, B. (2020). Linking dimensions of data on global marine animal diversity. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1814), 20190445. https://doi.org/10.1098/rstb.2019.0445

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.

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
6. Patel, S. H., Winton, M. V., Hatch, J. M., Haas, H. L., Saba, V. S., Fay, G., & Smolowitz, R. J. (2021). Projected shifts in loggerhead sea turtle thermal habitat in the Northwest Atlantic Ocean due to climate change. Scientific Reports, 11(1). doi:10.1038/s41598-021-88290-9

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.

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
3. Laceby, J. P., Batista, P. V. G., Taube, N., Kruk, M. K., Chung, C., Evrard, O., … Kerr, J. G. (2021). Tracing total and dissolved material in a western Canadian basin using quality control samples to guide the selection of fingerprinting parameters for modelling. CATENA, 200, 105095. https://doi.org/10.1016/j.catena.2020.105095

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-interactions.github.io/mangal-api/).

ReLTER provides access to DEIMS-SDR (https://deims.org/), and allows interaction with data and software implemented by eLTER Research Infrastructure (RI) thus improving data sharing among European LTER projects. ReLTER uses the R language to access and interact with the DEIMS-SDR archive of information shared by the Long Term Ecological Research (LTER) network. This package grew within eLTER H2020 as a major project that will help advance the development of European Long-Term Ecosystem Research Infrastructures (eLTER RI - https://elter-ri.eu). The ReLTER package functions in particular allow to: - retrieve the information about entities (e.g. sites, datasets, and activities) shared by DEIMS-SDR (see e.g. get_site_info function); - interact with the ODSEurope starting with the dataset shared by DEIMS-SDR (see e.g. get_site_ODS function); - use the eLTER site informations to download and crop geospatial data from other platforms (see e.g. get_site_ODS function); - improve the quality of the dataset (see e.g. get_id_worms). Functions currently implemented are derived from discussions of the needs among the eLTER users community. The ReLTER package will continue to follow the progress of eLTER-RI and evolve, adding new tools and improvements as required.

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.

A set of convenience functions as well as geographical/political data about Nigeria, aimed at simplifying work with data and information that are specific to the country.

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

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.net/ij/. Also supports text image I/O.

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
3. Germani, E., Lelouard, H., & Fallet, M. (2020). SAPHIR: a Shiny application to analyze tissue section images. F1000Research, 9, 1276. https://doi.org/10.12688/f1000research.27062.1

Generate estimated body size distributions for populations or communities of birds, given either species ID or species’ mean body size. Designed to work naturally with the North American Breeding Bird Survey, or with any dataset of bird species, abundance, and/or mean size data.

Allows users to access the Oregon State Prism climate data (https://prism.nacse.org/). 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.

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.

Utility to parse a bibtex file.

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.

An interface to the fast_matrix_market C++ library, this package offers efficient read and write operations for Matrix Market files in R. It supports both sparse and dense matrix formats. Peer-reviewed at ROpenSci (https://github.com/ropensci/software-review/issues/606).

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.

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

A high performance interface to the Global Biodiversity Information Facility, GBIF. In contrast to rgbif, which can access small subsets of GBIF data through web-based queries to a central server, gbifdb provides enhanced performance for R users performing large-scale analyses on servers and cloud computing providers, providing full support for arbitrary SQL or dplyr operations on the complete GBIF data tables (now over 1 billion records, and over a terabyte in size). gbifdb accesses a copy of the GBIF data in parquet format, which is already readily available in commercial computing clouds such as the Amazon Open Data portal and the Microsoft Planetary Computer, or can be accessed directly without downloading, or downloaded to any server with suitable bandwidth and storage space. The high-performance techniques for local and remote access are described in https://duckdb.org/why_duckdb and https://arrow.apache.org/docs/r/articles/fs.html respectively.

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 users computer where the file is stored, making it simple to use suppdata() with standard functions like read.csv().

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

There are a number of binary files associated with the Webdriver/Selenium project. This package provides functions to download these binaries and to manage processes involving them.

A client for the Environmental Data Initiative repository REST API. The EDI data repository https://portal.edirepository.org/nis/home.jsp is for publication and reuse of ecological data with emphasis on metadata accuracy and completeness. It is built upon the PASTA+ software stack https://pastaplus-core.readthedocs.io/en/latest/index.html# and was developed in collaboration with the US LTER Network https://lternet.edu/. EDIutils includes functions to search and access existing data, evaluate and upload new data, and assist other data management tasks common to repository users.

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.

Calculate magnetic field at a given location and time according to the World Magnetic Model (WMM). Both the main field and secular variation components are returned. This functionality is useful for physicists and geophysicists who need orthogonal components from WMM. Currently, this package supports annualized time inputs between 2000 and 2025. If desired, users can specify which WMM version to use, e.g., the original WMM2015 release or the recent out-of-cycle WMM2015 release. Methods used to implement WMM, including the Gauss coefficients for each release, are described in the following publications: Chulliat et al (2020) doi:10.25923/ytk1-yx35, Chulliat et al (2019) doi:10.25921/xhr3-0t19, Chulliat et al (2015) doi:10.7289/V5TB14V7, Maus et al (2010) https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/WMM2010_Report.pdf, McLean et al (2004) https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/TRWMM_2005.pdf, and Macmillian et al (2000) https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/wmm2000.pdf.

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.

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

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 (https://github.com/beast-dev/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.

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.

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
3. Parent, S.-É., Lafond, J., Paré, M. C., Parent, L. E., & Ziadi, N. (2020). Conditioning Machine Learning Models to Adjust Lowbush Blueberry Crop Management to the Local Agroecosystem. Plants, 9(10), 1401. https://doi.org/10.3390/plants9101401
4. Layton, K. K. S., Snelgrove, P. V. R., Dempson, J. B., Kess, T., Lehnert, S. J., Bentzen, P., … Bradbury, I. R. (2021). Genomic evidence of past and future climate-linked loss in a migratory Arctic fish. Nature Climate Change, 11(2), 158–165. doi:10.1038/s41558-020-00959-7

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.

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.

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.

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.

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; and serializing to/from Geobuf binary GeoJSON format.

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.

Provides a set of R bindings for the Selenium 2.0 WebDriver (see https://www.selenium.dev/documentation/ 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.

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
16. Göbel, S. 2020. Voting and Social Media-Based Political Participation. https://doi.org/10.31235/osf.io/sjq4g
17. Mancosu, M., & Vegetti, F. (2020). What You Can Scrape and What Is Right to Scrape: A Proposal for a Tool to Collect Public Facebook Data. Social Media + Society, 6(3), 205630512094070. https://doi.org/10.1177/2056305120940703
18. Gessa, A., Jiménez, A., & Sancha, P. (2020). Open Innovation in Digital Healthcare: Users’ Discrimination between Certified and Non-Certified mHealth Applications. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 130. https://doi.org/10.3390/joitmc6040130
19. Simpson, R. B., Gottlieb, J., Zhou, B., Hartwick, M. A., & Naumova, E. N. (2021). Completeness of open access FluNet influenza surveillance data for Pan-America in 2005–2019. Scientific Reports, 11(1). doi:10.1038/s41598-020-80842-9

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.

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

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.

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.

1. August, T. A., Pescott, O. L., Joly, A., & Bonnet, P. (2020). AI Naturalists Might Hold the Key to Unlocking Biodiversity Data in Social Media Imagery. Patterns, 1(7), 100116. https://doi.org/10.1016/j.patter.2020.100116

Search plant phenology data aggregated from several sources and available on the Global Plant Phenology Data Portal.

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.

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
57. Davies, A. D., Lewis, Z., & Dougherty, L. R. (2020). A meta-analysis of factors influencing the strength of mate-choice copying in animals. Behavioral Ecology. https://doi.org/10.1093/beheco/araa064
58. Kuchta, R., Řehulková, E., Francová, K., Scholz, T., Morand, S., & Šimková, A. (2020). Diversity of monogeneans and tapeworms in cypriniform fishes across two continents. International Journal for Parasitology, 50(10-11), 771–786. https://doi.org/10.1016/j.ijpara.2020.06.005
59. Atsumi, K., Lagisz, M., & Nakagawa, S. (2020). Non-additive genetic effects induce novel phenotypic distributions in male mating traits of F1 hybrids https://ecoevorxiv.org/kt3ud/download?format=pdf
60. Geffroy, B., Sadoul, B., Putman, B. J., Berger-Tal, O., Garamszegi, L. Z., Møller, A. P., & Blumstein, D. T. (2020). Evolutionary dynamics in the Anthropocene: Life history and intensity of human contact shape antipredator responses. PLOS Biology, 18(9), e3000818. https://doi.org/10.1371/journal.pbio.3000818
61. Xiao, W., Chen, C., Chen, X., Huang, Z., & Chen, H. Y. H. (2020). Functional and phylogenetic diversity promote litter decomposition across terrestrial ecosystems. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13181
62. Wilkes, M. A., Edwards, F., Jones, J. I., Murphy, J. F., England, J., Friberg, N., … Brown, L. E. (2020). Trait‐based ecology at large scales: Assessing functional trait correlations, phylogenetic constraints and spatial variability using open data. Global Change Biology. https://doi.org/10.1111/gcb.15344
63. Marshall, D. J., & Alvarez-Noriega, M. (2020). Projecting marine developmental diversity and connectivity in future oceans. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1814), 20190450. https://doi.org/10.1098/rstb.2019.0450
64. Kunc, H. P., & Schmidt, R. (2020). Species sensitivities to a global pollutant: A meta‐analysis on acoustic signals in response to anthropogenic noise. Global Change Biology, 27(3), 675–688. https://doi.org/10.1111/gcb.15428
65. Dániel-Ferreira, J., Bommarco, R., Wissman, J., & Öckinger, E. (2020). Linear infrastructure habitats increase landscape-scale diversity of plants but not of flower-visiting insects. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-78090-y
66. Sandoval-Herrera, N. I., Mastromonaco, G. F., Becker, D. J., Simmons, N. B., & Welch, K. C. (2021). Inter- and intra-specific variation in hair cortisol concentrations of Neotropical bats. https://doi.org/10.1101/2021.01.10.426004
67. Murphy, R., Palm, M., Mustonen, V., Warringer, J., Farewell, A., Parts, L., & Moradigaravand, D. (2021). Genomic Epidemiology and Evolution of Escherichia coli in Wild Animals in Mexico. mSphere, 6(1). doi:10.1128/msphere.00738-20
68. Dougherty, L. R. (2021). Meta‐analysis shows the evidence for context‐dependent mating behaviour is inconsistent or weak across animals. Ecology Letters, 24(4), 862–875. doi:10.1111/ele.13679
69. Andreu-Sánchez, S., Chen, W., Stiller, J., & Zhang, G. (2021). Multiple origins of a frameshift insertion in a mitochondrial gene in birds and turtles. GigaScience, 10(1). doi:10.1093/gigascience/giaa161

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/.

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.

1. Robitaille, A. L., Webber, Q. M. R., Turner, J. W., & Wal Eric, V. (2020). The problem and promise of scale in multilayer animal social networks. Current Zoology. https://doi.org/10.1093/cz/zoaa052

AIMS Data Platform API Client which provides easy access to AIMS Data Platform scientific data and information.

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 APIs for indices’, documents, nodes, clusters, an interface to the cat API, and more.

Provides functions to analyze the spatial distribution of biodiversity, in particular categorical analysis of neo- and paleo-endemism (CANAPE) as described in Mishler et al (2014) doi:10.1038/ncomms5473. canaper conducts statistical tests to determine the types of endemism that occur in a study area while accounting for the evolutionary relationships of species.

A tiny package to generate CRediT author statements (https://credit.niso.org/). It provides three functions: create a template, read it back and generate the CRediT author statement in a text file.

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.

Interface to Phylocom (https://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.

1. Perez, T. M., & Feeley, K. J. (2020). Weak phylogenetic and climatic signals in plant heat tolerance. Journal of Biogeography. https://doi.org/10.1111/jbi.13984
2. Perez, T. M., Socha, A., Tserej, O., & Feeley, K. J. (2021). Photosystem II heat tolerances characterize thermal generalists and the upper limit of carbon assimilation. Plant, Cell & Environment. https://doi.org/10.1111/pce.13990

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.

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.

Automate the Transformation of a Google Document into a Quarto Book source.

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.

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

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.

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
12. Dion, M. L., Mitchell, S. M., & Sumner, J. L. (2020). Gender, seniority, and self-citation practices in political science. Scientometrics, 125(1), 1–28. https://doi.org/10.1007/s11192-020-03615-1
13. Puschmann, C., & Pentzold, C. (2020). A field comes of age: tracking research on the internet within communication studies, 1994 to 2018. Internet Histories, 1–19. https://doi.org/10.1080/24701475.2020.1749805
14. Benard, S., & Correll, S. J. (2010). Normative Discrimination and the Motherhood Penalty. Gender & Society, 24(5), 616–646. https://doi.org/10.1177/0891243210383142
15. Clayson, P. E., Baldwin, S., & Larson, M. J. (2020). The Open Access Advantage for Studies of Human Electrophysiology: Impact on Citations and Altmetrics. https://doi.org/10.31234/osf.io/5xagd

Fast categorization of items based on external code data identified by regular expressions. A typical use case considers patient with medically coded data, such as codes from the International Classification of Diseases (ICD) or the Anatomic Therapeutic Chemical (ATC) classification system. Functions of the package relies on a triad of objects: (1) case data with unit id:s and possible dates of interest; (2) external code data for corresponding units in (1) and with optional dates of interest and; (3) a classification scheme (classcodes object) with regular expressions to identify and categorize relevant codes from (2). It is easy to introduce new classification schemes (classcodes objects) or to use default schemes included in the package. Use cases includes patient categorization based on comorbidity indices such as Charlson, Elixhauser, RxRisk V, or the comorbidity-polypharmacy score (CPS), as well as adverse events after hip and knee replacement surgery.

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.

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

Tools to import, clean, and visualize movement data, particularly from motion capture systems such as Optitracks Motive, the Straw Labs Flydra, or from other sources. We provide functions to remove artifacts, standardize tunnel position and tunnel axes, select a region of interest, isolate specific trajectories, fill gaps in trajectory data, and calculate 3D and per-axis velocity. For experiments of visual guidance, we also provide functions that use subject position to estimate perception of visual stimuli.

Parse messy geographic coordinates from various character 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.

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

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

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

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.

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

Geocode with the OpenCage API, either from place name to longitude and latitude (forward geocoding) or from longitude and latitude to the name and address of a location (reverse geocoding), see https://opencagedata.com.

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

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.

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 graphql package.

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.

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

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.

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
14. Behrendt, I., Fasshauer, M., & Eichner, G. (2020). Gluten intake and metabolic health: conflicting findings from the UK Biobank. European Journal of Nutrition. https://doi.org/10.1007/s00394-020-02351-9
15. Aragão e Pina, J., Passos, A. M., Maynard, M. T., & Sinval, J. (2021). Self-efficacy, mental models and team adaptation: A first approach on football and futsal refereeing. Psychology of Sport and Exercise, 52, 101787. https://doi.org/10.1016/j.psychsport.2020.101787
16. España, S., Ochoa de Olza, M., Sala, N., Piulats, J. M., Ferrandiz, U., Etxaniz, O., … Font, A. (2020). PSA Kinetics as Prognostic Markers of Overall Survival in Patients with Metastatic Castration-Resistant Prostate Cancer Treated with Abiraterone Acetate. Cancer Management and Research, Volume 12, 10251–10260. https://doi.org/10.2147/cmar.s270392
17. Wadley, A. L., Venter, W. D. F., Moorhouse, M., Akpomiemie, G., Serenata, C., Hill, A., … Kamerman, P. R. (2020). High individual pain variability in people living with HIV: A graphical analysis. European Journal of Pain, 25(1), 160–170. https://doi.org/10.1002/ejp.1658
18. Wadley AL, Venter WDF, Moorhouse M, Akpomiemie G, Serenata C, Hill A, Sokhela S, Mqamelo N, Kamerman PR. High individual pain variability in people living with HIV: A graphical analysis. Eur J Pain 2020. https://doi.org/10.1002/ejp.1658
19. Schrag, N. F. D., Apley, M. D., Godden, S. M., Lubbers, B. V., & Singer, R. S. (2020). Antimicrobial use quantification in adult dairy cows – Part 1 – Standardized regimens as a method for describing antimicrobial use. Zoonoses and Public Health, 67(S1), 51–68. https://doi.org/10.1111/zph.12766
20. Nopp-Mayr, U., Reimoser, S., Reimoser, F., Sachser, F., Obermair, L., & Gratzer, G. (2020). Analyzing long-term impacts of ungulate herbivory on forest-recruitment dynamics at community and species level contrasting tree densities versus maximum heights. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-76843-3
21. Behrendt, I., Fasshauer, M., & Eichner, G. (2020). Gluten Intake and All-Cause and Cause-Specific Mortality: Prospective Findings from the UK Biobank. The Journal of Nutrition, 151(3), 591–597. https://doi.org/10.1093/jn/nxaa387

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 https://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.

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

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/).

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
17. Perez, T. M., & Feeley, K. J. (2020). Weak phylogenetic and climatic signals in plant heat tolerance. Journal of Biogeography. https://doi.org/10.1111/jbi.13984
18. Huang, M., Ding, L., Wang, J., Ding, C., & Tao, J. (2021). The impacts of climate change on fish growth: A summary of conducted studies and current knowledge. Ecological Indicators, 121, 106976. https://doi.org/10.1016/j.ecolind.2020.10697
19. Huang, M., Ding, L., Wang, J., Ding, C., & Tao, J. (2021). The impacts of climate change on fish growth: A summary of conducted studies and current knowledge. Ecological Indicators, 121, 106976. doi:10.1016/j.ecolind.2020.106976
20. Perez, T. M., Socha, A., Tserej, O., & Feeley, K. J. (2021). Photosystem II heat tolerances characterize thermal generalists and the upper limit of carbon assimilation. Plant, Cell & Environment. https://doi.org/10.1111/pce.13990

NOTE: This package is deprecated. Please use the neotoma2 package described at https://github.com/NeotomaDB/neotoma2. Access paleoecological datasets from the Neotoma Paleoecological Database using the published API (http://wnapi.neotomadb.org/), only containing datasets uploaded prior to June 2020. 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.

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
6. Fastovich, D., Russell, J. M., Jackson, S. T., Krause, T. R., Marcott, S. A., & Williams, J. W. (2020). Spatial Fingerprint of Younger Dryas Cooling and Warming in Eastern North America. Geophysical Research Letters. https://doi.org/10.1029/2020gl090031
7. Chevalier, M., Davis, B. A. S., Heiri, O., Seppä, H., Chase, B. M., Gajewski, K., … Kupriyanov, D. (2020). Pollen-based climate reconstruction techniques for late Quaternary studies. Earth-Science Reviews, 210, 103384. https://doi.org/10.1016/j.earscirev.2020.103384
8. Byun, E., Sato, H., Cowling, S. A., & Finkelstein, S. A. (2020). Extensive wetland development in mid-latitude North America during the Bølling–Allerød. Nature Geoscience, 14(1), 30–35. https://doi.org/10.1038/s41561-020-00670-4
9. Teale, C., & Chang, J. (2021). Fabaceae (legume) pollen as an anthropogenic indicator in eastern North America. Vegetation History and Archaeobotany. doi:10.1007/s00334-020-00815-w

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.

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

Access the United States National Provider Identifier Registry API https://npiregistry.cms.hhs.gov/api/. Obtain and transform administrative data linked to a specific individual or organizational healthcare provider, or perform advanced searches based on provider name, location, type of service, credentials, and other attributes exposed by the API.

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.

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

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.

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
15. Becker, D. J., & Han, B. A. (2020). The macroecology and evolution of avian competence for Borrelia burgdorferi. bioRxiv https://doi.org/10.1101/2020.04.15.040352
16. Greenville, A. C., Newsome, T. M., Wardle, G. M., Dickman, C. R., Ripple, W. J., & Murray, B. R. (2020). Simultaneously operating threats cannot predict extinction risk. Conservation Letters. https://doi.org/10.1111/conl.12758
17. Mothes, C. C., Stemle, L. R., Fonseca, T. N., Clements, S. L., Howell, H. J., & Searcy, C. A. (2020). Protect or perish: Quantitative analysis of state‐level species protection supports preservation of the Endangered Species Act. Conservation Letters. https://doi.org/10.1111/conl.12761
18. Alhajeri, B. H., Fourcade, Y., Upham, N. S., & Alhaddad, H. (2020). A global test of Allen’s rule in rodents. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13198
19. Etard, A., Morrill, S., & Newbold, T. (2020). Global gaps in trait data for terrestrial vertebrates. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13184
20. Alò, D., Lacy, S. N., Castillo, A., Samaniego, H. A., & Marquet, P. A. (2020). The macroecology of fish migration. Global Ecology and Biogeography, 30(1), 99–116. https://doi.org/10.1111/geb.13199
21. Murgier, J., McLean, M., Maire, A., Mouillot, D., Loiseau, N., Munoz, F., … Auber, A. (2021). Rebound in functional distinctiveness following warming and reduced fishing in the North Sea. Proceedings of the Royal Society B: Biological Sciences, 288(1942), 20201600. https://doi.org/10.1098/rspb.2020.1600
22. DE LA TORRE, G. M., & CAMPIÃO, K. M. (2021). Bird habitat preferences drive hemoparasite infection in the Neotropical region. Integrative Zoology. doi:10.1111/1749-4877.12515

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/.

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.

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

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

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).

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

The goal of exoplanets is to provide access to NASA’s Exoplanet Archive TAP Service. For more information regarding the API please read the documentation https://exoplanetarchive.ipac.caltech.edu/index.html.

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.

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

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

Aid the user in making queries to the EPA API site found at https://aqs.epa.gov/aqsweb/documents/data_api. This package combines API calling methods from various web scraping packages with specific strings to retrieve data from the EPA API. It also contains easy to use loaded variables that help a user navigate services offered by the API and aid the user in determining the appropriate way to make a an API call.

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.

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.

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

1. Milanesi, P., Mori, E., & Menchetti, M. (2020). Observer‐oriented approach improves species distribution models from citizen science data. Ecology and Evolution, 10(21), 12104–12114. https://doi.org/10.1002/ece3.6832

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.

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.

What the package does (one paragraph).

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/.

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.

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.

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

Download data from Symbiota2 portals using Symbiota’s API. Covers the Checklists, Collections, Crowdsource, Exsiccati, Glossary, ImageProcessor, Key, Media, Occurrence, Reference, Taxa, Traits, and UserRoles API families. Each Symbiota2 portal owner can load their own plugins (and modified code), and so this package may not cover every possible API endpoint from a given Symbiota2 instance.

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.

1. Buil-Gil, D. (2020). Small Area Estimation for Crime Analysis. SocArXiv. https://doi.org/10.31235/osf.io/gtbyu
2. Życzyńska-Ciołek, D., & Kołczyńska, M. (2020). Does Interviewers’ Age Affect Their Assessment of Respondents’ Understanding of Survey Questions? Evidence From the European Social Survey. International Journal of Public Opinion Research. https://doi.org/10.1093/ijpor/edaa015

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.

Standardize and simplify the tree biomass estimation process across globally distributed extratropical forests.

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.

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.

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.

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.

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, e.g. Gardner et. al 2010 doi:10.1176/appi.ajp.2009.09060802 and 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.

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

Contains functions to classify the pixels of an image file (jpeg or tiff) by its colour. It implements a simple form of the techniques known as Support Vector Machine adapted to this particular problem.

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 \codeassociations, which allows searching for fungus-host combinations.

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

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.

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://pubmed.ncbi.nlm.nih.gov/, process the results of those searches and pull data into their R sessions.

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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
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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
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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
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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
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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
50. Ponce, M., & Sandhel, A. (2020). covid19. analytics: An R Package to Obtain, Analyze and Visualize Data from the Corona Virus Disease Pandemic. arXiv preprint arXiv:2009.01091 https://arxiv.org/pdf/2009.01091.
51. Duarte, S., Vieira, P. E., & Costa, F. O. (2020). Assessment of species gaps in DNA barcode libraries of non-indigenous species (NIS) occurring in European coastal regions. Metabarcoding and Metagenomics, 4. https://doi.org/10.3897/mbmg.4.55162
52. Grenn, F. P., Kim, J. J., Makarious, M. B., Iwaki, H., Illarionova, A., … Brolin, K. (2020). The Parkinson’s Disease Genome‐Wide Association Study Locus Browser. Movement Disorders. https://doi.org/10.1002/mds.28197
53. Madritsch, S., Bomers, S., Posekany, A., Burg, A., Birke, R., Emerstorfer, F., … Sehr, E. M. (2020). Integrative transcriptomics reveals genotypic impact on sugar beet storability. Plant Molecular Biology. https://doi.org/10.1007/s11103-020-01041-8
54. Brandão, L. A. C., Agrelli, A., Bernardo, L., Paparella, F., Moura, R., & Crovella, S. (2020). PlatCOVID: A Novel Web Tool to Analyze, Curate and Share COVID-19 Literature. https://doi.org/10.21203/rs.3.rs-42169/v1
55. Carraro, L., Mächler, E., Wüthrich, R., & Altermatt, F. (2020). Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17337-8
56. Ihaka, R., & Gentleman, R. (1996). R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics, 5(3), 299–314. https://doi.org/10.1080/10618600.1996.10474713
57. Batista, E., Lopes, A., & Alves, A. (2020). Botryosphaeriaceae species on forest trees in Portugal: diversity, distribution and pathogenicity. European Journal of Plant Pathology, 158(3), 693–720. https://doi.org/10.1007/s10658-020-02112-8
58. Bandyopadhyay, S., Lysak, N., Adhikari, L., Velez, L. M., Sautina, L., Mohandas, R., … Bihorac, A. (2020). Discovery and Validation of Urinary Molecular Signature of Early Sepsis. Critical Care Explorations, 2(10), e0195. https://doi.org/10.1097/cce.0000000000000195
59. McColl‐Gausden, E. F., Weeks, A. R., Coleman, R. A., Robinson, K. L., Song, S., Raadik, T. A., & Tingley, R. (2020). Multispecies models reveal that eDNA metabarcoding is more sensitive than backpack electrofishing for conducting fish surveys in freshwater streams. Molecular Ecology. https://doi.org/10.1111/mec.15644
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64. Batovska, J., Piper, A., Valenzuela, I., Cunningham, J., & Blacket, M. (2020). Developing a Non-destructive Metabarcoding Protocol for Detection of Pest Insects in Bulk Trap Catches. https://doi.org/10.21203/rs.3.rs-125070/v1

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.

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

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

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.

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

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.

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

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.

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
2. Nguyen, H. Q., Nguyen, V. D., Van Nguyen, H., & Seo, T. S. (2020). Quantification of colorimetric isothermal amplification on the smartphone and its open-source app for point-of-care pathogen detection. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-72095-3

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

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

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.

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
2. Alvarado-Ortega, J., & Díaz-Cruz, J. A. (2021). Hastichthys totonacus sp. nov., a North American Turonian dercetid fish (Teleostei, Aulopiformes) from the Huehuetla quarry, Puebla, Mexico. Journal of South American Earth Sciences, 105, 102900. https://doi.org/10.1016/j.jsames.2020.102900

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

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.

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.