Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
52
datasets available to search
ShareScore release 0.9.0
Dataset results
52 results for “taxonomic database”
Linked collectors and determiners for: The InBIO Barcoding Initiative Database: A taxonomic revision of the Western Palaearctic genus Cacochroa Heinemann, 1870 (Lepidoptera, Depressariidae).
Natural history specimen data linked to collectors and determiners held within, "The InBIO Barcoding Initiative Database: A taxonomic revision of the Western Palaearctic genus Cacochroa Heinemann, 1870 (Lepidoptera, Depressariidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/900a387b-cf8b-479b-b983-69fa973f3bcd">https://bionomia.net/dataset/900a387b-cf8b-479b-b983-69fa973f3bcd</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/900a387b-cf8b-479b-b983-69fa973f3bcd">https://gbif.org/dataset/900a387b-cf8b-479b-b983-69fa973f3bcd</a>. Formatted as a Frictionless Data package.
The hsp65 metabarcoding DNA sequence database for taxonomic allocations using the Mothur (Version 1.0.0)
<ul> <li>The <em>hsp65</em> gene codes for an Heat Shock Protein (Telenti et al., 1993) and is widespread in the Actinobacteria phylum. It is well suited for the species allocation of the Nocardia genus (Rodriguez-Nava et al., 2006).</li> <li>The <em>hsp65</em> database, named ACTIhsp65, was designed to apply the <em>hsp65</em>-metabarcoding analytical scheme published in Vautrin et al. (2021). It includes the full <em>hsp65</em> identifiers, GenBank accession numbers, complete taxonomic records (domain down to strain code) of about 401 nucleotide-long <em>hsp65</em> sequences of 1066 unique taxa belonging to 198 genera.</li> <li>Nucleotide sequences of <em>hsp65</em> (range: 165-565 nucleotides) were either retrieved from public repositories (GenBank) or made available by Veronica Rodriguez-Nava.Vautrin et al. (2021) described the PCR and high throughput Illumina Miseq DNA sequencing procedures used to produce <em>hsp65</em> sequences.</li> <li>ACTIhsp65 V1.0.0 (June 2018 release) is made available under the Creative Commons Attribution 4.0 International Licence. It can be used for the taxonomic allocations of <em>hsp65 </em>sequences down to the species.</li> </ul>
Fig. 1 in A revised comprehensive checklist, relational database, and taxonomic system of reference for the bristly millipedes of the world (Diplopoda, Polyxenida)
Fig. 1. Polyxenus lagurus (Linnaeus, 1758). Habitus of an adult male, bisexual form, dorsal view, after Nguyen Duy - Jacquemin, 1996 (by kind permission of Millepattia). Drawing by Maurice Gaillard. Length = 4 mm.
Fig. 2 in A revised comprehensive checklist, relational database, and taxonomic system of reference for the bristly millipedes of the world (Diplopoda, Polyxenida)
Fig. 2. Propolyxenus forsteri Condé, 1951. Habitus of an adult male, dorsal view. Drawing by Christine Beau. Length = 3.5 mm.
Tallo database with World Flora Online taxonomic matches, also including information on presence in the TreeGOER and GlobalUsefulNativeTrees databases
<p>The <strong>Tallo database</strong> (v 1.0.0) provides tree measurements for 5,163 tree species and 61,856 globally distributed sites. The database can be accessed via <a href="https://zenodo.org/record/6637599">https://zenodo.org/record/6637599</a> and has been fully described in Jucker <em>et al.</em> 2022. <strong>Tallo: A global tree allometry and crown architecture database</strong>. Global Change Biology, 28, 5254–5268. <a href="https://doi.org/10.1111/gcb.16302">https://doi.org/10.1111/gcb.16302</a>. Data provided in this archive show the taxonomic matches with <a href="https://www.worldfloraonline.org/">World Flora Online</a> (Borsch et al. <a href="https://onlinelibrary.wiley.com/doi/10.1002/tax.12373">2020</a>) for two versions of its taxonomic backbone data set. The first version (<a href="https://www.worldfloraonline.org/downloadData">v. 2021.12</a>) was used also to standardize species names for the latest version of the <a href="https://www.worldagroforestry.org/output/agroforestry-species-switchboard-30">Agroforestry Species Switchboard</a> and during the compilation of the <strong>TreeGOER</strong> (<a href="https://zenodo.org/record/8052331">Tree Globally Observed Environmental Ranges</a>; Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>) and <strong>GlobalUsefulNativeTrees</strong> (<a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobUNT</a>; Kindt <em>et al.</em> <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) databases. The second version (<a href="https://www.worldfloraonline.org/downloadData">v. 2023.03</a>) was the most recent version available online. Taxonomic matching was done via the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora</a> R package (Kindt <a href="https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/aps3.11388">2020</a>, see example scripts for a different taxonomic matching exercise <a href="https://rpubs.com/Roeland-KINDT/996500">here</a>).</p> <p>Taxonomic matching was achieved for all taxa listed in the Tallo database, except for <em>Lithocarpus orocola</em>.</p> <p>Presence of a taxon in the TreeGOER and GlobalUsefulNativeTrees databases has been flagged by showing the number of records used in TreeGOER to calculate ranges for environmental variable bio01 and by the number of native countries in GlobUNT, respectively.</p> <p>The development of this archive was supported by the Darwin Initiative to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, and by the Green Climate Fund through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project.</p>
Table of hsp65 OTUs (cutoff 99%), their inferred taxonomic allocations according to the hsp65 database and, for selected OTUs, closest species obtained from GenBank (BLAST) with percent identity.
<p>This table is part of the paper intitled "Comparison of Actinobacteria communities from human-impacted and pristine karst caves"</p>
Database of 16S sequences from SILVA (r114), filtered, curated and annotated to be used easily by programs of taxonomic assignments
<p>The database used for the taxonomic assignment of reads generally comes from the SILVA database (http://www.arb-silva.de/). The logic behind this database is to use the information from the best one to the worst one. This is why the curated database was splitted in two parts : the [C] sequences for Complete sequences in terms of taxonomy, and the [I] and [E] sequences, for Incomplete and Environmental sequences.</p> <p>Each sequence included into the database must have a specific format summarizing all needed information (example below):<br> >[I]AACY020336309;Archaea(superkingdom);Euryarchaeota(phylum);Thermoplasmata(class);Thermoplasmatales(order);Marine_Group_II(no_rank);;marine_metagenome</p> <p>This sequence is an incomplete one ([I]), with a specific accession number from NCBI or SILVA, or another database (AACY020336309). Then, all taxonomic data is separated using ';' characters, for each considered level (superkingdom, phylum, <br> class, order, family, and genus). The species name is the last one and separated by two ';' characters from the rest of the descriptive line. Finally, the descriptive line must not contain specific characters like spaces. If one or several levels are unknown, this is indicated by 'no_rank'.</p> <p>Another example here for [C] sequences:<br> >[C]AAAK03000010;Bacteria(superkingdom);Firmicutes(phylum);Bacilli(class);Lactobacillales(order);Enterococcaceae(family);Enterococcus(genus);;Enterococcus_faecium_DO<br> This sequence is a complete one ([C]), with a specific accession number from NCBI or SILVA, or another database (AACY020187844). Then, all taxonomic data is separated using ';' characters, for each considered level (superkingdom, phylum, <br> class, order, family, and genus). The species is the last one and separated by two ';' characters from the rest of the descriptive line. Complete sequences must have six levels of information (superkingdom, phylum, class, order, family, and genus). If it is not the case, the sequence will be considered as Incomplete ([I]) (between three and five levels), or Environmental ([E]) (with only the superkingdom and the phylum levels).</p> <p>Another example here for [E] sequences:<br> >[E]U59968;Archaea(superkingdom);Thaumarchaeota(phylum);Soil_Crenarchaeotic_Group(SCG)(no_rank);;uncultured_crenarchaeote<br> This sequence is a environmental one ([E]), with a specific accession number from NCBI or SILVA, or another database (U59968). Then, all taxonomic data is separated using ';' characters, for each considered level (superkingdom, phylum, class, order, family, and genus). The species is the last one and separated by two ';' characters from the rest of the descriptive line. Complete sequences <br> must have six levels of information (superkingdom, phylum, class, order, family, and genus). If it is not the case, the sequence will be considered as Incomplete ([I]) (between three and five levels), or Environmental ([E]) (with only the superkingdom and the phylum levels).</p> <p>More details on the steps defined to clean and define this new database can be available on demand (sebastien.terrat@inra.fr).</p>
African wood density database with matches to the taxonomic backbone data sets of World Flora Online (version 2023.12) and the World Checklist of Vascular Plants (version 11)
<p>The <strong><span>African Wood Density Database </span></strong><span>provides air-dry wood density data for over 750 tree species grown in Africa.</span></p> <p>This archive provides taxonomic matches with recent versions of <strong>World Flora Online</strong> (WFO; <a href="../records/10425161">version 2023.12 downloaded from Zenodo</a>; Borch et al. <a href="https://onlinelibrary.wiley.com/doi/10.1002/tax.12373">2020</a>) and the <strong>World Checklist of Vascular Plants</strong> (WCVP; <a href="https://sftp.kew.org/pub/data-repositories/WCVP/Archive/">version 11 downloaded from the Kew data depository</a>; Govaerts et al. <a href="https://doi.org/10.1038/s41597-021-00997-6">2021</a>). Matching was done via the <strong>WorldFlora</strong> package (<a href="https://cran.r-project.org/package=WorldFlora">version 1.14-3</a>; Kindt <a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">2020</a>), using similar scripts as documented in this Rpub: <a href="https://rpubs.com/Roeland-KINDT/1134151">https://rpubs.com/Roeland-KINDT/1134151</a>.</p> <p> </p> <ul> <li><span>Carsan, S. Orwa, C. Harwood, C. Kindt, R. Stroebel, A. Neufeldt, H. and Jamnadass, R. 2012. African Wood Density Database. World Agroforestry Centre, Nairobi. <a href="https://apps.worldagroforestry.org/treesnmarkets/wood/">https://apps.worldagroforestry.org/treesnmarkets/wood/#</a> </span></li> <li><span>Borsch, T., Berendsohn, W., Dalcin, E., Delmas, M., Demissew, S., Elliott, A., Fritsch, P., Fuchs, A., Geltman, D., Güner, A., Haevermans, T., Knapp, S., le Roux, M.M., Loizeau, P.-A., Miller, C., Miller, J., Miller, J.T., Palese, R., Paton, A., Parnell, J., Pendry, C., Qin, H.-N., Sosa, V., Sosef, M., von Raab-Straube, E., Ranwashe, F., Raz, L., Salimov, R., Smets, E., Thiers, B., Thomas, W., Tulig, M., Ulate, W., Ung, V., Watson, M., Jackson, P.W. and Zamora, N. (2020), World Flora Online: Placing taxonomists at the heart of a definitive and comprehensive global resource on the world's plants. TAXON, 69: 1311-1341. <a href="https://doi.org/10.1002/tax.12373">https://doi.org/10.1002/tax.12373</a></span></li> <li><span>Govaerts, R., Nic Lughadha, E., Black, N. <em>et al.</em> The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. <em>Sci Data</em> <strong>8</strong>, 215 (2021). <a href="https://doi.org/10.1038/s41597-021-00997-6">https://doi.org/10.1038/s41597-021-00997-6</a></span></li> <li><span>Kindt, R. 2020. WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data. <em>Applications in Plant Sciences</em> 8(9): e11388. <a href="https://doi.org/10.1002/aps3.11388">https://doi.org/10.1002/aps3.11388</a></span></li> </ul> <p> </p> <p>Original funding for the database was provided <span>by the Carbon Benefits Project (CBP) supported by The Global Environment Facility (GEF). Development of the 2024 version </span>was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em>, by the <strong>Bezos Earth Fund</strong> to the <em>Bezos Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>. When using <strong>African Wood Density database</strong> in your work, cite the 2012 version (Carsan et al. <a href="https://apps.worldagroforestry.org/treesnmarkets/wood/">2012</a>) as well as this repository using the DOI.</p>
wikidata mapping to taxonomic ids from 11 other databases
Open the record for dataset details and reuse information.
Database on the taxonomical identification and potential toxigenic capacities of non-QPS
<p>The work performed constitutes the external scientific report of the EFSA contract OC/EFSA/FEED/2015/01. The aim of the project has been to provide EFSA with a database from a review on the taxonomical description and potential toxigenic capacities of microorganisms used for the industrial production of feed additives and food enzymes. The review includes microorganisms producing feed additives and food enzymes for which EFSA has received or can potentially receive applications for safety assessment, and which have not been recommended for Qualified Presumption of Safety (QPS) status. The database also comprises the molecular taxonomical identifiers and biosynthetic pathways involved in the production of toxic compounds and responsible genes. The main result of the project is shown as a database according to the EFSA data structure has been developed. The methodological aspects and the queries used in the systematic search and the procedure applied for the screening of retrieved scientific documents are described in this report. Details are available in supplementary appendices to this report.</p> <p>In total, 22970 scientific documents were screened in the literature search from which 411 were initially selected for providing pertinent data for the scope of the project. From the review of the selected articles, 474 bioactive secondary metabolites were recorded and 59 compounds were further studied for obtaining data on their toxicology and characteristic of their production by microorganisms used in industrial fermentations. The database generated in this project comprises details that characterized the conditions, genes involved and toxicity of these 59 compounds. This provides information that can be used to establish safety measures when using potentially toxigenic microorganisms in industrial fermentations.</p> <p>The searching strategy was defined after a preliminary study in which, general information about the fermentative process involving the microorganisms within the scope was obtained. This allowed to identify possible problems that can arise when retrieving data from this heterogeneous group of microorganisms.</p> <p>Several groups of species and groups of keywords were established to perform the searching strategy. The groups of keywords are the following:</p> <ul> <li>Keywords group 1: Terms related to toxin production and hazards</li> <li>Keywords group 2: Terms related to feed additives and food enzymes</li> <li>Keywords group 3: Terms related to fermentative processes</li> <li>Keywords group 4: Terms related to toxicology</li> <li>Keywords group 5: Terms related to biosynthetic pathways</li> </ul> <p>The microbial species has been divided into 3 groups, <strong>Species I, Species II</strong>, and <strong>Species III,</strong> according to the preliminary outcome in PubMed search:</p> <p><strong>Species I</strong>: Microorganisms that produced ≤ 200 entries when searched by scientific name.</p> <p><strong>Species II</strong>: Microorganisms that produced ≤ 500 entries when searched by scientific name and keywords from group</p> <p><strong>Species III</strong>: Microorganisms that produced > 500 entries when searched by scientific name and keywords from group 1</p> <p> </p> <p><strong>Note</strong>: Version 2 includes an update in the TOXICITYRESULTS file, where the column "effect_concentration" has been added.</p>
General principles for assignments of communities from eDNA: Open versus closed taxonomic databases
<p><span>Metabarcoding of environmental DNA (eDNA) is a powerful tool for describing biodiversity, such as finding keystone species or detecting invasive species in environmental samples. Continuous improvements in the method and the advances in sequencing platforms over the last decade have meant this approach is now widely used in biodiversity sciences and biomonitoring. For its general use, the method hinges on a correct identification of taxa. However, past studies have shown how this crucially depends on important decisions during sampling, sample processing, and subsequent handling of sequencing data. With no clear consensus as to the best practice, particularly the latter has led to varied bioinformatic approaches and recommendations for data preparation and taxonomic identification. </span><span>In this study, using a large freshwater fish eDNA sequence dataset, we compared the frequently used zero-radius Operational Taxonomic Unit (zOTUs) approach of our raw reads and assigned it taxonomically i) in combination with publicly available reference sequences (open databases) or ii) with an OSU (Operational Sequence Units) database approach, using a curated database of reference sequences generated from specimen barcoding (closed database). </span><span>We show both approaches gave comparable results for common species. However, the commonalities between the approaches decreased with read abundance and were thus less reliable and not comparable for rare species. The success of the </span><span>zOTU</span><span> approach depended on the suitability, rather than the size, of a reference database. Contrastingly, the OSU approach used reliable DNA sequences and thus often enabled species-level identifications, yet this resolution decreased with the recent phylogenetic age of the species. We show the need to include target group coverage, outgroups and full taxonomic annotation in reference databases to avoid misleading annotations that can occur when using short amplicon sizes as commonly used in eDNA metabarcoding studies. Finally, we make general suggestions to improve the construction and use of reference databases for metabarcoding studies in the future.</span></p>
Exposing New Taxonomic Variation with Inflammation – A Model-Specific Genome Database for Microbiome Researchers
<p>Data deposit for CBAJ-DB v1.2</p>
General principles for assignments of communities from eDNA: Open versus closed taxonomic databases
Open the record for dataset details and reuse information.
Diamond database for taxonomic annotation of fungal metatranscriptomics
<p>This is a protein fasta dataset for use with <a href="https://github.com/bbuchfink/diamond">diamond</a>. The <strong>fasta.gz</strong> file contains protein sequences for the following:</p> <ul> <li>1,164 genomes downloaded from JGI (<strong>taxonomy_taxids.tsv</strong>)</li> <li>121 genomes that are part of the <a href="https://bitbucket.org/dbeisser/taxmapper/src/master/">taxmapper</a> database (<strong>taxmapper_taxonomy_taxids.tsv</strong>) of which 6 were fungal</li> <li>the <em>Hygrophorus russula </em>MG78<em> </em>genome downloaded from NCBI.</li> </ul> <p>For the <em>H. russula</em> genome, genes were predicted using Augustus (v. 3.2.3) with the laccaria_bicolor model.</p> <p>The final protein database consists of a total of 17,694,143 protein sequences (14,976,193 from JGI, 2,708,401 from taxmapper and 9,549 from <em>H. russula</em>).</p> <p>The fasta file and associated taxonomic information files (nodes.dmp.gz & taxonmap.gz) can be used to build a diamond database compatible with diamond version 0.9.22:</p> <pre><code class="language-bash">zcat fasta.gz | diamond makedb -d diamond --taxonmap taxonmap.gz --taxonnodes nodes.dmp</code></pre> <p> </p>
FIGURE 1 in The Wild Silkmoths (Lepidoptera: Bombycoidea: Saturniidae) of Colombia: a database of occurrence points and taxonomic checklist
FIGURE 1. Distribution records for Saturniidae moths in Colombia. Warm colours indicate areas with higher densities of occurrences records, while colder colours and white areas represent a lower number of records and lack thereof, respectively.
APPENDIX. List of sequenced specimens of Triphosa, with identification, Sampling sites collecting data, Accession numbers, and process ID in BOLD database. Data taken from BOLD and generated by Axel Hausmann (1); Bernd Müller (2); Dirk Stadie (3); Iva Mihoci 4); Marco Infusino, Stefano Scalercio (5); Norbert Poell (6); Wanke et al. (7). in An integrative taxonomic revision of the genus Triphosa Stephens, 1829 (Geometridae: Larentiinae) in the Middle East and Central Asia, with description of two new species
APPENDIX. List of sequenced specimens of Triphosa, with identification, Sampling sites collecting data, Accession numbers, and process ID in BOLD database. Data taken from BOLD and generated by Axel Hausmann (1); Bernd Müller (2); Dirk Stadie (3); Iva Mihoci 4); Marco Infusino, Stefano Scalercio (5); Norbert Poell (6); Wanke et al. (7).
3I Interactive Keys and Taxonomic Databases: Deltocephalinae
Open the record for dataset details and reuse information.
3I Interactive Keys and Taxonomic Databases: Cicadellinae
Open the record for dataset details and reuse information.
3I Interactive Keys and Taxonomic Databases: Typhlocybinae
Open the record for dataset details and reuse information.
Turbellarian Taxonomic Database
Tyler S, Schilling S, Hooge M, and Bush LF (comp.) (2006-2016) Turbellarian taxonomic database. Version 1.7 <p></p>http://turbellaria.umaine.edu
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.