Data Extraction

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

There are a lot of different typical tasks that have to be solved during phonetic research and experiments. This includes creating a presentation that will contain all stimuli, renaming and concatenating multiple sound files recorded during a session, automatic annotation in Praat TextGrids (this is one of the sound annotation standards provided by Praat software, see Boersma & Weenink 2020 https://www.fon.hum.uva.nl/praat/), creating an html table with annotations and spectrograms, and converting multiple formats (Praat TextGrid, ELAN, EXMARaLDA, Audacity, subtitles .srt, and FLEx flextext). All of these tasks can be solved by a mixture of different tools (any programming language has programs for automatic renaming, and Praat contains scripts for concatenating and renaming files, etc.). phonfieldwork provides a functionality that will make it easier to solve those tasks independently of any additional tools. You can also compare the functionality with other packages: rPraat https://CRAN.R-project.org/package=rPraat, textgRid https://CRAN.R-project.org/package=textgRid.

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

1. Yu, G., Tsan-Yuk Lam, T., Zhu, H., & Guan, Y. (2018). Two methods for mapping and visualizing associated data on phylogeny using ggtree. Molecular Biology and Evolution. https://doi.org/10.1093/molbev/msy194
2. Paudyal, N., Pan, H., Elbediwi, M., Zhou, X., Peng, X., Li, X., … Yue, M. (2019). Characterization of Salmonella Dublin isolated from bovine and human hosts. BMC Microbiology, 19(1). https://doi.org/10.1186/s12866-019-1598-0
3. Callanan, J., Stockdale, S. R., Shkoporov, A., Draper, L. A., Ross, R. P., & Hill, C. (2020). Expansion of known ssRNA phage genomes: From tens to over a thousand. Science Advances, 6(6), eaay5981. https://doi.org/10.1126/sciadv.aay5981
4. Ahrenfeldt, J., Waisi, M., Loft, I. C., Clausen, P. T. L. C., Allesøe, R., Szarvas, J., … Lund, O. (2020). Metaphylogenetic analysis of global sewage reveals that bacterial strains associated with human disease show less degree of geographic clustering. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-59292-w
5. Ryt-Hansen, P., Pedersen, A. G., Larsen, I., Kristensen, C. S., Krog, J. S., Wacheck, S., & Larsen, L. E. (2020). Substantial Antigenic Drift in the Hemagglutinin Protein of Swine Influenza A Viruses. Viruses, 12(2), 248. https://doi.org/10.3390/v12020248
6. Yu, G. (2020). Using ggtree to Visualize Data on Tree‐Like Structures. Current Protocols in Bioinformatics, 69(1). https://doi.org/10.1002/cpbi.96
7. Lequime, S., Bastide, P., Dellicour, S., Lemey, P., & Baele, G. (2020). nosoi: a stochastic agent-based transmission chain simulation framework in R. https://doi.org/10.1101/2020.03.03.973107
8. Bastide, P., Ho, L. S. T., Baele, G., Lemey, P., & Suchard, M. A. (2020). Efficient Bayesian Inference of General Gaussian Models on Large Phylogenetic Trees. arXiv preprint arXiv:2003.10336. https://arxiv.org/pdf/2003.10336
9. Ordynets, A., Liebisch, R., Lysenko, L., Scherf, D., Volobuev, S., Saitta, A., … Langer, E. (2020). Morphologically similar but not closely related: the long-spored species of Subulicystidium (Trechisporales, Basidiomycota). Mycological Progress, 19(7), 691–703. https://doi.org/10.1007/s11557-020-01587-3
10. Carroll, L. M., Huisman, J. S., & Wiedmann, M. (2020). Twentieth-century emergence of antimicrobial resistant human- and bovine-associated Salmonella enterica serotype Typhimurium lineages in New York State. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-71344-9
11. Whitmer, S. L. M., Lo, M. K., Sazzad, H. M. S., Zufan, S., Gurley, E. S., Sultana, S., … Klena, J. D. (2020). Inference of Nipah virus Evolution, 1999-2015. Virus Evolution. https://doi.org/10.1093/ve/veaa062
12. Ettinger, C. L., & Eisen, J. A. (2020). Fungi, bacteria and oomycota opportunistically isolated from the seagrass, Zostera marina. PLOS ONE, 15(7), e0236135. https://doi.org/10.1371/journal.pone.0236135
13. Huang, R., Soneson, C., Ernst, F. G. M., Rue-Albrecht, K. C., Yu, G., Hicks, S. C., & Robinson, M. D. (2020). TreeSummarizedExperiment: a S4 class for data with hierarchical structure. F1000Research, 9, 1246. https://doi.org/10.12688/f1000research.26669.1
14. Figueroa, H., & Smith, S. A. (2020). A targeted phylogenetic approach helps explain New World functional diversity patterns of two eudicot lineages. Journal of Biogeography. https://doi.org/10.1111/jbi.13993
15. Alvarado-Ortega, J., & Díaz-Cruz, J. A. (2021). Hastichthys totonacus sp. nov., a North American Turonian dercetid fish (Teleostei, Aulopiformes) from the Huehuetla quarry, Puebla, Mexico. Journal of South American Earth Sciences, 105, 102900. https://doi.org/10.1016/j.jsames.2020.102900
16. Chak, S. T. C., Baeza, J. A., & Barden, P. (2020). Eusociality Shapes Convergent Patterns of Molecular Evolution across Mitochondrial Genomes of Snapping Shrimps. Molecular Biology and Evolution. https://doi.org/10.1093/molbev/msaa297
17. Wagner, E., Zaiser, A., Leitner, R., Quijada, N. M., Pracser, N., Pietzka, A., … Rychli, K. (2020). Virulence characterization and comparative genomics of Listeria monocytogenes sequence type 155 strains. BMC Genomics, 21(1). https://doi.org/10.1186/s12864-020-07263-w

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

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

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

1. Perez, T. M., & Feeley, K. J. (2020). Weak phylogenetic and climatic signals in plant heat tolerance. Journal of Biogeography. https://doi.org/10.1111/jbi.13984

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

The git2rdata package is an R package for writing and reading dataframes as plain text files. A metadata file stores important information. 1) Storing metadata allows to maintain the classes of variables. By default, git2rdata optimizes the data for file storage. The optimization is most effective on data containing factors. The optimization makes the data less human readable. The user can turn this off when they prefer a human readable format over smaller files. Details on the implementation are available in vignette(“plain_text”, package = “git2rdata”). 2) Storing metadata also allows smaller row based diffs between two consecutive commits. This is a useful feature when storing data as plain text files under version control. Details on this part of the implementation are available in vignette(“version_control”, package = “git2rdata”). Although we envisioned git2rdata with a git workflow in mind, you can use it in combination with other version control systems like subversion or mercurial. 3) git2rdata is a useful tool in a reproducible and traceable workflow. vignette(“workflow”, package = “git2rdata”) gives a toy example. 4) vignette(“efficiency”, package = “git2rdata”) provides some insight into the efficiency of file storage, git repository size and speed for writing and reading. Please cite using doi:10.5281/zenodo.1485309.

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

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

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

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

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

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

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

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

1. Milla, R., Bastida, J. M., Turcotte, M. M., Jones, G., Violle, C., Osborne, C. P., … Byun, C. (2018). Phylogenetic patterns and phenotypic profiles of the species of plants and mammals farmed for food. Nature Ecology & Evolution, 2(11), 1808–1817. https://doi.org/10.1038/s41559-018-0690-4
2. Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C., Edler, D., … Antonelli, A. (2019). CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13152
3. Rice, A., Šmarda, P., Novosolov, M., Drori, M., Glick, L., Sabath, N., … Mayrose, I. (2019). The global biogeography of polyploid plants. Nature Ecology & Evolution, 3(2), 265–273. https://doi.org/10.1038/s41559-018-0787-9
4. Karger, D. N., Kessler, M., Conrad, O., Weigelt, P., Kreft, H., König, C., & Zimmermann, N. E. (2019). Why tree lines are lower on islands-Climatic and biogeographic effects hold the answer. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12897
5. De Frenne, P., Zellweger, F., Rodríguez-Sánchez, F., Scheffers, B. R., Hylander, K., Luoto, M., … Lenoir, J. (2019). Global buffering of temperatures under forest canopies. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-019-0842-1
6. Colli‐Silva, M., Vasconcelos, T. N. C., & Pirani, J. R. (2019). Outstanding plant endemism levels strongly support the recognition of campo rupestre provinces in mountaintops of eastern South America. Journal of Biogeography. https://doi.org/10.1111/jbi.13585
7. Waller, J. (2019). Data Location Quality at GBIF. Biodiversity Information Science and Standards, 3. https://doi.org/10.3897/biss.3.35829
8. Butterfield, B. J., Holmgren, C. A., Anderson, R. S., & Betancourt, J. L. (2019). Life history traits predict colonization and extinction lags of desert plant species since the Last Glacial Maximum. Ecology. https://doi.org/10.1002/ecy.2817
9. Wüest, R. O., Zimmermann, N. E., Zurell, D., Alexander, J. M., Fritz, S. A., Hof, C., … Karger, D. N. (2019). Macroecology in the age of Big Data – Where to go from here? Journal of Biogeography. https://doi.org/10.1111/jbi.13633
10. Pender, J. E., Hipp, A. L., Hahn, M., Kartesz, J., Nishino, M., & Starr, J. R. (2019). How sensitive are climatic niche inferences to distribution data sampling? A comparison of Biota of North America Program (BONAP) and Global Biodiversity Information Facility (GBIF) datasets. Ecological Informatics, 100991. https://doi.org/10.1016/j.ecoinf.2019.100991
11. Feng, X., Park, D. S., Walker, C., Peterson, A. T., Merow, C., & Papeş, M. (2019). A checklist for maximizing reproducibility of ecological niche models. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-019-0972-5
12. Espinosa, B. S., D’Apolito, C., Silva-Caminha, S. A. F., Ferreira, M. G., & Absy, M. L. (2020). Neogene paleoecology and biogeography of a Malvoid pollen in northwestern South America. Review of Palaeobotany and Palynology, 273, 104131. https://doi.org/10.1016/j.revpalbo.2019.104131
13. Jin, J., & Yang, J. (2020). BDcleaner: A workflow for cleaning taxonomic and geographic errors in occurrence data archived in biodiversity databases. Global Ecology and Conservation, 21, e00852. https://doi.org/10.1016/j.gecco.2019.e00852
14. Zizka, A., Azevedo, J., Leme, E., Neves, B., Costa, A. F., Caceres, D., & Zizka, G. (2019). Biogeography and conservation status of the pineapple family (Bromeliaceae). Diversity and Distributions, 26(2), 183–195. https://doi.org/10.1111/ddi.13004
15. Marshall, B. M., & Strine, C. T. (2019). Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media. PeerJ, 7, e8059. https://doi.org/10.7717/peerj.8059
16. Asevedo, L., D’Apolito, C., Misumi, S. Y., Barros, M. A. de, Barth, O. M., & Avilla, L. dos S. (2020). Palynological analysis of dental calculus from Pleistocene proboscideans of southern Brazil: A new approach for paleodiet and paleoenvironmental reconstructions. Palaeogeography, Palaeoclimatology, Palaeoecology, 540, 109523. https://doi.org/10.1016/j.palaeo.2019.109523
17. Léveillé-Bourret, É., Chen, B.-H., Garon-Labrecque, M.-È., Ford, B. A., & Starr, J. R. (2020). RAD sequencing resolves the phylogeny, taxonomy and biogeography of Trichophoreae despite a recent rapid radiation (Cyperaceae). Molecular Phylogenetics and Evolution, 145, 106727. https://doi.org/10.1016/j.ympev.2019.106727
18. Moudrý, V., & Devillers, R. (2020). Quality and usability challenges of global marine biodiversity databases: An example for marine mammal data. Ecological Informatics, 56, 101051. https://doi.org/10.1016/j.ecoinf.2020.101051
19. Alfaro-Ramírez, F. U., Ramírez-Albores, J. E., Vargas-Hernández, J. J., Franco-Maass, S., & Pérez-Suárez, M. (2020). Potential reduction of Hartweg´s Pine (Pinus hartwegii Lindl.) geographic distribution. PLOS ONE, 15(2), e0229178. https://doi.org/10.1371/journal.pone.0229178
20. Armitage, D. W., & Jones, S. E. (2020). Barriers to coexistence limit the poleward range of a globally-distributed plant. https://doi.org/10.1101/2020.02.24.946574
21. Zizka, A., Carvalho‐Sobrinho, J. G., Pennington, R. T., Queiroz, L. P., Alcantara, S., Baum, D. A., … Antonelli, A. (2020). Transitions between biomes are common and directional in Bombacoideae (Malvaceae). Journal of Biogeography. https://doi.org/10.1111/jbi.13815
22. Bernardi, A. P., Lauterjung, M. B., Mantovani, A., & dos Reis, M. S. (2020). Phylogeography and species distribution modeling reveal a historic disjunction for the conifer Podocarpus lambertii. Tree Genetics & Genomes, 16(3). https://doi.org/10.1007/s11295-020-01434-2
23. Gaynor, M. L., Fu, C., Gao, L., Lu, L., Soltis, D. E., & Soltis, P. S. (2020). Biogeography and ecological niche evolution in Diapensiaceae inferred from phylogenetic analysis. Journal of Systematics and Evolution. https://doi.org/10.1111/jse.12646
24. Pacifico, R., Almeda, F., Frota, A., & Fidanza, K. (2020). Areas of endemism on Brazilian mountaintops revealed by taxonomically verified records of Microlicieae (Melastomataceae). Phytotaxa, 450(2), 119–148. https://doi.org/10.11646/phytotaxa.450.2.1
25. Waldock, C. A., De Palma, A., Borges, P. A. V., & Purvis, A. (2020). Insect occurrence in agricultural land‐uses depends on realized niche and geographic range properties. Ecography. https://doi.org/10.1111/ecog.05162
26. Colli‐Silva, M., Reginato, M., Cabral, A., Forzza, R. C., Pirani, J. R., & Vasconcelos, T. N. da C. (2020). Evaluating shortfalls and spatial accuracy of biodiversity documentation in the Atlantic Forest, the most diverse and threatened Brazilian phytogeographic domain. TAXON, 69(3), 567–577. https://doi.org/10.1002/tax.12239
27. Sanchez‐Martinez, P., Martínez‐Vilalta, J., Dexter, K. G., Segovia, R. A., & Mencuccini, M. (2020). Adaptation and coordinated evolution of plant hydraulic traits. Ecology Letters. https://doi.org/10.1111/ele.13584
28. Reimuth, J., & Zotz, G. (2020). The biogeography of the megadiverse genus Anthurium (Araceae). Botanical Journal of the Linnean Society, 194(2), 164-176. https://doi.org/10.1093/botlinnean/boaa044
29. Polaina, E., Pärt, T., & Recio, M. R. (2020). Identifying hotspots of invasive alien terrestrial vertebrates in Europe to assist transboundary prevention and control. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-68387-3
30. Mothes, C. C., Howell, H. J., & Searcy, C. A. (2020). Habitat suitability models for the imperiled wood turtle (Glyptemys insculpta) raise concerns for the species’ persistence under future climate change. Global Ecology and Conservation, 24, e01247. https://doi.org/10.1016/j.gecco.2020.e01247
31. Nania, D., Flecks, M., & Rödder, D. (2020). Continuous expansion of the geographic range linked to realized niche expansion in the invasive Mourning gecko Lepidodactylus lugubris (Duméril & Bibron, 1836). PLOS ONE, 15(7), e0235060. https://doi.org/10.1371/journal.pone.0235060
32. Brightly, W. H., Hartley, S. E., Osborne, C. P., Simpson, K. J., & Strömberg, C. A. E. (2020). High silicon concentrations in grasses are linked to environmental conditions and not associated with C4 photosynthesis. Global Change Biology. https://doi.org/10.1111/gcb.15343
33. Paton, A., Antonelli, A., Carine, M., Forzza, R. C., Davies, N., Demissew, S., … Dickie, J. (2020). Plant and fungal collections: Current status, future perspectives. PLANTS, PEOPLE, PLANET, 2(5), 499–514. https://doi.org/10.1002/ppp3.10141
34. Carrillo, J. D., Faurby, S., Silvestro, D., Zizka, A., Jaramillo, C., Bacon, C. D., & Antonelli, A. (2020). Disproportionate extinction of South American mammals drove the asymmetry of the Great American Biotic Interchange. Proceedings of the National Academy of Sciences, 117(42), 26281–26287. https://doi.org/10.1073/pnas.2009397117
35. Figueroa, H., & Smith, S. A. (2020). A targeted phylogenetic approach helps explain New World functional diversity patterns of two eudicot lineages. Journal of Biogeography. https://doi.org/10.1111/jbi.13993
36. Simpson, K. J., Jardine, E. C., Archibald, S., Forrestel, E. J., Lehmann, C. E. R., Thomas, G. H., & Osborne, C. P. (2020). Resprouting grasses are associated with less frequent fire than seeders. New Phytologist. https://doi.org/10.1111/nph.17069
37. Bello, C., Cintra, A. L. P., Barreto, E., Vancine, M. H., Sobral-Souza, T., Graham, C. H., & Galetti, M. (2020). Environmental niche and functional role similarity between invasive and native palms in the Atlantic Forest. Biological Invasions. https://doi.org/10.1007/s10530-020-02400-8
38. Roigé, M., & Phillips, C. B. (2021). Validation and uncertainty analysis of the match climates regional algorithm (CLIMEX) for Pest risk analysis. Ecological Informatics, 61, 101196. https://doi.org/10.1016/j.ecoinf.2020.101196
39. Panter, C. T., Clegg, R. L., Moat, J., Bachman, S. P., Klitgård, B. B., & White, R. L. (2020). To clean or not to clean: Cleaning open‐source data improves extinction risk assessments for threatened plant species. Conservation Science and Practice, 2(12). https://doi.org/10.1111/csp2.311
40. Chowdhury, S., Braby, M. F., Fuller, R. A., & Zalucki, M. P. (2020). Coasting along to a wider range: niche conservatism in the recent range expansion of the Tawny Coster, Acraea terpsicore (Lepidoptera: Nymphalidae). Diversity and Distributions. https://doi.org/10.1111/ddi.13200
41. Farooq, H., Azevedo, J. A. R., Soares, A., Antonelli, A., & Faurby, S. (2020). Mapping Africa’s Biodiversity: More of the Same Is Just Not Good Enough. Systematic Biology. https://doi.org/10.1093/sysbio/syaa090

Reads Genbank files.

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

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

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

1. Garmendia, A., Raigón, M. D., Marques, O., Ferriol, M., Royo, J., & Merle, H. (2018). Effects of nettle slurry (Urtica dioica L.) used as foliar fertilizer on potato (Solanum tuberosum L.) yield and plant growth. PeerJ, 6, e4729. https://doi.org/10.7717/peerj.4729
2. Garmendia, A., Merle, H., Ruiz, P., & Ferriol, M. (2018). Distribution and ecological segregation on regional and microgeographic scales of the diploid Centaurea aspera L., the tetraploid C. seridis L., and their triploid hybrids (Compositae). PeerJ, 6, e5209. https://doi.org/10.7717/peerj.5209
3. Garmendia, A., Beltrán, R., Zornoza, C., Breijo, F., Reig, J., Bayona, I., & Merle, H. (2019). Insect repellent and chemical agronomic treatments to reduce seed number in ‘Afourer’ mandarin - Effect on yield and fruit diameter. Scientia Horticulturae. 246, 437–447. https://doi.org/10.1016/j.scienta.2018.11.025
4. Ktenioudaki, A., O’Donnell, C. P., & do Nascimento Nunes, M. C. (2019). Modelling the biochemical and sensory changes of strawberries during storage under diverse relative humidity conditions. Postharvest Biology and Technology, 154, 148–158. https://doi.org/10.1016/j.postharvbio.2019.04.023
5. Ayodele Benjamin, E., Vincent, E., Claudius, A., Olatomiwa, L., & Dickson, E. (2019). Data-based investigation on the performance of an independent Gas turbine for electricity generation using real power measurements and other closely related parameters. Data in Brief, 104444. https://doi.org/10.1016/j.dib.2019.104444
6. Ehlers, M., Nold, J., Kuhn, M., Klingelhöfer-Jens, M., & Lonsdorf, T. (2020). Natural variations in brain morphology do not account for inter-individual differences in defensive responding during fear acquisition training and extinction. https://psyarxiv.com/q2kyf/download?format=pdf
7. Wiley, M., & Wiley, J. F. (2020). Data Input and Output. Beginning R 4, 33–46. https://doi.org/10.1007/978-1-4842-6053-1_3
8. Yan, T., Wang, Q., Maodzeka, A., Wu, D., & Jiang, L. (2020). BnaSNPDB: An interactive web portal for the efficient retrieval and analysis of SNPs among 1,007 rapeseed accessions. Computational and Structural Biotechnology Journal, 18, 2766–2773. https://doi.org/10.1016/j.csbj.2020.09.031
9. Munzert, S., & Ramirez-Ruiz, S. (2020, October 10). Meta-Analysis of the Effects of Voting Advice Applications. https://doi.org/10.31219/osf.io/utdn4

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

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

1. Scharmüller, A., Schreiner, V. C., & Schäfer, R. B. (2020). Standartox: Standardizing Toxicity Data. Data, 5(2), 46. https://doi.org/10.3390/data5020046
2. LeBeau, B. C., & Aloe, A. M. (2020). Evolution of Statistical Software and Quantitative Methods. https://doi.org/10.17077/pp.005273
3. Benjamens, S., Banning, L. B., van den Berg, T. A., & Pol, R. A. (2020). Gender Disparities in Authorships and Citations in Transplantation Research. Transplantation direct, 6(11). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575186/

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

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

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

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

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

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

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

1. Ahmed, M., Lai, T. H., & Kim, D. R. (2019). colocr: An R package for conducting co-localization analysis on fluorescence microscopy images. https://doi.org/10.7287/peerj.preprints.27613v1
2. Nguyen, H. Q., Nguyen, V. D., Van Nguyen, H., & Seo, T. S. (2020). Quantification of colorimetric isothermal amplification on the smartphone and its open-source app for point-of-care pathogen detection. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-72095-3

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

1. Sawa, T., Momiyama, K., Mihara, T., Kainuma, A., Kinoshita, M., & Moriyama, K. (2020). Molecular epidemiology of clinically high‐risk Pseudomonas aeruginosa strains: Practical overview. Microbiology and Immunology. https://doi.org/10.1111/1348-0421.12776
2. Alvarado-Ortega, J., & Díaz-Cruz, J. A. (2021). Hastichthys totonacus sp. nov., a North American Turonian dercetid fish (Teleostei, Aulopiformes) from the Huehuetla quarry, Puebla, Mexico. Journal of South American Earth Sciences, 105, 102900. https://doi.org/10.1016/j.jsames.2020.102900

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

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

1. Salmon, M., Milà, C., Bhogadi, S., Addanki, S., Madhira, P., Muddepaka, N., … Tonne, C. (2018). Wearable camera-derived microenvironments in relation to personal exposure to PM 2.5. Environment International, 117, 300–307. https://doi.org/10.1016/j.envint.2018.05.021
2. Milà, C., Curto, A., Dimitrova, A., Sreekanth, V., Kinra, S., Marshall, J. D., & Tonne, C. (2020). Identifying predictors of personal exposure to air temperature in peri-urban India. Science of The Total Environment, 707, 136114. https://doi.org/10.1016/j.scitotenv.2019.136114
3. Upadhya, A., Agrawal, P., Vakacherla, S., & Kushwaha, M. (2020). mmaqshiny v1.0: R-Shiny package to explore Air-Quality Mobile-Monitoring data. Journal of Open Source Software, 5(50), 2250. https://doi.org/10.21105/joss.02250
4. Riederer, A. M., Krenz, J. E., Tchong‐French, M. I., Torres, E., Perez, A., Younglove, L. R., … Karr, C. J. (2020). Effectiveness of portable HEPA air cleaners on reducing indoor PM2.5 and NH3 in an agricultural cohort of children with asthma: A randomized intervention trial. Indoor Air. https://doi.org/10.1111/ina.12753

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

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