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3,696 results for “Reference”
Default SingleM reference "metapackage" data
<p>SingleM is a tool for profiling shotgun metagenomes. It has a particular strength in detecting microbial lineages which are not in reference databases. The method it uses also makes it suitable for some related tasks, such as assessing eukaryotic contamination, finding bias in genome recovery, computing ecological diversity metrics, and lineage-targeted MAG recovery.</p> <p>The data here is the singlem "metapackage" which is the reference package to be used with SingleM in e.g. "pipe" mode.</p> <p>SingleM is available at <a href="https://github.com/wwood/singlem">https://github.com/wwood/singlem</a>.</p> <p>The newest version is built from Genome Taxonomy Database (GTDB) version 10-RS226, but older versions available in the history of this record should still work with the newest SingleM software.</p> <p> </p> <h2>Changelog</h2> <p>version 5.4.0</p> <ul> <li>Updated to GTDB 10-RS226.</li> <li>The 5.x version number indicates the metapackage format. Old 4.x versions still work with the current software version.</li> </ul> <p>version 4.3.0</p> <ul> <li>Updated to GTDB 09-RS220.</li> </ul> <p>version 4.2.2</p> <ul> <li>Fixed name of .zb folder to be correct version</li> </ul> <p>version 4.2.1</p> <ul> <li>Changed name of .zb folder to be standard</li> </ul> <p>version 4.2.0</p> <ul> <li>Updated GTDB 08-RS214 package to metapackage version 5, and smafa database version 2.</li> </ul> <p>version 4.1.0</p> <ul> <li>Updated GTDB 07-RS207 package to metapackage version 5, and smafa database version 2 (this is the same as version 3.1.2, but with an updated version number).</li> </ul> <p>version 3.2.1</p> <ul> <li>Updated genome sizes for GTDB genomes (for use with `read_fraction`) corrected based on CheckM v2 estimates of completeness and contamination.</li> </ul> <p>version 3.2.0</p> <ul> <li>Updated to GTDB 08-RS214.</li> </ul> <p>version 3.1.2</p> <ul> <li>Updated GTDB 07-RS207 package to metapackage version 5, and smafa database version 2.</li> </ul>
Simulation of the SLR Space Segment Evolution to Improve the Realization of Terrestrial Reference Frames and Determination of Low-Degree Gravity Field Parameters
<p>These are data obtained from simulation studies of the development of the space segment of the SLR technique. Detailed information can be found in Najder et al. (2025). Najder, J., Sośnica, K., Zajdel, R., & Kur, T. (2025). Simulation of the SLR space segment evolution to improve the realization of terrestrial reference frames and determination of low-degree gravity field parameters. <em>Journal of Geodesy</em>, <em>99</em>(6), 46. https://doi.org/10.1007/s00190-025-01971-5</p>
AirMLP - SPS30 low-cost sensors and Tecora reference station PM 2.5 data
<p>The information below describes a dataset related to a study conducted in Turin, Italy, involving low-cost laser-scattering SPS30 sensors placed by Wiseair SRL and a Tecora reference station placed by Arpa Piemonte (Italian Air Quality Agency). This dataset spans two different time periods in 2022, specifically from March 1, 2022, to April 29, 2022, and from October 26, 2022, to December 30, 2022. The data in this dataset pertains to the mass concentration of PM2.5 (particulate matter with a diameter of 2.5 micrometres or less).</p><p> </p><p>The reference station's data is divided into two periods and is provided in files named "rf_x.csv." These files contain hourly data and timestamps in GMT+1. Each file has three columns:</p><ul><li>"valid_at" (in Rome local hour, GMT+1)</li><li>"valore_originale" (PM 2.5 raw mass concentration values recorded by the reference station)</li><li>"pm2p5" (PM 2.5 mass concentration validated values by the air quality agency)</li></ul><p>The low-cost sensors, referred to as "ari_xxxx.csv," provide data at approximately 15-minute frequency. These files contain the following columns:</p><ul><li>"valid_at" (in GMT)</li><li>"pm2p5" (PM 2.5 raw mass concentration measured by the SPS30 sensor)</li><li>"relative_humidity" (expressed as a percentage)</li><li>"temperature" (in degrees Celsius)</li><li>"pressure" (in hPA)</li><li>"wind_speed" (in meters per second)</li><li>"cloud_coverage" (expressed as a percentage)</li></ul><p>Notably, the "relative_humidity" and "temperature" values are gathered from sensors placed within a device containing the SPS30 low-cost sensor.</p><p> </p><p>Here's a summary of the specific data files in this dataset:</p><ul><li>"<strong>rf_1.csv</strong>": Hourly data provided by the Air Quality Agency for the first period.</li><li>"<strong>rf_2.csv</strong>": Hourly data provided by the Air Quality Agency for the second period.</li><li>"<strong>arpa_1727.csv</strong>," "<strong>arpa_1952.csv</strong>," and "<strong>arpa_1953.csv</strong>": Three low-cost sensors placed by Wiseair, which refer to the first period.</li><li>"<strong>arpa_1885.csv</strong>" and "<strong>arpa_2049.csv</strong>": Two low-cost sensors placed by Wiseair, that refer to the second period.</li></ul>
18S V9 metabarcoding reference databases and naive-bayes classifier
<p>18S metabarcoding databases and naive-bayes classifiers specific to the V9 region. Built from the <a href="https://pr2-database.org/">PR2 database</a> using Qiime2 (version 2023.2)<a href="https://github.com/BenKaehler/q2-clawback">.</a> Includes a naive-bayes classifier for use with Qiime2. Sequences were dereplicated with Rescript --p-mode 'uniq' , retaining identical sequence records that have differing taxonomies.</p><p>Primers used:</p><p>EMP 18S 1391f: GTACACACCGCCCGTC</p><p>EMP 18S EukBr: TGATCCTTCTGCAGGTTCACCTAC</p><p><strong>Stats</strong></p><p>19,470 unique sequences</p><p>39,170 total sequences</p><p>11,748 unique taxa </p><p>Note: there were 221,085 sequences in the original PR2 database. Many were filtered out due to the in-silico extraction with our V9 primers.</p><h3>File Descriptions</h3><p><strong>Files in bold are recommended for taxonomic classification.</strong></p><p>Create naive-bayes classifier for 18S PR2 database.md: Markdown with code used to generate databases |</p><p><strong>pr2_v5.0.0_SSU_18S-V9_uniq-classifier.qza</strong>: Unweighted naive-bayes classifier for 18S V9 (primers 1391f, EukBr), extracted from PR2 v5.0.1, dereplicated, generated by qiime2-2023.2 |</p><p><strong>pr2_version_5.0.0_SSU_18S-V9_uniq_seqs.qza</strong>: Sequences for 18S V9 (primers 1391f, EukBr), extracted from PR2 v5.0.1, dereplicated, generated by qiime2-2023.2 |</p><p><strong>pr2_version_5.0.0_SSU_18S-V9_uniq_tax.qza</strong>: Taxa for pr2_version_5.0.0_SSU_18S-V9_uniq_seqs.qza (dereplicated) |</p><p>pr2_version_5.0.0_SSU_18S-V9_seqs.qza: Sequences for 18S V9 (primers 1391f, EukBr), extracted from PR2 v5.0.1, NOT dereplicated, generated by qiime2-2023.2 |</p><p>pr2_version_5.0.0_SSU_18S-V9_tax.qza: Taxa for pr2_version_5.0.0_SSU_18S-V9_seqs.qza (NOT dereplicated) </p><p>pr2_version_5.0.0_SSU_mothur.fasta: SSU sequences downloaded from PR2 v 5.0.1 |</p><p>pr2_version_5.0.0_SSU_mothur.tax: SSU taxa downloaded from PR2 v5.0.1 |</p><p>pr2_version_5.0.0_taxonomy.xlsx: Detailed taxonomy downloaded from PR2 v5.0.1 |</p>
Reference data for neural retina atlas
<p>This repository contains reference files used in analysis for the manuscript 'A proteogenomic atlas of the human neural retina' by Riepe et al. (2024). The code is available at https://github.com/cmbi/Neural-Retina-Atlas.</p>
12S metabarcoding reference data from the Research Institute for Nature and Forest (INBO)
<p>This dataset contains metabarcoding reference data with 12S sequences from fish and other vertebrates for Teleo and Riaz primers suited for use with the <a href="https://git.metabarcoding.org/obitools/obitools/wikis/home/">OBITools</a> package.</p> <p><strong>Files</strong><br>- <strong>all_seqs_INBO_riaz_amplified.fasta</strong>: reference data for Riaz marker<br>- <strong>all_seqs_INBO_Valentini_teleo_amplified.fasta</strong>: reference data for Teleo marker<br>- <strong>species_INBO_riaz.csv</strong>: species for which reference data is included in Riaz dataset<br>- <strong>species_INBO_teleo.csv</strong>: species for which reference data is included in Teleo dataset</p>
NICHE Flanders: reference values for the (a)biotic requirements of vegetation types in Flanders, Belgium
<p>This dataset contains site requirements/tolerance limits (or "reference values") for 28 vegetation types found in Flanders. It gives the lower and upper limits or the classes within which these vegetation types can occur, for 7 site factors that determine potential vegetation development. These reference values can be used to determine the potential distribution of the different vegetation types with the ecohydrological model NICHE Flanders (<a href="https://purews.inbo.be/ws/portalfiles/portal/5370206/Callebaut_etal_2007_NicheVlaanderen.pdf">Callebaut et al. 2007</a>, in Dutch).</p> <p>See the Technical info (available in English and Dutch) for more information.</p>
MarFERReT: an open-source, version-controlled reference library of marine microbial eukaryote functional genes
<p>Metatranscriptomics generates large volumes of sequence data about transcribed genes in natural environments. Taxonomic annotation of these datasets depends on availability of curated reference sequences. For marine microbial eukaryotes, current reference libraries are limited by gaps in sequenced organism diversity and barriers to updating libraries with new sequence data, resulting in taxonomic annotation of only about half of eukaryotic environmental transcripts. Here, we introduce version 1.0 of the Marine Functional EukaRyotic Reference Taxa (MarFERReT), an updated marine microbial eukaryotic sequence library with a version-controlled framework designed for taxonomic annotation of eukaryotic metatranscriptomes. We gathered 902 marine eukaryote genomes and transcriptomes from multiple sources and assessed these candidate entries for sequence quality and cross-contamination issues, selecting 800 validated entries for inclusion in the library. MarFERReT v1 contains reference sequences from 800 marine eukaryotic genomes and transcriptomes, covering 453 species- and strain-level taxa, totaling nearly 28 million protein sequences with associated NCBI and PR2 Taxonomy identifiers and Pfam functional annotations. An accompanying MarFERReT project repository hosts containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT.<br><br>MarFERReT is linked to a code repository hosting containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT here: <a href="https://github.com/armbrustlab/marferret">https://github.com/armbrustlab/marferret</a></p> <p>The raw source data for the 902 candidate entries considered for MarFERReT v1.1.1, including the 800 accepted entries, are available for download from their respective online locations. The source URL for each of the entries is listed here in MarFERReT.v1.1.1.entry_curation.csv, and detailed instructions and code for downloading the raw sequence data from source are available in the MarFERReT code repository (<a href="https://github.com/armbrustlab/marferret/blob/main/docs/process_clean_marmicrodb.log.sh">link</a>). </p> <p>This repository release contains MarFERReT database files from the v1.1.1 MarFERReT release using the following MarFERReT library build scripts: <strong>assemble_marferret.sh</strong>, <strong>pfam_annotate.sh</strong>, and <strong>build_diamond_db.sh</strong><br><br>The following MarFERReT data products are available in this repository:</p> <p><strong>MarFERReT.v1.1.1.metadata.csv</strong><br>This CSV file contains descriptors of each of the 902 database entries, including data source, taxonomy, and sequence descriptors. Data fields are as follows:</p> <ol> <li><strong>entry_id</strong>: Unique MarFERReT sequence entry identifier.</li> <li><strong>accepted: </strong>Acceptance into the final MarFERReT build (Y/N). The Y/N values can be adjusted to customize the final build output according to user-specific needs.</li> <li><strong>marferret_name</strong>: A human and machine friendly string derived from the NCBI Taxonomy organism name; maintaining strain-level designation wherever possible.</li> <li><strong>tax_id</strong>: The NCBI Taxonomy ID (taxID).</li> <li><strong>pr2_accession</strong>: Best-matching PR2 accession ID associated with entry</li> <li><strong>pr2_rank</strong>: The lowest shared rank between the entry and the pr2_accession</li> <li><strong>pr2_taxonomy</strong>: PR2 Taxonomy classification scheme of the pr2_accession</li> <li><strong>data_type</strong>: Type of sequence data; transcriptome shotgun assemblies (TSA), gene models from assembled genomes (genome), and single-cell amplified genomes (SAG) or transcriptomes (SAT).</li> <li><strong>data_source</strong>: Online location of sequence data; the Zenodo data repository (<a href="../">Zenodo</a>), the datadryad.org repository (<a href="http://datadryad.org/">datadryad.org</a>), MMETSP re-assemblies on Zenodo (MMETSP)17, NCBI GenBank (<a href="https://www.ncbi.nlm.nih.gov/genbank/">NCBI</a>), JGI Phycocosm (<a href="https://phycocosm.jgi.doe.gov/phycocosm/home">JGI-Phycocosm</a>), the TARA Oceans portal on Genoscope (<a href="http://www.genoscope.cns.fr/tara/">TARA</a>), or entries from the Roscoff Culture Collection through the METdb database repository (<a href="https://metdb.sb-roscoff.fr/metdb/">METdb</a>).</li> <li><strong>source_link</strong>: URL where the original sequence data and/or metadata was collected.</li> <li><strong>pub_year</strong>: Year of data release or publication of linked reference.</li> <li><strong>ref_link</strong>: Pubmed URL directs to the published reference for entry, if available.</li> <li><strong>ref_doi</strong>: DOI of entry data from source, if available.</li> <li><strong>source_filename</strong>: Name of the original sequence file name from the data source.</li> <li><strong>seq_type</strong>: Entry sequence data retrieved in nucleotide (nt) or amino acid (aa) alphabets.</li> <li><strong>n_seqs_raw</strong>: Number of sequences in the original sequence file.</li> <li><strong>source_name:</strong> Full organism name from entry source</li> <li><strong>original_taxID</strong>: Original NCBI taxID from entry data source metadata, if available</li> <li><strong>alias:</strong> Additional identifiers for the entry, if available</li> </ol> <p><br><strong>MarFERReT.v1.1.1.curation.csv</strong><br>This CSV file contains curation and quality-control information on the 902 candidate entries considered for incorporation into MarFERReT v1, including curated NCBI Taxonomy IDs and entry validation statistics. Data fields are as follows:</p> <ol> <li><strong>entry_id:</strong> Unique MarFERReT sequence entry identifier</li> <li><strong>marferret_name: </strong>Organism name in human and machine friendly format, including additional NCBI taxonomy strain identifiers if available.</li> <li><strong>tax_id</strong>: Verified NCBI taxID used in MarFERReT</li> <li><strong>taxID_status</strong>: Status of the final NCBI taxID (Assigned, Updated, or Unchanged)</li> <li><strong>taxID_notes</strong>: Notes on the original_taxID</li> <li><strong>n_seqs_raw</strong>: Number of sequences in the original sequence file</li> <li><strong>n_pfams</strong>: Number of Pfam domains identified in protein sequences</li> <li><strong>qc_flag</strong>: Early validation quality control flags for the following: LOW_SEQS; less than 1,200 raw sequences; LOW_PFAMS; less than 500 Pfam domain annotations.</li> <li><strong>flag_Lasek</strong>: Flag notes from Lasek-Nesselquist and Johnson (2019); contains the flag 'FLAG_LASEK' indicating ciliate samples reported as contaminated in this study.</li> <li><strong>VV_contam_pct</strong>: Estimated contamination reported for MMETSP entries in Van Vlierberghe et al., (2021).</li> <li><strong>flag_VanVlierberghe: </strong>Flag for a high level of estimated contamination, from 'flag_VanVlierberghe' values over 50%: FLAG_VV.</li> <li><strong>rp63_npfams</strong>: Number of ribosomal protein Pfam domains out of 63 total.</li> <li><strong>rp63_contam_pct</strong>: Percent of total ribosomal protein sequences with an inferred taxonomic identity in any lineage other than the recorded identity, as described in the Technical Validation section from analysis of 63 Pfam ribosomal protein domains.</li> <li><strong>flag_rp63</strong>: Flag for a high level of estimated contamination, from 'rp63_contam_pct' values over 50%: FLAG_RP63.</li> <li><strong>flag_sum: </strong>Count of the number of flag columns (`qc_flag`, `flag_Lasek`, `flag_VanVlierberghe`, and `flag_rp63`). All entries with one or more flag are nominally rejected ('accepted' = N); entries without any flags are validated and accepted ('accepted' = Y).</li> <li><strong>accepted: </strong>Acceptance into the final MarFERReT build (Y or N).</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.proteins.faa.gz</strong><br>This Gzip-compressed FASTA file contains the 27,951,013 final translated and clustered protein sequences for all 800 accepted MarFERReT entries. The sequence defline contains the unique identifier for the sequence and its reference (mftX, where 'X' is a ten-digit integer value). </p> <p> </p> <p><strong>MarFERReT.v1.1.1.taxonomies.tab.gz</strong><br>This Gzip-compressed tab-separated file is formatted for interoperability with the DIAMOND protein alignment tool commonly used for downstream analyses and contains some columns without any data. Each row contains an entry for one of the MarFERReT protein sequences in MarFERReT.v1.proteins.faa.gz. Note that 'accession.version' and 'taxid' are populated columns while 'accession' and 'gi' have NA values; the latter columns are required for back-compatibility as input for the DIAMOND alignment software and LCA analysis. </p> <p>The columns in this file contain the following information:</p> <ol> <li><strong>accession</strong>: (NA)</li> <li><strong>accession.version</strong>: The unique MarFERReT sequence identifier ('mftX').</li> <li><strong>taxid</strong>: The NCBI Taxonomy ID associated with this reference sequence.</li> <li><strong>gi</strong>: (NA).</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.proteins_info.tab.gz</strong><br>This Gzip-compressed tab-separated file contains a row for each final MarFERReT protein sequence with the following columns:</p> <ol> <li><strong>aa_id</strong>: the unique identifier for each MarFERReT protein sequence.</li> <li><strong>entry_id</strong>: The unique numeric identifier for each MarFERReT entry.</li> <li><strong>source_defline</strong>: The original, unformatted sequence identifier</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.best_pfam_annotations.csv.gz<br></strong>This Gzip-compressed CSV file contains the best-scoring Pfam annotation for intra-species clustered protein sequences from the 800 validated MarFERReT entries; derived from the hmmsearch annotations against Pfam 34.0 functional domains. This file contains the following fields:</p> <ol> <li><strong>aa_id</strong>: The unique MarFERReT protein sequence ID ('mftX').</li> <li><strong>pfam_name</strong>: The shorthand Pfam protein family name.</li> <li><strong>pfam_id</strong>: The Pfam identifier.</li> <li><strong>pfam_eval</strong>: hmm profile match e-value score</li> <li><strong>pfam_score:</strong> hmm profile match bitscore</li> </ol> <p><br><strong>MarFERReT.v1.1.1.dmnd</strong><br>This binary file is the indexed database of the MarFERReT protein library with embedded NCBI taxonomic information generated by the DIAMOND makedb tool using the build_diamond_db.sh script from the MarFERReT /scripts/ library. This can be used as the reference DIAMOND database for annotating environment sequences from eukaryotic metatranscriptomes. <br><br></p>
Wind power plants layouts according to arbitrary reference points, Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array
<p><strong>If this dataset helps your research, please cite it and the papers further below (according to which OWPP you study).</strong></p> <p>This dataset contains the layouts of the Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array offshore wind power plants (OWPPs), which can be utilized in a variety of studies. </p> <p>The X and Y coordinates, in kilometers, were written according to arbitrary reference points. The positions of wind turbine generators (WTGs) and substations (SS) for the OWPPs came from the sources below.</p> <p><strong>Thanet</strong>: WTGs from [1] (page 7), SS based on [3] (page 9).<br><strong>West of Duddon Sands</strong>: WTGs from [2] (page 5), SS based on [3] (page 9).<br><strong>Ormonde</strong>: WTGs and SS from [4] (page 2).<br><strong>Westermost Rough</strong>: WTGs and SS from [5] (page 4).<br><strong>Horns Rev 1</strong>: WTGs and SS from [6] (page 6).<br><strong>Horns Rev 2</strong>: WTGs and SS from [7] (page 5).<br><strong>Anholt</strong>: WTGs and SS from [8] (page 3).<br><strong>London Array</strong>: WTGs from [9] (page 15), SS based on [10] (page 2).</p> <p>From the OWPPs' layout figures [1-9], I used Graph Grabber 2.0.2* to extract the data points. Then, based on visual inspection of layouts in [1-9], I utilized 2D projections to align WTGs that should be aligned. References [1], [2], and [9] do not provide the SS positions. Thus, I carefully overlapped the layouts with other layouts from [3] and [10] to approximate the SS locations.</p> <p>Regarding the arbitrary reference points for the coordinates, although the values in the X and Y axes differ from [1-9], note that the distances among the WTGs are the same from [1-9]. Furthermore, there are no axes in [1,5]. Instead, distances are given, which are enough to obtain the layout. Values in meters were converted to kilometers.</p> <p>As seen in the attachments, the coordinates can be obtained via either "h5" or "csv" files, which can be easily read by Matlab, Julia, and Python, among others. <strong>The name of the datasets in the "h5" file are</strong>: "Thanet", "WDS", "Ormonde", "WMR", "HornsRev1", "HornsRev2", "Anholt", and "LondonArray".</p> <p><strong>Except for the London Array OWPP, the first row in all data matrices represents the SS coordinates, whereas the subsequent rows represent the WTGs. London Array has 2 substations, thus the first and second rows represent their coordinates. In all matrices, the first and second columns are the X and Y coordinates, respectively.</strong></p> <p>*<a href="https://www.quintessa.org/software/downloads-and-demos/graph-grabber-2.0.2">Graph Grabber 2.0.2 | Downloads And Demos | Software | Quintessa Limited | Scientific and Mathematical Consultancy</a></p>
Reference grids (vector) and their centroids for harmonization of analysis
<p>This dataset contains reference grids (vector) and their centroids for harmonized analysis. All the files are <em>geoparquet</em>, they are described below. Production procedure is available at projects GitHub repository (https://github.com/aavotins/HiQBioDiv/blob/main/Templates/TemplateGrids_Vector.R):</p> <ul> <li>"tikls100_sauzeme.parquet" contains terrestrial territory of Latvia divided in 100-by-100 m polygon grid cells. Contains fields: <ul> <li>"id" feature ID;</li> <li>"yes" a fields with values "1";</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"rinda300" with ID's matching file "tikls300_sauszeme.parquet";</li> <li>"ID1km" with ID's matching file "tikls1km_sauszeme.parquet";</li> <li>"rinda500" with ID's matching file "tikls500_sauszeme.parquet";</li> <li>"geom" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls300_sauzeme.parquet" contains terrestrial territory of Latvia divided in 300-by-300 m polygon grid cells. Contains fields: <ul> <li>"rinda300" feature ID;</li> <li>"x" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls500_sauzeme.parquet" contains terrestrial territory of Latvia divided in 500-by-500 m polygon grid cells. Contains fields: <ul> <li>"rinda500" feature ID;</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"x" a {sf} geometry definition field.</li> </ul> </li> <li>"tikls1km_sauzeme.parquet" contains terrestrial territory of Latvia divided in 1000-by-1000 m polygon grid cells. Contains fields: <ul> <li>"ID1km" feature ID;</li> <li>"yes" a fields with values "1";</li> <li>"tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"geometry" a {sf} geometry definition field.</li> </ul> </li> <li>"pts100_sauszeme.parquet" contains centroids of file "tikls100_sauszeme.parquet" with their attribute fields;</li> <li>"pts300_sauszeme.parquet" contains centroids of file "tikls300_sauszeme.parquet" with their attribute fields and additionally "tks50km" with ID's of topographic map of Latvia pages (TKS-93 M:50000);</li> <li>"pts500_sauszeme.parquet" contains centroids of file "tikls500_sauszeme.parquet" with their attribute fields;</li> <li>"pts1000_sauszeme.parquet" contains centroids of file "tikls1km_sauszeme.parquet" with their attribute fields;</li> <li>"tks93_50km.parquet" contains topographic map of Latvia pages (TKS-93 M:50000). Contains fields: <ul> <li>"NOSAUKUMS" with a page name;</li> <li>"NUMURS" with a page number;</li> <li>"Shape_Length" an attribute from ESRI File Geodatabase;</li> <li>"Shape_Area" an attribute from ESRI File Geodatabase;</li> <li>"Shape" a {sf} geometry definition field;</li> </ul> </li> <li>All the above mentioned files are stored also as layers in geopakage file "vector_grids.gpkg" having the same names and attributes.</li> </ul>
Mar2025 reference datasets for Mitohelper
<p><strong>Reference datasets (Mar 2025 update) for Mitohelper (</strong><a href="https://github.com/aomlomics/mitohelper"><em><strong>https://github.com/aomlomics/mitohelper</strong></em></a><strong>)</strong></p> <p><a href="http://github.com/aomlomics/mitohelper">Mitohelper</a> is a repository built to facilitate experimental design, alignment visualization, and reference sequence analysis in fish eDNA studies. Refer to our <a href="https://doi.org/10.1002/edn3.187">paper</a> and Mitohelper's <a href="https://github.com/aomlomics/mitohelper/wiki">wiki</a> for database construction pipeline.</p> <p>I. Reference database files in tab-separated format, containing gene, taxonomy, and sequence information:</p> <ul> <li>mitofish.all.Mar2025.tsv (883,519 records)</li> <li>mitofish.12S.Mar2025.tsv (61,379 records of 12S rRNA gene >50 bp long)</li> <li>mitofish.12S.Mar2025_NR.fasta (FASTA file of 12S rRNA gene records)</li> <li>mitofish.COI.Mar2025.tsv (323,337 records)</li> </ul> <p>II. De-replicated QIIME 2-compatible 12S/12S+16S+18S rRNA reference datasets:</p> <ul> <li>12S-seqs-derep-uniq.qza</li> <li>12S-tax-derep-uniq.qza</li> <li>12S-16S-18S-seqs.qza</li> <li>12S-16S-18S-tax.qza</li> </ul> <p>If you use Mitohelper, please cite:<br>Jean Lim, S, Thompson, LR. Mitohelper: A mitochondrial reference sequence analysis tool for fish eDNA studies. Environmental DNA. 2021; 00: 1– 10. <a href="https://doi.org/10.1002/edn3.187">https://doi.org/10.1002/edn3.187</a></p>
Alignment between type of landmark in different sources and the concept in the spatial reference objects ontology
<p>The five datasets represent a manually alignment between the landmark type of five different datasets archived <a href="https://doi.org/10.5281/zenodo.6480986">here</a> and a common vocabulary extracted from an application ontology defined for mountain rescue purposes, named <a href="https://hamac.ign.fr/owa/redir.aspx?C=cjlWje9SCaYsVOTLbxbOoIBLZUCS56nVb248cRSMTEDSENDFzybaCA..&URL=http%3a%2f%2fchoucas.ign.fr%2fdoc%2fontologies%2foor.owl%2f">Ontology of landmarks</a> (OOR).</p> <p>Each file represents the alignment for features belonging to a data source with the same OOR ontology.</p> <p>For example, the type «bivouac» from camptocamp.org source is aligned with the uri <a href="http://purl.org/choucas.ign.fr/oor#abri">http://purl.org/choucas.ign.fr/oor#abri</a> of the corresponding class «Shelter » in the ontology of landmark. The alignments models can be considered as a ground truth data.</p> <p>The alignments results are obtained using an ontology application named <a href="http://choucas.ign.fr/doc/ontologies/index-fr.html">OOR</a>. These specific results are obtained using the version of OOR V1.0.1 which is an improved version and contains new concepts compared to the first release 1.0.0. The new version of OOR (i.e. 1.0.1) will be released by the end of May 31 2022. The new link will be added here.</p> <p>This archive is released for transparency and reproducibility purposes.</p>
Epidemiological and clinical characteristics predictive of ICU mortality of traumatic brain injury patients treated at a trauma reference hospital – A cohort study - Dataset
<p><strong>Dataset of a cohort whose summary is described below.</strong></p> <p><strong>ABSTRACT</strong></p> <p><strong>Background</strong>: Traumatic brain injury (TBI) has substantial physical, psychological, social and economic impacts, with high rates of morbidity and mortality. Considering its high incidence, the aim of this study was to identify epidemiological and clinical characteristics that predict mortality in patients hospitalized for TBI in intensive care units (ICUs). <strong>Methods</strong>: A retrospective cohort study was carried out with patients over 18 years old with TBI admitted to an ICU of a Brazilian trauma referral hospital between January 2012 and August 2019. TBI was compared with other traumas in terms of clinical characteristics of ICU admission and outcome. Univariate and multivariate analyses were used to estimate the odds ratio for mortality. <strong>Results</strong>: Of the 4816 patients included, 1114 had TBI, with a predominance of males (85.1%). Compared with patients with other traumas, patients with TBI had a lower mean age (45.3 ± 19.1 versus 57.1 ± 24.1 years, p < 0.001), higher median APACHE II (19 versus 15, p <0.001) and SOFA (6 versus 3, p < 0.001) scores, lower median Glasgow Coma Scale (GCS) score (10 versus 15, p < 0.001), higher median length of stay (7 days versus 4 days, p < 0.001) and higher mortality (27.6% versus 13.3%, p < 0.001). In the multivariate analysis, the predictors of mortality were older age (OR: 1.008 [1.002-1.015], p = 0.016), higher APACHE II score (OR: 1.180 [1.155-1.204], p < 0.001), lower GCS score for the first 24 hours (OR: 0.730 [0.700-0.760], p < 0.001), and greater number of brain injuries and presence of associated chest trauma (OR: 1.727 [1.192-2.501], p < 0.001). <strong>Conclusion</strong>: Patients admitted to the ICU for TBI were younger and had worse prognostic scores, longer hospital stays and higher mortality than those admitted to the ICU for other traumas. The independent predictors of mortality were advanced age, APACHE II score, first 24-hour GCS score, number of brain injuries and chest trauma.</p>
The Object Detection for Olfactory References (ODOR) Dataset
<p><strong>The Object Detection for Olfactory References (ODOR) Dataset</strong></p> <p>Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. </p> <p>Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. </p> <p>It has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. </p> <p>Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.</p> <p><strong>How to use</strong></p> <p>The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes or image descriptions, as well as formal annotations, such as artist, license, or creation year. </p> <p>In addition to a zip containing the dataset images, we provide links to their source collections in the metadata file and a Python script to conveniently download the artwork images (`download_imgs.py`).</p> <p>The mapping between the `images` array of the `annotations.json` and the `metadata.csv` file can be accomplished via the `file_name` attribute of the elements of the `images` array and the unique `File Name` column of the `metadata.csv` file, respectively.</p>
Testing absolute plate reference frames and the implications for the generation of geodynamic mantle heterogeneity structure
<div>Description of Resources - Shephard et al. (2012)</div> <div> </div> <div>This file provides a detailed description of all of the files that make up the data collection associated with the publication: Shephard, G. E., Bunge, H. P., Schuberth, B. S., Müller, R. D., Talsma, A. S., Moder, C., & Landgrebe, T. C. W. (2012). Testing absolute plate reference frames and the implications for the generation of geodynamic mantle heterogeneity structure. Earth and Planetary Science Letters, 317, 204-217. doi: <a href="https://doi.org/10.1016/j.epsl.2011.11.027" target="_blank" rel="noopener">10.1016/j.epsl.2011.11.027</a></div> <div> </div> <div>Note: For information on file formats and what programs to use to interact with various file formats, see "File Formats and Recommended Programs”.</div> <div> </div> <div>This data collection includes both the rotations and topologically closed polygons* for each of the 5 absolute reference frames that were tested in the publication. They are to be loaded in GPlates (<a href="http://www.gplates.org" target="_blank" rel="noopener">http://www.gplates.org</a>).</div> <div> </div> <div>*Topologically closed plate polygons are constructed from the intersection of ridges, transforms, subduction zones and other plate boundary geometries. These 'resolved topologies' are valid at 1 Myr intervals. The plate boundary geometries and plate polygons have been assigned plate reconstruction IDs to allow them to be reconstructed using the supplied rotation files. </div> <div> </div> <div>The files associated with this data collection include:</div> <div>• <strong>Hybrid hotspot model (Moving and Fixed hotspots) (HHS)</strong></div> <div>* Caltech_Global_20110311HHS.gpml (37 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412HHS.rot (287 KB)- global rotation model</div> <div> </div> <div>• <strong>Fixed hotspot model (FHS)</strong></div> <div>* Caltech_Global_20110311FHS.gpml (36.8 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412FHS.rot (291 KB) - global rotation model</div> <div> </div> <div>•<strong> Hybrid hotspot and palaeomagnetic model (PMG)</strong></div> <div>* Caltech_Global_20110311PMG.gpml (35.9 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412PMG.rot (287 KB) - global rotation model</div> <div> </div> <div>• <strong>Subduction reference frame model (SUB)</strong></div> <div>* Caltech_Global_20110311SUB.gpml (36 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412SUB.rot (287 KB) - global rotation model</div> <div> </div> <div>• <strong>Hybrid hotspot and TPW-corrected palaeomagnetic model (TPW)</strong></div> <div>* Caltech_Global_20110311TPW.gpml (36.8 MB) - topologically closed plate polygons and plate boundary geometries</div> <div>* Caltech_Global_20110412TPW.rot (287 KB)- global rotation model</div> <div> </div> <div>Project files (.gproj) are included for each .gpml/.rot pair.</div> <div> </div> <div>This article has additional supplementary data available with the online publication.</div> <div> </div> <div> </div> <div>Additional notes:</div> <div>*.rot contains the rotations for all plates and topological polygons.</div> <div>Each model is specific according to the African Plate (Plate ID 701) rotations. The rotations for all other plates are the same across each of the five models with the exception of cross-overs involving Pacific/Panthalassa plates for times earlier than 83.5Ma; these must be absolute reference frame specific and were re-calculated for each model. Programs used to calculate the new finite rotations include "adder" and "seaflow" </div> <div> </div> <div>*.gpml and .shp files contain continuously closing plate polygons i.e. from plate boundaries, from 140 Ma to present-day in 1 million year increments. </div> <div>These files differ slightly from those used in the paper, but are the most up-to-date version (as at May 2011) and are based on an updated model, Seton et al. (2012).</div> <div>They are specific to each of the five absolute reference frames. </div> <div> </div> <div>Note on velocity calculations in GPlates:</div> <div>GPlates calculates the velocity within each plate based on the stage rotation for that time period and averages for that respective period. For this reason, the velocities of a plate do not change incrementally within the time period and then abruptly change according to the next time period/stage rotation. </div> <div>This is also why there appears to be a "jump" in velocity magnitude and direction between 140 and 139 Ma.</div>
Protein haplotype sequences obtained by ProHap from the Human Pangenome Reference Consotruim dataset
<p>Database of protein sequences obtained using ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) on the data set of phased genotypes published by the Human Pangenome Reference Consotruim (HPRC), first release (<a href="https://github.com/human-pangenomics/hpp_pangenome_resources">https://github.com/human-pangenomics/hpp_pangenome_resources</a>), 44 samples. We used Ensembl v.110 for the mapping of coordinates between genes, exons, and transcripts.</p> <p>This repository contains one database created using all 43 samples of the HPRC release (the haplotypes of the sample NA21309 did not encode any non-canonical sequences), and then a database for each of the 43 samples separately. No filtering on allele frequency or haplotype frequency was applied in any of the databases. The complete configuration file for the ProHap run is attached to this repository.</p> <p>There is one compressed directory for each of the databases, containing the following files:</p> <ul> <li>F1: The concatenated fasta file ready to be used with search engines, contains the following: <ul> <li>Protein haplotype sequences obtained by ProHap</li> <li>Reference proteome as per Ensembl v. 110</li> <li>Contaminant sequences from the cRAP project (<a href="https://www.thegpm.org/crap/">https://www.thegpm.org/crap/</a>)</li> <li>For this dataset, only the simplified format is provided. The simplified fasta contains only the artificial protein identifier and the matching gene name, and is optimised for compatibility with a wide range of tools. For annotation of peptides using the PeptideAnnotator, please provide the header (F1.2) in addition to the fasta file. </li> </ul> </li> <li>F2: Additional information about the haplotype sequences, to be used for mapping identified peptides to the original haplotypes</li> <li>F3: Translations of haplotype cDNA sequences, before merging with the reference proteome</li> </ul> <p>For further description of the files, please refer to <a href="https://github.com/ProGenNo/ProHap/wiki/Output-files">https://github.com/ProGenNo/ProHap/wiki/Output-files</a>.</p> <p>For the usage of these databases with search engines, and downstream anaylsis of identified peptides, please refer to the project's wiki page: <a href="https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches">https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches</a>.</p> <p>When using these databases in your publication, please cite: Vašíček, J., Kuznetsova, K.G., Skiadopoulou, D. <em>et al.</em> ProHap enables human proteomic database generation accounting for population diversity. <em>Nat Methods</em> (2024). <a href="https://doi.org/10.1038/s41592-024-02506-0">https://doi.org/10.1038/s41592-024-02506-0</a></p>
Projected fresh water use from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016
<p>The dataset contains projections of fresh water withdrawal and consumption from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016.</p> <p>The energy sector in this scope includes energy production (production of coal, oil and gas) and energy transformation in oil refineries and power plants (nuclear, solid fuels, oil, gas, biomass and geothermal).</p> <p>The information in provided on NUTS 2 level following the NUTS2 2013 definition.</p> <p>The dataset is explained in more detail in the report <a href="https://ec.europa.eu/jrc/en/publication/projected-fresh-water-use-european-energy-sector">Projected fresh water use from the European energy sector</a>.</p>
EvoRef: EvoBib Reference Browser
<p>This is the dump of the data underlying <a href="http://calc.digling.org/evoref/">EvoRef</a>, a collection of quotes from different bibliographic references, which is in part connected to the EvoBib bibliography.</p>
Rye Reference NLRome
<p>Annotation of nucleotide-binding leucine-rich repeat, NB-LRR, or NLR, the major family of resistance genes, in the recent 'Lo7' and 'Weining' rye reference genomes. </p> <p>Shared in the hope that it provides a valuable and easily-accessible resource for further studies in the NLRome of rye and breeding of novel resistant cultivars.</p>
A Fully-Parameterized Object-Side Light Field Dataset and Theory for Using Entrance and Exit Pupils as Natural Light Field Reference Planes for an Unfocused Plenoptic Camera
<p>We describe a dataset of light fields with full object-side parameterizations. The dataset contains PNG and ESLF files for all 32 images. 12 of them additionally contain depth maps and point clouds.</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.