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18,745 results for “Taxonomy”
Taxonomy and Biogeography of Nematode Communities at Harvard Forest 2014
Our overall goal is to describe and map nematode biodiversity in North America. Specifically we will concentrate on Criconematina, a suborder of plant parasitic, soil-dwelling nematodes. Criconematina, commonly referred to as ring-nematodes, are distributed globally associated with a wide range of hosts and habitats. In native grasslands and forests, they may constitute as much as 30% of the below-ground nematode community. Their abundance often approaches 500 individuals per 100cc of soil with as many as a dozen species recorded from a single habitat. Host associations may be broad, covering entire plant families, or they may specialize in feeding on a few closely related plant species. Several are known agronomic pest species, but the vast majority is known only from native habitats and responds negatively to soil disturbance. Due to their sensitivity to disturbance and associations with a range of plant species, some ecologists have suggested that ring nematodes could serve as a below-ground biological indicator of habitat quality. Before this application is possible taxonomic boundaries need to be evaluated and a reference database needs to be established.
Plum Island LTER phytoplankton identification using HPLC and Chem Taxonomy along transects in the Plum Island Sound estuary, Massachusetts. (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-pie/404/4. The abstract below was extracted from the Level 0 data package and is included for context: Water column samples are collected along an estuarine salinity gradient as part of our monitoring surveys of the Parker River estuary each spring and late summer (typically high vs low freshwater input). Samples are filtered, and stored frozen for later pigment analyses by HPLC. Pigment data are then analyzed by CHEMTAX, calibrated to a matrix of pigment ratios based on taxonomy and enumeration of selected subsamples by microspcopy. Data are presented in terms of chlorophyll a concentrations partitionaed among the major phytoplankton groups as determined by CHEMTAX. For 2003-2006, sampling stations along the Plum Island Sound-Parker River were at fixed geographic locations at specific "Bends" in the river. In 2008, we began sampling the water column in salinity space rather than at specific geographic locations along the river. This sampling approach was adopted in order to follow particular water masses in this macrotidal estuary. In practical terms, it means that sampling locations, or stations, are not static. Therefore, we have mapped the 11 sampling locations (latitude and longitude are logged at each station) from each transect along the mainstem of the estuary, so each station may be placed along the river (to the nearest 0.5km) as well as in salinity space. We have also used the km marker to assign the sampling locations from each survey to one of four bounding boxes : the Sound (Plum Island Sound; EST-PR-SoundBND) which encompasses approximatly the first 9.5 km or the transect, with Okm at the mouth of the sound; the Lower Parker River (EST-PR-LowerParkerBND) , ~9.5 - 1
Plum Island LTER phytoplankton identification using HPLC and Chem Taxonomy along transects in the Plum Island Sound estuary, Massachusetts. (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/338/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-pie/404/4. The abstract below was extracted from the Level 0 data package and is included for context: Water column samples are collected along an estuarine salinity gradient as part of our monitoring surveys of the Parker River estuary each spring and late summer (typically high vs low freshwater input). Samples are filtered, and stored frozen for later pigment analyses by HPLC. Pigment data are then analyzed by CHEMTAX, calibrated to a matrix of pigment ratios based on taxonomy and enumeration of selected subsamples by microspcopy. Data are presented in terms of chlorophyll a concentrations partitionaed among the major phytoplankton groups as determined by CHEMTAX. For 2003-2006, sampling stations along the Plum Island Sound-Parker River were at fixed geographic locations at specific "Bends" in the river. In 2008, we began sampling the water column in salinity space rather than at specific geographic locations along the river. This sampling approach was adopted in order to follow particular water masses in this macrotidal estuary. In practical terms, it means that sampling locations, or stations, are not static. Therefore, we have mapped the 11 sampling locations (latitude and longitude are logged at each station) from each transect along the mainstem of the estuary, so each station may be placed along the river (to the nearest 0.5km) as well as in salinity space. We have also used the km marker to assign the sampling locations from each survey to one of four bounding boxes : the Sound (Plum Island Sound; EST-PR-SoundBND) which encompasses approximatly the first 9
Dataset for 'Measuring and explaining disagreement about bird taxonomy'
<p>Dataset used for a research project that measures, classifies and explains disagreement about bird taxonomy. This is version 3, which has new data on research effort for each of the birdlife concepts, and no longer contains data about ecological and geographical predictors. This version of the data is used in the submission to EJT in november 2023.</p>
Density independent prey choice, taxonomy, life history and web characteristics determine the diet and biocontrol potential of spiders (Linyphiidae and Lycosidae) in cereal crops - Dataset
<p>Materials and Methods</p> <p>Fieldwork</p> <p>Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were the two most common families present in these field surveys, so were prioritised for collection. Spiders were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from occupied webs and the ground, between April and September 2018. Surveys and sampling were conducted five days per week across this period. Each transect was adjacent to a randomly selected tramline and they were distributed across the entire field. The areas searched were 4 m<sup>2</sup> quadrats at least 10 m apart and all observed linyphiids and lycosids were collected in approximately 15-minute searches. The spiders included in this study were taken from 64 locations across 24 days (Supplementary Table 3) along the aforementioned transects. Spiders were individually placed into 1.5 ml microcentrifuge tubes containing 100 % ethanol using an aspirator, regularly changing meshing, at least every five spiders, to limit potential cross-contamination between spiders (spiders were also subsequently washed during transferral to fresh ethanol at the identification and, separately, dissection stages). Linyphiids occupying webs were prioritised for collection, but ground-active linyphiid spiders were also collected. For each spider taken from a web, the height of the web from the ground and its approximate dimensions were recorded, the latter calculated as approximate web area. Spiders were taken to Cardiff University, transferred to fresh ethanol, adults identified to species-level and juveniles to genus, and stored at -80 °C in 100 % ethanol until subsequent DNA extraction. To obtain data on local prey density, 4 m<sup>2</sup> of ground and crop stems were suction sampled using a ‘G-vac’ for 30 seconds at each quadrat from which spiders were collected, with the collected material emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab.</p> <p>All invertebrates were identified to family level due to the restriction of many of the metabarcoding-derived dietary data to this level, and the difficulty associated with finer taxonomic resolution of many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae, which were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level and wasps of the superfamily Ichneumonoidea (which were identified no further due to obscurity of wing venation due to damage).</p> <p> </p> <p>Extraction and high-throughput sequencing of spider gut DNA</p> <p>Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera <em>Erigone</em>, <em>Tenuiphantes</em>, <em>Bathyphantes</em> and <em>Microlinyphia </em>(Araneae: Linyphiidae), and the Lycosidae genus <em>Pardosa</em>. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key <sup>1</sup>. Abdomens were removed from spiders and again washed in and transferred to fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens <sup>2</sup>. At least one extraction negative (blank tubes treated identically to samples) was included per 12 spiders (each extraction typically contained 24 spiders, thus two extraction negatives), which was included in subsequent PCR and high-throughput sequencing to detect instances of lab/reagent contamination.</p> <p>For amplification of DNA, two primer pairs were used. BerenF-LuthienR <sup>3</sup> amplified a broad range of invertebrates including spiders, and TelperionF-LaureR, amplified a range of invertebrates but fewer spiders (modified from TelperionF-LaurelinR <sup>3</sup> via one base-pair change from Laurelin; 5’-ggrtawacwgttcawccagt-3’). Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively.</p> <p>Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet <em>et al. </em>(2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions (Supplementary Table 1) and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads). Bioinformatic analysis followed (Drake et al., 2021; Supplementary Information 1).</p> <p> </p> <p>Statistical analysis</p> <p>All analyses were conducted in R v4.0.0 <sup>6</sup>. Initial multivariate analyses used binary data (i.e., presence/absence) given the various problems inherent to quantifying metabarcoding data <sup>7,8</sup>. Prey species that occurred only once across all of the dietary samples were removed before further analyses to prevent outliers skewing the results, which is particularly problematic for non-metric multidimensional scaling. Spider diets were compared between variables using multivariate generalized linear models (MGLMs) via ‘manyglm’ in the ‘mvabund’ package <sup>9</sup> with a binomial error family and Monte Carlo resampling. Model independent variables included spider genus, spider life stage (juvenile or adult, the latter defined by fully developed genitalia), spider sex and all two-way interactions between these variables. Pairwise two-way interactions were also included between the aforementioned variables and Julian day to account for how seasonality may affect these relationships.</p> <p>Coarse dietary differences were visualised by non-metric multidimensional scaling (NMDS) via metaMDS in the ‘vegan’ package <sup>10</sup> with Jaccard distance in two dimensions and 999 tries. For NMDS, outliers (usually samples containing rare taxa) were identified by plotting and subsequently removed to facilitate separation of samples and achieve minimum stress. For visualisation of the effect of categorical variables against the dietary NMDS, spider plots were created using ‘ordispider’ with ‘ggplot’ and the ‘RColorBrewer’ ‘Accent’ colour palette <sup>11</sup>. Spider diet was compared against web characteristics for spiders for which both data were available using the MGLM process outlined above, but with starting models containing web height, web area, an interaction between the two, and pairwise interactions between genus, life stage and sex with the two web variables. This model used the same binomial error family as above, but with a ‘cloglog’ link function. For visualisation of the effect of continuous variables against the NMDS, surf plots were created with scaled coloured contours using the function “ordisurf” of the “ggplot” package in R.</p> <p>All prey taxa were classified as agricultural pests, natural enemies or excluded from subsequent analyses of intraguild predation and biocontrol (Supplementary Table 2). Intraguild predation and biocontrol variables were created by counting the number of natural enemy taxa, and, separately, of agriculturally relevant “pest” taxa (taxa containing species that commonly adversely affect agricultural productivity; Supplementary Table 2) in each spider’s diet. These resultant count data (effectively the diversity of pests and natural enemies predated by each individual spider) were separately analysed against spider genus, life stage and sex via GLM. “Site” (denoting the 4 m<sup>2</sup> area from which spiders were collected within fields) was initially included as a random effect in generalized linear mixed-models, but no significant effect was observed when comparing this model against a standard GLM via a likelihood ratio test of nested models using the ‘lrtest’ command in the ‘lmtest’ package <sup>12</sup>. Standard GLMs were thus used to avoid issues relating to singularity in the mixed models. The assumptions for the resultant Poisson error family GLMs were tested using the “testResiduals” function of the ‘DHARMa’ package <sup>13</sup>. Intraguild predation and biocontrol differences between significant terms were visualised using violin plots with the quartiles, median and 95 % upper limit annotated using the ‘geom_violin’ function in ‘ggplot2’.</p> <p><em>In situ</em> spider prey choice was analysed using network-based null models in the ‘econullnetr’ package <sup>14</sup> with the ‘generate_null_net’ command, visually represented with the ‘plot_preferences’ command. Binary dietary data were used alongside suction sample count data to represent prey availability. These suction sample data, as described above, were collected at the same sites as the spiders three days after spider collection. Prior to the taxonomic prey choice analysis, an hemipteran identified no further than order level through dietary analysis was removed due to the inability to pair it to any present prey taxa with certainty. Standardised effect sizes (SES) were extracted for all comparisons for each individual spider and compared between genera, life stages and sexes using permutational multivariate analysis of variance (PerMANOVA) using the ‘adonis’ function of the ’vegan’ package with 9999 permutations and a Euclidean distance matrix to determine overall differences in prey choice.</p> <p> </p> <p>References</p> <p>1. Roberts, M. J. <em>The Spiders of Great Britain and Ireland (Compact Edition)</em>. (Harley Books, 1993).</p> <p>2. Krehenwinkel, H., Kennedy, S., Pekár, S. & Gillespie, R. G. A cost-efficient and simple protocol to enrich prey DNA from extractions of predatory arthropods for large-scale gut content analysis by Illumina sequencing. <em>Methods Ecol. Evol.</em> <strong>8</strong>, 126–134 (2017).</p> <p>3. Cuff, J. P. <em>et al.</em> Money spider dietary choice in pre- and post-harvest cereal crops using metabarcoding. <em>Ecol. Entomol.</em> <strong>46</strong>, 249–261 (2021).</p> <p>4. Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. <em>Environmental DNA</em>. (Oxford University Press, 2018).</p> <p>5. Drake, L. E. <em>et al.</em> An assessment of minimum sequence copy thresholds for identifying and reducing the prevalence of artefacts in dietary metabarcoding data. <em>Methods Ecol. Evol.</em> <strong>in press</strong>, (2021).</p> <p>6. R Core Team. R: A language and environment for statistical computing. (2020).</p> <p>7. Deagle, B. E., Thomas, A. C., Shaffer, A. K. & Trites, A. W. Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count? <em>Mol. Ecol. Resour.</em> <strong>13</strong>, 620–633 (2013).</p> <p>8. Deagle, B. E. <em>et al.</em> Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? <em>Mol. Ecol.</em> <strong>28</strong>, 391–406 (2019).</p> <p>9. Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund – an R package for model-based analysis of multivariate abundance data. <em>Methods Ecol. Evol.</em> <strong>3</strong>, 471–474 (2012).</p> <p>10. Oksanen, J. <em>et al.</em> vegan: Community Ecology Package. (2016).</p> <p>11. Neuwirth, E. RColorBrewer: ColorBrewer palettes. (2014).</p> <p>12. Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. <em>R News</em> <strong>2</strong>, 7–10 (2002).</p> <p>13. Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. (2020).</p> <p>14. Vaughan, I. P. <em>et al.</em> econullnetr: an r package using null models to analyse the structure of ecological networks and identify resource selection. <em>Methods Ecol. Evol.</em> <strong>9</strong>, 728–733 (2018).</p>
CORINE land cover - Catalan land cover taxonomy dictionary for metropolitan Barcelona
<p>This dataset includes Catalan Land Cover categories that are relevant for metropolitan Barcelona and their translation to CORINE land cover categories.</p> <p>This work has been developed for the ERC project <a href="https://urbag.eu/">URBAG</a></p> <p>Original Catalan land cover map: <a href="https://www.creaf.uab.es/mcsc/">https://www.creaf.uab.es/mcsc/ </a></p> <p>CORINE land cover: <a href="https://land.copernicus.eu/pan-european/corine-land-cover">https://land.copernicus.eu/pan-european/corine-land-cover</a></p>
A Taxonomy of Tools and Approaches for FAIRification
<p>Datasets accompanying the study “A Taxonomy of Tools and Approaches for FAIRification” on the tools and approaches emerging from stakeholders’ experiences adopting the FAIR principles in practice.</p> <p> </p> <p>Datasets:</p> <ol> <li> <p>queryResults.csv</p> <ul> <li> <p>Description</p> <ul> <li> <p>The dataset consists of the query results returned by OpenAIRE Explore and defines the corpus at the base of our study.</p> </li> </ul> </li> <li> <p>Structure</p> <ul> <li> <p>11 columns:</p> <ol> <li> <p>Query</p> <ul> <li> <p>Type of query entered</p> <ul> <li> <p>FAIR, FAIRification (all fields)</p> </li> <li> <p>OpenAIRE subjects (subject)</p> </li> </ul> </li> </ul> </li> <li> <p>Result Type [OpenAIRE label]</p> <ul> <li> <p>Type of the research output (publication|data|software|other)</p> </li> </ul> </li> <li> <p>Title [OpenAIRE label]</p> </li> <li> <p>Authors [OpenAIRE label]</p> </li> <li> <p>Publication Year [OpenAIRE label]</p> </li> <li> <p>DOI [OpenAIRE label]</p> </li> <li> <p>Download from [OpenAIRE label]</p> </li> <li> <p>Type [OpenAIRE label]</p> <ul> <li> <p>Subtype of the research output</p> </li> </ul> </li> <li> <p>Journal [OpenAIRE label]</p> </li> <li> <p>Funder|Project Name (GA Number) [OpenAIRE label]</p> </li> <li> <p>Access [OpenAIRE label]</p> <ul> <li> <p>Access rights</p> </li> </ul> </li> </ol> </li> </ul> </li> </ul> </li> <li> <p>publicationsTools.csv</p> <ul> <li> <p>Description</p> <ul> <li> <p>The dataset pairs the tools/services extracted from the corpus to their respective source.</p> </li> </ul> </li> <li> <p>Structure</p> <ul> <li> <p>2 columns:</p> <ol> <li> <p>source</p> <ul> <li> <p>reference to the publication or software citation</p> </li> </ul> </li> <li> <p>name</p> <ul> <li> <p>name of the tool/service/technology</p> </li> </ul> </li> </ol> </li> </ul> </li> </ul> </li> <li> <p>toolsAll.csv</p> <ul> <li> <p>Description</p> <ul> <li> <p>The dataset lists all the unique tool/service entries, distinguishing between those that were considered relevant for the study (further categorised into tools, technologies or services) and those that were excluded.</p> </li> </ul> </li> <li> <p>Structure</p> <ul> <li> <p>3 columns:</p> <ol> <li> <p>entryType</p> <ul> <li> <p>entry categorisation (tool|service|technology|excluded)</p> </li> </ul> </li> <li> <p>name</p> <ul> <li> <p>name of the tool/service/technology</p> </li> </ul> </li> <li> <p>URL</p> <ul> <li> <p>URL of the tool/service web page or description</p> </li> </ul> </li> </ol> </li> </ul> </li> </ul> </li> <li> <p>toolsType.csv</p> <ul> <li> <p>Description</p> <ul> <li> <p>Classification of the tools/services/technologies into the study-defined classes.</p> </li> </ul> </li> <li> <p>Structure</p> <ul> <li> <p>19 columns:</p> <ol> <li> <p>name</p> <ul> <li> <p>name of the tool/service/technology</p> </li> </ul> </li> <li> <p>URL</p> <ul> <li> <p>URL of the tool/service web page or description</p> </li> </ul> </li> <li> <p>GUPRI helper - GUPRI creation and management service</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>GUPRI helper - GUPRI Indexing and discovery service</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Metadata helper - Metadata editor</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Metadata helper - Metadata extractor</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Metadata helper - Metadata tracker</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Metadata helper - Metadata validator</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Metadata helper - Metadata assistant</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Indexing and discovery service - registry</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Indexing and discovery service - repository</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Indexing and discovery service - Indexing and discovery service finder</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Converter - metadata</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Converter - data</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Licence helper</p> <ul> <li> <p>‘class’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Assessment tool - automated</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Assessment tool - manual</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>Assessment tool - Assessment tool finder</p> <ul> <li> <p>‘class - subclass’ of the tool/service/technology</p> </li> </ul> </li> <li> <p>DMP tool</p> <ul> <li> <p>‘class’ of the tool/service/technology</p> </li> </ul> </li> </ol> </li> </ul> </li> </ul> </li> <li> <p>toolsFAIR.csv</p> <ul> <li> <p>Description</p> <ul> <li> <p>The dataset relates the tool/service/technology to the FAIR principles it enables.</p> </li> </ul> </li> <li> <p>Structure</p> <ul> <li> <p>12 columns:</p> <ol> <li> <p>name</p> <ul> <li> <p>name of the tool/service/technology</p> </li> </ul> </li> <li> <p>URL</p> <ul> <li> <p>URL of the tool/service web page or description</p> </li> </ul> </li> <li> <p>F1</p> <ul> <li> <p>reference to the FAIR principle</p> </li> </ul> </li> <li> <p>F2</p> <ul> <li> <p>reference to the FAIR principle</p> </li> </ul> </li> <li> <p>F3</p> <ul> <li> <p>reference to the FAIR principle</p> </li> </ul> </li> <li> <p>F4</p> <ul> <li> <p>reference to the FAIR principle</p> </li> </ul> </li> <li> <p>A</p> <ul> <li> <p>generic reference to the accessibility principles (see the paper)</p> </li> </ul> </li> <li> <p>I1</p> <ul> <li> <p>reference to the FAIR principle</p> </li> </ul> </li> <li> <p>I3</p> <ul> <li> <p>reference to the FAIR principle</p> </li> </ul> </li> <li> <p>R1.1</p> <ul> <li> <p>reference to the FAIR principle</p> </li> </ul> </li> <li> <p>R1.2</p> <ul> <li> <p>reference to the FAIR principle</p> </li> </ul> </li> <li> <p>R1.3</p> <ul> <li> <p>reference to the FAIR principle</p> </li> </ul> </li> </ol> </li> </ul> </li> </ul> </li> <li> <p>toolsScope.csv</p> <ul> <li> <p>Description</p> <ul> <li> <p>Since the FAIR principles have been specified for different types of resources ((meta)data, semantic artefacts, software and workflows), the dataset correlates the tool/service/technology and the types of FAIR-specific resources it covers.</p> </li> </ul> </li> <li> <p>Structure</p> <ul> <li> <p>6 columns:</p> <ol> <li> <p>name</p> <ul> <li> <p>name of the tool/service/technology</p> </li> </ul> </li> <li> <p>URL</p> <ul> <li> <p>URL of the tool/service web page or description</p> </li> </ul> </li> <li> <p>(meta)data</p> <ul> <li> <p>reference to the FAIR-specific resource</p> </li> </ul> </li> <li> <p>semantic artefact</p> <ul> <li> <p>reference to the FAIR-specific resource</p> </li> </ul> </li> <li> <p>software</p> <ul> <li> <p>reference to the FAIR-specific resource</p> </li> </ul> </li> <li> <p>workflow</p> <ul> <li> <p>reference to the FAIR-specific resource</p> </li> </ul> </li> </ol> </li> </ul> </li> </ul> </li> <li> <p>toolsDomain.csv</p> <ul> <li> <p>Description</p> <ul> <li> <p>Classification of the tools/services/technologies into the Frascati framework-defined domains.</p> </li> </ul> </li> <li> <p>Structure</p> <ul> <li> <p>9 columns:</p> <ol> <li> <p>name</p> <ul> <li> <p>name of the tool/service/technology</p> </li> </ul> </li> <li> <p>URL</p> <ul> <li> <p>URL of the tool/service web page or description</p> </li> </ul> </li> <li> <p>cross-domain</p> <ul> <li> <p>domain</p> </li> </ul> </li> <li> <p>Agricultural and veterinary sciences</p> <ul> <li> <p>domain</p> </li> </ul> </li> <li> <p>Engineering and technology</p> <ul> <li> <p>domain</p> </li> </ul> </li> <li> <p>Humanities and the arts</p> <ul> <li> <p>domain</p> </li> </ul> </li> <li> <p>Medical and health sciences</p> <ul> <li> <p>domain</p> </li> </ul> </li> <li> <p>Natural sciences</p> <ul> <li> <p>domain</p> </li> </ul> </li> <li> <p>Social sciences</p> <ul> <li> <p>domain</p> </li> </ul> </li> </ol> </li> </ul> </li> </ul> </li> </ol>
Density-dependent effects of exotic brook trout on aquatic communities in mountain lakes revealed by environmental DNA and morphological taxonomy
Invasion of non-native fishes threatens freshwater biodiversity worldwide. Yet, detailed estimates of population demography for invasive species, that estimate population size and body size of the invasive species, are rarely integrated in evaluating aquatic community responses. Our study capitalized on detailed brook trout population demographic data collected for a replicated whole lake ecosystem experiment involving experimental harvesting of exotic brook trout in nine mountain lakes. We applied environmental DNA (eDNA) metabarcoding and morphological taxonomy to examine the response of crustacean zooplankton and macroinvertebrate communities to gradients in brook trout effective density and lake elevation. Density-dependent effects of brook trout on crustacean zooplankton and macroinvertebrate communities were detected even decades after their first introductions (between 1926 and 1980). However, they were moderated by environmental factors such as elevation, lake maximum depth and dissolved organic carbon. Elevation was important in structuring crustacean zooplankton and macroinvertebrate community composition. While there were differences in explanatory variables when describing communities characterized by eDNA metabarcoding and morphological taxonomy, the principal environmental factors that structured the communities were similar. Our paper highlights persisting density-dependent impacts of exotic trout on invertebrate communities even decades after first introduction, and it considers the conservation implications for lake restoration.
Plum Island LTER phytoplankton identification using HPLC and Chem Taxonomy along transects in the Plum Island Sound estuary, Massachusetts.
Water column samples are collected along an estuarine salinity gradient as part of our monitoring surveys of the Parker River estuary each spring and late summer (typically high vs low freshwater input). Samples are filtered, and stored frozen for later pigment analyses by HPLC. Pigment data are then analyzed by CHEMTAX, calibrated to a matrix of pigment ratios based on taxonomy and enumeration of selected subsamples by microspcopy. Data are presented in terms of chlorophyll a concentrations partitionaed among the major phytoplankton groups as determined by CHEMTAX. For 2003-2006, sampling stations along the Plum Island Sound-Parker River were at fixed geographic locations at specific "Bends" in the river. In 2008, we began sampling the water column in salinity space rather than at specific geographic locations along the river. This sampling approach was adopted in order to follow particular water masses in this macrotidal estuary. In practical terms, it means that sampling locations, or stations, are not static. Therefore, we have mapped the 11 sampling locations (latitude and longitude are logged at each station) from each transect along the mainstem of the estuary, so each station may be placed along the river (to the nearest 0.5km) as well as in salinity space. We have also used the km marker to assign the sampling locations from each survey to one of four bounding boxes : the Sound (Plum Island Sound; EST-PR-SoundBND) which encompasses approximatly the first 9.5 km or the transect, with Okm at the mouth of the sound; the Lower Parker River (EST-PR-LowerParkerBND) , ~9.5 - 14.5 km; the Middle Parker River (EST-PRMiddleParkerBND), ~14.5 - 18.75 km, and the Upper Parker River (EST-PR-UpperParker BND)., ~18.75 to 24.25 km (the Parker R. Dam).
InChI OER Taxonomy Structure
<p>This diagram shows both the hierarchy and flow of the filters for using the Open Education Resource (OER) on the InChI-Trust Website to find materials.</p>
RDP Classifier 2.14 and the RDP bacterial and archaeal taxonomy training set No. 19
<p>RDP Classifier 2.14 (August 2023) Release Note:</p><p>The Bacteria and Archaea hierarchy model used by RDP Classifier has been updated to training set No. 19. The new version has over 600 genera and 2500 species added since last version No. 18 released in July 2020. The information that is used to update the RDP taxonomy to training set version No. 19, and RDP Classifier version 2.14 came from publicly available scientific articles and public sequence repository, mostly from International Journal of Systematic and Evolutionary Microbiology (IJSEM), the All-Species Living Tree Project (LTP) and GenBank. </p><p>It is worth noting that most of the phyla have new names, according to article "</p><p>Oren A, Garrity GM. Valid publication of the names of forty-two phyla of prokaryotes. Int J Syst Evol Microbiol. 2021 Oct;71(10). doi: 10.1099/ijsem.0.005056. PMID: 34694987."</p><p>In addition to the files to train and run the RDP Classifier, new file formats are made available to accommodate the needs of users:</p><p>1. A new file trainset19_072023_speciesrank.fa has been added to the release in RDPClassifier_16S_trainsetNo19_rawtrainingdata.zip. This file is NOT needed to train the classifier. In addition to sequences, it contains genus, species, strain, type status and taxonomy rank, which are useful for closest species identification using third-party tools (e.g. BLAST).</p><p>2. Two new files in RDPClassifier_16S_trainsetNo19_QiimeFormat.zip to retrain the RDP Classifier included in Qiime2 package.</p>
A taxonomy to map evidence on the co-benefits, challenges, and limits of carbon dioxide removal
<p>This repository is linked to the following article:</p> <ul> <li>Prütz, R., Fuss, S., Lück, S. Stephan, L. & Rogelj, J., A taxonomy to map evidence on the co-benefits, challenges, and limits of carbon dioxide removal. <em>Commun Earth Environ</em> <strong>5</strong>, 197 (2024). <a href="https://doi.org/10.1038/s43247-024-01365-z">https://doi.org/10.1038/s43247-024-01365-z</a></li> </ul> <p>This repository includes: </p> <ul> <li>The literature-based data set that was compiled and used to develop the taxonomy of carbon dioxide removal side effects</li> <li>Code to run the machine learning classifier and to process and visualise the data</li> <li>Training data and an abbreviation list which is required to run the provided code</li> </ul>
Micro and Macro Open Science Perspective Taxonomy
<h1>Micro and Macro Open Science (OS) Perspective Taxonomy</h1> <h2>Micro OS Perspective</h2> <p>Taxonomy related to terminologies and knowledge around the practice (workflow) of the OS knowledge generator (e.g., researcher), based on reading by <a title="Taxonomia da Ciência Aberta: revisada e ampliada" href="https://doi.org/10.5007/1518-2924.2023.e91712" target="_blank" rel="noopener">Silveira et al. (2023)</a> and <a title="UNESCO Recommendation on Open Science" href="https://unesdoc.unesco.org/ark:/48223/pf0000379949" target="_blank" rel="noopener">UNESCO (2021)</a>.</p> <h2>Macro OS Perspective</h2> <p>Taxonomy related to the conceptual ramifications of OS concerning (public) policies, infrastructure, open involvement of social actors (society) and open dialogue with other knowledge systems, based on reading by <a title="Taxonomia da Ciência Aberta: revisada e ampliada" href="https://doi.org/10.5007/1518-2924.2023.e91712" target="_blank" rel="noopener">Silveira et al. (2023)</a> and <a title="UNESCO Recommendation on Open Science" href="https://unesdoc.unesco.org/ark:/48223/pf0000379949" target="_blank" rel="noopener">UNESCO (2021)</a>.</p> <h3>Instructions:</h3> <ul> <li>There is nothing new in this repository. This is just the taxonomy revised and expanded by <a title="Taxonomia da Ciência Aberta: revisada e ampliada" href="https://doi.org/10.5007/1518-2924.2023.e91712" target="_blank" rel="noopener">Silveira et al. (2023)</a> segregated into two perspectives: Micro and Macro;</li> <li>The reason for this segregation was to document the terminologies and knowledge surrounding the practice (workflow) of OS, which from an individual (micro) point of view, is most interest to the researcher;</li> <li>These two concepts are presented in the form of a mindmap in English and Portuguese (four .png files);</li> <li>If the user wishes to develop another mindmap, with another theme and/or colors, or another flowchart, there are also eight *.txt files with the markdown and memaid hierarchies.</li> </ul>
EU Taxonomy on Sustainable Activities (Tidy)
<p>In order to meet the EU’s climate and energy targets for 2030 and reach the objectives of the European green deal, it is vital that we direct investments towards sustainable projects and activities. To achieve this, a common language and a clear definition of what is ‘sustainable’ is needed. This is why the action plan on financing sustainable growth called for the creation of a common classification system for sustainable economic activities, or an EU taxonomy.</p> <p>The EU taxonomy is a classification system, establishing a list of environmentally sustainable economic activities in the areas of Climate mitigation, Climate adaptation, Biodiversity, Circular economy, Water, Pollution prevention. It could play an important role helping the EU scale up sustainable investment and implement the European green deal. The EU taxonomy would provide companies, investors and policymakers with appropriate definitions for which economic activities can be considered environmentally sustainable. It was defined by the Regulation (EU) 2020/852</p> <p>The European Commmission created an EU Taxonomy Compass provides a visual representation of the contents of the EU Taxonomy, starting with the Delegated Act on the climate objectives, as adopted on 4 June 2021. Whilst you can download the EU Taxonomy in xlsx or json format, they are not tidy datasets, and they are not particularly well-suited for calculations or filtering.</p> <p>Reprex created a tidy version of the EU Taxonomy for developing better sustainability indicators into the Green Deal Data Observatory. This tidy version has subjective weights given to broader NACE sections and divisions. We plan to add better, more objective weights for NACE sections or divisions which are only partially matching the EU Taxonomy. For example, H49.32 is part of the sustainable taxonomy, but the entire H49 division is not. Therefore, we use weight=0.5 for this division. The A2 division is fully part of the taxonomy, and we use a weight=1 to refer to this fact. A better weighting would consider the weight of the H492.32 activity within the H49 division in the European economy. What would make such a weighting tricy is that this weight is different for each European country and the EU as a whole.</p>
Lineage and role in integrative taxonomy of a heterotrophic orchid complex
<p>Lineage-based species definitions applying coalescent approaches to species delimitation have become increasingly popular. Yet, the application of these methods and the recognition of lineage-only definitions have recently been questioned. Species delimitation criteria that explicitly consider both lineages and evidence for ecological ‘role’ shifts provide an opportunity to incorporate ecologically meaningful data from multiple sources in studies of species boundaries. Here, such criteria were applied to a problematic group of mycoheterotrophic orchids, the <em>Corallorhiza striata</em> complex, analyzing genomic, morphological, phenological, reproductive-mode, niche, and fungal host data. A recently developed method for generating genomic polymorphism data–ISSRseq–demonstrates evidence for four distinct lineages, including a previously unidentified lineage in the Coast Ranges and Cascades of California and Oregon, USA. There is divergence in morphology, phenology, reproductive mode, and fungal associates among the four lineages. Integrative analyses, conducted in population assignment and redundancy analysis frameworks, provide evidence of distinct genomic lineages and a similar pattern of divergence in the ‘extended’ data, albeit with weaker signal. However, none of the ‘extended’ datasets fully satisfy the condition of a significant ‘role’ shift, which requires evidence of fixed differences. The four lineages identified in the current study are recognized at the level of variety, short of comprising different species. This study represents the most comprehensive application of ‘lineage+role’ to date and illustrates the advantages of such an approach.</p>
PRJNA860062 Assigned Taxonomy and QIIME2 Pipeline
<p><strong>PRJNA860062 Assigned Taxonomy:</strong></p> <p>This upload comprises two datasets with the assigned taxonomy for sequence variants of BioProject PRNJA860062.</p> <ul> <li>PRJNA860062_ASVCounts_NCBItaxonomy.txt</li> <li>PRJNA860062_ASVCounts_SILVAtaxonomy.txt</li> </ul> <p>BioProject PRNJN860062 compares bacterial profiles of zebrafish larvae microbiota resulting from two different microbial colonization methods. The full description and sequence data for this project can be obtained from the Sequence Read Archive (<a href="https://www.ncbi.nlm.nih.gov/bioproject">https://www.ncbi.nlm.nih.gov/bioproject</a>). </p> <p>The dataset with the SILVA taxonomy can directly be obtained using the QIIME2 script included in this upload ('PRJNA860062_QIIME2Script.txt'). As previously noted by Lesack and Birol (2018), SILVA species annotations include nomenclature errors (<a href="https://doi.org/10.1101/441576">DOI: 10.1101/441576</a>). Therefore, the dataset with the NCBI taxonomy comprises a manually corrected taxonomy for BioProject PRNJA860062, based on the family to phylum level nomenclature of the NCBI taxonomy browser (<a href="https://www.ncbi.nlm.nih.gov/taxonomy">https://www.ncbi.nlm.nih.gov/taxonomy</a>).</p> <p>Both files are tab-delimited text files, include the domain to species level taxonomy in the first 7 columns, and include the number of assigned sequence variants (ASVs) per taxon in the final 6 colums, corresponding to BioSample SAMN29820940, SAMN29820941, SAMN29820942, SAMN29820943, SAMN29820944, and SAMN29820945.</p> <p> </p> <p><strong>QIIME 2 Pipeline:</strong></p> <p>The QIIME2 script that was used to obtain the assigned SILVA taxonomy BioProject PRNJA860062 is uploaded as:</p> <ul> <li>PRJNA860062_QIIME2Script.txt</li> </ul> <p>Input files that are required to run this script, including a manifest text file, sample metadata, and the reference sequences and taxonomy from the SILVA 138 small subunit (16S/18S) rRNA database Ref NR 99, are uploaded in the zipped file:</p> <ul> <li>PRJNA860062_InputFiles.zip</li> </ul> <p>FASTQ sequence data for BioSample SAMN29820940, SAMN29820941, SAMN29820942, SAMN29820943, SAMN29820944, and SAMN29820945, can be obtained from the Sequence Read Archive under BioProject PRNJA860062 (<a href="https://www.ncbi.nlm.nih.gov/bioproject">https://www.ncbi.nlm.nih.gov/bioproject</a>).</p> <p>All output files are uploaded in the zipped file:</p> <ul> <li>PRJNA860062_OutputFiles.zip</li> </ul> <p>Data provenance, including the versions of python (3.6.7) and python packages, can be acquired by dragging QIIME2 Visualizations (.qzv output files) into the QIIME2 viewing interface (<a href="http://view.qiime2.org">http://view.qiime2.org</a>).</p>
Taxonomy of Security Weaknesses in Java and Kotlin Android Apps
<p>This is the replication package for the paper "Taxonomy of security weaknesses in Java and Kotlin Android apps" accepted for inclusion in The Journal of Systems & Software</p>
Taxonomy, occurrences, phylogeny, traits and uses of the entire plant genus Scleria (Cyperaceae)
<p>This resource includes several datasets:</p> <p>(1) Taxonomy (261 species): Updated taxonomy of the genus Scleria at the species level based on Bauters et al. 2016 and 2019.</p> <p>(2) Occurrences (latitude/longitude): data was compiled using observations from the Global Biodiversity Information Facility, Red List, research grade identifications from iNaturalist (accessed 13/08/2023) and records for collections from BR, K, GENT, L, MO, NY, P, US and WAG which were georeferenced using Google Earth. The dataset includes 22,759 observations from 248 species. Methodology follows Larridon et al. (2021).</p> <p>(3) Phylogeny of the genus based on three markers (ITS, ndhF, rps16) from Larridon et al. (2021) (136 species).</p> <p>(4) Traits. (i) We measured maximum height, maximum blade length, maximum blade width, stem width, nutlet length and nutlet width from 1,254 specimens of 209 species housed at Royal Botanic Gardens, Kew and the Muséum National d'Histoire Naturelle in Paris. (ii) We also compiled another dataset of 16 continuous and categorical traits for all 261 Scleria species derived from protologues and descriptions from regional floras. (iii) We measured nutlet weight for 141 species.</p> <p>(5) Uses & ecology: ethnobotany (mostly medicinal uses) and references to its ecology in several ecosystems (e.g., pollination, dispersal, ecological role). This data was gathered from several bibliographical sources, also provided.</p> <p>(6) Pictures of nutlets from 141 species.</p>
A molecular taxonomy of tumors independent of tissue-of-origin
<p>This tarball contains the pre-processed data in .Rda files required to compile our manuscript entitled "A molecular taxonomy of tumors independent of tissue-of-origin"</p>
Taxonomy, distribution and classification of ecosystem-types, integrating the recent IUCN function-based typology and local conceptualizations
<p>1. Introduction:</p> <p>This dataset is a work in progress. It compiles data gathered on ecosystem-types and their distribution based on a series of field studies led by the author, in Seychelles and West and Central Africa (Senterre 2014, Senterre & Wagner 2014, Senterre 2016, Senterre et al. 2017, 2019, 2020, 2021a, 2022). The aims of this dataset are:</p> <p>a. To share in an explicit and transparent way data on proposed taxonomies of ecosystems, i.e. conceptualizations of ecosystem-types, including explicit ecosystem names and management of synonymies.</p> <p>b. To develop ecosystem red listing based on transparent and falsifiable distribution raw data, combining distribution modeling (maps) and in situ observation of individual stand occurrences.</p> <p>c. To illustrate in detail how to deal with ecosystem data following the approach described in Senterre et al. (2021b) (i.e. "ecosystemology" approach).</p> <p>d. To integrate the above approach with the newly developed function-based typology of ecosystems (Keith et al. 2022), therefore contributing to bridging the persistent gap between the global and the local scales in ecosystem descriptions and classifications.</p> <p> </p> <p>2. Context and versions:</p> <p>This dataset was initially planned for publication on GBIF (Global Biodiversity Information Facility), as part of a project developed for the review of Key Biodiversity Areas in Seychelles: "Mainstreaming recent species and ecosystem distribution data into Key Biodiversity Areas assessments in Seychelles" (<a href="https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf">https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf</a>).</p> <p>In the first version of the GBIF dataset (<a href="https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf">https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf</a>), we proposed an analysis of the potential 'core' and 'extension' files available in GBIF for a publication of ecosystem-type names (and synonymies) and their corresponding occurrences recorded from field observations. This is an original analysis of taxonomic principles managed entirely at the scale of local observable objects, and their history of identifications or interpretations.</p> <p>Toward the end of the above-mentioned GBIF project, considering the limitations and gaps currently present in GBIF, it was decided to restrict the GBIF dataset to a simple 'metadata' entry and to publish the complete version of this dataset in Zenodo. This allows to include all tables needed, as well as all required fields without having to accommodate them within the limited GBIF structure (see metadata description on GBIF for more details). The fields of the tables published here are described in the GBIF metadata entry and in the ecosystemology paper (Senterre et al. 2021b).</p> <p> </p> <p>3. New development on typology aspects:</p> <p>In addition, considering that the new IUCN global typology of ecosystems is now published (Keith et al. 2022), we have reviewed in detail the possibility of integration of ecosystems conceptualized using our ecosystemology approach within the new IUCN typology. The result of this analysis is being considered for a publication, and this Zenodo dataset would then be published in full (i.e. including all typology aspects) as supplementary materials. In the meantime, I would be happy to discuss any of these aspects with whoever is interested.</p> <p> </p> <p>4. Access to ecosystem data for conservation actors:</p> <p>Finally, the actual data (published here) on ecosystem-types, their names, synonymies, classification, distribution, and red list status are compiled into a format that we designed to be useful to conservation actors in the form of interactive webpages (produced with R as shiny apps). This development is based on very limited resources, and the author is still quite new to R, so any help or feedback on ways to improve the scripts would be very much welcomed.</p> <p>The interactive page is available here (currently filtered to Seychelles' data only, although the dataset contains data beyond the Seychelles): https://shiny.bio.gov.sc/bioeco/</p> <p>The R scripts are available on Github: https://github.com/bsenterre/ecosystemology</p> <p> </p> <p>5. Tables contained in this dataset:</p> <p>a. Ecosystem taxonomy tables:</p> <p>ecoSpecies: Contains the list of all ecosystem-type names with their unique identifier.</p> <p>ecoOccurrences: Contains the list of individual stand occurrences, including ecosystem characters as standardized in Senterre et al. (2021b; i.e. virtual ecosystem specimen).</p> <p>ecoSpeciesProfiles: Contains basic metadata on ecosystem-types, such as their Red List evaluations.</p> <p>ecoIdentifications: Contains all the different interpretations/identifications (referring to the table ecoSpecies or to higher levels of classification, see below) made on the stands observed in the ecoOccurrences table.</p> <p> </p> <p>b. Ecosystem typology tables (TO BE ADDED LATER):</p> <p>IUCNL3: This is just a transcription, as is, of the IUCN global typology version 2.1.</p> <p>IUCNL3BIOCrossover: This table defines and comments correspondences between BIOL2 (the level 2 of the typology used by us) and the IUCN typology L3 (level 3).</p> <p>BIOL2: This is a variation based on the IUCN typology, here our level 2.</p> <p>BIOL3: This is a variation based on the IUCN typology, here our level 3.</p> <p>BIOL4: This is a variation based on the IUCN typology, here our level 4.</p> <p>ecoGenus: This is a general type of stand (thus excluding any regional ecosystem connotation), defined at a local scale and never combined with any geographic connotation (see ecosystemology paper: Senterre et al. 2021b).</p> <p>ecoFamily: This is a generalized version of the ecoGenus (i.e. still excluding any regional, sub-regional or geographic aspect).</p> <p>ecoOrder: This is a further generalized version of the ecoGenus (see also Senterre et al. 2020).</p> <p>lifeZone: This is a basic and incomplete list of life zones as defined following the Holdridge (1967) approach, with some additional elements proposed in Senterre et al. (2021b).</p> <p> </p> <p>6. Literature cited:</p> <p>Holdridge, L. R. 1967. Life zone ecology. Tropical Science Center, San Jose, Costa Rica.</p> <p>Keith, D. A., J. R. Ferrer-Paris, E. Nicholson, M. J. Bishop, B. A. Polidoro, E. Ramirez-Llodra, M. G. Tozer, J. L. Nel, R. Mac Nally, E. J. Gregr, K. E. Watermeyer, F. Essl, D. Faber-Langendoen, J. Franklin, C. E. R. Lehmann, A. Etter, D. J. Roux, J. S. Stark, J. A. Rowland, N. A. Brummitt, U. C. Fernandez-Arcaya, I. M. Suthers, S. K. Wiser, I. Donohue, L. J. Jackson, R. T. Pennington, T. M. Iliffe, V. Gerovasileiou, P. Giller, B. J. Robson, N. Pettorelli, A. Andrade, A. Lindgaard, T. Tahvanainen, A. Terauds, M. A. Chadwick, N. J. Murray, J. Moat, P. Pliscoff, I. Zager, and R. T. Kingsford. 2022. A function-based typology for Earth’s ecosystems. . Nature 610:513–518. doi:10.1038/s41586-022-05318-4.</p> <p>Senterre, B. 2014. Mapping habitat-types within the Hummingbird site at Dugbe (Liberia, West Africa). Consultancy Report, Missouri Botanical Garden. P. 56. https://doi.org/10.13140/RG.2.2.32628.48003.</p> <p>Senterre, B. 2016. Habitat-type ground-truthing and assessment of ecosystem conservation value in the Bel Air Alufer mining site (Guinea, West Africa), with recommendations for improving the draft map of land cover types. Consultancy Report, Missouri Botanical Garden, A study conducted for Alufer Mining Limited. P. 54.</p> <p>Senterre, B., E. Bidault, and T. Stévart. 2019. Identification et évaluation des écosystèmes menacés du Mont Nimba. Rapport de consultance, Missouri Botanical Garden (MBG), Africa and Madagascar Department. P. 106. https://doi.org/10.13140/RG.2.2.13242.93129.</p> <p>Senterre, B., E. Bidault, T. Stévart, and P. P. Lowry II. 2020. Assessment of Key Biodiversity Areas in the Lofa-Gola-Mano & Nimba complexes (West Africa) using ecosystem criteria. Final Report, Missouri Botanical Garden. P. 146. 10.13140/RG.2.2.17934.89924.</p> <p>Senterre, B., E. Bidault, T. Stévart, M. Wagner, and P. Lowry. 2017. Mapping habitat-types in south-east Kouilou (Republic of Congo). Consultancy Report, Missouri Botanical Garden (MBG), Africa and Madagascar Department, St. Louis, Missouri, USA. P. 163.</p> <p>Senterre, B., R. M. Bristol, G. Gendron, and E. Henriette. 2021a. Fine-tuning conservation priorities in Seychelles at the landscape scale, using global KBA guidelines with both species and ecosystem criteria. Consultancy Report, United Nations Development Programme, GOS/UNDP/GEF Programme Coordination Unit, Victoria, Seychelles.</p> <p>Senterre, B., P. P. Lowry II, E. Bidault, and T. Stévart. 2021b. Ecosystemology: a new approach toward a taxonomy of ecosystems. . Ecological Complexity 47:100945. doi:https://doi.org/10.1016/j.ecocom.2021.100945.</p> <p>Senterre, B., A.-H. Paradis, E. Bidault, T. Stévart, and P. P. Lowry II. 2022. Qualité et distribution des savanes montagnardes du Nimba. Rapport de consultance, Missouri Botanical Garden (MBG), Africa and Madagascar Department. P. 73. http://dx.doi.org/10.13140/RG.2.2.13433.34401.</p> <p>Senterre, B., and M. Wagner. 2014. Mapping Seychelles habitat-types on Mahé, Praslin, Silhouette, La Digue and Curieuse. Consultancy Report, Government of Seychelles, United Nations Development Programme, Victoria, Seychelles. P. 119. https://doi.org/10.13140/RG.2.1.4558.6009.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.