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spocc 10.1002/ece3.3163 research AlfsnesEtal2017Ecology&Evolution.pdf Observational data of the species were obtained from the gBif database using R with the rgbif package v0.8.0 and the spocc package v0.4.0. From gBif we obtained for each species; observations of the maximum absolute latitude (the most northern or southern extent) (in degrees) (MAL), maximum depth (in meters, crustaceans only) (MDE) and maximum elevation (in meters, insects only) (MEL). use GBIF data to explore genome size variation against many variables
spocc 10.3897/rio.3.e13414 research VanderhoevenEtal2017Rio.pdf grant proposal: Species occurrence data gathering (and dynamic updating) will be facilitated through the use of designated R packages that directly communicate with these databases (e.g. the ‘rgbif’ (Chamberlain et al. 2016) and ‘spocc’ packages of the rOpenSci project grant proposal: data will be collected with spocc
spocc NA research Perez-EscobarEtal2017Phytotaxa.pdf Species distributions of morphologically related and sympatric taxa were obtained from herbarium specimens (including type localities and type specimens) and the GBIF data repository using the package ‘spocc’ use GBIF data to construct species distributions
spocc 10.1111/ele.12860 research DallasEtal2017EcoLett.pdf An assumption of the main text analyses was that the range of each species – or at least that the centroid of the species range – was adequately estimated the sampled populations ... To investigate this further, we calculated species range and niche centroids using freely available occurrence data obtained from the Global Biodiversity Information Facility using the R package spocc (Chamberlain, 2017). Species geographic range and niche centroids were estimated by forming a minimum convex hull around the sampling occurrence points ... This allowed us the opportunity to compare species range estimates from abundance and occurrence data. use GBIF data to construct species range and niche centroids
spocc 10.3119/16-13 research Oldham&Weeks2017Rhodora.pdf Latitude and longitude coordinatedata necessary for developing a range map of the species were collected using spocc (Chamberlain et al. 2016) from the Global BiodiversityInformation Facility (www.gbif.org), Integrated Digitized Biocollections (www.idigbio.org), and United States Geological Survey Biodiversity Information Serving Our Nation use GBIF, iDigBio, and BISON data to construct range maps
spocc 10.1111/1365-2656.12721 research SalesEtal2017JAnimEcol.pdf We collected occurrence data for the wild boar and its hybrids ... from different databases, ... GBIF ... VertNet ... BISON ... Ecoengine ... iN aturalist and the Invasive Species Compendium (ICS). In addition, we exhaustively searched nonformal sources of wild boar occurrence information ... All occurrences from databases were downloaded with the function occ from R package ... spocc ..., and occurrences from other sources were downloaded manually. use GBIF, VertNet, BISON, Ecoengine, iNaturalist data to construct species niche models
spocc 10.1016/S0140-6736(18)31224-8 research LongbottomEtal2018TheLancet.pdf We assembled a list of snake species using WHO guidelines ... We also obtained occurrence data for each snake species from a variety of websites, such as VertNet and iNaturalist, using the spocc R package (version 0.7.0) use Vertnet and iNaturalist data to identify most vulernable populations for snakebites
spocc 10.1016/j.actatropica.2018.08.014 research SamyEtal2018ActaTropica.pdf Vector occurrences were drawn from ... GBIF ... and the VectorMap project (www.vectormap.org). Vector occurrences included records of five Culex spp.; Cx. tritaeniorhynchus, Cx. pseudovishnui, Cx. vishnui, Cx. fuscocephala, and Cx. gelidus. Records for reservoir species were obtained from GBIF; these records included E. garzetta, E. intermedia, and N. nycticorax. Records from the GBIF were obtained using the spocc package in R via the online Niche ToolBox repository in http://shiny.conabio.gob.mx:3838/nichetoolb2. Finally, occurrence records of JEV were compiled from the Food and Agriculture Organization Emergency Prevention System for Transboundary Animal and Plant Pests and Diseases Information System (EMPRES-i; http://empres-i.fao.org), and HealthMap (http://www.healthmap.org) use GBIF data to map potential distributions of mosquito vectors based on ecological niche models
spocc 10.1186/s12936-018-2500-5 software PfefferEtal2018MalariaJournal.pdf mosquito presence data (red triangles) were obtained from GBIF using the SpOcc zoon module (https://github.com/zoonproject/modules/blob/master/R/SpOcc.R) use GBIF data via zoon in malariaAtlas R pkg
spocc 10.1098/rstb.2017.0390 research PerezEtal2018PhilosophicalTransactionsB.pdf To create this last subset of species, we estimated the climatic distributions of species in the FTBG and FRED datasets by retrieving all available georeferenced records for our target species from ... GBIF (... GBIF accessed via the ‘spocc’ R package [66] on 23 February 2018). For all species with 􏰀25 georeferenced records, we estimated the mean annual temperatures (MATs) and annual precipitation (PPT) at each collection location for all occurrences by extracting the MAT (BIO1) and PPT (BIO12) values from the Worldclim v.1.4 database at a spatial resolution of 30 arc seconds [67]. To account for potentially erroneous georeferenced locations, we removed any occurrences lying outside the 2.5 and 97.5% quantiles of MAT and PPT for each species. We then selected the woody species from the FRED and FTBG with mean MAT 􏰀158C and 􏰂308C, and mean PPT 􏰀1000 and 􏰂3500 mm use GBIF data to estimate climatic distributions of species
spocc NA research Chelick2019Thesis.pdf The final species list included 1,541 species (Appendix A1), comprising 1,221 native species and 320 exotic species, 982 forbs, 262 graminoids, 81 shrubs, 130 subshrubs (low growing shrubs under 1.0 m tall at maturity), and 86 trees. Occurrence data for all species in the final plant list were extracted from ... (GBIF) using the spocc package version 0.7.0. Occurrences extracted from GBIF were limited to those found within North America, and records from 1980 to present. Kane et al. (2017) express that it is good practice to model distributions based on a larger area that encompasses the smaller geographic area, in order to increase the background data provided for the MaxEnt model and ensure that a broad range of environmental conditions are represented for each species. Also, by encompassing a much broader extent than the focal area, we ensure that when predicting future distributions in response to climate change (see below), we accommodate species whose ranges may shift into the focal region from elsewhere use GBIF data to construct species distributions models
spocc 10.1007/s11104-018-03915-9 research ZuquimEtal2019PlantSoil.pdf Data from GBIF and iDigBio were downloaded using the package spocc (Chamberlain 2016) and geographical outliers were removed with the package biogeo (Robertson 2016) use GBIF and iDigBio data to construct future species ranges
rnoaa 10.1016/j.cageo.2015.02.013 research Bowman&Lees2015ComputersGeosciences.pdf just cited, not used not used
rnoaa 10.1080/01584197.2017.1315310 research GrosserEtal2017AustralOrnithology.pdf To test for Bergmann’s Rule we calculated linear regressions, with body size as the response variable and latitude (or SST) as the predictor. To test for Allen’s Rule, we performed multiple linear regressions, with bill or foot size as response variables, and latitude (or SST) and body size as predictors. Partial residuals for each variable of interest (e.g. bill size and latitude) were calculated by separately regressing that variable against body size. We then tested for correlations between the variables’ partial residuals (i.e. calculated partial correlation coefficients). Mean summer and winter SST (2000–2015) for locality data determined from museum records were extracted from NOAA’s Extended Reconstructed Sea Surface Temperature (ERSST) project using the RNOAA package in R (Chamberlain et al. 2016) fetch sea surface temperature (SST) data to check if latitude/SST can explain variation in body size
rnoaa 10.2514/6.2018-3670 research ZhuEtal2018AIAA_Aviation_Forum.pdf ASOS data are generated from the automatic observation of runway visual range, wind speed and direction, etc around ASOS sites. There are three different temporal resolution ASOS data: 1-minute, 5-minute and hourly. ASOS data, if reported hourly, is compiled into Integrated Surface Database (ISD). Because ISD data is integrated from many data sources, thus has more columns than ASOS data. We use rnoaa package to get ISD data from three ASOS stations near DEN, ORD and IAH use many variables from ISD to predict flight time
rnoaa NA research Pierson2018Thesis.pdf ... Data were obtained using the R package ‘rnoaa’, developed by the National Oceanic and Atmospheric Administration (NOAA) (Chamberlain, 2017). The function `ghcnd` was used to download mean daily precipitation and minimum, maximum, and average daily temperature records for the full period or record stored in the U.S. Historical Climatology Network (USHCN) use climate data to identify opportunities for stream restoration
rnoaa NA research DezordiEtal2018.pdf just cited, not used not used
rnoaa 10.1038/s41467-019-08540-3 research Fitzpatrick&Dunn2019NatureCommunications.pdf We estimated historical ICV in the 12 temperature and precipitation variables for each urban area using NOAA weather station records. For each urban area, we used the rnoaa package in R and custom R scripts to find all weather stations within the urban area boundary that contained a complete time series of climatological records (monthly means of minimum and maximum temperature and total precipitation) for the contemporary reference period (1960–1990). If fewer than five weather stations within the urban area boundary met these requirements, stations outside the urban area boundary were searched, up to a maximum of 50 km from the urban area centroid, until five stations meeting the data requirements were found. Climate records were then averaged across stations using inverse distance weighting to down weight the contribution of records more distant from the urban area centroid. The monthly records were then aggregated to the four climatological seasons to match the contemporary climate normals and future climate projections estimate interannual climatic variability in urban areas
rnoaa NA research Angel&Markus2019Bulletin.pdf Three precipitation data sources were used in this study ... primary data source was the Global Historical Climatology Network Daily (GHCN-Daily) ... Data were downloaded using the CRAN R package ‘rnoaa’. A total of 761 stations was downloaded from Illinois and from adjacent counties of neighboring states (Missouri, Iowa, Wisconsin, Indiana, and Kentucky) government report on precipitation
taxize 10.1017/s0960428615000256 research BadenEtal2015EdinburgJBotany.pdf All species names were checked in TROPICOS, and new records were also checked in Flora Mesoamericana (2014). The species list was matched with the Catalogue of Life (CoL; Roskov et al., 2014), and IUCN Red List status of each species was retrieved from the IUCN Red List official website, using the R package Taxize check sci. names against COL, get IUCN Red List status, in botanical inventory
taxize 10.1016/j.ecoinf.2015.05.004 research BergheEtal2015EcolInformatics.pdf just cited, not used not used
taxize 10.1111/2041-210x.12327 software Bocci2014MEE.pdf To avoid possible problems, users are also urged to check plant species names (e.g. using the taxize package (Chamberlain & Szo€cs 2013), before using tr8: this package can also be used to remove authors’ names from species names) not used
taxize 10.1007/s10530-015-0970-8 research Bradie&Leung2016JBiog.pdf We identified 1081 articles that cited MaxEnt ... articles were reviewed, and 227 studies identified that generated a MaxEnt SDM for a plant or animal and reported the contribution of variables to the model. ... Taxonomic information was obtained using the package ‘Taxize’ (Chamberlain & Szocs, 2013) in the R language for statistical computing (R Core Team, 2016); missing data were supplemented using Internet databases (Catalogue of Life, ITIS database ...) collect taxonomic information
taxize 10.1111/ddi.12351 research DoddEtal2015DivDist.pdf they may have cited the wrong package none
taxize 10.2478/cszma-2013-0004 research Drozd&Sipos2013.pdf just cited, not used not used
taxize 10.12688/f1000research.2-191.v1 software Chamberlain&Szocs2013F1000Research.pdf taxize paper none
taxize 10.1111/mec.13026 research HodginsEtal2014MolEcol.pdf We used previously published de novo transcriptome assemblies ... for 39 species, which include 35 Asteraceae and four out group taxa ... We classified each of the species as invasive or noninvasive ... using the Encyclopedia of Life invasive species comprehensive list, which was accessed programmatically on August 12, 2014 using the taxize package in R classify species invasive or not
taxize 10.1186/s40709-015-0032-5 research LapatasEtal2015JBiolResearch.pdf just cited, not used not used
taxize 10.1111/2041-210x.12600 software NiedballaEtal2016MEE.pdf Users are free to use any species names (or abbreviations or codes) they wish. If scientific or common species names are used, the function checkSpeciesNames can check them against the ITIS taxonomic database (www.itis.gov) and returns their matching counterparts (utilising the R package taxize (Chamberlain & Szöcs, 2013) internally), making sure species names and spelling are standardised and taxonomically sound, and thus making it easier to combine data sets from different studies software uses taxize to check user names against ITIS
taxize 10.1002/pca.2520 research NingthoujamEtal2014PhytoChemAnal.pdf ... many of these published names included synonyms, informal names and unresolved names, along with orthographic errors. ... the Taxonomic Name Resolution Service of iPlant Collaborative was used for bulk checking of plants names, by cross-checking with records in the Plant Database of the US Department of Agriculture, Missouri Botanical Garden’s Tropicos Database, the Global Compositae Checklist database and the National Center for Biotechnology Information’s Taxonomy database ... In a second step, ‘Taxonstand’ and ‘taxize’ packages ... were used to check plants recorded in The Plant List database and other resources such as Encyclopaedia of Life, Catalogue of Life, IUCN Red List and uBio ... When there was still inconsistency in the authority name ... names recorded in the International Plant Name Index (www.ipni.org) database were manually assessed for correction check names against TPL, EOL, COL, IUCN, uBIO
taxize 10.3897/zookeys.552.6934 research Perez-LuqueEtal2016Zookeys.pdf For field identification, several field guides were used ... The scientific names were checked with database of the IOC World Bird List ... We also used the R package taxize ... to verify the taxonomical classification verify taxonomical classification (birds)
taxize 10.4033/iee.2015.8.8.f research Poisot2015IdeaEcolEvol.pdf just cited, not used not used
taxize 10.1002/ece3.1246 research PosEtal2014EcolEvol.pdf just cited, not used not used
taxize 10.1890/15-1397.1 research BachelotEtal2016EcologicalApplications.pdf For taxonomic assignments, we used Basic Local Alignment Search Tool with the nucleotide database, excluding sequences not associated with known organisms, and the R package ‘taxize’ (Chamberlain and Szocs 2013) to extract the best taxonomy for each OTU from the GenBank taxonomic database (See supporting information for details). [in supplement]: We created a script so that for each OTU, the BLAST search returned the top 10 hit results along with the coverage query, the maximum identity, the E value, and the taxon ID associated with each hit. Using the software R and the package TAXIZE (Chamberlain and Szocs 2013), we created a second algorithm to extract the best taxonomy identity for each OTU from GenBank ... get name data for NCBI sequence data
taxize 10.3897/phytokeys.46.9116 research Perez-LuqueEtal2015Phytokeys.pdf The specimens were taxonomically identified using Flora Iberica ... and others reference floras: Flora de Andalucía Oriental ..., Flora Vascular de Andalucía Oriental ... and Flora Europaea ... The scientific names were checked with databases of International Plant Names Index (IPNI 2013) and Catalogue of Life/Species 2000 ... We also used the R packages taxize ... and Taxostand ... to verify the taxonomical classification verify taxonomical classification (plants)
taxize 10.1111/ecog.01941 research PoisotEtal2015biorxiv.pdf We then used the name-checking functions from the taxize package ... to perform the following steps. First, all names were resolved, and one of the following was applied: valid names were conserved, invalid names with a close replacement were corrected, and invalid names with no replacement were removed. In most situations, invalid names were typos in the spelling of valid ones. After this step, 74 genera with 189 interactions remained, representing a high quality genus-level food-web from the original sampling validate genus names for a food web
taxize 10.5194/bg-13-2537-2016 research WagnerEtal2016Biogeosciences.pdf The names of all recorded species were checked using the Taxonomic Name Resolution Service and corrected as necessary ... Botanical identifications were made at the species level for 11,967 trees, at the genus level for 1,613 trees, family level for 171 trees and unidentified for 730 trees compiled dataset of tropical forest tree species names were checked with TNRS
taxize 10.7717/peerj-cs.56 software Schwery&Omeara2016PeerJCompSci.pdf The package MonoPhy is written in R ... It builds on the existing packages ape, phytools, phangorn, RColorBrewer and taxize ... To avoid having to manually compose a taxonomy file for a taxon-rich phylogeny, MonoPhy can automatically download desired taxonomic levels from ITIS or NCBI using taxize software uses taxize to get taxon classifications
taxize 10.1111/jbi.12894 research Bradie&Leung2016JBiog.pdf Taxonomic information was obtained using the package ‘Taxize’ ... missing data were supplemented using Internet databases (Catalogue of Life, ITIS database) add taxonomic classification data to meta-analysis dataset
taxize 10.1111/nph.14077 research BuffordEtal2016NewPhyt.pdf Database processing and all analyses were performed in R ... We modified code from the packages ‘TAXIZE’ (Chamberlain & Sz€ocs, 2013) and ‘TAXONSTAND’ (Cayuela et al., 2012) to access the global taxonomic databases (accessed June 2015) validate/clean names for plant-fungal association study
rgbif 10.1093/biosci/biw022 research AmanoEtal2016BioScience.pdf We first collected the number of species occurrence records (from any sources) in each country in each year stored in the GBIF, using the occ_search function of the rgbif package occurrence counts globally through time, comparing birds to other taxa
rgbif 10.1111/ele.12170 research BartomeusEtal2013EcoLett.pdf only cited in acknowledegments none
rgbif 10.1016/j.ecoinf.2014.08.008 research Barve2014EcolInformatics.pdf GBIF data for these two species was downloaded using rgbif package (Chamberlain et al., 2014) in R and was used in the map to compare with SNS harvested data records occurrence records for mapping two species
rgbif 10.1111/nph.13572 research BoneEtal2015NewPhyt.pdf The ecological niches of CAM and C3 Eulophiinae taxa were inferred from species occurrence data and corresponding climatic variables. Species occurrences were based on herbarium data and were downloaded from GBIF using rgbif and complemented by data available at RBG Kew. We retrieved 19 climatic variables (BioClim) reflecting temperature and precipitation regimes for all available herbarium specimens occurrence records to construct niche models
rgbif 10.3897/bdj.3.e4162 research CollinsEtal2015BiodDataJournal.pdf ... assess the current knowledge of the catfishes of the Rio Nhamundá we carried out a survey of Web databases as well as published checklists and literature. The Web databases searched were FishBase, Catalog of Fishes, and GBIF. FishBase and GBIF searches were conducted using rfishbase 0.2-2 and rgbif 0.5.0 packages ... a script to repeat these searches is presented in Suppl. material 1 ... We additionally checked records for the neighbouring Trombetas and Uatumã rivers using rfishbase, rgbif, Catalog of Fishes, and Checklist of catfishes collect occurrence records for catfishes in a Brazilian river
rgbif 10.2478/cszma-2013-0004 review Drozd&Sipos2013.pdf just cited, not used not used
rgbif 10.1371/journal.pone.0128295 software KongEtal2015PlosOne.pdf just cited, not used not used
rgbif 10.2989/20702620.2014.999305 research RichardsonEtal2015SouthernForests.pdf (Fig 2 caption): Data were extracted using the function rgbif on 8 July 2014 occurrence records of Acacia species in Australia through time
rgbif 10.1002/ece3.1599 research TurnerEtal2015Ecology&Evolution.pdf we investigated evidence of a climatic niche expansion in the invaded range of C. diffusa. 592 geo-referenced occurrence locations for C.diffusa from North America, Europe, and western Asia were retrieved from GBIF using rgbif ... This was combined with 70 seed collection locations from previous sampling efforts ... For each occurrence record, corresponding climate data were retrieved from the WorldClim database as above assessing niche expansion of invasive plants with occurrence records
rgbif 10.1111/nph.13623 research VerheijenEtal2015NewPhyt.pdf To associate each species to a climate zone, species distributions were determined based on their spatial occurrences in GBIF, extracted with the R-package RGBIF. The climate zone within the K€oppen–Geiger climate classification ... with most occurrences was taken to represent the correct climate zone that a species originated from. For species occurring mostly in deserts, the climate zone with the second most occurrences for this species was taken test of plant functional type classification with occurrences records
rgbif 10.1101/032755 software Zizka&Antonelli2015Biorxiv.pdf just cited, not used not used
rgbif 10.1111/rec.12381 research ButterfieldEtal2016RestEcol.pdf occurrence records for grasses were extracted from within the geographic spaces encompassed by the climate projections. Occurrence records were extracted from GBIF using rgbif ... Records before 1950, outside of the United States, or with georeferencing errors were excluded. Non-native species (those with native ranges outside of the United States) were then removed by filtering through the species list introduced by the U.S. Department of Agriculture use occurrence records with climate data to predict potential restoration plant species
rgbif 10.1111/nph.13694 research DellingerEtal2016NewPhyt.pdf cited, but probably wrong package none
rgbif 10.1590/0102-33062014abb3711 research FeitosaEtal2015ActaBotanicaBrasilica.pdf (looked up records for a small reference in the paper) The pollen from these species often relies on anemophilous transport for dispersal ..., and there are records of Hedyosmum specimens collected near the town of Humaita, close to our study area small note in manuscript about a species being in a study area
rgbif 10.1111/jvs.12301 research MalhadoEtal2015JVegSci.pdf We retrieved 200,080 specimen occurrence records for 211 tree genera distributed throughout the Amazonia from GBIF using the ‘occ_search’ function of ‘rgbif’. It is important to note that GBIF data come from a wide range of forest types, including those degraded by human actions. We identified and excluded duplicate specimens by screening for unique combinations of genera and species name (when provided), and the date of collection. The validity of tree genera names was checked using the Taxonomic Names Resolution Service occurrence records of trees in the Amazon to test predictors of seed size
rfishbase 10.2478/cszma-2013-0004 research Drozd&Sipos2013.pdf just cited, not used not used
rfishbase 10.1016/j.aquaculture.2016.04.018 research FroehlichEtal2016Aquaculture.pdf data were extracted from FishBase, SeaLifeBase, and/or EOL ..., including ... rfishbase ... and Reol ... All values from the database repositories and missing information for each species were checked and filled in through literature searches, ... combinations of the following terms were searched in Google and Google Scholar: 'scientific name', with 'temperature range', 'hypoxia tolerance' or 'oxygen range', 'trophic level', and 'von Bertalanffy growth or size' collect fish life history traits
rfishbase 10.1126/science.aab0800 research McGeeEtal2015Science.pdf We used ... 'rfishbase' to obtain a list of all described species of marine spiny-finned (acanthopterygian) fish. We then used the package to obtain a depth range for marine pharyngognathous fishes ... We also checked whether our comparative diet dataset was biased towards large predatory fishes. We used the ‘getSize’ function in rfishbase to obtain size data (body length) for all available species of marine ray-finned fish living shallower than 510m depth (n=3128) obtain species within a group, and two traits
rfishbase 10.1007/s11538-016-0143-7 research PlankEtal2016BullMathBiol.pdf The FishBase database (www.fishbase.org) is the principal repository for fish data ... Of the 62 identified species studied by Fisher et al. (2005), FishBase provides fecundity estimates for four species: Lutjanus carponotatus (speed 52 cm/s, fecundity 7,074–748,959); Oxymonacanthus longirostris (speed 31.1 cm/s, fecundity 200–300); Dascyllus aruanus (speed 24 cm/s, fecundity 1,500–2,000); Plectropomus leopardus (speed 31.5 cm/s, fecundity 457,900) (fecundity data extracted using rfishbase) extract fecundity data for four fish species
rfishbase 10.1098/rspb.2015.1428 research PriceEtal2015RSPB.pdf Each species in the phylogeny was assigned to a family using the FishBase systematic classification, which was accessed via the RFISHBASE package get family name for each species
rfishbase 10.1111/ecog.01348 research SagouisEtal2015Ecography.pdf Trophic position values were collected for 769 species using FishBase ... and the R package ‘rfishbase’ ... According to their trophic position (TP), species were grouped into three trophic guilds: primary consumers (TP􏰔2.5), secondary consumers (2.5􏰒TP􏰔3.5) and top predators (TP 􏰓 3.5) group species into trophic guilds using trophic position from rfishbase
rfishbase 10.1111/zsc.12098 research BoegerEtal2015ZoolScripta.pdf the percentage of marine species that can withstand reduced salinity (i.e. species that inhabit brackish water environments) among all marine, brackish and anadromous species was considered as a measure of the inherited euryhalinity of each fish family included in the analysis. The number of species associated with each type of environmental salinity was obtained from FishBase, aided by the rfishbase package fetch salinity associated traits for many fish species
rfishbase 10.1111/1365-2656.12471 research MindelEtalJAnimEcol.pdf Fractional size of an individual was calculated as its total length divided by the potential maximum length of that species (Lmax) ... For most species, this value was downloaded from FishBase using the R package rfishbase calculate fish fractional size from max size for many fish species
rfishbase 10.1371/journal.pone.0073535 research MiyaEtal2013PLOSOne.pdf Depth ranges of ingroup species were accessed from FishBase using the package rfishbase ... Mean depths were calculated for those species where minimum and maximum depths were given. We then mapped depth ecology onto our time-scaled phylogenies acquire depth ranges for many species to determine a phylogenetic signal
rfishbase 10.1007/s00338-015-1326-7 research PriceEtal2015CoralReefs.pdf For each family, we calculated the percentage of extant species living on reefs using FishBase, accessed through functions in the R package rfishbase ... This was compared to the extant species richness, which was calculated using the number of species listed in each family in FishBase using rfishbase. Estimates of species richness from FishBase were checked to ensure that they were in general agreement with existing taxonomic estimates percentage of extant species living on reefs
rentrez 10.2478/cszma-2013-0004 research Drozd&Sipos2013.pdf just cited, not used not used
rentrez 10.1890/es14-00402.1 research HamptonEtal2015Ecosphere.pdf Increasing usage of the term 'open science' in the literature since 1995 in Web of Science and PubMed databases. Data from PubMed were downloaded via the rentrez (Winter and Chamberlain 2014) package in R, and Web of Science data were collected from manual searches search PubMed for mentions of phrases
rentrez 10.1371/journal.pcbi.1003472 research NguyenEtal2014PlosCompBiol.pdf We searched the key word '(myocardial infarction) AND (plasma OR serum)'' on PubMed with 'Homo Sapiens' as species from Jan 1, 2005 until May 31, 2013. This search resulted in 4326 abstracts. To reduce laborious manual effort, we developed a data mining program written in R using available XML parser and text mining software [58,59] search PubMed for mentions of phrases
rentrez NA research LeeEtal2017Code4Lib.pdf By using the ‘rentrez’ package for R and the NLM’s National Center for Biotechnology Information (NCBI) Entrez Utilities API, we are able to efficiently and accurately select repository records for inclusion in the NCBI LinkOut for IRs program demonstrate how to use rentrez to search NCBI API for articles in institutional repositories
rentrez 10.7287/peerj.preprints.3179v1 software Winter2017PeerJPreprint.pdf rentrez paper none
rentrez 10.1093/nar/gkx1321 research KrawczykEtal2018NucleicAcidsResearch.pdf Taxonomic information was obtained using the rentrez ... package in R based on 'taxon_id' provided in the assembly summary.txt file from the NCBI FTP, and were further manually curated to filter out eukaryotic, archaeal and viral sequences fetch NCBI taxonomic information for sequence data
rentrez 10.1101/260398 research Claypool&Patel2018Biorxiv.pdf We cataloged and downloaded gene expression and sample data from twenty (20) publicly available microarray studies that assayed gene expression before and after physical activity in healthy participants. We selected these studies from Gene Expression Omnibus (GEO) ... We used the rentrez R package [46] to execute the query on GEO use NCBI's Gene Expression Omnibus service
rentrez 10.1093/database/bay004 research ChenEtal2018Database.pdf For citation network data, we extracted the information from PubMed by CRAN R package rentrez extract citations (presumably from PubMed) using rentrez
rotl 10.1111/2041-210x.12593 software MichonneauEtal2016MEE.pdf rotl paper none
rotl 10.1111/jfb.13195 research KillenEtal2016JFishBiol.pdf Data were also analysed with phylogenetic information, but with fewer species (i.e. only those for which phylogenetic information is available). For the latter, the phylogenetic generalized least squares (PGLS) method ... was employed via the ape package ... applying a phylogeny ... from the comprehensive tree of life ... using rotl ... which was then manually augmented. The branch lengths were estimated using Grafen’s branch-length transformation extract a phylogeny
rotl 10.1038/sdata.2016.56 research Estrada-Pena&Fuente2016ScientificData.pdf The set of scientific names was updated using the services of the Open Tree of Life (OTL) using the package rOTL [35] for R to obtain the accepted official name of the vertebrate. Hosts reported by their common (not Latin) names were checked against the GBIF database. The Sixty-one percent of common names reported in the literature search were unambiguously resolved to their scientific names validate scientific names
rotl 10.1111/jav.01580 research MatthewsEtal2017JAvianBiology.pdf We obtained genetic data for host species from the Open Tree of Life ... as deposited by Lovette et al. (2010) using package rotl ver. 3.0.1 extract genetic data (maybe they mean phylogenetic data?)
rotl 10.1038/s41598-018-20596-7 research SantorelliEtal2018ScientificReports.pdf The hypothesis of the existence of endemism areas based on large rivers was tested for 717 species ... To indicate if the river worked as a vicariance barrier independent of the taxonomic or functional group, we constructed a phylogenetic hypothesis separately for each group. For small, large and non-flying mammals (72 spp), snakes (66 spp), lizards (35 spp) and frogs (98 spp), the phylogenetic relationships were obtained with the R package “rotl” [68], and for birds (446 spp) the information was obtained through the website birdtree.org [69–71] extract phylogenies
rotl 10.1038/s41467-018-03500-9 research FarquharsonEtal2018NatureCommunications.pdf Fig 1 caption) Phylogenetic tree of 44 species included in the meta-analyses. The tree was created using the ‘rotl’ package; To account for the non-independence of effect sizes as a result of the shared evolutionary history of closely related species, we obtained the phylogenetic correlation between the species in our meta-analysis using the ‘rotl’ package [59], ... Taxon names were matched to records in the Open Tree Taxonomy, to obtain relationships between species. ... Due to the diverse species in our meta-analysis, accurately estimating branch lengths was not plausible, so we computed branch lengths based on topology using the ‘ape’ package extract phylogenies and validate scientific names
rotl 10.1111/2041-210x.13013 research Portugal2018MEE.pdf Species names were checked against the taxonomic reference associated with the online tree of life ... using the rotl package validate scientific names
rotl 10.1126/science.aao6868 research BarnecheEtal2018Science.pdf Phylogenetic relatedness might influence broad-scale variation in life-history traits (28) ... we created a tree from the Open Tree of Life (OTL) using the rotl R package (31) v. 3.0.3 in order to test for significant phylogenetic heritability in our models (32). We first downloaded the full Actinopterygii tree from OTL (n = 38,941 tips) and then added species from our dataset that were missing in the tree extract phylogenies
rotl 10.1111/faf.12297 research Morais&Bellwood2018Fish&Fisheries.pdf Our dataset included species with varying degrees of shared ancestry, as well as multiple observations from the same species. These features result in nonindependence among observations and require a phylogenetic correlation structure to be specified ... To do this, we first created a supertree by combining phylogenies that included our species using the “rotl” package extract phylogenies
rotl 10.1016/j.ecoinf.2018.06.008 software GastauerEtal2018EcologicalInformatics.pdf Beginning with a user-specific list of plant names in the phylomatic format (i.e., family/genus/genus_epithet), the function ComTreeOpt correlates the species names to the Open Tree Reference Taxonomy (OTT Taxonomy) using tnrs_match_names from rotl ... subtrees are built using tol_induced_subtree from the rotl package software uses rotl to get scientific names and get phylogenetic trees
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