Data Access

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

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

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

1. Amano, T., Lamming, J. D. L., & Sutherland, W. J. (2016). Spatial Gaps in Global Biodiversity Information and the Role of Citizen Science. BioScience, 66(5), 393–400. https://doi.org/10.1093/biosci/biw022
2. Bartomeus, I., Park, M. G., Gibbs, J., Danforth, B. N., Lakso, A. N., & Winfree, R. (2013). Biodiversity ensures plant-pollinator phenological synchrony against climate change. Ecol Lett, 16(11), 1331–1338. https://doi.org/10.1111/ele.12170
3. Barve, V. (2014). Discovering and developing primary biodiversity data from social networking sites: A novel approach. Ecological Informatics, 24, 194–199. https://doi.org/10.1016/j.ecoinf.2014.08.008
4. Bone, R. E., Smith, J. A. C., Arrigo, N., & Buerki, S. (2015). A macro-ecological perspective on crassulacean acid metabolism (CAM) photosynthesis evolution in Afro-Madagascan drylands: Eulophiinae orchids as a case study. New Phytol, 208(2), 469–481. https://doi.org/10.1111/nph.13572
5. Collins, R., Duarte Ribeiro, E., Nogueira Machado, V., Hrbek, T., & Farias, I. (2015). A preliminary inventory of the catfishes of the lower Rio Nhamundá, Brazil (Ostariophysi, Siluriformes). Biodiversity Data Journal, 3, e4162. https://doi.org/10.3897/bdj.3.e4162
6. Drozd, P., & Šipoš, J. (2013). R for all (I): Introduction to the new age of biological analyses. Casopis Slezskeho Zemskeho Muzea A, 62(1). https://doi.org/10.2478/cszma-2013-0004
7. Kong, X., Huang, M., & Duan, R. (2015). SDMdata: A Web-Based Software Tool for Collecting Species Occurrence Records. PLoS ONE, 10(6), e0128295. https://doi.org/10.1371/journal.pone.0128295
8. Richardson, D. M., Le Roux, J. J., & Wilson, J. R. (2015). Australian acacias as invasive species: lessons to be learnt from regions with long planting histories. Southern Forests: a Journal of Forest Science, 77(1), 31–39. https://doi.org/10.2989/20702620.2014.999305
9. Turner, K. G., Fréville, H., & Rieseberg, L. H. (2015). Adaptive plasticity and niche expansion in an invasive thistle. Ecology and Evolution, 5(15), 3183–3197. https://doi.org/10.1002/ece3.1599
10. Verheijen, L. M., Aerts, R., Bönisch, G., Kattge, J., & Van Bodegom, P. M. (2015). Variation in trait trade-offs allows differentiation among predefined plant functional types: implications for predictive ecology. New Phytol, 209(2), 563–575. https://doi.org/10.1111/nph.13623
11. Zizka, A., & Antonelli, A. (2015). speciesgeocodeR: An R package for linking species occurrences, user-defined regions and phylogenetic trees for biogeography, ecology and evolution. https://doi.org/10.1101/032755
12. Butterfield, B. J., Copeland, S. M., Munson, S. M., Roybal, C. M., & Wood, T. E. (2016). Prestoration: using species in restoration that will persist now and into the future. Restoration Ecology. https://doi.org/10.1111/rec.12381
13. Dellinger, A. S., Essl, F., Hojsgaard, D., Kirchheimer, B., Klatt, S., Dawson, W., … Dullinger, S. (2015). Niche dynamics of alien species do not differ among sexual and apomictic flowering plants. New Phytol, 209(3), 1313–1323. https://doi.org/10.1111/nph.13694
14. Feitosa, Y. O., Absy, M. L., Latrubesse, E. M., & Stevaux, J. C. (2015). Late Quaternary vegetation dynamics from central parts of the Madeira River in Brazil. Acta Botanica Brasilica, 29(1), 120–128. https://doi.org/10.1590/0102-33062014abb3711
15. Malhado, A. C. M., Oliveira-Neto, J. A., Stropp, J., Strona, G., Dias, L. C. P., Pinto, L. B., & Ladle, R. J. (2015). Climatological correlates of seed size in Amazonian forest trees. Journal of Vegetation Science, 26(5), 956–963. https://doi.org/10.1111/jvs.12301
16. Werner, G. D. A., Cornwell, W. K., Cornelissen, J. H. C., & Kiers, E. T. (2015). Evolutionary signals of symbiotic persistence in the legume–rhizobia mutualism. Proc Natl Acad Sci USA, 112(33), 10262–10269. https://doi.org/10.1073/pnas.1424030112
17. Robertson, M. P., Visser, V., & Hui, C. (2016). Biogeo: an R package for assessing and improving data quality of occurrence record datasets. Ecography, 39(4), 394–401. https://doi.org/10.1111/ecog.02118
18. Davison, J., Moora, M., Opik, M., Adholeya, A., Ainsaar, L., Ba, A., … Zobel, M. (2015). Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science, 349(6251), 970–973. https://doi.org/10.1126/science.aab1161
19. Curtis, C. A., & Bradley, B. A. (2016). Plant Distribution Data Show Broader Climatic Limits than Expert-Based Climatic Tolerance Estimates. PLOS ONE, 11(11), e0166407. https://doi.org/10.1371/journal.pone.0166407
20. Dullinger, I., Wessely, J., Bossdorf, O., Dawson, W., Essl, F., Gattringer, A., … Dullinger, S. (2016). Climate change will increase the naturalization risk from garden plants in Europe. Global Ecol. Biogeogr. https://doi.org/10.1111/geb.12512
21. Groom, Q., Weatherdon, L., & Geijzendorffer, I. R. (2016). Is citizen science an open science in the case of biodiversity observations? Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.12767
22. Janssens, S. B., Vandelook, F., De Langhe, E., Verstraete, B., Smets, E., Vandenhouwe, I., & Swennen, R. (2016). Evolutionary dynamics and biogeography of Musaceae reveal a correlation between the diversification of the banana family and the geological and climatic history of Southeast Asia. New Phytol, 210(4), 1453–1465. https://doi.org/10.1111/nph.13856
23. Sanyal, A., & Decocq, G. (2016). Adaptive evolution of seed oil content in angiosperms: accounting for the global patterns of seed oils. BMC Evolutionary Biology, 16(1). https://doi.org/10.1186/s12862-016-0752-7
24. Gilles, D., Zaiss, R., Blach-Overgaard, A., Catarino, L., Damen, T., Deblauwe, V., et al. (2016). RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys, 74, 1–18. https://doi.org/10.3897/phytokeys.74.9723
25. Lundgren, M. R., & Christin, P.-A. (2016). Despite phylogenetic effects, C3-C4 lineages bridge the ecological gap to C4 photosynthesis. Journal of Experimental Botany, erw451. https://doi.org/10.1093/jxb/erw451
26. Rai, K., Bhattarai, N. R., Vanaerschot, M., Imamura, H., Gebru, G., Khanal, B., … Van der Auwera, G. (2017). Single locus genotyping to track Leishmania donovani in the Indian subcontinent: Application in Nepal. PLOS Neglected Tropical Diseases, 11(3), e0005420. https://doi.org/10.1371/journal.pntd.0005420
27. Balao, F., Trucchi, E., Wolfe, T., Hao, B.-H., Lorenzo, M. T., Baar, J., … Paun, O. (2017). Adaptive sequence evolution is driven by biotic stress in a pair of orchid species (Dactylorhiza) with distinct ecological optima. Molecular Ecology. https://doi.org/10.1111/mec.14123
28. Carvajal-Endara, S., Hendry, A. P., Emery, N. C., & Davies, T. J. (2017). Habitat filtering not dispersal limitation shapes oceanic island floras: species assembly of the Galápagos archipelago. Ecology Letters, 20(4), 495–504. https://doi.org/10.1111/ele.12753
29. Mounce, R., Smith, P., & Brockington, S. (2017). Ex situ conservation of plant diversity in the world’s botanic gardens. Nature Plants, 3(10), 795–802. https://doi.org/10.1038/s41477-017-0019-3
30. Alfsnes, K., Leinaas, H. P., & Hessen, D. O. (2017). Genome size in arthropods: different roles of phylogeny, habitat and life history in insects and crustaceans. Ecology and Evolution. https://doi.org/10.1002/ece3.3163
31. Chamberlain SA, Boettiger C. (2017) R Python, and Ruby clients for GBIF species occurrence data. PeerJ Preprints 5:e3304v1 https://doi.org/10.7287/peerj.preprints.3304v1
32. Ludt, W. B., Morgan, L., Bishop, J., & Chakrabarty, P. (2017). A quantitative and statistical biological comparison of three semi-enclosed seas: the Red Sea, the Persian (Arabian) Gulf, and the Gulf of California. Marine Biodiversity. https://doi.org/10.1007/s12526-017-0740-1
33. Vanderhoeven, S., Adriaens, T., Desmet, P., Strubbe, D., Backeljau, T., Barbier, Y., … Groom, Q. (2017). Tracking Invasive Alien Species (TrIAS): Building a data-driven framework to inform policy. Research Ideas and Outcomes, 3, e13414. https://doi.org/10.3897/rio.3.e13414
34. Aedo, C., & Pando, F. (2017). A distribution and taxonomic reference dataset of Geranium in the New World. Scientific Data, 4, 170049. https://doi.org/10.1038/sdata.2017.49
35. Cardoso, D., Särkinen, T., Alexander, S., Amorim, A. M., Bittrich, V., Celis, M., … Forzza, R. C. (2017). Amazon plant diversity revealed by a taxonomically verified species list. Proceedings of the National Academy of Sciences, 201706756. https://doi.org/10.1073/pnas.1706756114
36. Duffy, G. A., Coetzee, B. W. T., Latombe, G., Akerman, A. H., McGeoch, M. A., & Chown, S. L. (2017). Barriers to globally invasive species are weakening across the Antarctic. Diversity and Distributions. https://doi.org/10.1111/ddi.12593
37. Pereira, A. G., Sterli, J., Moreira, F. R. R., & Schrago, C. G. (2017). Multilocus phylogeny and statistical biogeography clarify the evolutionary history of major lineages of turtles. Molecular Phylogenetics and Evolution. https://doi.org/10.1016/j.ympev.2017.05.008
38. Mayer, K., Haeuser, E., Dawson, W., Essl, F., Kreft, H., Pergl, J., … van Kleunen, M. (2017). Naturalization of ornamental plant species in public green spaces and private gardens. Biological Invasions. https://doi.org/10.1007/s10530-017-1594-y
39. Chalmandrier, L., Albouy, C., & Pellissier, L. (2017). Species pool distributions along functional trade-offs shape plant productivity–diversity relationships. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-15334-4
40. Serra-Diaz, J. M., Enquist, B. J., Maitner, B., Merow, C., & Svenning, J.-C. (2017). Big data of tree species distributions: how big and how good? Forest Ecosystems, 4(1). https://doi.org/10.1186/s40663-017-0120-0
41. Sanyal, A., Lenoir, J., O’Neill, C., Dubois, F., & Decocq, G. (2018). Intraspecific and interspecific adaptive latitudinal cline in Brassicaceae seed oil traits. American Journal of Botany, 105(1), 85–94. https://doi.org/10.1002/ajb2.1014
42. Bemmels, J. B., Wright, S. J., Garwood, N. C., Queenborough, S. A., Valencia, R., & Dick, C. W. (2018). Filter-dispersal assembly of lowland Neotropical rainforests across the Andes. Ecography. https://doi.org/10.1111/ecog.03473
43. Schweiger, A. H., & Svenning, J.-C. (2018). Down-sizing of dung beetle assemblages over the last 53 000 years is consistent with a dominant effect of megafauna losses. Oikos. https://doi.org/10.1111/oik.04995
44. Saad, N. J., Lynch, V. D., Antillón, M., Yang, C., Crump, J. A., & Pitzer, V. E. (2018). Seasonal dynamics of typhoid and paratyphoid fever. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-25234-w
45. De Oliveira, H., Oprea, M., & Dias, R. (2018). Distributional Patterns and Ecological Determinants of Bat Occurrence Inside Caves: A Broad Scale Meta-Analysis. Diversity, 10(3), 49. https://doi.org/10.3390/d10030049
46. Lortie, C. J., Filazzola, A., Kelsey, R., Hart, A. K., & Butterfield, H. S. (2018). Better late than never: a synthesis of strategic land retirement and restoration in California. Ecosphere, 9(8), e02367. https://doi.org/10.1002/ecs2.2367
47. Boria, R. A., & Blois, J. L. (2018). The effect of large sample sizes on ecological niche models: Analysis using a North American rodent, Peromyscus maniculatus. Ecological Modelling, 386, 83–88. https://doi.org/10.1016/j.ecolmodel.2018.08.013
48. Lusseau, D., & Mancini, F. (2018). A global assessment of tourism and recreation conservation threats to prioritise interventions. arXiv preprint https://arxiv.org/abs/1808.08399
49. Dallas, T. A., & Hastings, A. (2018). Habitat suitability estimated by niche models is largely unrelated to species abundance. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12820
50. Gadelha Jr, L. M., de Siracusa, P. C., Ziviani, A., Dalcin, E. C., Affe, H. M., de Siqueira, M. F., … & Costa, R. L. (2018). A Survey of e-Biodiversity: Concepts, Practices, and Challenges. arXiv preprint arXiv:1810.00224 https://arxiv.org/abs/1810.00224
51. Testo, W. L., Sessa, E., & Barrington, D. S. (2018). The rise of the Andes promoted rapid diversification in Neotropical Phlegmariurus (Lycopodiaceae). New Phytologist. https://doi.org/10.1111/nph.15544
52. Milla, R., Bastida, J. M., Turcotte, M. M., Jones, G., Violle, C., Osborne, C. P., … Byun, C. (2018). Phylogenetic patterns and phenotypic profiles of the species of plants and mammals farmed for food. Nature Ecology & Evolution, 2(11), 1808–1817. https://doi.org/10.1038/s41559-018-0690-4
53. Smith, J. R., Letten, A. D., Ke, P.-J., Anderson, C. B., Hendershot, J. N., Dhami, M. K., … Daily, G. C. (2018). A global test of ecoregions. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-018-0709-x
54. Collins, R. A., Wangensteen, O. S., O’Gorman, E. J., Mariani, S., Sims, D. W., & Genner, M. J. (2018). Persistence of environmental DNA in marine systems. Communications Biology, 1(1). https://doi.org/10.1038/s42003-018-0192-6
55. Bentz, C., Dediu, D., Verkerk, A., & Jäger, G. (2018). The evolution of language families is shaped by the environment beyond neutral drift. Nature Human Behaviour, 2(11), 816–821. https://doi.org/10.1038/s41562-018-0457-6
56. Menegotto, A., & Rangel, T. F. (2018). Mapping knowledge gaps in marine diversity reveals a latitudinal gradient of missing species richness. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-07217-7
57. Bartomeus, I., Stavert, J. R., Ward, D., & Aguado, O. (2018). Historical collections as a tool for assessing the global pollination crisis. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1763), 20170389. https://doi.org/10.1098/rstb.2017.0389
58. Hanson, J. O., Fuller, R. A., & Rhodes, J. R. (2018). Conventional methods for enhancing connectivity in conservation planning do not always maintain gene flow. Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.13315
59. Vidal, J. de D., de Souza, A. P., & Koch, I. (2018). Impacts of landscape composition, marginality of distribution, soil fertility, and climatic stability on the patterns of woody plant endemism in the Cerrado. https://doi.org/10.1101/362475
60. López-Jurado, J., Mateos-Naranjo, E., & Balao, F. (2018). Niche divergence and limits to expansion in the high polyploid Dianthus broteri complex. New Phytologist. https://doi.org/10.1111/nph.15663
61. Spalink, D., MacKay, R., & Sytsma, K. J. (2019). Phylogeography, population genetics, and distribution modeling reveal vulnerability of Scirpus longii (Cyperaceae) and the Atlantic Coastal Plain Flora to climate change. Molecular Ecology. https://doi.org/10.1111/mec.15006
62. Lee, C. K. F., Keith, D. A., Nicholson, E., & Murray, N. J. (2019). REDLISTR: Tools for the IUCN Red Lists of Ecosystems and Threatened Species in R. Ecography. https://doi.org/10.1111/ecog.04143
63. Ladwig, L. M., Chandler, J. L., Guiden, P. W., & Henn, J. J. (2019). Extreme winter warm event causes exceptionally early bud break for many woody species. Ecosphere, 10(1), e02542. https://doi.org/10.1002/ecs2.2542
64. Lu, M., & Hedin, L. O. (2019). Global plant–symbiont organization and emergence of biogeochemical cycles resolved by evolution-based trait modelling. Nature Ecology & Evolution, 3(2), 239–250. https://doi.org/10.1038/s41559-018-0759-0
65. Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C., Edler, D., … Antonelli, A. (2019). CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13152
66. Rice, A., Šmarda, P., Novosolov, M., Drori, M., Glick, L., Sabath, N., … Mayrose, I. (2019). The global biogeography of polyploid plants. Nature Ecology & Evolution, 3(2), 265–273. https://doi.org/10.1038/s41559-018-0787-9
67. Mittermeier, T. et al. 2019. A season for all things: Phenological imprints in Wikipedia usage and their relevance toconservation. PLoS Biology https://research.birmingham.ac.uk/portal/files/58082037/pbio.3000146_1.pdf
68. Daru, B. H., le Roux, P. C., Gopalraj, J., Park, D. S., Holt, B. G., & Greve, M. (2019). Spatial overlaps between the global protected areas network and terrestrial hotspots of evolutionary diversity. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12888
69. Dillen, M., Groom, Q., Chagnoux, S., Güntsch, A., Hardisty, A., Haston, E., … Phillips, S. (2019). A benchmark dataset of herbarium specimen images with label data. Biodiversity Data Journal, 7. https://10.3897/bdj.7.e31817
70. Piñar, G., Poyntner, C., Tafer, H., & Sterflinger, K. (2019). A time travel story: metagenomic analyses decipher the unknown geographical shift and the storage history of possibly smuggled antique marble statues. Annals of Microbiology. https://doi.org/10.1007/s13213-019-1446-3
71. Dreyer, J. B. B., Higuchi, P., & Silva, A. C. (2019). Ligustrum lucidum W. T. Aiton (broad-leaf privet) demonstrates climatic niche shifts during global-scale invasion. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-40531-8
72. Ludt, W. B., Bernal, M. A., Kenworthy, E., Salas, E., & Chakrabarty, P. (2019). Genomic, ecological, and morphological approaches to investigating species limits: A case study in modern taxonomy from Tropical Eastern Pacific surgeonfishes. Ecology and Evolution. https://doi.org/10.1002/ece3.5029
73. Medina, I. (2019). The role of the environment in the evolution of nest shape in Australian passerines. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-41948-x
74. Miranda, L. S., Imperatriz-Fonseca, V. L., & Giannini, T. C. (2019). Climate change impact on ecosystem functions provided by birds in southeastern Amazonia. PLOS ONE, 14(4), e0215229. https://doi.org/10.1371/journal.pone.0215229
75. Van de Perre, F., Leirs, H., & Verheyen, E. (2019). Paleoclimate, ecoregion size, and degree of isolation explain regional biodiversity differences among terrestrial vertebrates within the Congo Basin. Belgian Journal of Zoology, 149(1). https://doi.org/10.26496/bjz.2019.28
76. Hoban, S., Dawson, A., Robinson, J. D., Smith, A. B., & Strand, A. E. (2019). Inference of biogeographic history by formally integrating distinct lines of evidence: genetic, environmental niche, and fossil. Ecography. https://doi.org/10.1111/ecog.04327
77. Bacci, L. F., Michelangeli, F. A., & Goldenberg, R. (2019). Revisiting the classification of Melastomataceae: implications for habit and fruit evolution. Botanical Journal of the Linnean Society, 190(1), 1–24. https://doi.org/10.1093/botlinnean/boz006
78. Baliga, V. B., & Mehta, R. S. (2019). Morphology, ecology, and biogeography of independent origins of cleaning behavior around the world. Integrative and comparative biology. https://doi.org/10.1093/icb/icz030
79. Kadereit, J. W., Lauterbach, M., Kandziora, M., Spillmann, J., & Nyffeler, R. (2019). Dual colonization of European high-altitude areas from Asia by Callianthemum (Ranunculaceae). Plant Systematics and Evolution. https://doi.org/10.1007/s00606-019-01583-5
80. Schubert, M., Marcussen, T., Meseguer, A. S., & Fjellheim, S. (2019). The grass subfamily Pooideae: Cretaceous–Palaeocene origin and climate‐driven Cenozoic diversification. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12923
81. Westmeijer, G., Everaert, G., Pirlet, H., De Clerck, O., & Vandegehuchte, M. B. (2019). Mechanistic niche modelling to identify favorable growth sites of temperate macroalgae. Algal Research, 41, 101529. https://doi.org/10.1016/j.algal.2019.101529
82. Alhajeri, B. H., & Fourcade, Y. (2019). High correlation between species‐level environmental data estimates extracted from IUCN expert range maps and from GBIF occurrence data. Journal of Biogeography. https://doi.org/10.1111/jbi.13619
83. Ros-Candeira, A., Pérez-Luque, A. J., Suárez-Muñoz, M., Bonet-García, F. J., Hódar, J. A., Giménez de Azcárate, F., & Ortega-Díaz, E. (2019). Dataset of occurrence and incidence of pine processionary moth in Andalusia, south Spain. ZooKeys, 852, 125–136. https://doi.org/10.3897/zookeys.852.28567
84. McTavish, E. J. (2019). Linking Biodiversity Data Using Evolutionary History. Bio/diversity Information Science and Standards, 3. https://doi.org/10.3897/biss.3.36207
85. Uzma, Jiménez-Mejías, P., Amir, R., Hayat, M. Q., & Hipp, A. L. (2019). Timing and ecological priority shaped the diversification of sedges in the Himalayas. PeerJ, 7, e6792. https://doi.org/10.7717/peerj.6792
86. Butterfield, B. J., Holmgren, C. A., Anderson, R. S., & Betancourt, J. L. (2019). Life history traits predict colonization and extinction lags of desert plant species since the Last Glacial Maximum. Ecology. https://doi.org/10.1002/ecy.2817
87. Correia, R. A., Ruete, A., Stropp, J., Malhado, A. C. M., dos Santos, J. W., Lessa, T., … Ladle, R. J. (2019). Using ignorance scores to explore biodiversity recording effort for multiple taxa in the Caatinga. Ecological Indicators, 106, 105539. https://doi.org/10.1016/j.ecolind.2019.105539
88. Collins, R. A., Bakker, J., Wangensteen, O. S., Soto, A. Z., Corrigan, L., Sims, D. W., … Mariani, S. (2019). Non‐specific amplification compromises environmental DNA metabarcoding with COI. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13276
89. Jaganathan, G. K., & Dalrymple, S. E. (2019). Internal Seed Structure of Alpine Plants and Extreme Cold Exposure. Data, 4(3), 107. https://doi.org/10.3390/data4030107
90. Hayden, B., Palomares, M. L. D., Smith, B. E., & Poelen, J. H. (2019). Biological and environmental drivers of trophic ecology in marine fishes - a global perspective. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-47618-2
91. Pender, J. E., Hipp, A. L., Hahn, M., Kartesz, J., Nishino, M., & Starr, J. R. (2019). How sensitive are climatic niche inferences to distribution data sampling? A comparison of Biota of North America Program (BONAP) and Global Biodiversity Information Facility (GBIF) datasets. Ecological Informatics, 100991. https://doi.org/10.1016/j.ecoinf.2019.100991
92. Esperon‐Rodriguez, M., Power, S. A., Tjoelker, M. G., Beaumont, L. J., Burley, H., Caballero‐Rodriguez, D., & Rymer, P. D. (2019). Assessing the vulnerability of Australia’s urban forests to climate extremes. Plants, People, Planet. https://doi.org/10.1002/ppp3.10064
93. De Luca, D., Kooistra, W. H. C. F., Sarno, D., Gaonkar, C. C., & Piredda, R. (2019). Global distribution and diversity of Chaetoceros (Bacillariophyta, Mediophyceae): integration of classical and novel strategies. PeerJ, 7, e7410. https://doi.org/10.7717/peerj.7410
94. Havinga, I., Hein, L., Vega-Araya, M., & Languillaume, A. (2020). Spatial quantification to examine the effectiveness of payments for ecosystem services: A case study of Costa Rica’s Pago de Servicios Ambientales. Ecological Indicators, 108, 105766. https://doi.org/10.1016/j.ecolind.2019.105766
95. Ahmad, S., Yang, L., Khan, T. U., Wanghe, K., Li, M., & Luan, X. (2020). Using an ensemble modelling approach to predict the potential distribution of Himalayan gray goral (Naemorhedus goral bedfordi) in Pakistan. Global Ecology and Conservation, 21, e00845. https://doi.org/10.1016/j.gecco.2019.e00845
96. Faltýnek Fric, Z., Rindoš, M., & Konvička, M. (2019). Phenology responses of temperate butterflies to latitude depend on ecological traits. Ecology Letters, 23(1), 172–180. https://doi.org/10.1111/ele.13419
97. Stévart, T., Dauby, G., Lowry, P. P., Blach-Overgaard, A., Droissart, V., Harris, D. J., … Couvreur, T. L. P. (2019). A third of the tropical African flora is potentially threatened with extinction. Science Advances, 5(11), eaax9444. https://doi.org/10.1126/sciadv.aax9444
98. D’Amen, M., & Azzurro, E. (2019). Lessepsian fish invasion in Mediterranean marine protected areas: a risk assessment under climate change scenarios. ICES Journal of Marine Science, 77(1), 388–397. https://doi.org/10.1093/icesjms/fsz207
99. Yusri, S., Siregar, V. P., & Suharsono. (2019). Distribution Modelling of Porites (Poritidae) in Indonesia. IOP Conference Series: Earth and Environmental Science, 363, 012025. https://doi.org/10.1088/1755-1315/363/1/012025
100. Ekroos, J., Kleijn, D., Batáry, P., Albrecht, M., Báldi, A., Blüthgen, N., … Smith, H. G. (2020). High land-use intensity in grasslands constrains wild bee species richness in Europe. Biological Conservation, 241, 108255. https://doi.org/10.1016/j.biocon.2019.108255
101. Marshall, B. M., & Strine, C. T. (2019). Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media. PeerJ, 7, e8059. https://doi.org/10.7717/peerj.8059
102. Mienna, I. M., Speed, J. D. M., Bendiksby, M., Thornhill, A. H., Mishler, B. D., & Martin, M. D. (2019). Differential patterns of floristic phylogenetic diversity across a post‐glacial landscape. Journal of Biogeography. https://doi.org/10.1111/jbi.13789
103. D’Amen, M., & Azzurro, E. (2019). Integrating univariate niche dynamics in species distribution models: A step forward for marine research on biological invasions. Journal of Biogeography, 47(3), 686–697. https://doi.org/10.1111/jbi.13761
104. Alves, D. M. C. C., Eduardo, A. A., da Silva Oliveira, E. V., Villalobos, F., Dobrovolski, R., Pereira, T. C., … Gouveia, S. F. (2020). Unveiling geographical gradients of species richness from scant occurrence data. Global Ecology and Biogeography, 29(4), 748–759. https://doi.org/10.1111/geb.13055
105. Léveillé-Bourret, É., Chen, B.-H., Garon-Labrecque, M.-È., Ford, B. A., & Starr, J. R. (2020). RAD sequencing resolves the phylogeny, taxonomy and biogeography of Trichophoreae despite a recent rapid radiation (Cyperaceae). Molecular Phylogenetics and Evolution, 145, 106727. https://doi.org/10.1016/j.ympev.2019.106727
106. Wraith, J., Norman, P., & Pickering, C. (2020). Orchid conservation and research: An analysis of gaps and priorities for globally Red Listed species. Ambio. https://doi.org/10.1007/s13280-019-01306-7
107. Bachman, S., Walker, B., Barrios, S., Copeland, A., & Moat, J. (2020). Rapid Least Concern: towards automating Red List assessments. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e47018
108. Ceschin, D. G., Pires, N. S., Mardirosian, M. N., Lascano, C. I., & Venturino, A. (2020). The Rhinella arenarum transcriptome: de novo assembly, annotation and gene prediction. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-57961-4
109. Van Zonneveld, M., Rakha, M., Tan, S. yee, Chou, Y.-Y., Chang, C.-H., Yen, J.-Y., … Solberg, S. Ø. (2020). Mapping patterns of abiotic and biotic stress resilience uncovers conservation gaps and breeding potential of Vigna wild relatives. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-58646-8
110. Benhadi‐Marín, J., Santos, S. A. P., Baptista, P., & Pereira, J. A. (2020). Distribution of Bactrocera oleae (Rossi, 1790) throughout the Iberian Peninsula based on a maximum entropy modeling approach. Annals of Applied Biology. https://doi.org/10.1111/aab.12584
111. Shivambu, T. C., Shivambu, N., & Downs, C. T. (2020). Impact assessment of seven alien invasive bird species already introduced to South Africa. Biological Invasions. https://doi.org/10.1007/s10530-020-02221-9
112. Li, X., & Guo, B. (2020). Substantially adaptive potential in polyploid cyprinid fishes: evidence from biogeographic, phylogenetic and genomic studies. Proceedings of the Royal Society B: Biological Sciences, 287(1920), 20193008. https://doi.org/10.1098/rspb.2019.3008
113. Hannah, L., Roehrdanz, P. R., Marquet, P. A., Enquist, B. J., Midgley, G., Foden, W., … Svenning, J. (2020). 30% land conservation and climate action reduces tropical extinction risk by more than 50%. Ecography. https://doi.org/10.1111/ecog.05166
114. Zizka, A., Carvalho‐Sobrinho, J. G., Pennington, R. T., Queiroz, L. P., Alcantara, S., Baum, D. A., … Antonelli, A. (2020). Transitions between biomes are common and directional in Bombacoideae (Malvaceae). Journal of Biogeography. https://doi.org/10.1111/jbi.13815
115. Jung, E.-Y., Gaviria, J., Sun, S., & Engelbrecht, B. M. J. (2020). Comparative drought resistance of temperate grassland species: testing performance trade-offs and the relation to distribution. Oecologia, 192(4), 1023–1036. https://doi.org/10.1007/s00442-020-04625-9
116. Howard, C. C., & Cellinese, N. (2020). Tunicate bulb size variation in monocots explained by temperature and phenology. Ecology and Evolution, 10(5), 2299–2309. https://doi.org/10.1002/ece3.5996
117. Du, C., Chen, J., Jiang, L., & Qiao, G. (2020). High correlation of species diversity patterns between specialist herbivorous insects and their specific hosts. Journal of Biogeography. https://doi.org/10.1111/jbi.13816
118. Kusumoto, B., Costello, M. J., Kubota, Y., Shiono, T., Wei, C., Yasuhara, M., & Chao, A. (2020). Global distribution of coral diversity: Biodiversity knowledge gradients related to spatial resolution. Ecological Research, 35(2), 315–326. https://doi.org/10.1111/1440-1703.12096
119. Young, N. E., Jarnevich, C. S., Sofaer, H. R., Pearse, I., Sullivan, J., Engelstad, P., & Stohlgren, T. J. (2020). A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales. PLOS ONE, 15(3), e0229253. https://doi.org/10.1371/journal.pone.0229253
120. Chapman, A., Belbin, L., Zermoglio, P., Wieczorek, J., Morris, P., Nicholls, M., … Schigel, D. (2020). Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards, 4. https://doi.org/10.3897/biss.4.50889
121. Stropp, J., Umbelino, B., Correia, R. A., Campos-Silva, J. V., Ladle, R. J., & Malhado, A. C. M. (2020). The ghosts of forests past and future: deforestation and botanical sampling in the Brazilian Amazon. Ecography. https://doi.org/10.1111/ecog.05026
122. Hernández‐Rojas, A. C., Kluge, J., Krömer, T., Carvajal‐Hernández, C., Silva‐Mijangos, L., Miehe, G., … Kessler, M. (2020). Latitudinal patterns of species richness and range size of ferns along elevational gradients at the transition from tropics to subtropics. Journal of Biogeography, 47(6), 1383–1397. https://doi.org/10.1111/jbi.13841
123. Scharmüller, A., Schreiner, V. C., & Schäfer, R. B. (2020). Standartox: Standardizing Toxicity Data. Data, 5(2), 46. https://doi.org/10.3390/data5020046
124. Bohora Schlickmann, M., da Silva, A. C., de Oliveira, L. M., Oliveira Matteucci, D., Domingos Machado, F., Cuchi, T., … Higuchi, P. (2020). Specific leaf area is a potential indicator of tree species sensitive to future climate change in the mixed Subtropical Forests of southern Brazil. Ecological Indicators, 116, 106477. https://doi.org/10.1016/j.ecolind.2020.106477
125. Joyce, E., Thiele, K., Slik, F., & Crayn, D. (2020). Checklist of the vascular flora of the Sunda-Sahul Convergence Zone. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e51094
126. Petersen, T. K., Speed, J. D. M., Grøtan, V., & Austrheim, G. (2020). Urban aliens and threatened near-naturals: Land-cover affects the species richness of alien- and threatened species in an urban-rural setting. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-65459-2
127. Lindberg, C. L., Hanslin, H. M., Schubert, M., Marcussen, T., Trevaskis, B., Preston, J. C., & Fjellheim, S. (2020). Increased above‐ground resource allocation is a likely precursor for independent evolutionary origins of annuality in the Pooideae grass subfamily. New Phytologist. https://doi.org/10.1111/nph.16666
128. Lenoir, J., Bertrand, R., Comte, L., Bourgeaud, L., Hattab, T., Murienne, J., & Grenouillet, G. (2020). Species better track climate warming in the oceans than on land. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-020-1198-2
129. Sun, M., Folk, R. A., Gitzendanner, M. A., Soltis, P. S., Chen, Z., Soltis, D. E., & Guralnick, R. P. (2020). Recent accelerated diversification in rosids occurred outside the tropics. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17116-5
130. Hock, M., Hofmann, R., Essl, F., Pyšek, P., Bruelheide, H., & Erfmeier, A. (2020). Native distribution characteristics rather than functional traits explain preadaptation of invasive species to high‐UV‐B environments. Diversity and Distributions. https://doi.org/10.1111/ddi.13113
131. Schertler, A., Rabitsch, W., Moser, D., Wessely, J., & Essl, F. (2020). The potential current distribution of the coypu (Myocastor coypus) in Europe and climate change induced shifts in the near future. NeoBiota, 58, 129–160. https://doi.org/10.3897/neobiota.58.33118
132. Weyna, A., & Romiguier, J. (2020). Relaxation of purifying selection suggests low effective population size in eusocial Hymenoptera and solitary pollinating bees. https://doi.org/10.1101/2020.04.14.038893
133. Sanchez‐Martinez, P., Martínez‐Vilalta, J., Dexter, K. G., Segovia, R. A., & Mencuccini, M. (2020). Adaptation and coordinated evolution of plant hydraulic traits. Ecology Letters. https://doi.org/10.1111/ele.13584
134. Koch, L. K., Cunze, S., Kochmann, J., & Klimpel, S. (2020). Bats as putative Zaire ebolavirus reservoir hosts and their habitat suitability in Africa. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-71226-0
135. De Sousa, K., & Solberg, S. Ø. (2020). Conservation Gaps in Traditional Vegetables Native to Europe and Fennoscandia. Agriculture, 10(8), 340. https://doi.org/10.3390/agriculture10080340
136. Polaina, E., Pärt, T., & Recio, M. R. (2020). Identifying hotspots of invasive alien terrestrial vertebrates in Europe to assist transboundary prevention and control. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-68387-3
137. Falconi, N., Fuller, T. K., DeStefano, S., & Organ, J. F. (2020). An open-access occurrence database for Andean bears in Peru. Ursus, 2020(31e11), 1. https://doi.org/10.2192/ursus-d-19-00012.1
138. Brightly, W. H., Hartley, S. E., Osborne, C. P., Simpson, K. J., & Strömberg, C. A. E. (2020). High silicon concentrations in grasses are linked to environmental conditions and not associated with C4 photosynthesis. Global Change Biology. https://doi.org/10.1111/gcb.15343
139. Dianzinga, N. T., Moutoussamy, M., Sadeyen, J., Ravaomanarivo, L. H. R., & Frago, E. (2020). The interacting effect of habitat amount, habitat diversity and fragmentation on insect diversity along elevational gradients. Journal of Biogeography, 47(11), 2377–2391. https://doi.org/10.1111/jbi.13959
140. Grattarola, F., González, A., Mai, P., Cappuccio, L., Fagúndez-Pachón, C., Rossi, F., … Pincheira-Donoso, D. (2020). Biodiversidata: A novel dataset for the vascular plant species diversity in Uruguay. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e56850
141. Romain Courault, Marianne Cohen, Mathilde Pottier. Disentangling the influence of geographical laws and sampling biais to model distribution of Birch tree from Open-acess Biodiversity Dataset (OBDs) in Swedish Lapland. 2020. ird-02973852 https://hal.ird.fr/ird-02973852/file/151020_Courault_Cohen_et_al_AGIS_SI_GPAF.pdf
142. Higgins, S. I., Larcombe, M. J., Beeton, N. J., Conradi, T., & Nottebrock, H. (2020). Predictive ability of a process‐based versus a correlative species distribution model. Ecology and Evolution, 10(20), 11043–11054. https://doi.org/10.1002/ece3.6712
143. Nelsen, M. P., & Lumbsch, H. T. (2020). A data-driven evaluation of lichen climate change indicators in Central Europe. Biodiversity and Conservation, 29(14), 3959–3971. https://doi.org/10.1007/s10531-020-02057-8
144. Alroy, J. (2020). A simple graph theoretic method provides accurate range area estimates. Authorea Preprints. https://d197for5662m48.cloudfront.net/documents/publicationstatus/49087/preprint_pdf/f0cf787b22dacb984cfd2771d532870b.pdf
145. Gadelha, L. M. R., Siracusa, P. C., Dalcin, E. C., Silva, L. A. E., Augusto, D. A., Krempser, E., … Siqueira, M. F. (2020). A survey of biodiversity informatics: Concepts, practices, and challenges. WIREs Data Mining and Knowledge Discovery, 11(1). https://doi.org/10.1002/widm.1394
146. Brandt, A. J., Bellingham, P. J., Duncan, R. P., Etherington, T. R., Fridley, J. D., Howell, C. J., … Peltzer, D. A. (2020). Naturalised plants transform the composition and function of the New Zealand flora. Biological Invasions. https://doi.org/10.1007/s10530-020-02393-4
147. Alò, D., Lacy, S. N., Castillo, A., Samaniego, H. A., & Marquet, P. A. (2020). The macroecology of fish migration. Global Ecology and Biogeography, 30(1), 99–116. https://doi.org/10.1111/geb.13199
148. Simpson, K. J., Jardine, E. C., Archibald, S., Forrestel, E. J., Lehmann, C. E. R., Thomas, G. H., & Osborne, C. P. (2020). Resprouting grasses are associated with less frequent fire than seeders. New Phytologist. https://doi.org/10.1111/nph.17069
149. Basooma, A., Nakiyende, H., Olokotum, M., Nkalubo, W., Musinguzi, L., & Natugonza, V. (2020). Using the novel priority index in prioritizing the selection of inland water bodies for site-based fish species conservation. bioRxiv. https://www.biorxiv.org/content/biorxiv/early/2020/11/14/2020.11.12.380618.full.pdf
150. Bombi, P. (2020). Potential conflict extent between two invasive alien pests, Rhynchophorus ferrugineus and Paysandisia archon, and the native populations of the Mediterranean fan palm. Journal for Nature Conservation, 58, 125927. https://doi.org/10.1016/j.jnc.2020.125927
151. Capurucho, J. M. G., Ashley, M. V., Tsuru, B. R., Cooper, J. C., & Bates, J. M. (2020). Dispersal ability correlates with range size in Amazonian habitat-restricted birds. Proceedings of the Royal Society B: Biological Sciences, 287(1939), 20201450. https://doi.org/10.1098/rspb.2020.1450
152. Panter, C. T., Clegg, R. L., Moat, J., Bachman, S. P., Klitgård, B. B., & White, R. L. (2020). To clean or not to clean: Cleaning open‐source data improves extinction risk assessments for threatened plant species. Conservation Science and Practice, 2(12). https://doi.org/10.1111/csp2.311
153. Pons, J., Campión, D., Chiozzi, G., Ettwein, A., Grangé, J., Kajtoch, Ł., … Fuchs, J. (2020). Phylogeography of a widespread Palaearctic forest bird species: The White‐backed Woodpecker (Aves, Picidae). Zoologica Scripta, 50(2), 155–172. https://doi.org/10.1111/zsc.12466
154. Ludt, W. B., & Myers, C. E. (2020). Distinguishing between dispersal and vicariance: A novel approach using anti‐tropical taxa across the fish Tree of Life. Journal of Biogeography, 48(3), 577–589. https://doi.org/10.1111/jbi.14021
155. Mayani-Parás, F., Botello, F., Castañeda, S., Munguía-Carrara, M., & Sánchez-Cordero, V. (2021). Cumulative habitat loss increases conservation threats on endemic species of terrestrial vertebrates in Mexico. Biological Conservation, 253, 108864. https://doi.org/10.1016/j.biocon.2020.108864
156. Zonneveld, M., Kindt, R., Solberg, S. Ø., N’Danikou, S., & Dawson, I. K. (2020). Diversity and conservation of traditional African vegetables: Priorities for action. Diversity and Distributions, 27(2), 216–232. https://doi.org/10.1111/ddi.13188
157. Santos, G. N. dos, Silva, A. C. da, & Higuchi, P. (2020). IMPACT OF CLIMATE CHANGE ON THE GEOGRAPHICAL DISTRIBUTION OF A CLOUD FOREST INDICATOR TREE SPECIES. Revista Árvore, 44. https://doi.org/10.1590/1806-908820200000032
158. Liao, H., Li, D., Zhou, T., Huang, B., Zhang, H., Chen, B., & Peng, S. (2020). The role of functional strategies in global plant distribution. Ecography, 44(4), 493–503. https://doi.org/10.1111/ecog.05476
159. Pineda-Munoz, S., Wang, Y., Lyons, S. K., Tóth, A. B., & McGuire, J. L. (2021). Mammal species occupy different climates following the expansion of human impacts. Proceedings of the National Academy of Sciences, 118(2), e1922859118. https://doi.org/10.1073/pnas.1922859118
160. Freimuth, J., Bossdorf, O., Scheepens, J. F., & Willems, F. M. (2021). Climate warming changes synchrony of plants and pollinators. https://doi.org/10.1101/2021.01.10.425984
161. Ascensão, F., D’Amico, M., Martins, R. C., Rebelo, R., Barbosa, A. M., Bencatel, J., … Capinha, C. (2021). Distribution of alien tetrapods in the Iberian Peninsula. NeoBiota, 64, 1–21. https://doi.org/10.3897/neobiota.64.55597
162. Schwery, O., & O’Meara, B. C. (2021). Age, Origin, and Biogeography: Unveiling the Factors Behind the Diversification of Dung Beetles. doi:10.1101/2021.01.26.428346

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

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

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

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

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

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

1. H Sparks, A., Hengl, T., & Nelson, A. (2017). GSODR: Global Summary Daily Weather Data in R. The Journal of Open Source Software, 2(10). https://doi.org/10.21105/joss.00177
2. Halimubieke, N., Kupán, K., Valdebenito, J. O., Kubelka, V., Carmona-Isunza, M. C., Burgas, D., … Székely, T. (2020). Successful breeding predicts divorce in plovers. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-72521-6

A flexible tool that allows generating bespoke air transport statistics for urban studies based on publicly available data from the Bureau of Transport Statistics (BTS) in the United States https://www.transtats.bts.gov/databases.asp?Z1qr_VQ=E&Z1qr_Qr5p=N8vn6v10&f7owrp6_VQF=D.

1. Teixeira, F., & Derudder, B. (2018). SKYNET: An R package for generating air passenger networks for urban studies. Urban Studies, 004209801880325. https://doi.org/10.1177/0042098018803258
2. Teixeira, F. M., & Derudder, B. (2021). Spatio-temporal dynamics in airport catchment areas: The case of the New York Multi Airport Region. Journal of Transport Geography, 90, 102916. https://doi.org/10.1016/j.jtrangeo.2020.102916

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

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.

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

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

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

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

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

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

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

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

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

1. Watson, O. J., Sumner, K. M., Janko, M., Goel, V., Winskill, P., Slater, H. C., … Parr, J. B. (2019). False-negative malaria rapid diagnostic test results and their impact on community-based malaria surveys in sub-Saharan Africa. BMJ Global Health, 4(4), e001582. https://doi.org/10.1136/bmjgh-2019-001582
2. Sánchez-Páez, D. A., & Ortega, J. A. (2019). Reported patterns of pregnancy termination from Demographic and Health Surveys. PLOS ONE, 14(8), e0221178. https://doi.org/10.1371/journal.pone.0221178
3. Finnegan, A., Sao, S. S., & Huchko, M. J. (2019). Using a Chord Diagram to Visualize Dynamics in Contraceptive Use: Bringing Data Into Practice. Global Health: Science and Practice, 7(4), 598–605. https://doi.org/10.9745/ghsp-d-19-00205
4. Walker, P. G. T., Whittaker, C., Watson, O. J., Baguelin, M., Winskill, P., Hamlet, A., … Ghani, A. C. (2020). The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science, eabc0035. https://doi.org/10.1126/science.abc0035
5. Li, Z. R., Martin, B. D., Dong, T. Q., Fuglstad, G. A., Paige, J., Riebler, A., … & Wakefield, J. (2020). Space-Time Smoothing of Demographic and Health Indicators using the R Package SUMMER. arXiv preprint arXiv:2007.05117 https://arxiv.org/pdf/2007.05117.
6. Stresman, G., Whittaker, C., Slater, H. C., Bousema, T., & Cook, J. (2020). Quantifying Plasmodium falciparum infections clustering within households to inform household-based intervention strategies for malaria control programs: An observational study and meta-analysis from 41 malaria-endemic countries. PLOS Medicine, 17(10), e1003370. https://doi.org/10.1371/journal.pmed.1003370
7. Fu, H., Lewnard, J. A., Frost, I., Laxminarayan, R., & Arinaminpathy, N. (2021). Modelling the global burden of drug-resistant tuberculosis avertable by a post-exposure vaccine. Nature Communications, 12(1). doi:10.1038/s41467-020-20731-x

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

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

1. Drozd, P., & Šipoš, J. (2013). R for all (I): Introduction to the new age of biological analyses. Casopis Slezskeho Zemskeho Muzea A, 62(1). https://doi.org/10.2478/cszma-2013-0004
2. Froehlich, H. E., Gentry, R. R., & Halpern, B. S. (2016). Synthesis and comparative analysis of physiological tolerance and life-history growth traits of marine aquaculture species. Aquaculture, 460, 75–82. https://doi.org/10.1016/j.aquaculture.2016.04.018
3. McGee, M. D., Borstein, S. R., Neches, R. Y., Buescher, H. H., Seehausen, O., & Wainwright, P. C. (2015). A pharyngeal jaw evolutionary innovation facilitated extinction in Lake Victoria cichlids. Science, 350(6264), 1077–1079. https://doi.org/10.1126/science.aab0800
4. Plank, M. J., Pitchford, J. W., & James, A. (2016). Evolutionarily Stable Strategies for Fecundity and Swimming Speed of Fish. Bull Math Biol, 78(2), 280–292. https://doi.org/10.1007/s11538-016-0143-7
5. Price, S. A., Friedman, S. T., & Wainwright, P. C. (2015). How predation shaped fish: the impact of fin spines on body form evolution across teleosts. Proc. R. Soc. B, 282(1819), 20151428. https://doi.org/10.1098/rspb.2015.1428
6. Sagouis, A., Cucherousset, J., Villéger, S., Santoul, F., & Boulêtreau, S. (2015). Non-native species modify the isotopic structure of freshwater fish communities across the globe. Ecography, 38(10), 979–985. https://doi.org/10.1111/ecog.01348
7. Boeger, W. A., Marteleto, F. M., Zagonel, L., & Braga, M. P. (2014). Tracking the history of an invasion: the freshwater croakers (Teleostei: Sciaenidae) in South America. Zool Scr, 44(3), 250–262. https://doi.org/10.1111/zsc.12098
8. Mindel, B. L., Webb, T. J., Neat, F. C., & Blanchard, J. L. (2016). A trait-based metric sheds new light on the nature of the body size-depth relationship in the deep sea. J Anim Ecol, 85(2), 427–436. https://doi.org/10.1111/1365-2656.12471
9. Miya, M., Friedman, M., Satoh, T. P., Takeshima, H., Sado, T., Iwasaki, W., … Nishida, M. (2013). Evolutionary Origin of the Scombridae (Tunas and Mackerels): Members of a Paleogene Adaptive Radiation with 14 Other Pelagic Fish Families. PLoS ONE, 8(9), e73535. https://doi.org/10.1371/journal.pone.0073535
10. Price, S. A., Claverie, T., Near, T. J., & Wainwright, P. C. (2015). Phylogenetic insights into the history and diversification of fishes on reefs. Coral Reefs, 34(4), 997–1009. https://doi.org/10.1007/s00338-015-1326-7
11. Collins, R. A., Britz, R., & Rüber, L. (2015). Phylogenetic systematics of leaffishes - Teleostei: Polycentridae, Nandidae. Journal of Zoological Systematics and Evolutionary Research. 53(4), 259–272. https://doi.org/10.1111/jzs.12103
12. Schaefer, J., Frazier, N., & Barr, J. (2015). Dynamics of Near-Coastal Fish Assemblages following the Deepwater Horizon Oil Spill in the Northern Gulf of Mexico. Transactions of the American Fisheries Society, 145(1), 108–119. https://doi.org/10.1080/00028487.2015.1111253
13. Bezerra, L. A. V., Padial, A. A., Mariano, F. B., Garcez, D. S., & Sánchez-Botero, J. I. (2017). Fish diversity in tidepools: assembling effects of environmental heterogeneity. Environmental Biology of Fishes. https://doi.org/10.1007/s10641-017-0584-3
14. Tedesco, P. A., Beauchard, O., Bigorne, R., Blanchet, S., Buisson, L., Conti, L., … Oberdorff, T. (2017). A global database on freshwater fish species occurrence in drainage basins. Scientific Data, 4, 170141. https://doi.org/10.1038/sdata.2017.141
15. Dulvy, N. K., & Kindsvater, H. K. (2017). The Future Species of Anthropocene Seas. Conservation for the Anthropocene Ocean, 39–64. https://doi.org/10.1016/b978-0-12-805375-1.00003-9
16. Pedersen, E. J., Thompson, P. L., Ball, R. A., Fortin, M.-J., Gouhier, T. C., Link, H., … Pepin, P. (2017). Signatures of the collapse and incipient recovery of an overexploited marine ecosystem. Royal Society Open Science, 4(7), 170215. https://doi.org/10.1098/rsos.170215
17. Martin, B. T., Heintz, R., Danner, E. M., & Nisbet, R. M. (2017). Integrating lipid storage into general representations of fish energetics. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.12667
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
56. Anderson, D. M., & Gillooly, J. F. (2020). Predicting egg size across temperatures in marine teleost fishes. Fish and Fisheries, 21(5), 1027–1033. https://doi.org/10.1111/faf.12486
57. Larouche, O., Hodge, J. R., Alencar, L. R. V., Camper, B., Adams, D. S., Zapfe, K., … Price, S. A. (2020). Do key innovations unlock diversification? A case-study on the morphological and ecological impact of pharyngognathy in acanthomorph fishes. Current Zoology. https://doi.org/10.1093/cz/zoaa048
58. Bayley, D. T. I., Purvis, A., Nellas, A. C., Arias, M., & Koldewey, H. J. (2020). Measuring the long-term success of small-scale marine protected areas in a Philippine reef fishery. Coral Reefs. https://doi.org/10.1007/s00338-020-01987-7
59. Larouche, O., Benton, B., Corn, K. A., Friedman, S. T., Gross, D., Iwan, M., … Price, S. A. (2020). Reef-associated fishes have more maneuverable body shapes at a macroevolutionary scale. Coral Reefs, 39(5), 1427–1439. https://doi.org/10.1007/s00338-020-01976-w
60. Keppeler, F. W., Montaña, C. G., & Winemiller, K. O. (2020). The relationship between trophic level and body size in fishes depends on functional traits. Ecological Monographs. https://doi.org/10.1002/ecm.1415
61. Huang, M., Ding, L., Wang, J., Ding, C., & Tao, J. (2021). The impacts of climate change on fish growth: A summary of conducted studies and current knowledge. Ecological Indicators, 121, 106976. https://doi.org/10.1016/j.ecolind.2020.10697
62. Borstein, S. R. (2020). dietr: an R package for calculating fractional trophic levels from quantitative and qualitative diet data. Hydrobiologia, 847(20), 4285–4294. https://doi.org/10.1007/s10750-020-04417-5
63. Denderen, D., Gislason, H., Heuvel, J., & Andersen, K. H. (2020). Global analysis of fish growth rates shows weaker responses to temperature than metabolic predictions. Global Ecology and Biogeography, 29(12), 2203–2213. https://doi.org/10.1111/geb.13189
64. Oegelund Nielsen, R., da Silva, R., Juergens, J., Staerk, J., Lindholm Sørensen, L., Jackson, J., … Conde, D. A. (2020). Standardized data to support conservation prioritization for sharks and batoids (Elasmobranchii). Data in Brief, 33, 106337. https://doi.org/10.1016/j.dib.2020.106337
65. Guerra, A. S., Kao, A. B., McCauley, D. J., & Berdahl, A. M. (2020). Fisheries-induced selection against schooling behaviour in marine fishes. Proceedings of the Royal Society B: Biological Sciences, 287(1935), 20201752. https://doi.org/10.1098/rspb.2020.1752
66. Webb, T. J., & Vanhoorne, B. (2020). Linking dimensions of data on global marine animal diversity. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1814), 20190445. https://doi.org/10.1098/rstb.2019.0445
67. Morat, F., Wicquart, J., Schiettekatte, N. M. D., de Sinéty, G., Bienvenu, J., Casey, J. M., … Parravicini, V. (2020). Individual back-calculated size-at-age based on otoliths from Pacific coral reef fish species. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-00711-y
68. Whalen, M. A., Whippo, R. D. B., Stachowicz, J. J., York, P. H., Aiello, E., Alcoverro, T., … Bresch, M. (2020). Climate drives the geography of marine consumption by changing predator communities. Proceedings of the National Academy of Sciences, 117(45), 28160–28166. https://doi.org/10.1073/pnas.2005255117
69. Palacios-Abrantes, J., Reygondeau, G., Wabnitz, C. C. C., & Cheung, W. W. L. (2020). The transboundary nature of the world’s exploited marine species. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-74644-2
70. Paillard, A., Shimada, K., & Pimiento, C. (2020). The fossil record of extant elasmobranchs. Journal of Fish Biology. https://doi.org/10.1111/jfb.14588
71. Huang, M., Ding, L., Wang, J., Ding, C., & Tao, J. (2021). The impacts of climate change on fish growth: A summary of conducted studies and current knowledge. Ecological Indicators, 121, 106976. doi:10.1016/j.ecolind.2020.106976
72. Comte, L., Carvajal‐Quintero, J., Tedesco, P. A., Giam, X., Brose, U., Erős, T., … Olden, J. D. (2020). RivFishTIME: A global database of fish time‐series to study global change ecology in riverine systems. Global Ecology and Biogeography, 30(1), 38–50. https://doi.org/10.1111/geb.13210
73. Leung, B., Hargreaves, A. L., Greenberg, D. A., McGill, B., Dornelas, M., & Freeman, R. (2020). Clustered versus catastrophic global vertebrate declines. Nature, 588(7837), 267–271. https://doi.org/10.1038/s41586-020-2920-6
74. Kopf, R. K., Yen, J. D. L., Nimmo, D. G., Brosse, S., & Villéger, S. (2020). Global patterns and predictors of trophic position, body size and jaw size in fishes. Global Ecology and Biogeography, 30(2), 414–428. https://doi.org/10.1111/geb.13227
75. Gandra, M., Assis, J., Martins, M. R., & Abecasis, D. (2020). Reduced Global Genetic Differentiation of Exploited Marine Fish Species. Molecular Biology and Evolution. https://doi.org/10.1093/molbev/msaa299
76. Palacios-Abrantes, J., Reygondeau, G., Wabnitz, C. C. C., & Cheung, W. W. L. (2020). The transboundary nature of the world’s exploited marine species. Scientific Reports, 10(1). doi:10.1038/s41598-020-74644-2
77. Parravicini, V., Casey, J. M., Schiettekatte, N. M. D., Brandl, S. J., Pozas-Schacre, C., Carlot, J., … Stuart-Smith, R. D. (2020). Delineating reef fish trophic guilds with global gut content data synthesis and phylogeny. PLOS Biology, 18(12), e3000702. https://doi.org/10.1371/journal.pbio.3000702
78. Murgier, J., McLean, M., Maire, A., Mouillot, D., Loiseau, N., Munoz, F., … Auber, A. (2021). Rebound in functional distinctiveness following warming and reduced fishing in the North Sea. Proceedings of the Royal Society B: Biological Sciences, 288(1942), 20201600. https://doi.org/10.1098/rspb.2020.1600

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

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

1. Wang, S., He, Z., Wang, X., Li, H., & Liu, X.-S. (2019). Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction. eLife, 8. https://doi.org/10.7554/elife.49020
2. Li, Y., Ge, D., & Lu, C. (2019). The SMART App: an interactive web application for comprehensive DNA methylation analysis and visualization. Epigenetics & Chromatin, 12(1). https://doi.org/10.1186/s13072-019-0316-3
3. Kang, W., Zhang, M., Wang, Q., Gu, D., Huang, Z., Wang, H., … Jin, X. (2020). The SLC Family Are Candidate Diagnostic and Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma. BioMed Research International, 2020, 1–17. https://doi.org/10.1155/2020/1932948
4. Liu, Y., Wang, L., Lo, K.-W., & Lui, V. W. Y. (2020). Omics-wide quantitative B-cell infiltration analyses identify GPR18 for human cancer prognosis with superiority over CD20. Communications Biology, 3(1). https://doi.org/10.1038/s42003-020-0964-7
5. Wang, S., Xiong, Y., Gu, K., Zhao, L., Li, Y., Zhao, F., … Liu, X.-S. (2020). UCSCXenaShiny: An R Package for Exploring and Analyzing UCSC Xena Public Datasets in Web Browser. https://doi.org/10.20944/preprints202007.0179.v1
6. Gvaldin, D. Y., Pushkin, A. A., Timoshkina, N. N., Rostorguev, E. E., Nalgiev, A. M., & Kit, O. I. (2020). Integrative analysis of mRNA and miRNA sequencing data for gliomas of various grades. Egyptian Journal of Medical Human Genetics, 21(1). https://doi.org/10.1186/s43042-020-00119-8
7. Cui, Y., Hunt, A., Li, Z., Birkin, E., Lane, J., Ruge, F., & Jiang, W. G. (2021). Lead DEAD/H box helicase biomarkers with the therapeutic potential identified by integrated bioinformatic approaches in lung cancer. Computational and Structural Biotechnology Journal, 19, 261–278. https://doi.org/10.1016/j.csbj.2020.12.007

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

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

1. Lovelace, R., Goodman, A., Aldred, R., Berkoff, N., Abbas, A., & Woodcock, J. (2015). The Propensity to Cycle Tool: An open source online system for sustainable transport planning. arXiv preprint arXiv:1509.04425 http://arxiv.org/abs/1509.04425
2. Lovelace, R., Morgan, M., Hama, L., & Padgham, M. (2019). stats19: A package for working with open road crash data. Journal of Open Source Software, 4(33), 1181. https:://doi.org/10.21105/joss.01181
3. Yen, Y., Zhao, P., & Sohail, M. T. (2019). The morphology and circuity of walkable, bikeable, and drivable street networks in Phnom Penh, Cambodia. Environment and Planning B: Urban Analytics and City Science, 239980831985772. https://doi.org/10.1177/2399808319857726
4. Zhao, P., & Cao, Y. (2020). Commuting inequity and its determinants in Shanghai: New findings from big-data analytics. Transport Policy, 92, 20–37. https://doi.org/10.1016/j.tranpol.2020.03.006
5. Morgan, M., & Lovelace, R. (2020). Travel flow aggregation: Nationally scalable methods for interactive and online visualisation of transport behaviour at the road network level. Environment and Planning B: Urban Analytics and City Science, 239980832094277. https://doi.org/10.1177/2399808320942779
6. Baddeley, A., Nair, G., Rakshit, S., McSwiggan, G., & Davies, T. M. (2020). Analysing point patterns on networks — A review. Spatial Statistics, 100435. https://doi.org/10.1016/j.spasta.2020.100435
7. Bivand, R. S. (2020). Progress in the R ecosystem for representing and handling spatial data. Journal of Geographical Systems. https://doi.org/10.1007/s10109-020-00336-0
8. Fitzgerald, D. B., Henderson, A. R., Maloney, K. O., Freeman, M. C., Young, J. A., Rosenberger, A. E., … Smith, D. R. (2021). A Bayesian framework for assessing extinction risk based on ordinal categories of population condition and projected landscape change. Biological Conservation, 253, 108866. https://doi.org/10.1016/j.biocon.2020.108866
9. Lovelace, R. (2021). Open source tools for geographic analysis in transport planning. Journal of Geographical Systems. doi:10.1007/s10109-020-00342-2

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.

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

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

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

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

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

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

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

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

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.

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

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

1. Evans, K. M., Vidal-García, M., Tagliacollo, V. A., Taylor, S. J., & Fenolio, D. B. (2019). Bony Patchwork: Mosaic Patterns of Evolution in the Skull of Electric Fishes (Apteronotidae: Gymnotiformes). Integrative and Comparative Biology. https://doi.org/10.1093/icb/icz026
2. Ruiz-Sanchez, E., Maya-Lastra, C. A., Steinmann, V. W., Zamudio, S., Carranza, E., Murillo, R. M., & Rzedowski, J. (2019). Datataxa: a new script to extract metadata sequence information from GenBank, the Flora of Bajío as a case study. Botanical Sciences, 97(4), 754–760. https://doi.org/10.17129/botsci.2226
3. Crespo, L. C., Silva, I., Enguídanos, A., Cardoso, P., & Arnedo, M. A. (2020). Integrative taxonomic revision of the woodlouse-hunter spider genus Dysdera (Araneae: Dysderidae) in the Madeira archipelago with notes on its conservation status. Zoological Journal of the Linnean Society. https://doi.org./10.1093/zoolinnean/zlaa089

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

1. Vantas, K. (2018). hydroscoper: R interface to the Greek National Data Bank for Hydrological and Meteorological Information. Journal of Open Source Software, 3(23), 625. https://doi.org/10.21105/joss.00625
2. Vantas, K., Sidiropoulos, E., & Loukas, A. (2019). Robustness Spatiotemporal Clustering and Trend Detection of Rainfall Erosivity Density in Greece. Water, 11(5), 1050. https://doi.org/10.3390/w11051050
3. Vantas, K., Sidiropoulos, E., & Loukas, A. (2020). Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests. Water, 12(3), 687. https://doi.org/10.3390/w12030687
4. Vantas, K., Sidiropoulos, E., & Evangelides, C. (2020). Estimating Rainfall Erosivity from Daily Precipitation Using Generalized Additive Models. Environmental Sciences Proceedings, 2(1), 21. doi:10.3390/environsciproc2020002021

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

Allows automating the creation of time series of rasters derived from MODIS satellite land products data. It performs several typical preprocessing steps such as download, mosaicking, reprojecting and resizing data acquired on a specified time period. All processing parameters can be set using a user-friendly GUI. Users can select which layers of the original MODIS HDF files they want to process, which additional quality indicators should be extracted from aggregated MODIS quality assurance layers and, in the case of surface reflectance products, which spectral indexes should be computed from the original reflectance bands. For each output layer, outputs are saved as single-band raster files corresponding to each available acquisition date. Virtual files allowing access to the entire time series as a single file are also created. Command-line execution exploiting a previously saved processing options file is also possible, allowing users to automatically update time series related to a MODIS product whenever a new image is available. For additional documentation refer to the following article: Busetto and Ranghetti (2016) doi:10.1016/j.cageo.2016.08.020.

1. Busetto, L., & Ranghetti, L. (2016). MODIStsp : An R package for automatic preprocessing of MODIS Land Products time series. Computers & Geosciences, 97, 40–48. https://doi.org/10.1016/j.cageo.2016.08.020
2. Bellón, B., Bégué, A., Lo Seen, D., de Almeida, C., & Simões, M. (2017). A Remote Sensing Approach for Regional-Scale Mapping of Agricultural Land-Use Systems Based on NDVI Time Series. Remote Sensing, 9(6), 600. https://doi.org/10.3390/rs9060600
3. Hurtado, L. A., Calzada, J. E., Rigg, C. A., Castillo, M., & Chaves, L. F. (2018). Climatic fluctuations and malaria transmission dynamics, prior to elimination, in Guna Yala, República de Panamá. Malaria Journal, 17(1). https://doi.org/10.1186/s12936-018-2235-3
4. Ranghetti, L., Cardarelli, E., Boschetti, M., Busetto, L., & Fasola, M. (2018). Assessment of Water Management Changes in the Italian Rice Paddies from 2000 to 2016 Using Satellite Data: A Contribution to Agro-Ecological Studies. Remote Sensing, 10(3), 416. https://doi.org/10.3390/rs10030416
5. Bellón, B., Bégué, A., Lo Seen, D., Lebourgeois, V., Evangelista, B. A., Simões, M., & Demonte Ferraz, R. P. (2018). Improved regional-scale Brazilian cropping systems’ mapping based on a semi-automatic object-based clustering approach. International Journal of Applied Earth Observation and Geoinformation, 68, 127–138. https://doi.org/10.1016/j.jag.2018.01.019
6. Manfron, G., Delmotte, S., Busetto, L., Hossard, L., Ranghetti, L., Brivio, P. A., & Boschetti, M. (2017). Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue, France. International Journal of Applied Earth Observation and Geoinformation, 57, 190–201. https://doi.org/10.1016/j.jag.2017.01.001
7. Araya, S., Ostendorf, B., Lyle, G., & Lewis, M. (2018). CropPhenology: An R package for extracting crop phenology from time series remotely sensed vegetation index imagery. Ecological Informatics, 46, 45–56. https://doi.org/10.1016/j.ecoinf.2018.05.006
8. Adisa, O., Botai, J., Hassen, A., Darkey, D., Adeola, A., Tesfamariam, E., … Adisa, A. (2018). Variability of Satellite Derived Phenological Parameters across Maize Producing Areas of South Africa. Sustainability, 10(9), 3033. https://doi.org/10.3390/su10093033
9. Granell, C., Miralles, I., Rodríguez-Pupo, L., González-Pérez, A., Casteleyn, S., Busetto, L., … Huerta, J. (2017). Conceptual Architecture and Service-Oriented Implementation of a Regional Geoportal for Rice Monitoring. ISPRS International Journal of Geo-Information, 6(7), 191. https://doi.org/10.3390/ijgi6070191
10. Boschetti, M., Busetto, L., Manfron, G., Laborte, A., Asilo, S., Pazhanivelan, S., & Nelson, A. (2017). PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series. Remote Sensing of Environment, 194, 347–365. https://doi.org/10.1016/j.rse.2017.03.029
11. Nutini, F., Stroppiana, D., Busetto, L., Bellingeri, D., Corbari, C., Mancini, M., … Boschetti, M. (2017). A Weekly Indicator of Surface Moisture Status from Satellite Data for Operational Monitoring of Crop Conditions. Sensors, 17(6), 1338. https://doi.org/10.3390/s17061338
12. Moura, M. M., dos Santos, A. R., Pezzopane, J. E. M., Alexandre, R. S., da Silva, S. F., Pimentel, S. M., … de Carvalho, J. R. (2019). Relation of El Niño and La Niña phenomena to precipitation, evapotranspiration and temperature in the Amazon basin. Science of The Total Environment, 651, 1639–1651. https://doi.org/10.1016/j.scitotenv.2018.09.242
13. Hurtado, L., Rigg, C., Calzada, J., Dutary, S., Bernal, D., Koo, S., & Chaves, L. (2018). Population Dynamics of Anopheles albimanus (Diptera: Culicidae) at Ipetí-Guna, a Village in a Region Targeted for Malaria Elimination in Panamá. Insects, 9(4), 164. https://doi.org/10.3390/insects9040164
14. Sodnomov, B. V., Ayurzhanaev, A. A., Tsydypov, B. Z., Garmaev, E. Z., & Tulokhonov, A. K. (2018). Software for analysis of vegetation indices dynamics. IOP Conference Series: Earth and Environmental Science, 211, 012083. https://doi.org/10.1088/1755-1315/211/1/012083
15. Marcos, B., Gonçalves, J., Alcaraz-Segura, D., Cunha, M., & Honrado, J. P. (2019). Improving the detection of wildfire disturbances in space and time based on indicators extracted from MODIS data: a case study in northern Portugal. International Journal of Applied Earth Observation and Geoinformation, 78, 77–85. https://doi.org/10.1016/j.jag.2018.12.003
16. Rigg, C. A., Hurtado, L. A., Calzada, J. E., & Chaves, L. F. (2019). Malaria infection rates in Anopheles albimanus (Diptera: Culicidae) at Ipetí-Guna, a village within a region targeted for malaria elimination in Panamá. Infection, Genetics and Evolution, 69, 216–223. https://doi.org/10.1016/j.meegid.2019.02.003
17. Nghiem, J., Potter, C., & Baiman, R. (2019). Detection of Vegetation Cover Change in Renewable Energy Development Zones of Southern California Using MODIS NDVI Time Series Analysis, 2000 to 2018. Environments, 6(4), 40. https://doi.org/10.3390/environments6040040
18. Marcos, B., Gonçalves, J., Alcaraz-Segura, D., Cunha, M., & Honrado, J. P. (2019). Improving the detection of wildfire disturbances in space and time based on indicators extracted from MODIS data: a case study in northern Portugal. International Journal of Applied Earth Observation and Geoinformation, 78, 77-85. https://doi.org/10.1016/j.jag.2018.12.003
19. Bhattarai, N., Mallick, K., Stuart, J., Vishwakarma, B. D., Niraula, R., Sen, S., & Jain, M. (2019). An automated multi-model evapotranspiration mapping framework using remotely sensed and reanalysis data. Remote Sensing of Environment, 229, 69–92. https://doi.org/10.1016/j.rse.2019.04.026
20. Adeola, A. M., Botai, J. O., Mukarugwiza Olwoch, J., DeW. Rautenbach, H. C. J., Adisa, O. M., De Jager, C., … Aaron, M. (2019). Predicting malaria cases using remotely sensed environmental variables in Nkomazi, South Africa. Geospatial Health, 14(1). https://doi.org/10.4081/gh.2019.676
21. Nelli, L., Ferguson, H. M., & Matthiopoulos, J. (2019). Achieving explanatory depth and spatial breadth in infectious disease modelling: Integrating active and passive case surveillance. Statistical Methods in Medical Research, 096228021985638. https://doi.org/10.1177/0962280219856380
22. Verstraeten, W. W., Dujardin, S., Hoebeke, L., Bruffaerts, N., Kouznetsov, R., Dendoncker, N., … Delcloo, A. W. (2019). Spatio-temporal monitoring and modelling of birch pollen levels in Belgium. Aerobiologia. https://doi.org/10.1007/s10453-019-09607-w
23. Yoo, B. H., Kim, K. S., & Lee, J. (2019). MODIS 대기자료를 활용한 남북한 기상관측소에서의 냉방도일 추정. 한국농림기상학회지, 21(2), 97–109. https://doi.org/10.5532/KJAFM.2019.21.2.97
24. Mpandeli, S., Nhamo, L., Moeletsi, M., Masupha, T., Magidi, J., Tshikolomo, K., … Mabhaudhi, T. (2019). Assessing climate change and adaptive capacity at local scale using observed and remotely sensed data. Weather and Climate Extremes, 26, 100240. https://doi.org/10.1016/j.wace.2019.100240
25. Badreldin, N., Abu Hatab, A., & Lagerkvist, C.-J. (2019). Spatiotemporal dynamics of urbanization and cropland in the Nile Delta of Egypt using machine learning and satellite big data: implications for sustainable development. Environmental Monitoring and Assessment, 191(12). https://doi.org/10.1007/s10661-019-7934-x
26. Estrada-Peña, A., Nava, S., Tarragona, E., Bermúdez, S., de la Fuente, J., Domingos, A., … Guglielmone, A. A. (2019). Species occurrence of ticks in South America, and interactions with biotic and abiotic traits. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0314-0
27. Fatikhunnada, A., Liyantono, Solahudin, M., Buono, A., Kato, T., & Seminar, K. B. (2020). Assessment of pre-treatment and classification methods for Java paddy field cropping pattern detection on MODIS images. Remote Sensing Applications: Society and Environment, 17, 100281. https://doi.org/10.1016/j.rsase.2019.100281
28. Akpoti, K., Kabo-bah, A. T., Dossou-Yovo, E. R., Groen, T. A., & Zwart, S. J. (2020). Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling. Science of The Total Environment, 709, 136165. https://doi.org/10.1016/j.scitotenv.2019.136165
29. Pérez-Goya, U., Montesino-SanMartin, M., Militino, A. F., & Ugarte, M. D. (2020). RGISTools: Downloading, Customizing, and Processing Time Series of Remote Sensing Data in R. arXiv preprint arXiv:2002.01859 https://arxiv.org/pdf/2002.01859.pdf
30. Barela, I., Burger, L. M., Taylor, J., Evans, K. O., Ogawa, R., McClintic, L., & Wang, G. (2020). Relationships between survival and habitat suitability of semi‐aquatic mammals. Ecology and Evolution, 10(11), 4867–4875. https://doi.org/10.1002/ece3.6239
31. Nguyen, C. T., Nguyen, D. T. H., & Phan, D. K. (2020). Factors affecting urban electricity consumption: a case study in the Bangkok Metropolitan Area using an integrated approach of earth observation data and data analysis. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-020-09157-6
32. Fernández-Ruiz, N., & Estrada-Peña, A. (2020). Could climate trends disrupt the contact rates between Ixodes ricinus (Acari, Ixodidae) and the reservoirs of Borrelia burgdorferi s.l.? PLOS ONE, 15(5), e0233771. https://doi.org/10.1371/journal.pone.0233771
33. Liu, L., Huang, J., Xiong, Q., Zhang, H., Song, P., Huang, Y., … Wang, X. (2020). Optimal MODIS data processing for accurate multi-year paddy rice area mapping in China. GIScience & Remote Sensing, 57(5), 687–703. https://doi.org/10.1080/15481603.2020.1773012
34. Anton, C. B., Smith, D. W., Suraci, J. P., Stahler, D. R., Duane, T. P., & Wilmers, C. C. (2020). Gray wolf habitat use in response to visitor activity along roadways in Yellowstone National Park. Ecosphere, 11(6). https://doi.org/10.1002/ecs2.3164
35. Jayawardhana, W. G. N. N., & Chathurange, V. M. I. (2020). Investigate the sensitivity of the satellite-based agricultural drought indices to monitor the drought condition of paddy and introduction to enhanced multi-temporal agricultural drought indices. J Remote Sens GIS, 9, 271. https://www.longdom.org/open-access/investigate-the-sensitivity-of-the-satellitebased-agricultural-drought-indices-to-monitor-the-drought-condition-of-paddy.pdf
36. Da Silva Abel, E. L., Delgado, R. C., Vilanova, R. S., Teodoro, P. E., da Silva Junior, C. A., Abreu, M. C., & Silva, G. F. C. (2020). Environmental dynamics of the Juruá watershed in the Amazon. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-020-00890-z
37. Gutiérrez-Hernández, O. (2020). Fenología de los ecosistemas de alta montaña en Andalucía: Análisis de la tendencia estacional del SAVI (2000-2019). Pirineos, 175, 055. https://doi.org/10.3989/pirineos.2020.175005
38. Estrada-Peña, A., D’Amico, G., & Fernández-Ruiz, N. (2020). Modelling the potential spread of Hyalomma marginatum ticks in Europe by migratory birds. International Journal for Parasitology. doi:10.1016/j.ijpara.2020.08.004
39. De Andrade, C. F., Delgado, R. C., Barbosa, M. L. F., Teodoro, P. E., Junior, C. A. da S., Wanderley, H. S., & Capristo-Silva, G. F. (2020). Fire regime in Southern Brazil driven by atmospheric variation and vegetation cover. Agricultural and Forest Meteorology, 295, 108194. doi:10.1016/j.agrformet.2020.108194
40. Sao, D., Kato, T., Tu, L. H., Thouk, P., Fitriyah, A., & Oeurng, C. (2020). Evaluation of Different Objective Functions Used in the SUFI-2 Calibration Process of SWAT-CUP on Water Balance Analysis: A Case Study of the Pursat River Basin, Cambodia. Water, 12(10), 2901. https://doi.org/10.3390/w12102901
41. Chaves, L. F., & Friberg, M. D. (2021). Aedes albopictus and Aedes flavopictus (Diptera: Culicidae) pre-imaginal abundance patterns are associated with different environmental factors along an altitudinal gradient. Current Research in Insect Science, 1, 100001. https://doi.org/10.1016/j.cris.2020.100001
42. De Andrade, M. D., Delgado, R. C., da Costa de Menezes, S. J. M., de Ávila Rodrigues, R., Teodoro, P. E., da Silva Junior, C. A., & Pereira, M. G. (2021). Evaluation of the MOD11A2 product for canopy temperature monitoring in the Brazilian Atlantic Forest. Environmental Monitoring and Assessment, 193(1). doi:10.1007/s10661-020-08788-z
43. Akpoti, K., Dossou-Yovo, E. R., Zwart, S. J., & Kiepe, P. (2021). The potential for expansion of irrigated rice under alternate wetting and drying in Burkina Faso. Agricultural Water Management, 247, 106758. doi:10.1016/j.agwat.2021.106758

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

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

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

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

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

1. Drost, H.-G., Gabel, A., Liu, J., Quint, M., & Grosse, I. (2017). myTAI: evolutionary transcriptomics with R. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx835
2. Gogleva, A., Drost, H.-G., & Schornack, S. (2018). SecretSanta: flexible pipelines for functional secretome prediction. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty088
3. Ng, P. K.-S., Li, J., Jeong, K. J., Shao, S., Chen, H., Tsang, Y. H., … Mills, G. B. (2018). Systematic Functional Annotation of Somatic Mutations in Cancer. Cancer Cell, 33(3), 450–462.e10. https://doi.org/10.1016/j.ccell.2018.01.021
4. Schwalie, P. C., Dong, H., Zachara, M., Russeil, J., Alpern, D., Akchiche, N., … Deplancke, B. (2018). A stromal cell population that inhibits adipogenesis in mammalian fat depots. Nature. https://doi.org/10.1038/s41586-018-0226-8
5. Wegrzyn, J. L., Falk, T., Grau, E., Buehler, S., Ramnath, R., & Herndon, N. (2019). Cyberinfrastructure and resources to enable an integrative approach to studying forest trees. Evolutionary Applications. https://doi.org/10.1111/eva.12860
6. Karakülah, G., Arslan, N., Yandım, C., & Suner, A. (2019). TEffectR: an R package for studying the potential effects of transposable elements on gene expression with linear regression model. PeerJ, 7, e8192. https://doi.org/10.7717/peerj.8192
7. Noecker, C., Chiu, H. C., McNally, C. P., & Borenstein, E. (2019). Defining and evaluating microbial contributions to metabolite variation in microbiome-metabolome association studies. mSystems, 4(6). https://doi.org/10.1128/mSystems.00579-19
8. Kim, J., Yoon, S., & Nam, D. (2020). netGO: R-Shiny package for network-integrated pathway enrichment analysis. Bioinformatics. https://doi.org/10.1093/bioinformatics/btaa077
9. Drost, H.-G. (2020). LTRpred: de novo annotation of intact retrotransposons. Journal of Open Source Software, 5(50), 2170. https://doi.org/10.21105/joss.02170
10. Bailey, T. W., Santos, A., Nascimento, N. C. de, Sivasankar, M. P., & Cox, A. (2020). RNA sequencing identifies transcriptional changes in the rabbit larynx in response to low humidity challenge. https://doi.org/10.21203/rs.3.rs-45442/v1
11. Pimsler, M. L., Oyen, K. J., Herndon, J. D., Jackson, J. M., Strange, J. P., Dillon, M. E., & Lozier, J. D. (2020). Biogeographic parallels in thermal tolerance and gene expression variation under temperature stress in a widespread bumble bee. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-73391-8
12. Manjang, K., Tripathi, S., Yli-Harja, O., Dehmer, M., & Emmert-Streib, F. (2020). Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-73326-3
13. Thrupp, N., Sala Frigerio, C., Wolfs, L., Skene, N. G., Fattorelli, N., Poovathingal, S., … Fiers, M. (2020). Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans. Cell Reports, 32(13), 108189. https://doi.org/10.1016/j.celrep.2020.108189
14. Sarmah, D. T., Bairagi, N., & Chatterjee, S. (2020). Tracing the footsteps of autophagy in computational biology. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbaa286
15. Henkel, L., Rauscher, B., Schmitt, B., Winter, J., & Boutros, M. (2020). Genome-scale CRISPR screening at high sensitivity with an empirically designed sgRNA library. BMC Biology, 18(1). https://doi.org/10.1186/s12915-020-00905-1
16. Böttcher, A., Büttner, M., Tritschler, S., Sterr, M., Aliluev, A., Oppenländer, L., … Lickert, H. (2021). Non-canonical Wnt/PCP signalling regulates intestinal stem cell lineage priming towards enteroendocrine and Paneth cell fates. Nature Cell Biology, 23(1), 23–31. https://doi.org/10.1038/s41556-020-00617-2
17. Tjeldnes, H., Labun, K., Cleuren, Y. T., Chyżyńska, K., Świrski, M., & Valen, E. (2021). ORFik: a comprehensive R toolkit for the analysis of translation. doi:10.1101/2021.01.16.426936

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

1. Hassall, C., Owen, J., & Gilbert, F. (2016). Phenological shifts in hoverflies (Diptera: Syrphidae): linking measurement and mechanism. Ecography. https://doi.org/10.1111/ecog.02623
2. Bowser, M., Morton, J., Hanson, J., Magness, D., & Okuly, M. (2017). Arthropod and oligochaete assemblages from grasslands of the southern Kenai Peninsula, Alaska. Biodiversity Data Journal, 5, e10792. https://doi.org/10.3897/bdj.5.e10792
3. Divoll, T. J., Brown, V. A., Kinne, J., McCracken, G. F., & O’Keefe, J. M. (2018). Disparities in second-generation DNA metabarcoding results exposed with accessible and repeatable workflows. Molecular Ecology Resources. https://doi.org/10.1111/1755-0998.12770
4. Cravens, Z. M., Brown, V. A., Divoll, T. J., & Boyles, J. G. (2017). Illuminating prey selection in an insectivorous bat community exposed to artificial light at night. Journal of Applied Ecology, 55(2), 705–713. https://doi.org/10.1111/1365-2664.13036
5. Collins, R. A., Bakker, J., Wangensteen, O. S., Soto, A. Z., Corrigan, L., Sims, D. W., … Mariani, S. (2019). Non‐specific amplification compromises environmental DNA metabarcoding with COI. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13276
6. Piper, A. M., Batovska, J., Cogan, N. O. I., Weiss, J., Cunningham, J. P., Rodoni, B. C., & Blacket, M. J. (2019). Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance. GigaScience, 8(8). https://doi.org/10.1093/gigascience/giz092
7. Arranz, V., Pearman, W. S., Aguirre, J. D., & Liggins, L. (2020). MARES, a replicable pipeline and curated reference database for marine eukaryote metabarcoding. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0549-9
8. Wilkes, M. A., Edwards, F., Jones, J. I., Murphy, J. F., England, J., Friberg, N., … Brown, L. E. (2020). Trait‐based ecology at large scales: Assessing functional trait correlations, phylogenetic constraints and spatial variability using open data. Global Change Biology. https://doi.org/10.1111/gcb.15344
9. Sigsgaard, E. E., Olsen, K., Hansen, M. D. D., Hansen, O. L. P., Høye, T. T., Svenning, J., & Thomsen, P. F. (2020). Environmental DNA metabarcoding of cow dung reveals taxonomic and functional diversity of invertebrate assemblages. Molecular Ecology. https://doi.org/10.1111/mec.15734
10. Batovska, J., Piper, A., Valenzuela, I., Cunningham, J., & Blacket, M. (2020). Developing a Non-destructive Metabarcoding Protocol for Detection of Pest Insects in Bulk Trap Catches. https://doi.org/10.21203/rs.3.rs-125070/v1

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

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

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

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

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

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

An implementation of calls designed to collect and organize Twitter data via Twitter’s REST and stream Application Program Interfaces (API), which can be found at the following URL: https://developer.twitter.com/en/docs.

1. Firmansyah, F. M., & Jones, J. J. (2019). Did the Black Panther Movie Make Blacks Blacker? Examining Black Racial Identity on Twitter Before and After the Black Panther Movie Release. Social Informatics, 66–78. https://doi.org/10.1007/978-3-030-34971-4_5
2. Sansone, A., Cignarelli, A., Ciocca, G., Pozza, C., Giorgino, F., Romanelli, F., & Jannini, E. A. (2019). The Sentiment Analysis of Tweets as a New Tool to Measure Public Perception of Male Erectile and Ejaculatory Dysfunctions. Sexual Medicine, 7(4), 464–471. https://doi.org/10.1016/j.esxm.2019.07.001
3. Tancoigne, E. (2019). Invisible brokers: “citizen science” on Twitter. Journal of Science Communication, 18(06). https://doi.org/10.22323/2.18060205
4. Greenhalgh, S. P., Willet, K. B. S., & Koehler, M. J. (2019). Approaches to Mormon Identity and Practice in the #ldsconf Twitter Hashtag. Journal of Media and Religion, 18(4), 122–133. https://doi.org/10.1080/15348423.2019.1696121
5. Mingione, M., Cristofaro, M., & Mondi, D. (2020). If I give you my emotion, what do I get? Conceptualizing and measuring the co-created emotional value of the brand. Journal of Business Research, 109, 310–320. https://doi.org/10.1016/j.jbusres.2019.11.071
6. Wunderlich, F., & Memmert, D. (2020). Innovative Approaches in Sports Science—Lexicon-Based Sentiment Analysis as a Tool to Analyze Sports-Related Twitter Communication. Applied Sciences, 10(2), 431. https://doi.org/10.3390/app10020431
7. Fontanelli, O., & Mansilla, R. (2020). Modeling the Popularity of Twitter Hashtags with Master Equations. arXiv preprint, https://arxiv.org/pdf/2003.02672.pdf
8. Hagen, L., Neely, S., Keller, T. E., Scharf, R., & Vasquez, F. E. (2020). Rise of the Machines? Examining the Influence of Social Bots on a Political Discussion Network. Social Science Computer Review, 089443932090819. https://doi.org/10.1177/0894439320908190
9. Greenhalgh, S. P., Rosenberg, J. M., Staudt Willet, K. B., Koehler, M. J., & Akcaoglu, M. (2020). Identifying multiple learning spaces within a single teacher-focused Twitter hashtag. Computers & Education, 148, 103809. https://doi.org/10.1016/j.compedu.2020.103809
10. Bramlett, B. H., & Burge, R. P. (2020). God Talk in a Digital Age: How Members of Congress Use Religious Language on Twitter. Politics and Religion, 1–23. https://doi.org/10.1017/s1755048320000231
11. Rahman, M. M., Ali, G. G., Li, X. J., Paul, K. C., & Chong, P. H. (2020). Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment. Nawaz and Li, Xue Jun and Paul, Kamal Chandra and Chong, Peter HJ, Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment (June 30, 2020). https://arxiv.org/pdf/2007.00054.pdf
12. Greco, F., & La Rocca, G. (2020). The Topics-scape of the Pandemic Crisis: The Italian Sentiment on Political Leaders. Culture e Studi del Sociale, 5(1, Special), 335-346. http://www.cussoc.it/index.php/journal/article/view/134
13. Barrios‐O’Neill, D. (2020). Focus and social contagion of environmental organization advocacy on Twitter. Conservation Biology. https://doi.org/10.1111/cobi.13564
14. Puerta, P., Laguna, L., Vidal, L., Ares, G., Fiszman, S., & Tárrega, A. (2020). Co-occurrence networks of Twitter content after manual or automatic processing. A case-study on “gluten-free.” Food Quality and Preference, 86, 103993. https://doi.org/10.1016/j.foodqual.2020.103993
15. Stephens, M. (2020). A geospatial infodemic: Mapping Twitter conspiracy theories of COVID-19. Dialogues in Human Geography, 10(2), 276–281. https://doi.org/10.1177/2043820620935683
16. Green, J., Edgerton, J., Naftel, D., Shoub, K., & Cranmer, S. J. (2020). Elusive consensus: Polarization in elite communication on the COVID-19 pandemic. Science Advances, 6(28), eabc2717. https://doi.org/10.1126/sciadv.abc2717
17. Dogucu, M., & Çetinkaya-Rundel, M. (2020). Web Scraping in the Statistics and Data Science Curriculum: Challenges and Opportunities. Journal of Statistics Education, 1–11. https://doi.org/10.1080/10691898.2020.1787116
18. Xaudiera, S., & Cardenal, A. S. (2020). Ibuprofen Narratives in Five European Countries During the COVID-19 Pandemic. Harvard Kennedy School Misinformation Review. https://doi.org/10.37016/mr-2020-029
19. Ferster, C., Laberee, K., Nelson, T., Thigpen, C., Simeone, M., & Winters, M. (2020). From advocacy to acceptance: Social media discussions of protected bike lane installations. Urban Studies, 004209802093825. https://doi.org/10.1177/0042098020938252
20. Laguna, L., Fiszman, S., Puerta, P., Chaya, C., & Tárrega, A. (2020). The impact of COVID-19 lockdown on food priorities. Results from a preliminary study using social media and an online survey with Spanish consumers. Food Quality and Preference, 86, 104028. https://doi.org/10.1016/j.foodqual.2020.104028
21. Sass, C. A. B., Pimentel, T. C., Aleixo, M. G. B., Dantas, T. M., Cyrino Oliveira, F. L., Freitas, M. Q., … Esmerino, E. A. (2020). Exploring social media data to understand consumers’ perception of eggs: A multilingual study using Twitter. Journal of Sensory Studies. https://doi.org/10.1111/joss.12607
22. Kamiński, M., Szymańska, C., & Nowak, J. K. (2020). Whose Tweets on COVID-19 Gain the Most Attention: Celebrities, Political, or Scientific Authorities? Cyberpsychology, Behavior, and Social Networking. https://doi.org/10.1089/cyber.2020.0336
23. Xu, S., & Xiong, Y. (2020). Setting socially mediated engagement parameters: A topic modeling and text analytic approach to examining polarized discourses on Gillette’s campaign. Public Relations Review, 46(5), 101959. doi:10.1016/j.pubrev.2020.101959
24. Sältzer, M. (2020). Finding the bird’s wings: Dimensions of factional conflict on Twitter. Party Politics, 135406882095796. https://doi.org/10.1177/1354068820957960
25. Sutton, J., Renshaw, S. L., & Butts, C. T. (2020). The First 60 Days: American Public Health Agencies’ Social Media Strategies in the Emerging COVID-19 Pandemic. Health Security, 18(6), 454–460. https://doi.org/10.1089/hs.2020.0105
26. Lemay, D. J., & Doleck, T. (2020). Online Learning Communities in the COVID-19 Pandemic: Social Learning Network Analysis of Twitter during the Shutdown. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI), 2(1) https://onlinejour.journals.publicknowledgeproject.org/index.php/i-jai/article/view/15427
27. Hu, L., & Kearney, M. W. (2020). Gendered Tweets: Computational Text Analysis of Gender Differences in Political Discussion on Twitter. Journal of Language and Social Psychology, 0261927X2096975. https://doi.org/10.1177/0261927x20969752
28. Fuoli, M., Clarke, I., Wiegand, V., Ziezold, H., & Mahlberg, M. (2020). Responding Effectively to Customer Feedback on Twitter: A Mixed Methods Study of Webcare Styles. Applied Linguistics. https://doi.org/10.1093/applin/amaa046
29. Johnson, T., & Greenwell, M. P. (2020, November 12). Is sustainability advertising just a public relations stunt?. https://doi.org/10.31235/osf.io/avy4d
30. Koh, J. X., & Liew, T. M. (2020). How loneliness is talked about in social media during COVID-19 pandemic: Text mining of 4,492 Twitter feeds. Journal of Psychiatric Research. https://doi.org/10.1016/j.jpsychires.2020.11.015
31. Bittermann, A., Batzdorfer, V., Müller, S. M., & Steinmetz, H. (2021). Mining Twitter to detect hotspots in psychology. Zeitschrift für Psychologie. https://www.psycharchives.org/bitstream/20.500.12034/3956/1/ESM%201_methods.pdf
32. Lucas, B., & Landman, T. (2020). Social listening, modern slavery, and COVID-19. Journal of Risk Research, 1–21. https://doi.org/10.1080/13669877.2020.1864009
33. Boehm, F. J., & Hanlon, B. M. (2021). What Is Happening on Twitter? A Framework for Student Research Projects With Tweets. Journal of Statistics and Data Science Education, 29(sup1), S95–S102. https://doi.org/10.1080/10691898.2020.1848486
34. Gutiérrez García-Pardo, I., Guevara Gil, J. A., Gómez González, D., Castro Cantalejo, J., & Espínola Vílchez, R. (2021). Community Detection Problem Based on Polarization Measures. An application to Twitter: the COVID-19 case in Spain. https://doi.org/10.20944/preprints202101.0080.v1
35. Nkonde, M., Rodriguez, M. Y., Cortana, L., Mukogosi, J. K., King, S., Serrato, R., … Malik, M. M. (2021). Disinformation creep: ADOS and the strategic weapon-ization of breaking news. Harvard Kennedy School Misinformation Review. doi:10.37016/mr-2020-52
36. Heyerdahl, L. W., Vray, M., Leger, V., Le Fouler, L., Antouly, J., Troit, V., & Giles-Vernick, T. (2021). Evaluating the motivation of Red Cross Health volunteers in the COVID-19 pandemic: a mixed-methods study protocol. BMJ Open, 11(1), e042579. doi:10.1136/bmjopen-2020-042579
37. Adepeju, M., & Jimoh, F. (2021). An Analytical Framework for Measuring Inequality in the Public Opinion on Policing—Assessing the Impacts of COVID-19 Pandemic Using Twitter Data. Journal of Geographic Information System, 13(02), 122–147. doi:10.4236/jgis.2021.132008

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

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

Functions to automate downloading geospatial data available from several federated data sources (mainly sources maintained by the US Federal government). Currently, the package enables extraction from nine datasets: The National Elevation Dataset digital elevation models (1 and 1/3 arc-second; USGS); The National Hydrography Dataset (USGS); The Soil Survey Geographic (SSURGO) database from the National Cooperative Soil Survey (NCSS), which is led by the Natural Resources Conservation Service (NRCS) under the USDA; the Global Historical Climatology Network (GHCN), coordinated by National Climatic Data Center at NOAA; the Daymet gridded estimates of daily weather parameters for North America, version 4, available from the Oak Ridge National Laboratory’s Distributed Active Archive Center (DAAC); the International Tree Ring Data Bank; the National Land Cover Database (NLCD); the Cropland Data Layer from the National Agricultural Statistics Service; and the PAD-US dataset of protected area boundaries from the USGS.

1. McAfee, S. A., McCabe, G. J., Gray, S. T., & Pederson, G. T. (2018). Changing station coverage impacts temperature trends in the Upper Colorado River Basin. International Journal of Climatology. https://doi.org/10.1002/joc.5898
2. Medury, A., Griswold, J. B., Huang, L., & Grembek, O. (2019). Pedestrian Count Expansion Methods: Bridging the Gap between Land Use Groups and Empirical Clusters. Transportation Research Record: Journal of the Transportation Research Board, 036119811983826. https://doi.org/10.1177/0361198119838266
3. Meisner, J., Clifford, W. R., Wohrle, R. D., Kangiser, D., & Rabinowitz, P. (2019). Soil and climactic predictors of canine coccidioidomycosis seroprevalence in Washington State: an ecological cross‐sectional study. Transboundary and Emerging Diseases. https://doi.org/10.1111/tbed.13265
4. Saadi, M., Oudin, L., & Ribstein, P. (2019). Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters. Water, 11(8), 1540. https://doi.org/10.3390/w11081540
5. Martinez-Feria, R. A., & Basso, B. (2020). Unstable crop yields reveal opportunities for site-specific adaptations to climate variability. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-59494-2
6. Saadi, M., Oudin, L., & Ribstein, P. (2020). Beyond Imperviousness: The Role of Antecedent Wetness in Runoff Generation in Urbanized Catchments. Water Resources Research, 56(11). https://doi.org/10.1029/2020wr028060
7. Porter, W. T., Barrand, Z. A., Wachara, J., DaVall, K., Mihaljevic, J. R., Pearson, T., … Nieto, N. C. (2021). Predicting the current and future distribution of the western black-legged tick, Ixodes pacificus, across the Western US using citizen science collections. PLOS ONE, 16(1), e0244754. https://doi.org/10.1371/journal.pone.0244754

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

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

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

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

1. Alfsnes, K., Leinaas, H. P., & Hessen, D. O. (2017). Genome size in arthropods: different roles of phylogeny, habitat and life history in insects and crustaceans. Ecology and Evolution. https://doi.org/10.1002/ece3.3163
2. Vanderhoeven, S., Adriaens, T., Desmet, P., Strubbe, D., Backeljau, T., Barbier, Y., … Groom, Q. (2017). Tracking Invasive Alien Species (TrIAS): Building a data-driven framework to inform policy. Research Ideas and Outcomes, 3, e13414. https://doi.org/10.3897/rio.3.e13414
3. Pérez-Escobar, O. A., Rodriguez, L. K., & Martel, C. (2017). A new species of Telipogon (Oncidiinae: Orchidaceae) from the paramos of Colombia. Phytotaxa, 305(4), 262-268. http://www.biotaxa.org/Phytotaxa/article/view/phytotaxa.305.4.2
4. Dallas, T., Decker, R. R., & Hastings, A. (2017). Species are not most abundant in the centre of their geographic range or climatic niche. Ecology Letters. https://doi.org/10.1111/ele.12860
5. Oldham, K. A., & Weeks, A. (2017). Varieties of Melampyrum Lineare (Orobanchaceae) Revisited. Rhodora. http://www.rhodorajournal.org/doi/abs/10.3119/16-13
6. Sales, L. P., Ribeiro, B. R., Hayward, M. W., Paglia, A., Passamani, M., & Loyola, R. (2017). Niche conservatism and the invasive potential of the wild boar. Journal of Animal Ecology, 86(5), 1214–1223. https://doi.org/10.1111/1365-2656.12721
7. Longbottom, J., Shearer, F. M., Devine, M., Alcoba, G., Chappuis, F., Weiss, D. J., … Pigott, D. M. (2018). Vulnerability to snakebite envenoming: a global mapping of hotspots. The Lancet. https://doi.org/10.1016/S0140-6736(18)31224-8
8. Samy, A. M., Alkishe, A. A., Thomas, S., Wang, L., & Zhang, W. (2018). Mapping the potential distributions of etiological agent, vectors, and reservoirs of Japanese Encephalitis in Asia and Australia. Acta Tropica. https://doi.org/10.1016/j.actatropica.2018.08.014
9. Pfeffer, D. A., Lucas, T. C. D., May, D., Harris, J., Rozier, J., Twohig, K. A., … Gething, P. W. (2018). malariaAtlas: an R interface to global malariometric data hosted by the Malaria Atlas Project. Malaria Journal, 17(1). https://doi.org/10.1186/s12936-018-2500-5
10. Perez, T. M., Valverde-Barrantes, O., Bravo, C., Taylor, T. C., Fadrique, B., Hogan, J. A., … Feeley, K. J. (2018). Botanic gardens are an untapped resource for studying the functional ecology of tropical plants. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1763), 20170390. https://doi.org/10.1098/rstb.2017.0390
11. Zuquim, G., Costa, F. R. C., Tuomisto, H., Moulatlet, G. M., & Figueiredo, F. O. G. (2019). The importance of soils in predicting the future of plant habitat suitability in a tropical forest. Plant and Soil. https://doi.org/10.1007/s11104-018-03915-9
12. Myers, E. A., Xue, A. T., Gehara, M., Cox, C., Davis Rabosky, A. R., Lemos‐Espinal, J., … Burbrink, F. T. (2019). Environmental Heterogeneity and Not Vicariant Biogeographic Barriers Generate Community Wide Population Structure in Desert Adapted Snakes. Molecular Ecology. https://doi.org/10.1111/mec.15182
13. Pender, J. E., Hipp, A. L., Hahn, M., Kartesz, J., Nishino, M., & Starr, J. R. (2019). How sensitive are climatic niche inferences to distribution data sampling? A comparison of Biota of North America Program (BONAP) and Global Biodiversity Information Facility (GBIF) datasets. Ecological Informatics, 100991. https://doi.org/10.1016/j.ecoinf.2019.100991
14. Báez, J. C., Barbosa, A. M., Pascual, P., Ramos, M. L., & Abascal, F. (2019). Ensemble modeling of the potential distribution of the whale shark in the Atlantic Ocean. Ecology and Evolution, 10(1), 175–184. https://doi.org/10.1002/ece3.5884
15. Reyes, J. A., & Lira-Noriega, A. (2020). Current and future global potential distribution of the fruit fly Drosophila suzukii (Diptera: Drosophilidae). The Canadian Entomologist, 1–13. https://doi.org/10.4039/tce.2020.3
16. Sales, L., Culot, L., & Pires, M. M. (2020). Climate niche mismatch and the collapse of primate seed dispersal services in the Amazon. Biological Conservation, 247, 108628. https://doi.org/10.1016/j.biocon.2020.108628
17. Gaynor, M. L., Fu, C., Gao, L., Lu, L., Soltis, D. E., & Soltis, P. S. (2020). Biogeography and ecological niche evolution in Diapensiaceae inferred from phylogenetic analysis. Journal of Systematics and Evolution. https://doi.org/10.1111/jse.12646
18. Bonello, G., Grillo, M., Cecchetto, M., Giallain, M., Granata, A., Guglielmo, L., … Schiaparelli, S. (2020). Distributional records of Ross Sea (Antarctica) planktic Copepoda from bibliographic data and samples curated at the Italian National Antarctic Museum (MNA): checklist of species collected in the Ross Sea sector from 1987 to 1995. ZooKeys, 969, 1–22. https://doi.org/10.3897/zookeys.969.52334
19. Milanesi, P., Mori, E., & Menchetti, M. (2020). Observer‐oriented approach improves species distribution models from citizen science data. Ecology and Evolution, 10(21), 12104–12114. https://doi.org/10.1002/ece3.6832
20. Fassio, G., Russini, V., Buge, B., Schiaparelli, S., Modica, M. V., Bouchet, P., & Oliverio, M. (2020). High cryptic diversity in the kleptoparasitic genus Hyalorisia Dall, 1889 (Littorinimorpha: Capulidae) with the description of nine new species from the Indo-West Pacific. Journal of Molluscan Studies, 86(4), 401–421. https://doi.org/10.1093/mollus/eyaa028
21. Lozano, V. (2021). Distribution of Five Aquatic Plants Native to South America and Invasive Elsewhere under Current Climate. Ecologies, 2(1), 27–42. doi:10.3390/ecologies2010003
22. Escobar, S., Helmstetter, A. J., Jarvie, S., Montúfar, R., Balslev, H., & Couvreur, T. L. P. (2021). Pleistocene climatic fluctuations promoted alternative evolutionary histories in Phytelephas aequatorialis, an endemic palm from western Ecuador. Journal of Biogeography, 48(5), 1023–1037. doi:10.1111/jbi.14055

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

1. Varela, S., González-Hernández, J., Sgarbi, L. F., Marshall, C., Uhen, M. D., Peters, S., & McClennen, M. (2014). paleobioDB: an R package for downloading, visualizing and processing data from the Paleobiology Database. Ecography, 38(4), 419–425. https://doi.org/10.1111/ecog.01154
2. Read, J. S., Walker, J. I., Appling, A. P., Blodgett, D. L., Read, E. K., & Winslow, L. A. (2015). geoknife: reproducible web-processing of large gridded datasets. Ecography, 39(4), 354–360. https://doi.org/10.1111/ecog.01880
3. Springer, M. S., Emerling, C. A., Meredith, R. W., Janečka, J. E., Eizirik, E., & Murphy, W. J. (2016). Waking the undead: implications of a soft explosive model for the timing of placental mammal diversification. Molecular Phylogenetics and Evolution. https://doi.org/10.1016/j.ympev.2016.09.017
4. Pimiento, C., & Benton, M. J. (2020). The impact of the Pull of the Recent on extant elasmobranchs. Palaeontology. https://doi.org/10.1111/pala.12478
5. Carrillo, J. D., Faurby, S., Silvestro, D., Zizka, A., Jaramillo, C., Bacon, C. D., & Antonelli, A. (2020). Disproportionate extinction of South American mammals drove the asymmetry of the Great American Biotic Interchange. Proceedings of the National Academy of Sciences, 117(42), 26281–26287. https://doi.org/10.1073/pnas.2009397117

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

1. Van Schaik, P., Peng, Y., Ojelabi, A., & Ling, J. (2019). Explainable statistical learning in public health for policy development: the case of real-world suicide data. BMC medical research methodology, 19(1), 152. https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-019-0796-7
2. Rebolj, M., Parmar, D., Maroni, R., Blyuss, O., & Duffy, S. W. (In press). Concurrent participation in screening for cervical, breast, and bowel cancer in England. Journal of Medical Screening. https://doi.org/10.1177/0969141319871977
3. Senior, S. (2020, February 4). Does Sure Start spending improve school readiness? An ecological longitudinal study. https://doi.org/10.31235/osf.io/rbcz5
4. van Wieringen, W. N., & Binder, H. (2020). Transfer learning of regression models from a sequence of datasets by penalized estimation. arXiv preprint arXiv:2007.02117. https://arxiv.org/pdf/2007.02117
5. Stevens, M. C., Chen, Y., Stringer, A., Clemmow, C., & Jones, L. A. (2020). Key factors driving obesity in the UK. http://london.gisruk.org/gisruk2020_proceedings/GISRUK2020_paper_17.pdf

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

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

1. Pirhadi, S., Sunseri, J., & Koes, D. R. (2016). Open Source Molecular Modeling. Journal of Molecular Graphics and Modelling. https://doi.org/10.1016/j.jmgm.2016.07.008
2. Bergmann, A. J., Scott, R. P., Wilson, G., & Anderson, K. A. (2018). Development of quantitative screen for 1550 chemicals with GC-MS. Analytical and Bioanalytical Chemistry, 1-10. https://link.springer.com/article/10.1007/s00216-018-0997-7
3. Robert J. Allaway, Sara J. Gosline, Marco Nievo, Salvatore La Rosa, Annette Bakker and Justin Guinney 2018. Abstract 4643: Drug-Target Explorer: An interactive tool for examining chemical-biological interactions. Cancer Res July 1 2018 (78) (13 Supplement) 4643, https://doi.org/10.1158/1538-7445.AM2018-4643
4. Stanstrup, J., Broeckling, C., Helmus, R., Hoffmann, N., Mathé, E., Naake, T., … Neumann, S. (2019). The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites, 9(10), 200. https://doi.org/10.3390/metabo9100200
5. Tada, I., Tsugawa, H., Meister, I., Zhang, P., Shu, R., Katsumi, R., … Chaleckis, R. (2019). Creating a Reliable Mass Spectral–Retention Time Library for All Ion Fragmentation-Based Metabolomics. Metabolites, 9(11), 251. https://doi.org/10.3390/metabo9110251
6. Malaj, E., Liber, K., & Morrissey, C. A. (2019). Spatial distribution of agricultural pesticide use and predicted wetland exposure in the Canadian Prairie Pothole Region. Science of The Total Environment, 134765. https://doi.org/10.1016/j.scitotenv.2019.134765
7. Zushi, Y., Hanari, N., Nabi, D., & Lin, B.-L. (2020). Mixture Touch: A Web Platform for the Evaluation of Complex Chemical Mixtures. ACS Omega, 5(14), 8121–8126. https://doi.org/10.1021/acsomega.0c00340
8. Scharmüller, A., Schreiner, V. C., & Schäfer, R. B. (2020). Standartox: Standardizing Toxicity Data. Data, 5(2), 46. https://doi.org/10.3390/data5020046
9. Costanzi, S., Slavick, C. K., Hutcheson, B. O., Koblentz, G. D., & Cupitt, R. T. (2020). Lists of Chemical Warfare Agents and Precursors from International Nonproliferation Frameworks: Structural Annotation and Chemical Fingerprint Analysis. Journal of Chemical Information and Modeling, 60(10), 4804–4816. https://doi.org/10.1021/acs.jcim.0c00896
10. Islam, S. M., Hossain, S. M. M., & Ray, S. (2020). DTI-SNNFRA: Drug-Target interaction prediction by shared nearest neighbors and fuzzy-rough approximation. arXiv preprint arXiv:2009.10766 https://arxiv.org/abs/2009.10766.
11. Hammoud, Z., & Kramer, F. (2020). Multipath: An R Package to Generate Integrated Reproducible Pathway Models. Biology, 9(12), 483. https://doi.org/10.3390/biology9120483
12. Su, Q.-Z., Vera, P., Nerín, C., Lin, Q.-B., & Zhong, H.-N. (2021). Safety concerns of recycling postconsumer polyolefins for food contact uses: Regarding (semi-)volatile migrants untargetedly screened. Resources, Conservation and Recycling, 167, 105365. https://doi.org/10.1016/j.resconrec.2020.105365

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

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

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

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

1. Shabangu, F. W., Yemane, D., Stafford, K. M., Ensor, P., & Findlay, K. P. (2017). Modelling the effects of environmental conditions on the acoustic occurrence and behaviour of Antarctic blue whales. PLOS ONE, 12(2), e0172705. https://doi.org/10.1371/journal.pone.0172705
2. Mendez, L., Borsa, P., Cruz, S., de Grissac, S., Hennicke, J., Lallemand, J., … Weimerskirch, H. (2017). Geographical variation in the foraging behaviour of the pantropical red-footed booby. Marine Ecology Progress Series, 568, 217–230. https://doi.org/10.3354/meps12052
3. Abolaffio, M., Reynolds, A. M., Cecere, J. G., Paiva, V. H., & Focardi, S. (2018). Olfactory-cued navigation in shearwaters: linking movement patterns to mechanisms. Scientific Reports, 8(1). http://doi.org/10.1038/s41598-018-29919-0
4. Baylis, A. M. M., Tierney, M., Orben, R. A., Warwick-Evans, V., Wakefield, E., Grecian, W. J., … Brickle, P. (2019). Important At-Sea Areas of Colonial Breeding Marine Predators on the Southern Patagonian Shelf. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-44695-1
5. O’Farrell, S., Chollett, I., Sanchirico, J. N., & Perruso, L. (2019). Classifying fishing behavioral diversity using high-frequency movement data. Proceedings of the National Academy of Sciences, 201906766. https://doi.org/10.1073/pnas.1906766116
6. Patel, S. H., Winton, M. V., Hatch, J. M., Haas, H. L., Saba, V. S., Fay, G., & Smolowitz, R. J. (2021). Projected shifts in loggerhead sea turtle thermal habitat in the Northwest Atlantic Ocean due to climate change. Scientific Reports, 11(1). doi:10.1038/s41598-021-88290-9

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

1. Albers, S. (2017). tidyhydat: Extract and Tidy Canadian Hydrometric Data. The Journal of Open Source Software, 2(20), 511. https://doi.org/10.21105/joss.00511
2. Beaton, A., Whaley, R., Corston, K., & Kenny, F. (2019). Identifying historic river ice breakup timing using MODIS and Google Earth Engine in support of operational flood monitoring in Northern Ontario. https://doi.org/10.1016/j.rse.2019.02.011
3. Laceby, J. P., Batista, P. V. G., Taube, N., Kruk, M. K., Chung, C., Evrard, O., … Kerr, J. G. (2021). Tracing total and dissolved material in a western Canadian basin using quality control samples to guide the selection of fingerprinting parameters for modelling. CATENA, 200, 105095. https://doi.org/10.1016/j.catena.2020.105095

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

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

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

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

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

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

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

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

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

A programmatic interface to the web service methods provided by Global Biotic Interactions (GloBI) (https://www.globalbioticinteractions.org/). GloBI provides access to spatial-temporal species interaction records from sources all over the world. rglobi provides methods to search species interactions by location, interaction type, and taxonomic name.

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

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

1. Konzen, E., Shi, J. Q., & Wang, Z. (2019). Modelling Function-Valued Processes with Nonseparable Covariance Structure. arXiv preprint arXiv:1903.09981. https://arxiv.org/pdf/1903.09981.pdf
2. Hanes, C., Wotton, M., Woolford, D. G., Martell, D. L., & Flannigan, M. (2020). Preceding Fall Drought Conditions and Overwinter Precipitation Effects on Spring Wildland Fire Activity in Canada. Fire, 3(2), 24. https://www.mdpi.com/2571-6255/3/2/24/pdf
3. Parent, S.-É., Lafond, J., Paré, M. C., Parent, L. E., & Ziadi, N. (2020). Conditioning Machine Learning Models to Adjust Lowbush Blueberry Crop Management to the Local Agroecosystem. Plants, 9(10), 1401. https://doi.org/10.3390/plants9101401
4. Layton, K. K. S., Snelgrove, P. V. R., Dempson, J. B., Kess, T., Lehnert, S. J., Bentzen, P., … Bradbury, I. R. (2021). Genomic evidence of past and future climate-linked loss in a migratory Arctic fish. Nature Climate Change, 11(2), 158–165. doi:10.1038/s41558-020-00959-7

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

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

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

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

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

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

1. Michonneau, F., Brown, J. W., & Winter, D. J. (2016). rotl: an R package to interact with the Open Tree of Life data. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.12593
2. Killen, S. S., Norin, T., & Halsey, L. G. (2016). Do method and species lifestyle affect measures of maximum metabolic rate in fishes? Journal of Fish Biology. https://doi.org/10.1111/jfb.13195
3. Estrada-Peña, A., & de la Fuente, J. (2016). Species interactions in occurrence data for a community of tick-transmitted pathogens. Scientific Data, 3, 160056. https://doi.org/10.1038/sdata.2016.56
4. Matthews, A. E., Klimov, P. B., Proctor, H. C., Dowling, A. P. G., Diener, L., Hager, S. B., … Boves, T. J. (2017). Cophylogenetic assessment of New World warblers (Parulidae) and their symbiotic feather mites (Proctophyllodidae). Journal of Avian Biology. https://doi.org/10.1111/jav.01580
5. Santorelli, S., Magnusson, W. E., & Deus, C. P. (2018). Most species are not limited by an Amazonian river postulated to be a border between endemism areas. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-20596-7
6. Farquharson, K. A., Hogg, C. J., & Grueber, C. E. (2018). A meta-analysis of birth-origin effects on reproduction in diverse captive environments. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-03500-9
7. Portugal, S. J., & White, C. R. (2018). Miniaturisation of biologgers is not alleviating the 5% rule. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13013
8. Barneche, D. R., Robertson, D. R., White, C. R., & Marshall, D. J. (2018). Fish reproductive-energy output increases disproportionately with body size. Science, 360(6389), 642–645. https://doi.org/10.1126/science.aao6868
9. Morais, R. A., & Bellwood, D. R. (2018). Global drivers of reef fish growth. Fish and Fisheries. https://doi.org/10.1111/faf.12297
10. Gastauer, M., Caldeira, C. F., Trotter, I., Ramos, S. J., & Neto, J. A. A. M. (2018). Optimizing community trees using the open tree of life increases the reliability of phylogenetic diversity and dispersion indices. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2018.06.008
11. Paseka, R. E., & Grunberg, R. L. (2018). Allometric and trait-based patterns in parasite stoichiometry. Oikos. https://doi.org/10.1111/oik.05339
12. Barneche, D. R., Burgess, S. C., & Marshall, D. J. (2018). Global environmental drivers of marine fish egg size. Global Ecology and Biogeography, 27(8), 890–898. https://doi.org/10.1111/geb.12748
13. Merkling, T., Nakagawa, S., Lagisz, M., & Schwanz, L. E. (2017). Maternal Testosterone and Offspring Sex-Ratio in Birds and Mammals: A Meta-Analysis. Evolutionary Biology, 45(1), 96–104. https://doi.org/10.1007/s11692-017-9432-9
14. Becker, D., Czirják, G., Rynda-Apple, A., & Plowright, R. (2018). Handling stress and sample storage are associated with weaker complement-mediated bactericidal ability in birds but not bats. Physiological and Biochemical Zoology. https://doi.org/10.1086/701069
15. O’Dea, R. E., Lagisz, M., Hendry, A. P., & Nakagawa, S. (2018). Developmental temperature affects phenotypic means and variability: a meta-analysis of fish data. https://doi.org/10.32942/osf.io/ge7f8
16. Tresch, S., Frey, D., Le Bayon, R.-C., Zanetta, A., Rasche, F., Fliessbach, A., & Moretti, M. (2018). Litter decomposition driven by soil fauna, plant diversity and soil management in urban gardens. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2018.12.235
17. Green, D. M. (2019). Rarity of Size-Assortative Mating in Animals: Assessing the Evidence with Anuran Amphibians. The American Naturalist, 193(2) https://www.journals.uchicago.edu/doi/abs/10.1086/701124
18. Mathot, K. J., Dingemanse, N. J., & Nakagawa, S. (2018). The covariance between metabolic rate and behaviour varies across behaviours and thermal types: meta-analytic insights. Biological Reviews. https://doi.org/10.1111/brv.12491
19. Pettersen, A. K., White, C. R., Bryson-Richardson, R. J., & Marshall, D. J. (2019). Linking life-history theory and metabolic theory explains the offspring size-temperature relationship. Ecology Letters. https://doi.org/10.1111/ele.13213
20. Halsey, L. G., & White, C. R. (2019). Terrestrial locomotion energy costs vary considerably between species: no evidence that this is explained by rate of leg force production or ecology. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-018-36565-z
21. Ohmer, M. E. B., Cramp, R. L., White, C. R., Harlow, P. S., McFadden, M. S., Merino-Viteri, A., … Franklin, C. E. (2019). Phylogenetic investigation of skin sloughing rates in frogs: relationships with skin characteristics and disease-driven declines. Proceedings of the Royal Society B: Biological Sciences, 286(1896), 20182378. https://doi.org/10.1098/rspb.2018.2378
22. Shefferson, R. P., Bunch, W., Cowden, C. C., Lee, Y., Kartzinel, T. R., Yukawa, T., … Jiang, H. (2019). Does evolutionary history determine specificity in broad ecological interactions? Journal of Ecology. https://doi.org/10.1111/1365-2745.13170
23. Pinto, N. S., Palaoro, A. V., & Peixoto, P. E. C. (2019). All by myself? Meta‐analysis of animal contests shows stronger support for self than for mutual assessment models. Biological Reviews. https://doi.org/10.1111/brv.12509
24. Kovacevic, A., Latombe, G., & Chown, S. L. (2019). Rate dynamics of ectotherm responses to thermal stress. Proceedings of the Royal Society B: Biological Sciences, 286(1902), 20190174. https://doi.org/10.1098/rspb.2019.0174
25. Mihalitsis, M., & Bellwood, D. R. (2019). Morphological and functional diversity of piscivorous fishes on coral reefs. Coral Reefs. https://doi.org/10.1007/s00338-019-01820-w
26. Tetzlaff, S. J., Sperry, J. H., & DeGregorio, B. A. (2019). Effects of antipredator training, environmental enrichment, and soft release on wildlife translocations: A review and meta-analysis. Biological Conservation, 236, 324–331. https://doi.org/10.1016/j.biocon.2019.05.054
27. McTavish, E. J. (2019). Linking Biodiversity Data Using Evolutionary History. Biodiversity Information Science and Standards, 3. https://doi.org/10.3897/biss.3.36207
28. Peters, A., Delhey, K., Nakagawa, S., Aulsebrook, A., & Verhulst, S. (2019). Immunosenescence in wild animals: meta‐analysis and outlook. Ecology Letters. https://doi.org/10.1111/ele.13343
29. Park, A. W. (2019). Food web structure selects for parasite host range. Proceedings of the Royal Society B: Biological Sciences, 286(1908), 20191277. https://doi.org/10.1098/rspb.2019.1277
30. Mihalitsis, M., & Bellwood, D. (2019). Functional implications of dentition-based morphotypes in piscivorous fishes. Royal Society Open Science, 6(9), 190040. https://doi.org/10.1098/rsos.190040
31. Sánchez-Tójar, A., Moran, N. P., O’Dea, R. E., Reinhold, K., & Nakagawa, S. (2019). Illustrating the importance of meta-analysing variances alongside means in ecology and evolution. https://doi.org/10.32942/osf.io/yhfvk
32. Li, X., Zhu, H., Geisen, S., Bellard, C., Hu, F., Li, H., … Liu, M. (2019). Agriculture erases climate constraints on soil nematode communities across large spatial scales. Global Change Biology. https://doi.org/10.1111/gcb.14821
33. Maherali, H. (2019). Mutualism as a plant functional trait: linking variation in the mycorrhizal symbiosis to climatic tolerance, geographic range and population dynamics. International Journal of Plant Sciences. https://doi.org/10.1086/706187
34. Defolie, C., Merkling, T., & Fichtel, C. (2019). Patterns and variation in the mammal parasite–glucorticoid relationship. Biological Reviews. https://doi.org/10.1111/brv.12555
35. Estrada-Peña, A., Nava, S., Tarragona, E., Bermúdez, S., de la Fuente, J., Domingos, A., … Guglielmone, A. A. (2019). Species occurrence of ticks in South America, and interactions with biotic and abiotic traits. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0314-0
36. Godfrey, J. M., Riggio, J., Orozco, J., Guzmán‐Delgado, P., Chin, A. R. O., & Zwieniecki, M. A. (2020). Ray fractions and carbohydrate dynamics of tree species along a 2750 m elevation gradient indicate climate response, not spatial storage limitation. New Phytologist, 225(6), 2314–2330. https://doi.org/10.1111/nph.16361
37. Clark, T. J., & Luis, A. D. (2019). Nonlinear population dynamics are ubiquitous in animals. Nature Ecology & Evolution, 4(1), 75–81. https://doi.org/10.1038/s41559-019-1052-6
38. Shan, S., Soltis, P. S., Soltis, D. E., & Yang, B. (2020). Considerations in adapting CRISPR/Cas9 in nongenetic model plant systems. Applications in Plant Sciences, 8(1). https://doi.org/10.1002/aps3.11314
39. Horne, C. R., Hirst, A. G., & Atkinson, D. (2020). Selection for increased male size predicts variation in sexual size dimorphism among fish species. Proceedings of the Royal Society B: Biological Sciences, 287(1918), 20192640. https://doi.org/10.1098/rspb.2019.2640
40. Walczyńska, A., Gudowska, A., & Sobczyk, Ł. (2020). Should I shrink or should I flow? – body size adjustment to thermo-oxygenic niche. https://doi.org/10.1101/2020.01.14.905901
41. Gomez Isaza, D. F., Cramp, R. L., & Franklin, C. E. (2020). Living in polluted waters: A meta-analysis of the effects of nitrate and interactions with other environmental stressors on freshwater taxa. Environmental Pollution, 114091. https://doi.org/10.1016/j.envpol.2020.114091
42. Finoshin, A. D., Adameyko, K. I., Mikhailov, K. V., Kravchuk, O. I., Georgiev, A. A., Gornostaev, N. G., … Lyupina, Y. V. (2020). Iron metabolic pathways in the processes of sponge plasticity. PLOS ONE, 15(2), e0228722. https://doi.org/10.1371/journal.pone.0228722
43. Jhwueng, D.-C., & O’Meara, B. C. (2020). On the Matrix Condition of Phylogenetic Tree. Evolutionary Bioinformatics, 16, 117693432090172. https://doi.org/10.1177/1176934320901721
44. Perez‐Lamarque, B., Selosse, M., Öpik, M., Morlon, H., & Martos, F. (2020). Cheating in arbuscular mycorrhizal mutualism: a network and phylogenetic analysis of mycoheterotrophy. New Phytologist. https://doi.org/10.1111/nph.16474
45. Marshall, D. J., Pettersen, A. K., Bode, M., & White, C. R. (2020). Developmental cost theory predicts thermal environment and vulnerability to global warming. Nature Ecology & Evolution, 4(3), 406–411. https://doi.org/10.1038/s41559-020-1114-9
46. Wei, N., Kaczorowski, R. L., Arceo-Gómez, G., O’Neill, E. M., Hayes, R. A., & Ashman, T.-L. (2020). Pollinator niche partitioning and asymmetric facilitation contribute to the maintenance of diversity. https://doi.org/10.1101/2020.03.02.974022
47. Allen, D., & Kim, A. Y. (2020). A permutation test and spatial cross-validation approach to assess models of interspecific competition between trees. PLOS ONE, 15(3), e0229930. https://doi.org/10.1371/journal.pone.0229930
48. Moran, N. P., Sánchez-Tójar, A., Schielzeth, H., & Reinhold, K. (2020). Poor condition promotes high-risk behaviours but context-dependency is key: A systematic review and meta-analysis. Ecorxiv preprint. https://ecoevorxiv.org/xsehd/
49. Lindner, M., Gilhooley, M. J., Palumaa, T., Morton, A. J., Hughes, S., & Hankins, M. W. (2020). Expression and Localization of Kcne2 in the Vertebrate Retina. Investigative Opthalmology & Visual Science, 61(3), 33. https://doi.org/10.1167/iovs.61.3.33
50. Cui, X., Paterson, A. M., Wyse, S. V., Alam, M. A., Maurin, K. J. L., Pieper, R., … Curran, T. J. (2020). Shoot flammability of vascular plants is phylogenetically conserved and related to habitat fire-proneness and growth form. Nature Plants, 6(4), 355–359. https://doi.org/10.1038/s41477-020-0635-1
51. Morand, S., Chaisiri, K., Kritiyakan, A., & Kumlert, R. (2020). Disease Ecology of Rickettsial Species: A Data Science Approach. Tropical Medicine and Infectious Disease, 5(2), 64. https://doi.org/10.3390/tropicalmed5020064
52. Bubac, C. M., Miller, J. M., & Coltman, D. W. (2020). The genetic basis of animal behavioural diversity in natural populations. Molecular Ecology, 29(11), 1957–1971. https://doi.org/10.1111/mec.15461
53. Crowley, D., Becker, D., Washburne, A., & Plowright, R. (2020). Identifying Suspect Bat Reservoirs of Emerging Infections. Vaccines, 8(2), 228. https://doi.org/10.3390/vaccines8020228
54. Estrada-Peña, A., Nava, S., Tarragona, E., de la Fuente, J., & Guglielmone, A. A. (2020). A community approach to the Neotropical ticks-hosts interactions. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-66400-3
55. Burda, P.-C., Crosskey, T., Lauk, K., Zurborg, A., Söhnchen, C., Liffner, B., … Gilberger, T.-W. (2020). Structure-Based Identification and Functional Characterization of a Lipocalin in the Malaria Parasite Plasmodium falciparum. Cell Reports, 31(12), 107817. https://doi.org/10.1016/j.celrep.2020.107817
56. Álvarez-Noriega, M., Burgess, S. C., Byers, J. E., Pringle, J. M., Wares, J. P., & Marshall, D. J. (2020). Global biogeography of marine dispersal potential. Nature Ecology & Evolution, 4(9), 1196–1203. https://doi.org/10.1038/s41559-020-1238-y
57. Davies, A. D., Lewis, Z., & Dougherty, L. R. (2020). A meta-analysis of factors influencing the strength of mate-choice copying in animals. Behavioral Ecology. https://doi.org/10.1093/beheco/araa064
58. Kuchta, R., Řehulková, E., Francová, K., Scholz, T., Morand, S., & Šimková, A. (2020). Diversity of monogeneans and tapeworms in cypriniform fishes across two continents. International Journal for Parasitology, 50(10-11), 771–786. https://doi.org/10.1016/j.ijpara.2020.06.005
59. Atsumi, K., Lagisz, M., & Nakagawa, S. (2020). Non-additive genetic effects induce novel phenotypic distributions in male mating traits of F1 hybrids https://ecoevorxiv.org/kt3ud/download?format=pdf
60. Geffroy, B., Sadoul, B., Putman, B. J., Berger-Tal, O., Garamszegi, L. Z., Møller, A. P., & Blumstein, D. T. (2020). Evolutionary dynamics in the Anthropocene: Life history and intensity of human contact shape antipredator responses. PLOS Biology, 18(9), e3000818. https://doi.org/10.1371/journal.pbio.3000818
61. Xiao, W., Chen, C., Chen, X., Huang, Z., & Chen, H. Y. H. (2020). Functional and phylogenetic diversity promote litter decomposition across terrestrial ecosystems. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13181
62. Wilkes, M. A., Edwards, F., Jones, J. I., Murphy, J. F., England, J., Friberg, N., … Brown, L. E. (2020). Trait‐based ecology at large scales: Assessing functional trait correlations, phylogenetic constraints and spatial variability using open data. Global Change Biology. https://doi.org/10.1111/gcb.15344
63. Marshall, D. J., & Alvarez-Noriega, M. (2020). Projecting marine developmental diversity and connectivity in future oceans. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1814), 20190450. https://doi.org/10.1098/rstb.2019.0450
64. Kunc, H. P., & Schmidt, R. (2020). Species sensitivities to a global pollutant: A meta‐analysis on acoustic signals in response to anthropogenic noise. Global Change Biology, 27(3), 675–688. https://doi.org/10.1111/gcb.15428
65. Dániel-Ferreira, J., Bommarco, R., Wissman, J., & Öckinger, E. (2020). Linear infrastructure habitats increase landscape-scale diversity of plants but not of flower-visiting insects. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-78090-y
66. Sandoval-Herrera, N. I., Mastromonaco, G. F., Becker, D. J., Simmons, N. B., & Welch, K. C. (2021). Inter- and intra-specific variation in hair cortisol concentrations of Neotropical bats. https://doi.org/10.1101/2021.01.10.426004
67. Murphy, R., Palm, M., Mustonen, V., Warringer, J., Farewell, A., Parts, L., & Moradigaravand, D. (2021). Genomic Epidemiology and Evolution of Escherichia coli in Wild Animals in Mexico. mSphere, 6(1). doi:10.1128/msphere.00738-20
68. Dougherty, L. R. (2021). Meta‐analysis shows the evidence for context‐dependent mating behaviour is inconsistent or weak across animals. Ecology Letters, 24(4), 862–875. doi:10.1111/ele.13679
69. Andreu-Sánchez, S., Chen, W., Stiller, J., & Zhang, G. (2021). Multiple origins of a frameshift insertion in a mitochondrial gene in birds and turtles. GigaScience, 10(1). doi:10.1093/gigascience/giaa161

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

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

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.

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

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

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

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

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

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

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

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

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

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

1. Mayor, J. R., Sanders, N. J., Classen, A. T., Bardgett, R. D., Clément, J.-C., Fajardo, A., et al. (2017). Elevation alters ecosystem properties across temperate treelines globally. Nature, 542(7639), 91–95. https://doi.org/10.1038/nature21027
2. Giroldo, A. B., Scariot, A., & Hoffmann, W. A. (2017). Trait shifts associated with the subshrub life-history strategy in a tropical savanna. Oecologia. https://doi.org/10.1007/s00442-017-3930-4
3. Van de Peer, T., Mereu, S., Verheyen, K., María Costa Saura, J., Morillas, L., Roales, J., … Muys, B. (2018). Tree seedling vitality improves with functional diversity in a Mediterranean common garden experiment. Forest Ecology and Management, 409, 614–633. https://doi.org/10.1016/j.foreco.2017.12.001
4. Bemmels, J. B., Wright, S. J., Garwood, N. C., Queenborough, S. A., Valencia, R., & Dick, C. W. (2018). Filter-dispersal assembly of lowland Neotropical rainforests across the Andes. Ecography. https://doi.org/10.1111/ecog.03473
5. Gastauer, M., Caldeira, C. F., Trotter, I., Ramos, S. J., & Neto, J. A. A. M. (2018). Optimizing community trees using the open tree of life increases the reliability of phylogenetic diversity and dispersion indices. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2018.06.008
6. Albert, S., Flores, O., Rouget, M., Wilding, N., & Strasberg, D. (2018). Why are woody plants fleshy-fruited at low elevations? Evidence from a high-elevation oceanic island. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12676
7. Gill, B. A., Musili, P. M., Kurukura, S., Hassan, A. A., Goheen, J. R., Kress, W. J., … Kartzinel, T. R. (2019). Plant DNA-barcode library and community phylogeny for a semi-arid East African savanna. Molecular Ecology Resources. https://doi.org/10.1111/1755-0998.13001
8. Redmond, M. D., Morris, T. L., & Cramer, M. C. (2019). The cost of standing tall: wood nutrients associated with tree invasions in nutrient‐poor fynbos soils of South Africa. Ecosphere, 10(9). https://doi.org/10.1002/ecs2.2831
9. Vidal, M. C., Quinn, T. W., Stireman, J. O., Tinghitella, R. M., & Murphy, S. M. (2019). Geography is more important than host plant use for the population genetic structure of a generalist insect herbivore. Molecular Ecology. https://doi.org/10.1111/mec.15218
10. Bohner, T., & Diez, J. (2019). Extensive mismatches between species distributions and performance and their relationship to functional traits. Ecology Letters. https://doi.org/10.1111/ele.13396
11. Roddy, A. B., Théroux-Rancourt, G., Abbo, T., Benedetti, J. W., Brodersen, C. R., Castro, M., … Simonin, K. A. (2019). The Scaling of Genome Size and Cell Size Limits Maximum Rates of Photosynthesis with Implications for Ecological Strategies. International Journal of Plant Sciences. https://doi.org/10.1086/706186>
12. Herrera, C. M. (2020). Flower traits, habitat, and phylogeny as predictors of pollinator service: a plant community perspective. Ecological Monographs. https://doi.org/10.1002/ecm.1402
13. Théroux-Rancourt, G., Roddy, A. B., Earles, J. M., Gilbert, M. E., Zwieniecki, M. A., Boyce, C. K., … Brodersen, C. R. (2020). Maximum CO2 diffusion inside leaves is limited by the scaling of cell size and genome size. https://doi.org/10.1101/2020.01.16.904458
14. Larson, J. E., Anacker, B. L., Wanous, S., & Funk, J. L. (2020). Ecological strategies begin at germination: Traits, plasticity and survival in the first 4 days of plant life. Functional Ecology. https://doi.org/10.1111/1365-2435.13543
15. Trugman, A. T., Anderegg, L. D. L., Shaw, J. D., & Anderegg, W. R. L. (2020). Trait velocities reveal that mortality has driven widespread coordinated shifts in forest hydraulic trait composition. Proceedings of the National Academy of Sciences, 117(15), 8532–8538. https://doi.org/10.1073/pnas.1917521117
16. Santana, V. M., Alday, J. G., Adamo, I., Alloza, J. A., & Baeza, M. J. (2020). Climate, and not fire, drives the phylogenetic clustering of species with hard-coated seeds in Mediterranean Basin communities. Perspectives in Plant Ecology, Evolution and Systematics, 45, 125545. https://doi.org/10.1016/j.ppees.2020.125545
17. Perez, T. M., & Feeley, K. J. (2020). Weak phylogenetic and climatic signals in plant heat tolerance. Journal of Biogeography. https://doi.org/10.1111/jbi.13984
18. Huang, M., Ding, L., Wang, J., Ding, C., & Tao, J. (2021). The impacts of climate change on fish growth: A summary of conducted studies and current knowledge. Ecological Indicators, 121, 106976. https://doi.org/10.1016/j.ecolind.2020.10697
19. Huang, M., Ding, L., Wang, J., Ding, C., & Tao, J. (2021). The impacts of climate change on fish growth: A summary of conducted studies and current knowledge. Ecological Indicators, 121, 106976. doi:10.1016/j.ecolind.2020.106976
20. Perez, T. M., Socha, A., Tserej, O., & Feeley, K. J. (2021). Photosystem II heat tolerances characterize thermal generalists and the upper limit of carbon assimilation. Plant, Cell & Environment. https://doi.org/10.1111/pce.13990

NOTE: This package is deprecated. Please use the neotoma2 package described at https://github.com/NeotomaDB/neotoma2. Access paleoecological datasets from the Neotoma Paleoecological Database using the published API (http://wnapi.neotomadb.org/), only containing datasets uploaded prior to June 2020. The functions in this package access various pre-built API functions and attempt to return the results from Neotoma in a usable format for researchers and the public.

1. Nanavati, W. P., Whitlock, C., Iglesias, V., & de Porras, M. E. (2019). Postglacial vegetation, fire, and climate history along the eastern Andes, Argentina and Chile (lat. 41–55°S). Quaternary Science Reviews, 207, 145–160. https://doi.org/10.1016/j.quascirev.2019.01.014
2. Wang, Y., Goring, S. J., & McGuire, J. L. (2019). Bayesian ages for pollen records since the last glaciation in North America. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0182-7
3. Elmslie, B. G., Gushulak, C. A., Boreux, M. P., Lamoureux, S. F., Leavitt, P. R., & Cumming, B. F. (2019). Complex responses of phototrophic communities to climate warming during the Holocene of northeastern Ontario, Canada. The Holocene, 095968361988301. https://doi.org/10.1177/0959683619883014
4. Deza-Araujo, M., Morales-Molino, C., Tinner, W., Henne, P. D., Heitz, C., Pezzatti, G. B., … Conedera, M. (2020). A critical assessment of human-impact indices based on anthropogenic pollen indicators. Quaternary Science Reviews, 236, 106291. https://doi.org/10.1016/j.quascirev.2020.106291
5. Carroll, H. M., Wanamaker, A. D., Clark, L. G., & Wilsey, B. J. (2020). Ragweed and sagebrush pollen can distinguish between vegetation types at broad spatial scales. Ecosphere, 11(5). https://doi.org/10.1002/ecs2.3120
6. Fastovich, D., Russell, J. M., Jackson, S. T., Krause, T. R., Marcott, S. A., & Williams, J. W. (2020). Spatial Fingerprint of Younger Dryas Cooling and Warming in Eastern North America. Geophysical Research Letters. https://doi.org/10.1029/2020gl090031
7. Chevalier, M., Davis, B. A. S., Heiri, O., Seppä, H., Chase, B. M., Gajewski, K., … Kupriyanov, D. (2020). Pollen-based climate reconstruction techniques for late Quaternary studies. Earth-Science Reviews, 210, 103384. https://doi.org/10.1016/j.earscirev.2020.103384
8. Byun, E., Sato, H., Cowling, S. A., & Finkelstein, S. A. (2020). Extensive wetland development in mid-latitude North America during the Bølling–Allerød. Nature Geoscience, 14(1), 30–35. https://doi.org/10.1038/s41561-020-00670-4
9. Teale, C., & Chang, J. (2021). Fabaceae (legume) pollen as an anthropogenic indicator in eastern North America. Vegetation History and Archaeobotany. doi:10.1007/s00334-020-00815-w

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

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

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

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

1. Cardoso P (2017) red - an R package to facilitate species red list assessments according to the IUCN criteria. Biodiversity Data Journal 5: e20530. https://doi.org/10.3897/BDJ.5.e20530
2. Moat, J., Bachman, S. P., Field, R., & Boyd, D. S. (2018). Refining area of occupancy to address the modifiable areal unit problem in ecology and conservation. Conservation Biology. https://doi.org/10.1111/cobi.13139
3. Lusseau, D., & Mancini, F. (2018). A global assessment of tourism and recreation conservation threats to prioritise interventions. arXiv preprint https://arxiv.org/abs/1808.08399
4. Van de Perre, F., Leirs, H., & Verheyen, E. (2019). Paleoclimate, ecoregion size, and degree of isolation explain regional biodiversity differences among terrestrial vertebrates within the Congo Basin. Belgian Journal of Zoology, 149(1). https://doi.org/10.26496/bjz.2019.28
5. Alhajeri, B. H., & Fourcade, Y. (2019). High correlation between species‐level environmental data estimates extracted from IUCN expert range maps and from GBIF occurrence data. Journal of Biogeography. https://doi.org/10.1111/jbi.13619
6. Nyboer, E. A., Liang, C., & Chapman, L. J. (2019). Assessing the vulnerability of Africa’s freshwater fishes to climate change: A continent-wide trait-based analysis. Biological Conservation, 236, 505–520. https://doi.org/10.1016/j.biocon.2019.05.003
7. Grattarola, F., Botto, G., da Rosa, I., Gobel, N., González, E., González, J., … Pincheira-Donoso, D. (2019). Biodiversidata: An Open-Access Biodiversity Database for Uruguay. Biodiversity Data Journal, 7. https://doi.org/10.3897/bdj.7.e36226
8. Lennox, R. J., Veríssimo, D., Twardek, W. M., Davis, C. R., & Jarić, I. (2019). Sentiment analysis as a measure of conservation culture in scientific literature. Conservation Biology. https://doi.org/10.1111/cobi.13404
9. Dawson, A., Paciorek, C. J., Goring, S. J., Jackson, S. T., McLachlan, J. S., & Williams, J. W. (2019). Quantifying trends and uncertainty in prehistoric forest composition in the upper Midwestern United States. Ecology. https://doi.org/10.1002/ecy.2856
10. Bager Olsen, M. T., Geldmann, J., Harfoot, M., Tittensor, D. P., Price, B., Sinovas, P., … Burgess, N. D. (2019). Thirty-six years of legal and illegal wildlife trade entering the USA. Oryx, 1–10. https://doi.org/10.1017/s0030605319000541
11. Scheffers, B. R., Oliveira, B. F., Lamb, I., & Edwards, D. P. (2019). Global wildlife trade across the tree of life. Science, 366(6461), 71–76. https://doi.org/10.1126/science.aav5327
12. Stévart, T., Dauby, G., Lowry, P. P., Blach-Overgaard, A., Droissart, V., Harris, D. J., … Couvreur, T. L. P. (2019). A third of the tropical African flora is potentially threatened with extinction. Science Advances, 5(11), eaax9444. https://doi.org/10.1126/sciadv.aax9444
13. Cooke, R. S. C., Eigenbrod, F., & Bates, A. E. (2020). Ecological distinctiveness of birds and mammals at the global scale. Global Ecology and Conservation, 22, e00970. https://doi.org/10.1016/j.gecco.2020.e00970
14. Ji, Y., Baker, C. C., Li, Y., Popescu, V. D., Wang, Z., Wang, J., … Yu, D. W. (2020). Large-scale Quantification of Vertebrate Biodiversity in Ailaoshan Nature Reserve from Leech iDNA. https://doi.org/10.1101/2020.02.10.941336
15. Becker, D. J., & Han, B. A. (2020). The macroecology and evolution of avian competence for Borrelia burgdorferi. bioRxiv https://doi.org/10.1101/2020.04.15.040352
16. Greenville, A. C., Newsome, T. M., Wardle, G. M., Dickman, C. R., Ripple, W. J., & Murray, B. R. (2020). Simultaneously operating threats cannot predict extinction risk. Conservation Letters. https://doi.org/10.1111/conl.12758
17. Mothes, C. C., Stemle, L. R., Fonseca, T. N., Clements, S. L., Howell, H. J., & Searcy, C. A. (2020). Protect or perish: Quantitative analysis of state‐level species protection supports preservation of the Endangered Species Act. Conservation Letters. https://doi.org/10.1111/conl.12761
18. Alhajeri, B. H., Fourcade, Y., Upham, N. S., & Alhaddad, H. (2020). A global test of Allen’s rule in rodents. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13198
19. Etard, A., Morrill, S., & Newbold, T. (2020). Global gaps in trait data for terrestrial vertebrates. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13184
20. Alò, D., Lacy, S. N., Castillo, A., Samaniego, H. A., & Marquet, P. A. (2020). The macroecology of fish migration. Global Ecology and Biogeography, 30(1), 99–116. https://doi.org/10.1111/geb.13199
21. Murgier, J., McLean, M., Maire, A., Mouillot, D., Loiseau, N., Munoz, F., … Auber, A. (2021). Rebound in functional distinctiveness following warming and reduced fishing in the North Sea. Proceedings of the Royal Society B: Biological Sciences, 288(1942), 20201600. https://doi.org/10.1098/rspb.2020.1600
22. DE LA TORRE, G. M., & CAMPIÃO, K. M. (2021). Bird habitat preferences drive hemoparasite infection in the Neotropical region. Integrative Zoology. doi:10.1111/1749-4877.12515

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

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

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

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

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

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

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

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/.

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

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

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

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

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

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

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

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

1. Drozd, P., & Šipoš, J. (2013). R for all (I): Introduction to the new age of biological analyses. Casopis Slezskeho Zemskeho Muzea A, 62(1). https://doi.org/10.2478/cszma-2013-0004
2. Hampton, S. E., Anderson, S. S., Bagby, S. C., Gries, C., Han, X., Hart, E. M., et al. (2015). The Tao of open science for ecology. Ecosphere, 6(7), art120. https://doi.org/10.1890/es14-00402.1
3. Nguyen, N. T., Zhang, X., Wu, C., Lange, R. A., Chilton, R. J., Lindsey, M. L., & Jin, Y.-F. (2014). Integrative Computational and Experimental Approaches to Establish a Post-Myocardial Infarction Knowledge Map. PLoS Computational Biology, 10(3), e1003472. https://doi.org/10.1371/journal.pcbi.1003472
4. Lee, Y. Y., Foster, E. D., Polley, D. E., & Odell, J. Using the ‘rentrez’ R Package to Identify Repository Records for NCBI LinkOut. Code4lib Journal. http://journal.code4lib.org/articles/12792
5. Winter, D. J. (2017). rentrez: An R package for the NCBI eUtils API (Version 1). PeerJ Preprints. https://doi.org/10.7287/peerj.preprints.3179v1
6. Krawczyk, P. S., Lipinski, L., & Dziembowski, A. (2018). PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Research. https://doi.org/10.1093/nar/gkx1321
7. Claypool, K., & Patel, C. J. (2018). A transcript-wide association study in physical activity intervention implicates molecular pathways in chronic disease. https://doi.org/10.1101/260398
8. Chen, L., Heikkinen, L., Wang, C., Yang, Y., Knott, K. E., & Wong, G. (2018). miRToolsGallery: a tag-based and rankable microRNA bioinformatics resources database portal. Database, 2018. https://doi.org/10.1093/database/bay004
9. Lakiotaki, K., Vorniotakis, N., Tsagris, M., Georgakopoulos, G., & Tsamardinos, I. (2018). BioDataome: a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology. Database, 2018. https://doi.org/10.1093/database/bay011
10. Reibe, S., Hjorth, M., Febbraio, M. A., & Whitham, M. (2018). GeneXX: An online tool for the exploration of transcript changes in skeletal muscle associated with exercise. Physiological genomics. https://doi.org/10.1152/physiolgenomics.00127.2017
11. Barnett, A. (2018). Missing the point: are journals using the ideal number of decimal places? F1000Research, 7, 450. https://doi.org/10.12688/f1000research.14488.1
12. Spalink, D., Stoffel, K., Walden, G. K., Hulse-Kemp, A. M., Hill, T. A., Van Deynze, A., & Bohs, L. (2018). Comparative transcriptomics and genomic patterns of discordance in Capsiceae (Solanaceae). Molecular Phylogenetics and Evolution, 126, 293–302. https://doi.org/10.1016/j.ympev.2018.04.030
13. Han, X., Williams, S. R., & Zuckerman, B. L. (2018). A snapshot of translational research funded by the National Institutes of Health (NIH): A case study using behavioral and social science research awards and Clinical and Translational Science Awards funded publications. PLOS ONE, 13(5), e0196545. https://doi.org/10.1371/journal.pone.0196545
14. Machado, V. N., Collins, R. A., Ota, R. P., Andrade, M. C., Farias, I. P., & Hrbek, T. (2018). One thousand DNA barcodes of piranhas and pacus reveal geographic structure and unrecognised diversity in the Amazon. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-26550-x
15. Sun, B. B., Maranville, J. C., Peters, J. E., Stacey, D., Staley, J. R., Blackshaw, J., … Butterworth, A. S. (2018). Genomic atlas of the human plasma proteome. Nature, 558(7708), 73–79. https://doi.org/10.1038/s41586-018-0175-2
16. Mioduchowska, M., Czyż, M. J., Gołdyn, B., Kur, J., & Sell, J. (2018). Instances of erroneous DNA barcoding of metazoan invertebrates: Are universal cox1 gene primers too “universal”? PLOS ONE, 13(6), e0199609. https://doi.org/10.1371/journal.pone.0199609
17. Magoga, G., Sahin, D. C., Fontaneto, D., & Montagna, M. (2018). Barcoding of Chrysomelidae of Euro-Mediterranean area: efficiency and problematic species. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-31545-9
18. Otten, C., Knox, J., Boulday, G., Eymery, M., Haniszewski, M., Neuenschwander, M., … Abdelilah‐Seyfried, S. (2018). Systematic pharmacological screens uncover novel pathways involved in cerebral cavernous malformations. EMBO Molecular Medicine, e9155. https://doi.org/10.15252/emmm.201809155
19. Yángüez, E., Hunziker, A., Dobay, M. P., Yildiz, S., Schading, S., Elshina, E., … Stertz, S. (2018). Phosphoproteomic-based kinase profiling early in influenza virus infection identifies GRK2 as antiviral drug target. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-06119-y
20. Collins, R. A., Wangensteen, O. S., O’Gorman, E. J., Mariani, S., Sims, D. W., & Genner, M. J. (2018). Persistence of environmental DNA in marine systems. Communications Biology, 1(1). https://doi.org/10.1038/s42003-018-0192-6
21. Cholet, F., Ijaz, U. Z., & Smith, C. J. (2018). Differential ratio amplicons (Ramp) for the evaluation of RNA integrity extracted from complex environmental samples. Environmental Microbiology. https://doi.org/10.1111/1462-2920.14516
22. Die, J. V., Elmassry, M. M., Leblanc, K. H., Awe, O. I., Dillman, A., & Busby, B. (2018). GeneHummus: A pipeline to define gene families and their expression in legumes and beyond. https://doi.org/10.1101/436659
23. Mioduchowska, M., Czyż, M. J., Gołdyn, B., Kilikowska, A., Namiotko, T., Pinceel, T., … Sell, J. (2018). Detection of bacterial endosymbionts in freshwater crustaceans: the applicability of non-degenerate primers to amplify the bacterial 16S rRNA gene. PeerJ, 6, e6039. https://doi.org/10.7717/peerj.603
24. Bennett, D., Hettling, H., Silvestro, D., Vos, R., & Antonelli, A. (2018). restez: Create and Query a Local Copy of GenBank in R. Journal of Open Source Software, 3(31), 1102. https://doi.org/10.21105/joss.01102
25. Brooks, L., Kaze, M., & Sistrom, M. (2019). A Curated, Comprehensive Database of Plasmid Sequences. Microbiology Resource Announcements, 8(1). https://doi.org/10.1128/mra.01325-18
26. Poulin, R., Hay, E., & Jorge, F. (2019). Taxonomic and geographic bias in the genetic study of helminth parasites. International Journal for Parasitology. https://doi.org/10.1016/j.ijpara.2018.12.005
27. Phelps, K., Hamel, L., Alhmoud, N., Ali, S., Bilgin, R., Sidamonidze, K., … Olival, K. (2019). Bat Research Networks and Viral Surveillance: Gaps and Opportunities in Western Asia. Viruses, 11(3), 240. https://doi.org/10.3390/v11030240
28. Barnett, A. G., & Moher, D. (2019). Turning the tables: A university league-table based on quality not quantity. F1000Research, 8, 583. https://doi.org/10.12688/f1000research.18453.1
29. Mann, C. M., Martínez-Gálvez, G., Welker, J. M., Wierson, W. A., Ata, H., Almeida, M. P., … Dobbs, D. (2019). The Gene Sculpt Suite: a set of tools for genome editing. Nucleic Acids Research. https://doi.org/10.1093/nar/gkz405
30. Al-Mustanjid, A. (2019). Design of a common pathway drug for all types of cardiovascular diseases: A network biology approach. Network Biology, 9(2), 28. http://www.iaees.org/publications/journals/nb/articles/2019-9(2)/design-of-a-common-pathway-drug-for-cardiovascular-diseases.pdf
31. Shackleton, M. E., Rees, G. N., Watson, G., Campbell, C., & Nielsen, D. (2019). Environmental DNA reveals landscape mosaic of wetland plant communities. Global Ecology and Conservation, 19, e00689. https://doi.org/10.1016/j.gecco.2019.e00689
32. Koppelstaetter, C., Leierer, J., Rudnicki, M., Kerschbaum, J., Kronbichler, A., Melk, A., … Perco, P. (2019). Computational Drug Screening Identifies Compounds Targeting Renal Age-associated Molecular Profiles. Computational and Structural Biotechnology Journal, 17, 843–853. https://doi.org/10.1016/j.csbj.2019.06.019
33. Ferraz, M. de A. M. M., Carothers, A., Dahal, R., Noonan, M. J., & Songsasen, N. (2019). Oviductal extracellular vesicles interact with the spermatozoon’s head and mid-piece and improves its motility and fertilizing ability in the domestic cat. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-45857-x
34. Collins, R. A., Bakker, J., Wangensteen, O. S., Soto, A. Z., Corrigan, L., Sims, D. W., … Mariani, S. (2019). Non‐specific amplification compromises environmental DNA metabarcoding with COI. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13276
35. Die, J. V., Elmassry, M. M., LeBlanc, K. H., Awe, O. I., Dillman, A., & Busby, B. (2019). geneHummus: an R package to define gene families and their expression in legumes and beyond. BMC Genomics, 20(1). https://doi.org/10.1186/s12864-019-5952-2
36. Piper, A. M., Batovska, J., Cogan, N. O. I., Weiss, J., Cunningham, J. P., Rodoni, B. C., & Blacket, M. J. (2019). Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance. GigaScience, 8(8). https://doi.org/10.1093/gigascience/giz092
37. Neugebauer, K., El‐Serehy, H. A., George, T. S., McNicol, J. W., Moraes, M. F., Sorreano, M. C. M., & White, P. J. (2019). The influence of phylogeny and ecology on root, shoot and plant ionomes of fourteen native Brazilian species. Physiologia Plantarum. https://doi.org/10.1111/ppl.13018
38. Wittouck, S., Wuyts, S., Meehan, C. J., van Noort, V., & Lebeer, S. (2019). A Genome-Based Species Taxonomy of the Lactobacillus Genus Complex. mSystems, 4(5). https://doi.org/10.1128/msystems.00264-19
39. Alex Dornburg, Dustin J. Wcisel, J. Thomas Howard et al. Transcriptome Ortholog Alignment Sequence Tools (TOAST) for Phylogenomic Dataset Assembly, 21 October 2019, PREPRINT (Version 1) available at Research Square https://doi.org/10.21203/rs.2.16269/v1
40. Fu, D. Y., & Hughey, J. J. (2019). Releasing a preprint is associated with more attention and citations for the peer-reviewed article. eLife, 8. https://doi.org/10.7554/elife.52646
41. Vitale, O., Preste, R., Palmisano, D., & Attimonelli, M. (2019). A data and text mining pipeline to annotate human mitochondrial variants with functional and clinical information. Molecular Genetics & Genomic Medicine, 8(2). https://doi.org/10.1002/mgg3.1085
42. Die, J. V., Elmassry, M. M., LeBlanc, K. H., Awe, O. I., Dillman, A., & Busby, B. (2018). GeneHummus: A pipeline to define gene families and their expression in legumes and beyond. https://doi.org/10.1101/436659
43. Oliphant, K., Cochrane, K., Schroeter, K., Daigneault, M. C., Yen, S., Verdu, E. F., & Allen-Vercoe, E. (2020). Effects of Antibiotic Pretreatment of an Ulcerative Colitis-Derived Fecal Microbial Community on the Integration of Therapeutic Bacteria In Vitro. mSystems, 5(1). https://doi.org/10.1128/msystems.00404-19
44. Thompson, K. A. (2020). Experimental hybridization studies suggest that pleiotropic alleles commonly underlie adaptive divergence between natural populations. The American Naturalist. https://doi.org/10.1086/708722
45. Pavlovich, S. S., Darling, T., Hume, A. J., Davey, R. A., Feng, F., Mühlberger, E., & Kepler, T. B. (2020). Egyptian Rousette IFN-ω Subtypes Elicit Distinct Antiviral Effects and Transcriptional Responses in Conspecific Cells. Frontiers in Immunology, 11. https://doi.org/10.3389/fimmu.2020.00435
46. Bärenstrauch, M., Mann, S., Jacquemin, C., Bibi, S., Sylla, O.-K., Baudouin, E., … Kunz, C. (2020). Molecular crosstalk between the endophyte Paraconiothyrium variabile and the phytopathogen Fusarium oxysporum – Modulation of lipoxygenase activity and beauvericin production during the interaction. Fungal Genetics and Biology, 139, 103383. https://doi.org/10.1016/j.fgb.2020.103383
47. Martínez, A., Eckert, E. M., Artois, T., Careddu, G., Casu, M., Curini-Galletti, M., … Fontaneto, D. (2020). Human access impacts biodiversity of microscopic animals in sandy beaches. Communications Biology, 3(1). https://doi.org/10.1038/s42003-020-0912-6
48. De Almeida Monteiro Melo Ferraz, M., Fujihara, M., Nagashima, J. B., Noonan, M. J., Inoue-Murayama, M., & Songsasen, N. (2020). Follicular extracellular vesicles enhance meiotic resumption of domestic cat vitrified oocytes. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-65497-w
49. Oh, S., Yeom, J., Cho, H. J., Kim, J.-H., Yoon, S.-J., Kim, H., … Kim, H. S. (2020). Integrated pharmaco-proteogenomics defines two subgroups in isocitrate dehydrogenase wild-type glioblastoma with prognostic and therapeutic opportunities. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17139-y
50. Ponce, M., & Sandhel, A. (2020). covid19. analytics: An R Package to Obtain, Analyze and Visualize Data from the Corona Virus Disease Pandemic. arXiv preprint arXiv:2009.01091 https://arxiv.org/pdf/2009.01091.
51. Duarte, S., Vieira, P. E., & Costa, F. O. (2020). Assessment of species gaps in DNA barcode libraries of non-indigenous species (NIS) occurring in European coastal regions. Metabarcoding and Metagenomics, 4. https://doi.org/10.3897/mbmg.4.55162
52. Grenn, F. P., Kim, J. J., Makarious, M. B., Iwaki, H., Illarionova, A., … Brolin, K. (2020). The Parkinson’s Disease Genome‐Wide Association Study Locus Browser. Movement Disorders. https://doi.org/10.1002/mds.28197
53. Madritsch, S., Bomers, S., Posekany, A., Burg, A., Birke, R., Emerstorfer, F., … Sehr, E. M. (2020). Integrative transcriptomics reveals genotypic impact on sugar beet storability. Plant Molecular Biology. https://doi.org/10.1007/s11103-020-01041-8
54. Brandão, L. A. C., Agrelli, A., Bernardo, L., Paparella, F., Moura, R., & Crovella, S. (2020). PlatCOVID: A Novel Web Tool to Analyze, Curate and Share COVID-19 Literature. https://doi.org/10.21203/rs.3.rs-42169/v1
55. Carraro, L., Mächler, E., Wüthrich, R., & Altermatt, F. (2020). Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17337-8
56. Ihaka, R., & Gentleman, R. (1996). R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics, 5(3), 299–314. https://doi.org/10.1080/10618600.1996.10474713
57. Batista, E., Lopes, A., & Alves, A. (2020). Botryosphaeriaceae species on forest trees in Portugal: diversity, distribution and pathogenicity. European Journal of Plant Pathology, 158(3), 693–720. https://doi.org/10.1007/s10658-020-02112-8
58. Bandyopadhyay, S., Lysak, N., Adhikari, L., Velez, L. M., Sautina, L., Mohandas, R., … Bihorac, A. (2020). Discovery and Validation of Urinary Molecular Signature of Early Sepsis. Critical Care Explorations, 2(10), e0195. https://doi.org/10.1097/cce.0000000000000195
59. McColl‐Gausden, E. F., Weeks, A. R., Coleman, R. A., Robinson, K. L., Song, S., Raadik, T. A., & Tingley, R. (2020). Multispecies models reveal that eDNA metabarcoding is more sensitive than backpack electrofishing for conducting fish surveys in freshwater streams. Molecular Ecology. https://doi.org/10.1111/mec.15644
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Search the Internet Archive (https://archive.org), retrieve metadata, and download files.

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

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

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

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

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

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

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

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.

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
5. Meng, B., Loonen, R. C. G. M., & Hensen, J. L. M. (2020). Data-driven inference of unknown tilt and azimuth of distributed PV systems. Solar Energy, 211, 418–432. https://doi.org/10.1016/j.solener.2020.09.077

Browse and query the popler database.