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Access Data from the Oregon State Prism Climate Project

Alan Butler
Description

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

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Interface to the Open Tree of Life API

Francois Michonneau
Description

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

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

R. Kyle Bocinsky
Description

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

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

Scott Chamberlain
Description

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

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

Scott Chamberlain
Description

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

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

Matthew Strimas-Mackey
Description

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

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

Lorenzo Busetto
Description

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

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

Steffi LaZerte
Description

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

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

Download Time Series Data from Waterinfo.be

Stijn Van Hoey
Description

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

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Catalogue of Life Plus Client

Scott Chamberlain
Description

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

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clifro
CRAN

Easily Download and Visualise Climate Data from CliFlo

Blake Seers
Description

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

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

Scott Chamberlain
Description

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

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

Scott Chamberlain
Description

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

Scientific use cases
  1. Amano, T., Lamming, J. D. L., & Sutherland, W. J. (2016). Spatial Gaps in Global Biodiversity Information and the Role of Citizen Science. BioScience, 66(5), 393–400. https://doi.org/10.1093/biosci/biw022
  2. Bartomeus, I., Park, M. G., Gibbs, J., Danforth, B. N., Lakso, A. N., & Winfree, R. (2013). Biodiversity ensures plant-pollinator phenological synchrony against climate change. Ecol Lett, 16(11), 1331–1338. https://doi.org/10.1111/ele.12170
  3. Barve, V. (2014). Discovering and developing primary biodiversity data from social networking sites: A novel approach. Ecological Informatics, 24, 194–199. https://doi.org/10.1016/j.ecoinf.2014.08.008
  4. Bone, R. E., Smith, J. A. C., Arrigo, N., & Buerki, S. (2015). A macro-ecological perspective on crassulacean acid metabolism (CAM) photosynthesis evolution in Afro-Madagascan drylands: Eulophiinae orchids as a case study. New Phytol, 208(2), 469–481. https://doi.org/10.1111/nph.13572
  5. Collins, R., Duarte Ribeiro, E., Nogueira Machado, V., Hrbek, T., & Farias, I. (2015). A preliminary inventory of the catfishes of the lower Rio Nhamundá, Brazil (Ostariophysi, Siluriformes). Biodiversity Data Journal, 3, e4162. https://doi.org/10.3897/bdj.3.e4162
  6. Drozd, P., & Šipoš, J. (2013). R for all (I): Introduction to the new age of biological analyses. Casopis Slezskeho Zemskeho Muzea A, 62(1). https://doi.org/10.2478/cszma-2013-0004
  7. Kong, X., Huang, M., & Duan, R. (2015). SDMdata: A Web-Based Software Tool for Collecting Species Occurrence Records. PLoS ONE, 10(6), e0128295. https://doi.org/10.1371/journal.pone.0128295
  8. Richardson, D. M., Le Roux, J. J., & Wilson, J. R. (2015). Australian acacias as invasive species: lessons to be learnt from regions with long planting histories. Southern Forests: a Journal of Forest Science, 77(1), 31–39. https://doi.org/10.2989/20702620.2014.999305
  9. Turner, K. G., Fréville, H., & Rieseberg, L. H. (2015). Adaptive plasticity and niche expansion in an invasive thistle. Ecology and Evolution, 5(15), 3183–3197. https://doi.org/10.1002/ece3.1599
  10. Verheijen, L. M., Aerts, R., Bönisch, G., Kattge, J., & Van Bodegom, P. M. (2015). Variation in trait trade-offs allows differentiation among predefined plant functional types: implications for predictive ecology. New Phytol, 209(2), 563–575. https://doi.org/10.1111/nph.13623
  11. Zizka, A., & Antonelli, A. (2015). speciesgeocodeR: An R package for linking species occurrences, user-defined regions and phylogenetic trees for biogeography, ecology and evolution. https://doi.org/10.1101/032755
  12. Butterfield, B. J., Copeland, S. M., Munson, S. M., Roybal, C. M., & Wood, T. E. (2016). Prestoration: using species in restoration that will persist now and into the future. Restoration Ecology. https://doi.org/10.1111/rec.12381
  13. Dellinger, A. S., Essl, F., Hojsgaard, D., Kirchheimer, B., Klatt, S., Dawson, W., … Dullinger, S. (2015). Niche dynamics of alien species do not differ among sexual and apomictic flowering plants. New Phytol, 209(3), 1313–1323. https://doi.org/10.1111/nph.13694
  14. Feitosa, Y. O., Absy, M. L., Latrubesse, E. M., & Stevaux, J. C. (2015). Late Quaternary vegetation dynamics from central parts of the Madeira River in Brazil. Acta Botanica Brasilica, 29(1), 120–128. https://doi.org/10.1590/0102-33062014abb3711
  15. Malhado, A. C. M., Oliveira-Neto, J. A., Stropp, J., Strona, G., Dias, L. C. P., Pinto, L. B., & Ladle, R. J. (2015). Climatological correlates of seed size in Amazonian forest trees. Journal of Vegetation Science, 26(5), 956–963. https://doi.org/10.1111/jvs.12301
  16. Werner, G. D. A., Cornwell, W. K., Cornelissen, J. H. C., & Kiers, E. T. (2015). Evolutionary signals of symbiotic persistence in the legume–rhizobia mutualism. Proc Natl Acad Sci USA, 112(33), 10262–10269. https://doi.org/10.1073/pnas.1424030112
  17. Robertson, M. P., Visser, V., & Hui, C. (2016). Biogeo: an R package for assessing and improving data quality of occurrence record datasets. Ecography, 39(4), 394–401. https://doi.org/10.1111/ecog.02118
  18. Davison, J., Moora, M., Opik, M., Adholeya, A., Ainsaar, L., Ba, A., … Zobel, M. (2015). Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science, 349(6251), 970–973. https://doi.org/10.1126/science.aab1161
  19. Curtis, C. A., & Bradley, B. A. (2016). Plant Distribution Data Show Broader Climatic Limits than Expert-Based Climatic Tolerance Estimates. PLOS ONE, 11(11), e0166407. https://doi.org/10.1371/journal.pone.0166407
  20. Dullinger, I., Wessely, J., Bossdorf, O., Dawson, W., Essl, F., Gattringer, A., … Dullinger, S. (2016). Climate change will increase the naturalization risk from garden plants in Europe. Global Ecol. Biogeogr. https://doi.org/10.1111/geb.12512
  21. Groom, Q., Weatherdon, L., & Geijzendorffer, I. R. (2016). Is citizen science an open science in the case of biodiversity observations? Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.12767
  22. Janssens, S. B., Vandelook, F., De Langhe, E., Verstraete, B., Smets, E., Vandenhouwe, I., & Swennen, R. (2016). Evolutionary dynamics and biogeography of Musaceae reveal a correlation between the diversification of the banana family and the geological and climatic history of Southeast Asia. New Phytol, 210(4), 1453–1465. https://doi.org/10.1111/nph.13856
  23. Sanyal, A., & Decocq, G. (2016). Adaptive evolution of seed oil content in angiosperms: accounting for the global patterns of seed oils. BMC Evolutionary Biology, 16(1). https://doi.org/10.1186/s12862-016-0752-7
  24. Gilles, D., Zaiss, R., Blach-Overgaard, A., Catarino, L., Damen, T., Deblauwe, V., et al. (2016). RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys, 74, 1–18. https://doi.org/10.3897/phytokeys.74.9723
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  122. Hernández‐Rojas, A. C., Kluge, J., Krömer, T., Carvajal‐Hernández, C., Silva‐Mijangos, L., Miehe, G., … Kessler, M. (2020). Latitudinal patterns of species richness and range size of ferns along elevational gradients at the transition from tropics to subtropics. Journal of Biogeography, 47(6), 1383–1397. https://doi.org/10.1111/jbi.13841
  123. Scharmüller, A., Schreiner, V. C., & Schäfer, R. B. (2020). Standartox: Standardizing Toxicity Data. Data, 5(2), 46. https://doi.org/10.3390/data5020046
  124. Bohora Schlickmann, M., da Silva, A. C., de Oliveira, L. M., Oliveira Matteucci, D., Domingos Machado, F., Cuchi, T., … Higuchi, P. (2020). Specific leaf area is a potential indicator of tree species sensitive to future climate change in the mixed Subtropical Forests of southern Brazil. Ecological Indicators, 116, 106477. https://doi.org/10.1016/j.ecolind.2020.106477
  125. Joyce, E., Thiele, K., Slik, F., & Crayn, D. (2020). Checklist of the vascular flora of the Sunda-Sahul Convergence Zone. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e51094
  126. Petersen, T. K., Speed, J. D. M., Grøtan, V., & Austrheim, G. (2020). Urban aliens and threatened near-naturals: Land-cover affects the species richness of alien- and threatened species in an urban-rural setting. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-65459-2
  127. Lindberg, C. L., Hanslin, H. M., Schubert, M., Marcussen, T., Trevaskis, B., Preston, J. C., & Fjellheim, S. (2020). Increased above‐ground resource allocation is a likely precursor for independent evolutionary origins of annuality in the Pooideae grass subfamily. New Phytologist. https://doi.org/10.1111/nph.16666
  128. Lenoir, J., Bertrand, R., Comte, L., Bourgeaud, L., Hattab, T., Murienne, J., & Grenouillet, G. (2020). Species better track climate warming in the oceans than on land. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-020-1198-2
  129. Sun, M., Folk, R. A., Gitzendanner, M. A., Soltis, P. S., Chen, Z., Soltis, D. E., & Guralnick, R. P. (2020). Recent accelerated diversification in rosids occurred outside the tropics. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17116-5
  130. Hock, M., Hofmann, R., Essl, F., Pyšek, P., Bruelheide, H., & Erfmeier, A. (2020). Native distribution characteristics rather than functional traits explain preadaptation of invasive species to high‐UV‐B environments. Diversity and Distributions. https://doi.org/10.1111/ddi.13113
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rinat
CRAN

Access iNaturalist Data Through APIs

Stéphane Guillou
Description

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

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Bielefeld Academic Search Engine (BASE) Client

Scott Chamberlain
Description

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

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rrricanes
Peer-reviewed

Web scraper for Atlantic and east Pacific hurricanes and tropical storms

Tim Trice
Description

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

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Mangal Client

Steve Vissault
Description

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

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Accesses Weather Data from the Iowa Environment Mesonet

Maëlle Salmon
Description

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

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

Adam H. Sparks
Description

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

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

Adam H. Sparks
Description

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

Scientific use cases
  1. Charalampopoulos, I. (2020). The R Language as a Tool for Biometeorological Research. Atmosphere, 11(7), 682. https://doi.org/10.3390/atmos11070682
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Global Surface Summary of the Day (GSOD) Weather Data Client

Adam Sparks
Description

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

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

Kauê de Sousa
Description

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

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getCRUCLdata
CRAN Peer-reviewed

CRU CL v. 2.0 Climatology Client

Adam Sparks
Description

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

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Extract and Tidy Canadian Hydrometric Data

Sam Albers
Description

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

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

Scott Chamberlain
Description

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

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fingertipsR
Peer-reviewed

Fingertips Data for Public Health

Sebastian Fox
Description

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

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

William D. Pearse
Description

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

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

Robin Lovelace
Description

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

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

Filipe Teixeira
Description

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

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

Chemical Information from the Web

Tamás Stirling
Description

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

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

Download and Explore Datasets from UCSC Xena Data Hubs

Shixiang Wang
Description

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

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

Carl Boettiger
Description

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

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

Hufkens Koen
Description

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

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

Robin Lovelace
Description

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

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

Noam Ross
Description

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

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

R Interface to the Data Retriever

Henry Senyondo
Description

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

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Download and Process Public Domain Works from Project Gutenberg

David Robinson
Description

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

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neotoma
CRAN

Access to the Neotoma Paleoecological Database Through R

Simon J. Goring
Description

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

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

John Deck
Description

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

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tradestatistics
CRAN Peer-reviewed

Open Trade Statistics API Wrapper and Utility Program

Mauricio Vargas
Description

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

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c14bazAAR

Download and Prepare C14 Dates from Different Source Databases

Clemens Schmid
Description

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

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rsnps
CRAN

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

Julia Gustavsen
Description

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

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

Scott Chamberlain
Description

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

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

Ju Yeong Kim
Description

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

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Client for CCAFS GCM Data

Scott Chamberlain
Description

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

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DrugBank Database XML Parser

Mohammed Ali
Description

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

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Search Vertnet, a Database of Vertebrate Specimen Records

Scott Chamberlain
Description

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

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

Hajk-Georg Drost
Description

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

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

Maëlle Salmon
Description

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

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

Download and Process Data from the Paleobiology Database

Sara Varela
Description

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

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

Scott Chamberlain
Description

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

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

International Cricket Data

Rob Hyndman
Description

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

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Client for Neuroscience Information Framework APIs

Scott Chamberlain
Description

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

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Interface to the USGS BISON API

Scott Chamberlain
Description

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

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

Functions to mine endoscopic and associated pathology datasets

Sebastian Zeki
Description

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

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General Purpose Client for ERDDAP Servers

Scott Chamberlain
Description

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

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

Retrieve Data from the 1000 Plants Initiative (1KP)

Zebulun Arendsee
Description

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

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gender
CRAN

Predict Gender from Names Using Historical Data

Lincoln Mullen
Description

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

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Download Qualtrics Survey Data

Julia Silge
Description

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

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Client for the CORE API

Scott Chamberlain
Description

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

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Directory of Open Access Journals Client

Scott Chamberlain
Description

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

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R Interface to the Species+ Database

Kevin Cazelles
Description

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

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

Scott Chamberlain
Description

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

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

Scott Chamberlain
Description

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

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

Evan Odell
Description

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

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Automated Phylogenetic Sequence Cluster Identification from GenBank

Dom Bennett
Description

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

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

Scott Chamberlain
Description

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

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Interface to the Biodiversity Heritage Library

Scott Chamberlain
Description

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

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

Claudia Vitolo
Description

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

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

Maxwell Joseph
Description

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

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essurvey
CRAN

Download Data from the European Social Survey on the Fly

Jorge Cimentada
Description

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

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rusda
CRAN

Interface to USDA Databases

Franz-Sebastian Krah
Description

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

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

An API Client for the Internet Archive

Lincoln Mullen
Description

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

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rgpdd

R Interface to the Global Population Dynamics Database

Carl Boettiger
Description

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

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NatureServe Interface

Scott Chamberlain
Description

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

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Interface with the United Nations Comtrade API

Chris Muir
Description

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

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rebird
CRAN

R Client for the eBird Database of Bird Observations

Sebastian Pardo
Description

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

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

API Client for the Open Context Archeological Database

Ben Marwick
Description

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

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An R client for HathiTrust API

Scott Chamberlain
Description

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

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rglobi
CRAN

R Interface to Global Biotic Interactions

Jorrit Poelen
Description

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

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

Matthew Leonawicz
Description

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

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photosearcher

Photo Searcher

Nathan Fox
Description

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

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rWBclimate
CRAN

A package for accessing World Bank climate data

Edmund Hart
Description

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

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

Scott Chamberlain
Description

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

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Provides some helper functions for using the GitHub V4 API

Maëlle Salmon
Description

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

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Interact with the UK AIR Pollution Database from DEFRA

Claudia Vitolo
Description

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

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

Mahmoud Ahmed
Description

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

Scientific use cases
  1. Ahmed, M., Nguyen, H., Lai, T., & Kim, D. R. (2018). miRCancerdb: a database for correlation analysis between microRNA and gene expression in cancer. BMC Research Notes, 11(1). https://doi.org/10.1186/s13104-018-3160-9
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API Client and Dataset Management for the Demographic and Health Survey (DHS) Data

OJ Watson
Description

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

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

Scott Chamberlain
Description

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

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R Package Client for the Netherlands Biodiversity API

Hannes Hettling
Description

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

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hydroscoper
CRAN Peer-reviewed

Interface to the Greek National Data Bank for Hydrometeorological Information

Konstantinos Vantas
Description

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

Scientific use cases
  1. Vantas, K. (2018). hydroscoper: R interface to the Greek National Data Bank for Hydrological and Meteorological Information. Journal of Open Source Software, 3(23), 625. https://doi.org/10.21105/joss.00625
  2. Vantas, K., Sidiropoulos, E., & Loukas, A. (2019). Robustness Spatiotemporal Clustering and Trend Detection of Rainfall Erosivity Density in Greece. Water, 11(5), 1050. https://doi.org/10.3390/w11051050
  3. Vantas, K., Sidiropoulos, E., & Loukas, A. (2020). Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests. Water, 12(3), 687. https://doi.org/10.3390/w12030687
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Programmatic Interface to the Web Service Methods Provided by UC Berkeley's Natural History Data

Karthik Ram
Description

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

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Download and Aggregate Data from Public Hire Bicycle Systems

Mark Padgham
Description

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

Scientific use cases
  1. Hosford, K., & Winters, M. 2019. Quantifying the Bicycle Share Gender Gap. Transport Findings, November. https://doi.org/10.32866/10802
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Download Data from the Catchment Data Explorer Website

Rob Briers
Description

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

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Parse NOAA Integrated Surface Data Files

Scott Chamberlain
Description

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

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Client for the Pangaea Database

Scott Chamberlain
Description

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

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

Michael W. Kearney
Description

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

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

Get Australian Flight Data, 1985-2016

Mathew Ling
Description

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

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Programmatic Interface to the openfisheries.org API

Karthik Ram
Description

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

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

Access London Natural History Museum Host-Helminth Record Database

Tad Dallas
Description

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

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

A DoOR to the Complete Olfactome

Daniel Münch
Description

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

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DoOR.data
Peer-reviewed

A DoOR to the Complete Olfactome

Daniel Münch
Description

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

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treebase
CRAN

Discovery, Access and Manipulation of TreeBASE Phylogenies

Carl Boettiger
Description

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

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rrricanesdata
Peer-reviewed

Data for Atlantic and east Pacific tropical cyclones since 1998

Tim Trice
Description

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

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Get Texts from the Perseus Digital Library

David Ranzolin
Description

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

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rnaturalearthhires

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

Andy South
Description

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

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rAvis
CRAN

Interface to the Bird-Watching Dataset Proyecto AVIS

Sara Varela
Description

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

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Download and Read RAM Legacy Stock Assessment Database

Kshitiz Gupta
Description

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

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Client for Various Ocean Time Series Datasets

Scott Chamberlain
Description

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

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historydata
CRAN

Datasets for Historians

Lincoln Mullen
Description

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

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Get Landsat 8 Data from Amazon Public Data Sets

Scott Chamberlain
Description

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

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genderdata

Historical Datasets for Predicting Gender from Names

Lincoln Mullen
Description

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

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Client for CAMS Radiation Service

Lukas Lundstrom
Description

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

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

Michael Kane
Description

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

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programmatic interface to the AntWeb

Karthik Ram
Description

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

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

Ben Raymond
Description

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

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Interface to the National Phenology Network API

Scott Chamberlain
Description

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

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Accesses the Monkeylearn API for Text Classifiers and Extractors

Maëlle Salmon
Description

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

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OpenBIS API Access to the InfectX Data Repository

Nicolas Bennett
Description

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

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

Danton Noriega-Goodwin
Description

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

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chromer
CRAN

Interface to Chromosome Counts Database API

Paula Andrea Martinez
Description

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

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

Matthew Leonawicz
Description

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

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Popler R Package

Compagnoni Aldo
Description

Browse and query the popler database.

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rentrez
CRAN

Entrez in R

David Winter
Description

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

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

Joseph Stachelek
Description

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

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USAboundaries
CRAN

Historical and Contemporary Boundaries of the United States of America

Lincoln Mullen
Description

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

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ozbabynames

Australian Popular Baby Names

Rob Hyndman
Description

Data on the most popular baby names in Australia.

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rdopa

R client to Joint Research Centre's DOPA REST API

Joona Lehtomaki
Description

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

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USAboundariesData

Datasets for the USAboundaries package

Lincoln Mullen
Description

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

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Client for the Index Database of Remote Sensing Indices

Scott Chamberlain
Description

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

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riodata

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

Gabriela de Queiroz
Description

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

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