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4,283 results for “Database”
The FORCIS database: A global census of planktonic Foraminifera from ocean waters
<p>The FORCIS (Foraminifera Response to Climatic Stress) database is a synthesis grouping datasets on living planktonic foraminifera. We assembled foraminiferal diversity and distribution data in the global oceans from 1910 until 2018, curating published and unpublished datasets. This database includes data collected using plankton tows, continuous plankton recorder, sediment traps and plankton pump from the global ocean.</p> <p>The FORCIS database version 01 is composed of 5 files (“.csv” format). All data coming from different sampling devices were put into separate “.csv” files. Only the data of the CPR from the Southern Hemisphere have been separated from the Northern Hemisphere CPR data as the data structure is not the same (species counts resolved vs. binned total counts, respectively). </p> <p>Apart from the file of CPR data from the Northern Hemisphere that contains only metadata and binned total counts, all the remaining four files contain 4 blocks:</p> <ul> <li> <p>Block 1: metadata (from column 1 to 71)</p> </li> <li> <p>Block 2: original counts (from column 72 to 274)</p> </li> <li> <p>Block 3: generated counts based on the validated taxonomy (from column 275 to 331). We added “_VT” to each species name to distinguish it from other taxonomy levels. E.g. “g_bulloides” became “g_bulloides_VT”. The number of species counted per subsample is also reported in the column “number_of_species_counted_VT”</p> </li> <li> <p>Block 4: generated counts based on the lumped taxonomy (from column 332 to 379). In this case, we added “_LT” to each species name. E.g. “n_dutertrei” became “n_dutertrei_VT”. We also calculated the number of species counted per subsample and reported it in the column “number_of_species_counted_LT”</p> </li> </ul> <p>Foraminifera abundance data counts are reported in different categories in the blocks 1,2 and 3 and described in the table below:</p> <table> <tbody> <tr> <td> <p><strong>count_type</strong></p> </td> <td> <p><strong>unit</strong></p> </td> </tr> <tr> <td> <p>Absolute</p> </td> <td> <p>ind/m3</p> </td> </tr> <tr> <td> <p>Relative</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Raw</p> </td> <td> <p>number of individuals</p> </td> </tr> <tr> <td> <p>Fluxes</p> </td> <td> <p>ind/m2/day</p> </td> </tr> <tr> <td> <p>Bin_Absolute</p> </td> <td> <p>ind/m3</p> </td> </tr> <tr> <td> <p>Bin_Relative</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Bin_Raw</p> </td> <td> <p>number of individuals</p> </td> </tr> <tr> <td> <p>Bin_Fluxes</p> </td> <td> <p>ind/m2/day</p> </td> </tr> </tbody> </table> <p> </p> <p>For more details about the FORCIS database column description, please check the data descriptor paper <strong>Chaabane et al. (2023) (https://doi.org/10.1038/s41597-023-02264-2).</strong></p> <p>The database is kept open for any new entries and the updated version will be released in csv format. The labels of updated versions of the released “.csv” files will contain the date of their publication and versioning number.</p>
sistr_cmd v1.0.2 serotyping databases
<p><strong><a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147101">Salmonella In Silico Typing Resource (SISTR)</a> <a href="https://github.com/peterk87/sistr_cmd">sistr_cmd</a> version <a href="https://github.com/peterk87/sistr_cmd/releases/tag/v1.0.2">1.0.2</a> serotyping databases</strong></p> <p>File structure tree for <code>sistr_cmd</code> <code>data</code> folder:</p> <pre><code>. |-- [4.0K] antigens | |-- [1.0M] fliC.fasta | |-- [210K] fljB.fasta | |-- [126K] wzx.fasta | `-- [ 60K] wzy.fasta |-- [4.0K] cgmlst | |-- [7.4M] cgmlst-centroid.fasta | |-- [ 96M] cgmlst-full.fasta | |-- [134M] cgmlst-profiles.hdf | `-- [ 803] README.md |-- [1.1M] genomes-to-serovar.txt |-- [1.0M] genomes-to-subspecies.txt |-- [118K] Salmonella-serotype_serogroup_antigen_table-WHO_2007.csv `-- [ 92M] sistr.msh 2 directories, 12 files</code></pre> <p><strong>Description of files:</strong></p> <ul> <li><code>genomes-to-serovar.txt</code>: Each genome id to serovar designation delimited by tab character for the 52,790 Salmonella genomes.</li> <li><code>genomes-to-subspecies.txt</code>: Each genome id to subspecies designation delimited by tab character for the 52,790 Salmonella genomes.</li> <li><code>Salmonella-serotype_serogroup_antigen_table-WHO_2007.csv</code>: Serovar and antigenic formula information table used by `sistr_cmd` for looking up serovar designations from antigen results</li> <li><code>sistr.msh</code>: <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0997-x">Mash</a> sketch file of 11840 Salmonella genomes for Mash-based serotyping</li> <li><code>antigens</code>: for antigen gene search-based serotyping <ul> <li><code>fliC.fasta</code>: fliC gene alleles for H1-antigen typing</li> <li><code>fljB.fasta</code>: fljB gene alleles for H2-antigen typing</li> <li><code>wzx.fasta</code>: wzx gene alleles for O-antigen typing</li> <li><code>wzy.fasta</code>: wzy gene alleles for O-antigen typing</li> </ul> </li> <li><code>cgmlst</code> for core-genome multilocus sequence typing (cgMLST) and cgMLST-based serotyping <ul> <li><code>cgmlst-profiles.hdf</code>: HDF5 file with cgMLST allelic profiles of 52,790 Salmonella genomes <ul> <li>read in with Pandas, i.e. <pre><code class="language-python">pd.read_hdf(CGMLST_PROFILES_PATH, key='cgmlst')</code></pre> </li> </ul> </li> <li><code>cgmlst-centroid.fasta</code>: "Centroid" or representative alleles of 52,790 Salmonella genomes for rapid NCBI BLAST+ blastn searching. Centroid alleles were defined from the full set of alleles for the 52,790 Salmonella genomes as the alleles for each locus: <ul> <li>group alleles by length</li> <li>group length grouped alleles by ends (28bp at allele start and end; 28 is word size of blastn megablast)</li> <li>hierarchical clustering of length+end grouped alleles</li> <li>flat clusters at 2.5% distance</li> <li>within each cluster, pick allele with least distance to others in cluster</li> </ul> </li> </ul> </li> <li><code>cgmlst-full.fasta</code>: alleles for the 52,790 Salmonella genomes</li> </ul>
VSR Databases used in article "Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition"
<p>This dataset contains required volcano-seismic waveform DBs (<em>dec.95M.16c</em> and <em>dec.09U.4c</em>) used in the article:</p> <p>"<em>Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition</em>",</p> <p>published in the Seismological Research Letters (<a href="https://doi.org/10.1785/0220180334">https://doi.org/10.1785/0220180334</a>). The authors want to thank everyone at the Instituto Andaluz of Geofísica (<a href="http://iagpds.ugr.es">http://iagpds.ugr.es</a>), precisely to Prof. Jesús Ibáñez and Dr. Javier Almendros, IPs of several research projects which </p> <p>have made possible the monitoring of Deception Island since early 1990s.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).</p>
ncrncornell/ced2ar-nber-ces-codebook: Codebook for NBER-CES Manufacturing Industry Database
<p>Codebook for NBER-CES Manufacturing Industry Database (2009) [NAICS and SIC], by Randy A. Becker , Wayne B. Gray , Jordan Marvakov , and Eric J. Bartelsman</p> <p>Main website: <a href="https://www.nber.org/data/nberces5809.html">https://www.nber.org/data/nberces5809.html</a> (note: a newer version is available at <a href="http://www.nber.org/data/nberces.html">http://www.nber.org/data/nberces.html</a> - this codebook does not necessarily reflect the more recent version.)</p> <p>Live version of the DDI codebook at <a href="https://www2.ncrn.cornell.edu/ced2ar-web/codebooks/nber-ces/">https://www2.ncrn.cornell.edu/ced2ar-web/codebooks/nber-ces/</a></p>
International Soil Radiocarbon Database v1.0
<p>Synthesized soils data associated with v1.0 of the International Soil Radiocarbon Database. Detailed information about this dataset can be found at <a href="http://soilradiocarbon.org">soilradiocarbon.org</a>.</p>
S48 | CPPDBLISTA | Database of Chemicals likely (List A) associated with Plastic Packaging (CPPdb)
<p>This is the collection associated with list S48 CPPDBLISTA on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S48 | CPPDBLISTA | <strong>Database of Chemicals associated with Plastic Packaging (CPPdb)</strong></p> <p>CPPdb Original File (List A and B) <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListA_ListB_181009_ZenodoV1.xlsx">XLSX</a> (06/03/2019)<br> Mapped Files (06/03/2019):<br> Table 2 from Groh et al as <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/Table2_Groh_etal_stoten_mapped.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/Table2_Groh_etal_stoten_mapped.csv">CSV</a> <br> CPPdb List A <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListA_Mapped_06032019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListA_Mapped_06032019.csv">CSV</a> <br> CPPdb List B <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListB_Mapped_06032019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListB_Mapped_06032019.csv">CSV</a></p> <p>Table 2 Groh et al. <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/Table2_Groh_etal_InChIKeys.txt">InChIKeys</a><br> CPPdb List A <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListA_InChIKeys.txt">InChIKeys</a><br> CPPdb List B <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListB_InChIKeys.txt">InChIKeys</a><br> (all 06/03/2019)</p> <p>A database of chemicals likely (List A, 903) and possibly (List B, 3353 - in another upload) associated with plastic packaging, with hazard data, from Groh et al 2019 DOI: <a href="https://doi.org/10.1016/j.scitotenv.2018.10.015">10.1016/j.scitotenv.2018.10.015</a>. Mapped to structures by CAS/Name by K. Groh & E. Schymanski.</p> <p>Latest version of original data (last update Oct 2018): DOI: <a href="http://doi.org/10.5281/zenodo.1287773">10.5281/zenodo.1287773</a></p> <p> </p>
Emissions Database for Global Atmospheric Research, version v4.3.2 part I Greenhouse gases
<p>The Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2, partim Greenhouse gases compiles anthropogenic emissions data for CO2, CH4 and N2O based on international statistics and emission factors. The version v4.3.2 of the EDGAR emission inventory provides global estimates, broken down to IPCC-relevant source-sector levels, from 1970 (the year of EU’s first Air Quality Directive) to 2012 (the end year of the first commitment period of the Kyoto Protocol (KP)). Strengths of EDGAR v4.3.2 include global geo-coverage (226 countries), continuity in time, and comprehensiveness in activities. Emission sources of the multiple gases include all human activities except the land-use, land-use change and forestry sector and are compiled following a bottom-up and IPCC-compliant approach. The dataset provides in addition to the complete timeseries 1970-2012 also annual and global gridmaps of 0.1 degree by 0.1 degree resolution for each source-sector and each year. For 2010 also 12 monthly gridmaps per source-sector are provided.</p>
DS3_DITOs_Capacity_Building_Tools_Results-events-database
<p>This csv file is the dataset of events that were completed by the DITOs consortium during the 3 year H2020 Coordination and Support Action 1/6/16-31/5/19</p> <p>It contains the following fields:</p> <p>Partner - The consortium partner responsible for organising the event</p> <p>Title - The name of the event</p> <p>Name of event as described in the DoA - The type of the event as listed in the DoA, or the word 'additional' if the event was not envisaged in the DoA</p> <p>Page link - the link to the together science.eu page that held the event detail</p> <p>Status - the status of the event (planned, completed or cancelled)</p> <p>Date - the start date of the event in YYY-MM-DD format</p> <p>Time - the start time of the event in 24HH:MM format</p> <p>End date - the end date of the event in YYY-MM-DD format</p> <p>End time - the end time of the event in 24HH:MM format</p> <p>The event type - conference, exhibition, gaming competition, online, travelling bus or workshop</p> <p>Audience number - an estimate (from the hosting partner) of the number of attendees</p> <p>%Female - an estimate (from the hosting partner) of the percentage of attendees</p> <p>Workpackage - WP1-6 - the work package from the DoA relevant to that event</p> <p>Partner org name and facilitator - the partner to contact and name(s) of facilitators</p> <p>Lower age bracket - an estimate of the age of the youngest attendee</p> <p>Upper age bracket - an estimate of the age of the oldest attendee</p> <p>URLs - any associated websites for outputs or publicity</p> <p>Event ID - a unique event identifier of the format XXXX_YYYYMMDD(z) where XXXX is partner identifier (ECSA, eutema, UCL, UPD, RBINS, Tekiu, UNIGE, WS, meritum, KI, MP - as defined in the Grant agreement), YYYYMMDD is the start date of the event and z is an optional suffix (a through z) used if the partner ran more than one event on that date</p> <p>Location - the address where the event took place</p> <p>Reporting period - the Grant agreement reporting period the event relates to</p> <p>Phase - the DoA phase the event relates to</p> <p>NGO - A list of any NGOs involved in co-hosting / contributing to the event</p> <p>DIY and local communities - A list of any DIY and local communities s involved in co-hosting / contributing to the event</p> <p>Local and national government - A list of any government bodies involved in co-hosting / contributing to the event</p> <p>Industry, company and start-ups - A list of any industry/start-ups involved in co-hosting / contributing to the event</p> <p>Other - A list of any other organisations involved in co-hosting / contributing to the event</p> <p>Online resources - URLs for any related resources</p> <p>Geolocation, latitude and longitude - coordinates of the event location</p>
Prosopographical Database of Judeans in the Murašû Archive
<p>This is a prosopographical database of Judean persons attested in the Murašû archive. It relates to chapter 5 in Tero Alstola, <em>Judeans in Babylonia: A Study of Deportees in the Sixth and Fifth Centuries BCE</em> (Culture and History of the Ancient Near East. Leiden: Brill). For further information, see the readme file.</p>
Functional traits database for North American birds
<p>Estimation of functional diversity in biological communities requires extensive and complete data on numerous functional traits of species or even individuals. When estimating functional diversity at large scales, this fact possesses an issue that may be hard to overcome: for many species, there might not be sufficient data on their functional traits. In such cases, even if there is missing information on functional trait value for one species in a community, this makes the trait impossible to use for the estimation of the functional diversity of a community. On the other hand, there are available datasets on the functional traits of all extant species within certain lineages across the world, but such datasets are often limited to very few functional traits, missing some dimensions of species' ecological niches. In this dataset, I compiled the available data from various sources that describe 23 functional traits of 703 bird species that occur in Canada, the United States, and Mexico. These functional traits include the following: diet type, diurnal and nocturnal feeding, diet items, feeding methods, feeding substrate, nest type, nest substrates, breeding system, chick development at hatching, nest aggregation, clutch size, first breeding age, number of clutches a year, breeding success, adult annual survival, mean biomass, maximum lifespan, hand-wing index, kleptoparasitism, nest parasitism, and the extent of dependency on other species for building a nest.</p>
LukProt - an animal evolution-centric eukaryotic protein database
<p>LukProt is the EukProt database with additional species added, mostly the undersampled animal and some holozoan taxa. The database is composed of sequences translated from annotated genomes, transcriptomes or ESTs. <strong>The main purposes of the database are to consolidate sequences from undersampled animal taxa</strong> and provide usable search tools. The publication associated with LukProt can be found here: <a href="https://doi.org/10.1093/gbe/evae231">https://doi.org/10.1093/gbe/evae231</a>.</p> <p>The current version of the database (v1.5.1) is based on <a href="https://doi.org/10.24072/pcjournal.173">EukProt v3</a>. The home of all public versions of LukProt is this page (Zenodo).</p> <p>Proteomes that are novel in LukProt are denoted as LPXXXXX and those coming from AniProtDB are called APXXXXX. The sequence IDs from EukProt are conserved in LukProt. This means that each sequence is assigned an ID in the following format:</p> <pre><code>(A/E/L)PXXXXX_Species_epithet_(strain)_PYYYYYY</code></pre> <p>where XXXXX is a number from 00001 to 99999 and YYYYYY is a number from 000001 to 999999. Each sequence is assigned a unique number YYYYYY, and each taxon XXXXXX. All the IDs are compatible with BLAST v5 "-parse_seqids" option and the database can be readily deployed, for example on a server running <a href="https://doi.org/10.1093/molbev/msz185">SequenceServer</a>. Within each of the source fasta files, the source sequence identifier was kept after a blank space, so that it can still be retrieved if needed.</p> <p>A publicly available BLAST server providing LukProt search is available at: <a title="LukProt BLAST server" href="https://lukprot.hirszfeld.pl/" target="_blank" rel="noopener">https://lukprot.hirszfeld.pl/</a>.</p> <p>Comparison of EukProt v2/v3, LukProt 1.4.1 and LukProt v1.5.1 in their main areas of difference:</p> <table> <tbody> <tr> <th>Taxogroup</th> <th>EukProt v2</th> <th>EukProt v3</th> <th>LukProt v1.4.1</th> <th>LukProt v1.5.1</th> </tr> <tr> <th> <p>Holozoa</p> <p>(excluding Metazoa)</p> </th> <td>31</td> <td>40</td> <td>39</td> <td>43</td> </tr> <tr> <th>Ctenophora</th> <td>2</td> <td>2</td> <td>35</td> <td>38</td> </tr> <tr> <th>Porifera</th> <td>4</td> <td>5</td> <td>30</td> <td>47</td> </tr> <tr> <th>Placozoa</th> <td>2</td> <td>2</td> <td>3</td> <td>6</td> </tr> <tr> <th>Cnidaria</th> <td>3</td> <td>5</td> <td>65</td> <td>88</td> </tr> <tr> <th>Bilateria</th> <td>51</td> <td>51</td> <td>94</td> <td>142</td> </tr> </tbody> </table> <p>Included with the database are:</p> <ul> <li>ready to use main database files: <ul> <li><em>LukProt_v1.5.1_single_species_FASTA.7z</em> – a FASTA file with the sequences - <a href="https://en.wikipedia.org/wiki/7z">7-zipped</a>, <strong>uncompressed size: 17.6 GB</strong><br> <ul> <li>to concatenate all into one file, run this in the parent directory: <code>for file in $(find . -type f -name "*.fasta"); do awk 'FNR==1{print ""}1' $file >> LukProt_v1.5.1.fa; done</code>. This will create single FASTA file with all the sequences in the parent directory. <code>awk</code> is used to insert a new line after every file because <code>cat</code> would sometimes merge the last sequence with the header of the first sequence.</li> </ul> </li> <li><em>LukProt_v1.5.1_full_BLAST_db.7z</em> – a preformatted, full BLAST database (NCBI BLAST database format version: v5, masked with segmasker), <strong>uncompressed size: 28.3 GB</strong></li> <li><em>LukProt_v1.5.1_taxogroup_BLAST_db.7z</em> – a collection of BLAST databases where each proteome is one taxogroup and is placed within the eukaryotic tree of life directory structure, <strong>uncompressed size: 26.3 GB</strong></li> <li><em>LukProt_v1.5.1_single_species_BLAST_db.7z</em> – a collection of BLAST databases where each proteome is one BLAST database and is placed within the eukaryotic tree of life directory structure, <strong>uncompressed size: 26.4 GB</strong></li> </ul> </li> <li>auxiliary database files: <ul> <li><em>LukProt_v1.5.1.cdhit70.7z</em> – the full database clustered at 70% identity using CD-HIT with the following command: <code>cd-hit -g 1 -d 0 -T 20 -M 90000 -c 0.7 -uL 0.2 -uS 0.9 -s 0.2</code>, <strong>uncompressed sizes: fasta file - 11 GB, clstr file - 2.5 GB</strong></li> <li><em>LukProt_IDs_mapped.txt.gz</em> – a text file mapping the LukProt IDs to the AniProtDB IDs and EukProt IDs that are different</li> <li><em>BUSCO_tables.ods</em> – a spreadsheet with full result tables generated by BUSCO analysis</li> <li><em>OMAmer_output.zip</em> – a folder with full results of OMAmer analyses (includes per-sequence taxonomy classification)</li> <li><em>OMArk_output.zip</em> – a folder with the results of all OMArk analyses</li> </ul> </li> <li>metadata: <ul> <li><em>README.md</em> – a README file describing the metadata</li> <li><strong><em>LukProt_metadata_sheet.ods</em> – main metadata file. A spreadsheet with information about each proteome (in an open .ods format, most compatible with <a href="https://www.libreoffice.org/">LibreOffice</a>)</strong></li> <li><em>LukProt_metadata_other.zip</em> – an archive with other metadata files, documented in the README. Contents include:<br> <ul> <li>the LukProt taxonomy in various formats</li> <li>supporting scripts for data manipulation and visualization</li> </ul> </li> <li>a recoloring script (modified by LFS, originally by Dr. Celine Petitjean). The script is in <a title="formatFigtree2" href="https://doi.org/10.5281/zenodo.10654583">public domain</a> and reuploaded here only for convenience. </li> <li>other files - see README</li> </ul> </li> <li><em>changelog.md</em> – database changelog</li> </ul> <p>Words of caution:</p> <ul> <li>The database has been synchronized to EukProt v3 in version v1.5.1. This means that identifiers were modified in comparison to LukProt v1.4.1. The convention is not expected to change any more in future updates.</li> <li>Many proteomes, especially those transcriptome-based, may contain contamination from different species. In addition, the translation algorithms often introduce errors (e.g. the transcript may not represent a full length protein). For this reason, to get accurate sequences from each organism, users are directed to source data and to the included OMAmer, OMArk and BUSCO data for details.</li> <li>The taxonomy is different to UniEuk/EukMap, but UniEuk data were integrated where possible.</li> <li>A few NCBI taxids are missing and will be added in due course.</li> <li>Proteomes from NCBI and UniProt will be updated to current versions.</li> <li>A number of proteomes present in some metadata, are unpublished and were held back.</li> <li>While the database contains metadata that present a particular phylogeny of animals, holozoans and other eukaryotes, no particular claims or hypotheses are made by the author(s). However, in the future efforts will be made to name clades officially, once they are more firmly established.</li> </ul> <p><strong>Please report any problems or suggestions to Lukasz Sobala: lukasz.sobala (at) hirszfeld.pl.</strong></p> <p> </p> <p>Acknowledgements:</p> <ul> <li> <p>Andrew E. Allen Lab for creating the original <a href="https://allenlab.ucsd.edu/data/" target="_blank" rel="noopener">PhyloDB</a>.</p> </li> <li> <p>Daniel Richter <em>et al.</em> for creating <a href="https://doi.org/10.6084/m9.figshare.12417881">EukProt</a> and keeping it updated.</p> </li> <li> <p>Members of <a href="https://multicellgenome.com/">the Multicellgenome Lab</a>, especially Michelle Leger (for donating her database), for the bioinformatics support and for doing great science.</p> </li> <li> <p>All the authors of the original data.</p> </li> <li> <p>National Science Centre of Poland for funding of the project 2020/36/C/NZ8/00081, "The role of glycosylation in the emergence of animal multicellularity", which enabled the creation of this database.</p> </li> </ul>
S75 | CyanoMetDB | Comprehensive database of secondary metabolites from cyanobacteria
<p>This is the collection associated with list S75 CyanoMetDB Comprehensive database of secondary metabolites from cyanobacteria on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>CyanoMetDB is a comprehensive database of secondary metabolites from cyanobacteria manually curated from primary references described in Jones et al (2021), DOI: <a href="https://doi.org/10.1016/j.watres.2021.117017">10.1016/j.watres.2021.117017</a> (preprint DOI: <a href="https://doi.org/10.1101/2020.04.16.038703">10.1101/2020.04.16.038703</a>). This upload contains the 2024 release. Please cite Jones et al (2021) DOI: <a href="https://doi.org/10.1016/j.watres.2021.117017">10.1016/j.watres.2021.117017</a> and this record Janssen et al (2024) DOI: <a href="https://doi.org/10.5281/zenodo.13854577">10.5281/zenodo.13854577</a> when using this CyanoMetDB Version 3!</p> <p><em><strong>Contents: </strong></em></p> <p><em><strong>CyanoMetDB XLSX database (2024 release): <a href="https://zenodo.org/records/13854577/files/CyanoMetDB_Version03.xlsx?download=1">CyanoMetDB_Version03.xlsx</a></strong></em></p> <p>Additional files for workflows:</p> <p>CSV format: <a href="https://zenodo.org/records/13854577/files/CyanoMetDB_V03_2024.csv?download=1">CyanoMetDB_V03_2024.csv</a><br>MetFrag local CSV file (original database abridged and reformatted for use in MetFrag): <a href="https://zenodo.org/records/13854577/files/CyanoMetDB_V03_2024_MetFrag.csv?download=1">CyanoMetDB_V03_2024_MetFrag.csv</a><a href="https://zenodo.org/api/files/7d71e4a9-e5f2-4ca3-ac55-6467a356ab9a/CyanoMetDB_MetFrag_Feb2021.csv"> </a><br>Additional files for matching InChIKeys (rapid suspect flagging): <a href="https://zenodo.org/records/13854577/files/CyanoMetDB_V03_2024_InChIKeys.txt?download=1">CyanoMetDB_V03_2024_InChIKeys.txt</a></p> <p>Corresponding author: Elisabeth Janssen (Eawag): <a href="mailto:Elisabeth.Janssen@eawag.ch">Elisabeth.Janssen@eawag.ch</a></p>
Excel template for the aggregate database on descriptive representation of the ActEU project
<p>This is the Excel template used to structure the databases that provide data at the legislature / party level for each of the six countries studied in Tasks 4.1 and 4.2 of the ActEU project.</p>
ChSPD Chilean soil profile database V2
<p>ChSPD is a soil profile database for Chile. The data was compiled from different published and unpublished sources. This new soil database covers a wide range of ecosystems and climate conditions. It comprises 20 different soil physical, hydraulic, and chemical properties. Each soil property has its own number of observations, which is determined by the soil horizons surveyed and the measurements taken at each point. The ChSPD_V2 includes 19769 georeferenced records, which represent 14029 soil profiles. The properties with the most records are organic matter (15797 data points), texture distribution (clay, sand, and silt content, 4978 data points), bulk density (5088 data points), field capacity (2020), and permanent wilting point (2012). </p>
A database of FLASH Murine In-Vivo Studies
<p>The database contains peer-reviewed papers published until March 2024 on the FLASH in-vivo (murine) experiments. From each paper, previously defined parameters have been manually extracted and/or recalculated to ensure compatibility within the database entries. </p> <p>We provide two types of datasets: a user-friendly web-based Notion database and two spreadsheets. The database contains all the reviewed papers with extracted information in text or numeric form. Users can duplicate the database or view, search, filter, and reorganise online entries. The spreadsheets contain the data for the most analysed endpoints (skin toxicity, survival rate, and crypt cells), allowing a comparative analysis.</p> <p>Our goal with this database is to increase awareness of the results and their variability and provide the community with a useful research and analysis tool.</p>
Database of indicators to evaluate the contribution of urban nature-based solutions to climate change adaptation, biodiversity conservation, and social justice
<p>Supplementary data used within the publication: Goodwin, S., Olazabal, M., Castro, A. J., & Pascual, U. (2024). Measuring the contribution of nature-based solutions beyond climate adaptation in cities. <em>Global Environmental Change</em>, <em>89</em>, 102939. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102939">https://doi.org/10.1016/j.gloenvcha.2024.102939</a>. Please also cite this paper when citing this database.</p> <div> <div>Within this database, you can find a list of indicators used to evaluate the contribution of a collection of 74 nature-based solutions (NbS) to climate change adaptation and related biodiversity and social justice challenges in cities. This list of indicators may be useful to those working in cities to provide inspiration for similar indicators they may wish to use to evaluate NbS in their city. This collection of NbS was drawn from previous work published in <em>Nature Sustainability</em> <a href="https://rdcu.be/c4tjk">here</a>.</div> <div> </div> </div> <p><em>The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DI20/11780006”. Marta Olazabal’s research is funded by the European Union (ERC, IMAGINE adaptation, 101039429). This research is further supported by María de Maeztu Excellence Unit 2023-2027 (ref. CEX2021-001201-M), funded by the Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (AEI) (Spain) (MCIN/AEI/10.13039/501100011033/); and by the Basque Government through the BERC 2022-2025 program. </em></p> <p><em>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</em></p>
Database Gil-Monte Burnout Model in Brazilian Teachers
<p>This dataset contains data of non-university teachers collected by paper and pencil at the workplace. These data were collected the professor Mary Sandra Carlotto in the south of Brazil. The participation in the study was voluntary and anonymous. Prior to the start of the assessment the school principal was contacted to obtain the authorization to carry out the study and design the data collection. Teachers were informed about the purpose of the study, and it was clarified that their participation would not result in any individual or organizational assessment consequences. The questionnaire was distributed to teachers at the beginning of their workday, and they were asked to submit the completed questionnaire by placing it in a box in the teachers’ room at the end of the day.</p> <p>The file contains demographic variables, the responses to 8 items of the General Self-efficacy Scale, 6 ítems of the Social support scale of the UNIPSICO questionnaire, 20 ítems of the Spanish Burnout Inventory, and 9 ítems from the Psychosomatic disorders scale of the UNIPSICO questionnaire.</p> <p>The name of the variables and the value labels have been written in English to facilitate their understanding.</p> <p>Data and codebooks are provided in csv format, following the FAIR principles.</p> <p>Three files are provided:<br>1. Database, with the data related to sample characteristics and the answers to the items of the questionnaires and the other items.<br>2. Database codebook of variables, with information of the labels of the variables of the Database file.<br>3. Database variable values codebook, with the labels of the values of the variables in the Database file.</p>
Heritage Site Database Limpopo National Park
<p>Accompanying datafile with list of sites, chronological period, and assessment as explained in the following papers:</p> <p>An assessment system for archaeological sites, the example of Limpopo Valley (in review)</p> <p>Anneli Ekblom, Solange Macamo, Peter Bechtel, Frederico Regala, Susana Carvalho, Mussa Raja, Michel Notelid (2024) A Framework for Cultural Heritage Management in National Parks, Mozambique. Bull. Mus. Anthropol. préhist. Monaco, n° 63. </p> <p> </p>
Database of the Monita privata (secreta) editions/reprints/translations
<h2>Description</h2> <p>This dataset provides an expansive, scholarly resource on Monita privata (later referred to also as Monita secreta), a unique anti-Jesuit polemic that emerged in early seventeenth-century Europe—a period characterized by religious and political turbulence. Likely composed between 1606 and 1612, this text gained traction through multiple Latin and vernacular editions, ostensibly as a covert guide for Jesuit members on wielding influence, wealth, and power through subversive means.</p> <p>In this dataset, bibliographic information is systematically enriched with geographical identifiers and temporal references to provide clarity on the publication history and authenticity of various editions. The dataset reveals the impact of the Monita in catalyzing anti-Jesuit sentiment through the centuries, not only in Europe but also in the Americas. Rich in metadata, this resource sheds light on how Monita privata/secreta and its subsequent editions may have fueled conspiracy theories about Jesuit secrecy and manipulation.</p> <p>Additionally, the dataset reframes traditional historiographical views, challenging the attribution of authorship to the ex-Jesuit <a href="https://www.wikidata.org/wiki/Q9292123" target="_blank" rel="noopener">Hieronim Zahorowski</a> (d.1634) and positing that the Monita may have been a Venetian (-inspired) fabrication, designed to critique Jesuit influence in Habsburg territories in Central Europe. The inclusion of regional identifiers and contextual data supports a nuanced analysis, making this dataset invaluable for understanding the geographic and political dimensions of early modern anti-Jesuit subversive and parodic literature.</p> <h2>Dataset Contents</h2> <ul> <li> <p>Bibliographic Resource ID: A unique identifier for each bibliographic resource.</p> </li> <li> <p>Title: The title of the edition.</p> </li> <li> <p>Year of Publication: The estimated year or range of years when the manuscript was written.</p> </li> <li> <p>Place of Publication: A Geonames identifier of the publication place.</p> </li> <li> <p>Set of additional research-oriented metadata:</p> </li> <ul> <li> <p>monita:placeInText – identifier of a place appearing in the text</p> </li> <li> <p>monita:placeType – information on the veracity of the place appearing in the text</p> </li> <li> <p>monita:privataInTitle – boolean value for the phrase “privata” appearing in the text</p> </li> <li> <p>monita:publishedSeparately – boolean value for information on the book release</p> </li> <li> <p>monita:secretaInTitle – boolean value for the phrase “secreta” appearing in the text</p> </li> <li> <p>monita:yearCertainty – certainty about the year of publication</p> </li> <li> <p>monita:yearOrigin – origin of the year of publication.</p> </li> </ul> </ul> <h2>Data Format</h2> <p>Terse RDF Triple Language (TTL) with attached metadata and identifiers.</p> <h2>Data Source</h2> <p>Collected from national library catalogs, private archives, and research institutions focusing on (early) modern history, this dataset emerged from an extensive archival and library exploration conducted as part of a research project funded by the Polish National Science Centre (NCN). Research visits spanned Cracow, Vilnius, and Rome—each city chosen for its historical significance in Jesuit and anti-Jesuit narratives—along with targeted investigations in Lublin, Warsaw, and Berlin. In Cracow, the <a href="https://krakow.jezuici.pl/archiwum/" target="_blank" rel="noopener">Provincial Archives</a>, the <a href="https://mnk.pl/en/branch/mnk-the-czartoryski-library" target="_blank" rel="noopener">Czartoryski Library</a>, and the <a href="https://bj.uj.edu.pl/en_GB/start-en" target="_blank" rel="noopener">Jagiellonian Library</a> provided access to Jesuit manuscripts and records of religious conflicts. Vilnius offered insights into Jesuit opposition within the Lithuanian context. <a href="https://biblioteka.vu.lt/en" target="_blank" rel="noopener">The University Library</a> and the <a href="https://www.mab.lt/en/home/" target="_blank" rel="noopener">Wróblewski Library of the Lithuanian Academy of Sciences</a> contained rare anti-Jesuit manuscripts and correspondences from Polish writers critical of the Society’s role in Lithuanian education and culture. In Rome, the <a href="https://arsi.jesuits.global/en/home-eng/" target="_blank" rel="noopener">Archivum Romanum Societatis Iesu (ARSI)</a> and <a href="http://www.bncrm.beniculturali.it/" target="_blank" rel="noopener">Biblioteca Nazionale Centrale</a> yielded polemical texts within the Fondo Gesuitico collection and translations of Monita privata into Spanish, alongside rare Italian editions that circulated as political propaganda. The <a href="https://casanatense.cultura.gov.it/en/" target="_blank" rel="noopener">Biblioteca Casanatense</a> further contributed previously unknown prints, underscoring the Monita's pan-European impact. Additional research in Lublin, Warsaw, and Berlin focused on tracing the origins and dissemination of the Monita privata and re-examining its authorship. <a href="https://archiwa.gov.pl/en/siec-archiwow/state-archives-in-lublin/" target="_blank" rel="noopener">Lublin’s State Archives</a> documented Zahorowski’s biography and reconciliation with the Society of Jesus, while Warsaw’s libraries revealed subtle textual variations across Monita editions, enhancing understanding of its European transmission. In Berlin, the <a href="https://staatsbibliothek-berlin.de/en/" target="_blank" rel="noopener">Staatsbibliothek </a>provided rare early modern prints and manuscripts; cross-referencing findings from Lublin and Warsaw prompted a reevaluation of the traditional attribution of authorship to Zahorowski, instead suggesting that the Monita may have originated within the Venetian intellectual circles surrounding <a href="https://www.wikidata.org/wiki/Q1349158" target="_blank" rel="noopener">Fra Paolo Sarpi</a> (1552–1623).</p> <h2>Applications</h2> <p>The dataset has been used in the project “Literatura antyjezuicka w Rzeczypospolitej Obojga Narodów” [Anti-Jesuit literature in the Polish-Lithuanian Commonwealth] and can be used for research in literary history, Jesuit studies, political science, and cultural studies.</p>
PetroChron Antarctica – a geological database for interdisciplinary use
<p>PetroChron Antarctica is a relational database containing petrological, geochemical and geochronological datasets along with computed rock properties from sampled rocks across Antarctica. Dataset is associated with the submitted publication - PetroChron Antarctica – a geological database for interdisciplinary use.</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.