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150 results for “collection database”
WageIndicator Collective Agreements Database Dataset with Full Texts and Selected Clauses
<p>Since 2012, the <a href="https://wageindicator.org/">WageIndicator Foundation</a> has maintained a <a href="https://wageindicator.org/cbadatabase">Collective Agreements Database</a>, where the texts of 1600 collective agreements (CBAs) from 61 countries and in 27 languages have been uploaded, coded and annotated. This database is a unique example at global level: collective agreements are documents containing conditions of employment that result from negotiations between independent unions and employers, and their content is often surrounded by an atmosphere of secrecy. Under the <a href="https://sshopencloud.eu/">SSHOC project</a> and with the support of the <a href="https://www.clarin.eu/">CLARIN Research Infrastructure</a>, the agreements have been manually and automatically annotated on several levels: for each agreement, the team answers a series of questions and selects the appropriate piece of text (clause) for each. </p> <p>One of the results of the collective agreements' annotation process is the dataset which is available here and includes all the clauses selected for each variable (WageIndicator_CBADatabase_Selected_Clauses). The full collective agreements' texts are stored in another dataset, also available here (WageIndicator_CBADatabase_Full_Texts_211019). A codebook is also included (210125-wageindicator-cba-codebook.pdf).</p>
PROSEU Collective Renewable Energy Prosumers Stakeholders Database (Template)
<p>As part of work package nº2 of the H2020 PROSEU project, which aimed to establish a baseline review and characterisation of renewable energy sources (RES) prosumer (self-consumption) initiatives across Europe, databases identifying the diversity of collective forms of RES prosumers and related stakeholders were built by the project partners using the templates and respective variables presented here (English language). The databases served to create a stratified sample of RES prosumer initiatives for purposes of a survey, as well as distinguish them from other stakeholders in the field.</p>
A database of physical therapy exercises with variability of execution collected by wearable sensors
<p>The PHYTMO database contains data from physical therapy exercises and gait variations recorded with magneto-inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations commonly prescribed in physical therapies were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. Four magneto-inertial sensors were placed on the lower-or upper-limbs for the recording of the motions together with passive optical reflectors. The files include the specifications of the inertial sensors and the cameras. The database includes magneto-inertial data (linear acceleration, turn rate and magnetic field), together with a highly accurate location and orientation in the 3D space provided by the optical system (errors are lower than 1mm). The database files were stored in CSV format to ensure usability with common data processing software. The main aim of this dataset is the availability of inertial data for two main purposes: the analysis of different techniques for the identification and evaluation of exercises monitored with inertial wearable sensors and the validation of inertial sensor-based algorithms for human motion monitoring that obtains segments orientation in the 3D space. Furthermore, the database stores enough data to train and evaluate Machine Learning-based algorithms. The age range of the participants can be useful for establishing age-based metrics for the exercises evaluation or the study of differences in motions between different aged groups. Finally, the MATLAB function <em>features_extraction</em>, developed by the authors, is also given. This function splits signals using a sliding window, returning its segments, and extract signal features, in the time and frequency domains, based on prior studies of the literature.</p>
Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments
<p><strong>Supplemental data for the manuscript </strong></p> <p><strong>"Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments".</strong></p> <p>Content</p> <ul> <li>functional_annotations.tar.gz <ul> <li> functional annotations for 10 different functional annotation systems, for 5090 species</li> </ul> </li> <li> genome_info_and_statistics.tar.gz <ul> <li>basic genome info, annotation system statistics, user query statistics</li> </ul> </li> <li>example_user_queries.tar.gz <ul> <li>three example files for user query inputs used in the analysis </li> </ul> </li> <li>README.txt <ul> <li>details about the files and file formats</li> </ul> </li> </ul>
EvoBib: A Bibliographic Database and Quote Collection for Historical Linguistics
<p>This databases offers 4564 references dealing with computer-assisted language comparison in a broad sense. In addition, the database offers 8364 distinct quotes collected from 5063 references. The majority of the references in the quote database overlaps with those in the bibliographic database. The quotes are organized by keywords and can browsed with a full text and a keyword search. </p> <p>The data (references and quotes) underlying each new release are provided here, the data can be browsed at <a href="https://evobib.digling.org/">https://evobib.digling.org/</a>.</p> <p>If you use the database, I would appreciate if you could this in your research:</p> <blockquote> <p>List, Johann-Mattis (2024): EvoBib: A bibliographic database and quote collection [Database, Version 1.8.0]. Passau: Chair for Multilingual Computational Linguistics. URL: <a href="https://evobib.digling.org/" rel="nofollow">https://evobib.digling.org/</a></p> </blockquote> <p> </p> <p> </p> <p> </p>
Collectives, associations, magazines and cultural initiatives_database_1
<p>Collection of web pages of collectives, associations, magazines and cultural initiatives related to the Latin American world in Italy, as well as to gender topics. </p>
A collection of molecular formula databases for HERMES
<p>A compilation of different molecule databases ready to be used in <a href="https://www.biorxiv.org/content/10.1101/2021.03.08.434466v1">HERMES</a>. We have compiled different open-access DBs and adapted their format to the HERMES requisite columns. Since all databases share the "Name" and "MolecularFormula" columns, merges between databases can be easily generated.</p> <p>More databases and merges will be added in the future. If you have any suggestions or want to contribute, feel free to contact us!</p> <p>All rights reserved to the original authors of the databases.</p> <p>Description of the files:</p> <ul> <li>ECMDB.csv: Entries from <a href="https://ecmdb.ca/"><em>E. coli</em> Metabolome Database</a>. 3760 compounds.</li> <li>Merge_KEGG_ECMDB.csv: a merge between all metabolites from <a href="https://www.genome.jp/kegg/pathway.html">KEGG pathways</a> associated to E.coli K12 with the ECMDB.csv from above. 6107 compounds.</li> <li>Merge_LipidMaps_LipidBlast.csv: a merge between lipid entities from <a href="https://www.lipidmaps.org/resources/databases/index.php">LipidMaps</a> LMSD and the metadata (just Name and Molecular Formula) of <a href="https://fiehnlab.ucdavis.edu/projects/LipidBlast">LipidBlast</a> entries. 163453 compounds.</li> <li>norman.xls: Entries from <a href="https://www.norman-network.com/nds/susdat/susdatSearchShow.php">NORMAN SusDat</a>, containing common and emerging drugs, pollutants, etc. 52019 compounds.</li> <li>PubChemLite_31Oct2020.csv Adapted column names from <a href="https://zenodo.org/record/4183801">https://zenodo.org/record/4183801</a>. 371,663 compounds related to exposomics.</li> <li>MS1_2ID.csv. Merge of HMDB, ChEBI and NORMAN compounds. 183911 compounds related to Human Metabolism, drugs, etc..</li> <li>COCONUT_NP.csv: parsed collection of entries from the COlleCtion of Open Natural ProdUcTs (<a href="https://coconut.naturalproducts.net/">COCONUT</a>).406752 compounds.</li> <li>DiTriPeptides.csv: a list of all theoretically possible dipeptides (400) and tripeptides (8000) and their associated molecular formulas. 8400 compounds.</li> </ul>
FRIBAS database: collection of the main characteristics of 237 reinforced concrete and 71 masonry buildings
<p>FRIBAS-DB is a database that comprises information about 312 buildings (237 reinforced concrete moment resisting frame, 71 unreinforced masonry, 4 mixed type). For each building 37 parameters related to the main building characteristics (age, height, structural typology and main vibrational period), and foundation soil characteristics (e.g. resonance frequency, outcropping geology, seismic soil class, topographic class) are reported. The 312 buildings are located in Basilicata and Friuli Venezia Giulia regions (Southern and North-eastern Italy, respectively) and in different geological and built-environment settings. The FRIBAS-DB allows studying the influence of these parameters for the building dynamic response.</p> <p>In the following the details of each field of the FRIBAS-DB are given.</p> <p>ID_GIS: unique identifier for each building;</p> <p>ID: building identifier containing a number and the province acronym (MT=Matera; PZ=Potenza; VdA=Villa d’Agri; FVG=Friuli Venezia Giulia);</p> <p>Municipality: name of the municipality;</p> <p>COD_COM: municipality code according to the national institute of statistics (ISTAT);</p> <p>LAT, LONG: coordinates of the building in the WGS84-UTM - zone 33N (EPSG:32633);</p> <p>Construction material: RC for Reinforced Concrete Moment Resisting Frame; Masonry for unreinforced masonry buildings; Mixed refers to buildings with both reinforced concrete and load-bearing masonry elements;</p> <p>Soft storey: presence of a soft storey, i.e. a floor that can activate a weak-floor failure mechanism;</p> <p>Building use: Residential, Public, Industrial, Turistic;</p> <p>Age of Construction: < 1919; <1988*; 1919-1945; 1946-1961; 1962-1971; 1972-1975; 1976-1981; 1982-1991; 1992-1996; 1992-2001; 1997-2001; 2002-2008; >2008; (*a more accurate class attribution has not been possible);</p> <p># Floors: the last floor was included when its estimated volume was comparable with the those of other floors in the building;</p> <p>Presence of basement: all the floors that are partially or totally below ground are considered as basement;</p> <p>Building height from the ground to the top of the roof (m): if the building is located on a slope, the ground floor is considered to be the one at the higher side of the slope;</p> <p>Building height from the basement to the top of the roof (m): total height, including also the basement and the structures present at the top of the building; </p> <p>Building width B (m): the shorter dimension of a circumscribing polygon;</p> <p>Building length L (m): the longer dimension of a circumscribing polygon;</p> <p>B/L: ratio between building width and building length as a measure for regularity in plan;</p> <p>B/H: ratio between building width and building height (from the ground level to the top of the building);</p> <p>Floor area (m<sup>2</sup>): the building area calculated based on building footprints (e.g. from openstreetmaps or available national/regional digital maps);</p> <p>Polygon area (m<sup>2</sup>): the area of the circumscribing polygon;</p> <p>Area ratio: ratio between floor and polygon area</p> <p>Building shape: geometric shape of the building, R (rectangle), S (square), T (T-shape), L (L-shape), C (C-shape), H (H-shape), Tr (trapezoid);</p> <p>Seismic provisions (masonry): Description of any seismic provisions (e.g. additional pillars, ring beam, walls reinforcement, tie-rods) if present;</p> <p>Masonry openings (%): percentage of openings with respect to the building lateral surface;</p> <p>Masonry type: type of load-bearing masonry, including material (e.g. stone, bricks, concrete blocks), layout (regular, irregular) and quality;</p> <p>Slab: rigid or flexible, rigid floors often consist of reinforced concrete and hollow tiles;</p> <p>Roof type: wood (with or without hollow tiles), reinforced concrete (with or without hollow tiles);</p> <p>Additional floors: added afterwards to the building, but not included in the original project;</p> <p>Foundation type: shallow or deep;</p> <p>Position of the building: single block is for buildings that consist of a single unit; in case of multiple blocks (e.g. in the case of attached buildings), we distinguish between internal buildings (attached to two or more buildings) and buildings located at the edge (far end blocks attached only to one building). The presence of seismic joints or staircases is specified in the text field;</p> <p>F1_building (Hz): the experimental fundamental frequencies in two directions of the buildings (longitudinal and transversal) were considered, defined as F1_building (lower value) and as F2_buildings (higher value). The fundamental vibrational frequencies for all buildings have been estimated from single station ambient noise measurements analysed through the Horizontal-to-Vertical Spectral Ratio technique. The noise was recorded at the top of each building, aligning the horizontal axes of the sensor parallel to the two main building axes. Measurements were carried out using two different instruments (Tromino and Lunitek Sentinel GEO). The recording time varies between 10 and 30 minutes. The HVSRs have been estimated by the following procedure: each component was divided into non-overlapping windows of 20 s; each window was detrended, tapered (set to 0.5), padded, Fast Fourier Transformed and smoothed with triangular windows with a width equal to 5% of the central frequency. For each of the 20 s windows, the arithmetic mean of the two horizontal component’s spectra was used to combine E-W and N-S components in the single horizontal (H) spectrum; then the HVSR is computed. Finally, the average HVSR spectrum is obtained, providing also the relative ± 2 standard deviations. </p> <p>F2_building (Hz): see above</p> <p>F0_ Foundation Soil (Hz): the main resonance frequency obtained from HVSR analysis of single station ambient noise measurements. Measurements were carried out using two different instruments, Tromino or Reftek datalogger equipped with Lennartz 3D-Lite. The recording time varies between 10 and 30 minutes. For data analysis see “F1_building (Hz)” field. For some cases, the HVSR from microzonation studies were used;</p> <p>Geology: the geological classification was inferred from field surveys or the detailed geological maps of microzonation studies at the scale of 1:5000 or 1:10.000, if available. Otherwise the geological map at the scale 1:50.000 was considered. The outcropping geology classes present are: Gravina Calcarenite (coarse-grained carbonate sandstone); Calcari M.te Viggiano (Limestones and carbonate sandstones); Marsicovetere Breccia (massive calcareous breccias); Subappennine clay; Conglomeratic deposits; Sands and sandstones; Clean Gravels; Silts and Clays; Sand; Silty Gravels; Gravels and Sands, with silt and clay; Alluvial deposits; Colluvial deposits; Eluvial and colluvial deposits; Anthropic deposits;</p> <p>Soft soil/rigid soil: soils with Vs > 360 m/s have been considered as rigid soil. This class is composed mainly by outcropping bedrock (limestones, sandstones and breccias of the South-Appennine Units) and by clean coarse gravels of the Upper Friulian Plain. Soils with Vs < 360 m/s have been considered as soft soils. These are loose sediments (silts, clays, sands, gravels and their mixture) of different origin (alluvial, colluvial, eluvial or antropic).</p> <p>Seismic soil class: this soil classification refers to the national building code (NTC2018, § 3.2.2) based on Vs30. Vs profiles have been measured nearby the studied buildings. If deduced by microzonation studies, they are marked by star (*).</p> <p>Topographic class: The topographic class refers to the national building code classification (NTC2018, § 3.2.2).</p>
Specialized vitamin D databases- European collection and USDA SR26 collection
<p>These two data sets are collections of best quality data on vitamin D content in foods, available in Europe (hosted by EuroFIR<sup>TM</sup>) and in US (hosted by USDA- in SR26). Data was collected in period of 2014-2015. Search criteria included analytical and manufactures' data sources, and these are sorted in food groups and vitamin D forms- vitamin D total (expressed in ug/100g or IU, and converted to ug), vitamin D3 (ug/100g), vitamin D2 (ug/100g), vitamin 25OHD (ug/100g). </p> <p>A manuscript describing creation process of these two dataset is uploaded as well. </p>
SchemaPile: A Large Collection of Relational Database Schemas
<p>Access to fine-grained schema information is crucial for understanding how relational databases are designed and used in practice, and for building systems that help users interact with them. Furthermore, such information is required as training data to leverage the potential of large language models (LLMs) for improving data preparation, data integration and natural language querying.<br><br>Existing single-table corpora such as GitTables provide insights into how tables are structured in-the-wild, but lack detailed schema information about how tables relate to each other, as well as metadata like data types or integrity constraints. On the other hand, existing multi-table (or database schema) datasets are rather small and attribute-poor, leaving it unclear to what extent they actually represent typical real-world database schemas. </p> <p>In order to address these challenges, we present SchemaPile, a corpus of 221,171 database schemas, extracted from SQL files on GitHub. It contains 1.7 million tables with 10 million column definitions, 700 thousand foreign key relationships, seven million integrity constraints, and data content for more than 340 thousand tables. We conduct an in-depth analysis on the millions of schema metadata properties in our corpus, as well as its highly diverse language and topic distribution. In addition, we showcase the potential of SchemaPile to improve a variety of data management applications, e.g., fine-tuning LLMs for schema-only foreign key detection, improving CSV header detection and evaluating multi-dialect SQL parsers. We publish the code and data for recreating SchemaPile and a permissively licensed subset SchemaPile-Perm. </p> <p><a href="http://schemapile.github.io">http://schemapile.github.io</a></p>
Linked collectors and determiners for: Database of invertebrates collected in Mongolia.
Natural history specimen data linked to collectors and determiners held within, "Database of invertebrates collected in Mongolia". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/867dbe0a-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/867dbe0a-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/867dbe0a-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/867dbe0a-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Lichen specimen database of Faurie collection.
Natural history specimen data linked to collectors and determiners held within, "Lichen specimen database of Faurie collection". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/d06712f6-7ea3-4b31-b3c8-da5b2f7ebbc2">https://bionomia.net/dataset/d06712f6-7ea3-4b31-b3c8-da5b2f7ebbc2</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d06712f6-7ea3-4b31-b3c8-da5b2f7ebbc2">https://gbif.org/dataset/d06712f6-7ea3-4b31-b3c8-da5b2f7ebbc2</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Historical review, catalog of type specimens and online database of the ichthyology collection of the Instituto de Ciencias Naturales of the Universidad Nacional de Colombia (ICN-MHN).
Natural history specimen data linked to collectors and determiners held within, "Historical review, catalog of type specimens and online database of the ichthyology collection of the Instituto de Ciencias Naturales of the Universidad Nacional de Colombia (ICN-MHN)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/264fca2e-af6c-427c-8edd-853280f9b607">https://bionomia.net/dataset/264fca2e-af6c-427c-8edd-853280f9b607</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/264fca2e-af6c-427c-8edd-853280f9b607">https://gbif.org/dataset/264fca2e-af6c-427c-8edd-853280f9b607</a>. Formatted as a Frictionless Data package.
Unlocking natural history collections to improve eDNA reference databases and biodiversity monitoring
Open the record for dataset details and reuse information.
PROSEU Collective Renewable Energy Prosumers Database (Template)
<p>As part of work package nº2 of the H2020 PROSEU project, which aimed to establish a baseline review and characterisation of renewable energy sources (RES) prosumer (self-consumption) initiatives across Europe, databases identifying the diversity of collective forms of RES prosumers and related stakeholders were built by the project partners using the templates and respective variables presented here (English language). The databases served to create a stratified sample of RES prosumer initiatives for purposes of a survey, as well as distinguish them from other stakeholders in the field.</p>
Database of Soil Organic Carbon (SOC) stock up to 20 cm depth: A collection of studies from Uruguay
<p>This database comprises 667 soil organic carbon (SOC) stock measurements, collected from various studies conducted across Uruguay, with sampling depths extending up to 20 cm. The database aggregates data from 14 studies, including undergraduate and postgraduate theses and research projects, sampled between 2002 and 2024. It provides comprehensive details on the geographic location of sampling sites (latitude and longitude coordinates), the date of sample collection, the methodology employed for SOC determination, the technique used to estimate SOC stock values at the specified depths, and a description of land use at the moment of sampling.</p> <p>This data collection is particularly relevant for evaluating SOC stocks modeling exercises, as it contributes to a more comprehensive understanding of carbon dynamics across various soil types, environmental conditions, land use, and land cover types. The database is available in shapefile format and is expected to serve as a valuable resource for researchers and professionals in the field.</p> <p><strong> </strong></p> <h3>Data Table Description</h3> <p>The associated data table from this database contains the following key variables:</p> <ul> <li> <p>ID: Unique identifier for each sampling point.</p> </li> <li> <p>Latitude & Longitude: Geographic coordinates of the sampling site.</p> </li> <li> <p>SOC Stock: Soil Organic Carbon (SOC) stock calculated at a fixed depth using the equation SOC stock (MgC/ha) = C × Bd × d, where C represents carbon concentration (%C), Bd is the bulk density of the soil (g/cm³), and d is the depth (cm).</p> </li> <li> <p>Depth: Depth (cm) used for SOC stock calculation. Data were harmonized to a depth of 20 cm; however, lower values are reported for sites where the sampling scheme was conducted at depths less than 20 cm or where bedrock was encountered before reaching 20 cm.</p> </li> <li> <p>Land Use: Simplified land use classification with “grassland”, “agriculture”, or “forest” categories</p> </li> <li> <p>Land Use Extended: If available, the responsible party provides a more detailed explanation of land use.</p> </li> <li> <p>Month & Year: Month and year of sample collection.</p> </li> <li> <p>SOC Method: Methodology used for SOC determination, such as Walkley-Black or dry combustion.</p> </li> <li> <p>Published: Indicates whether the data have been published, with a URL provided if available. If not published, the data are marked as unpublished.</p> </li> <li> <p>SOC Calc: The approach used for calculating SOC stock at a fixed depth, detailed in two possible methods:</p> </li> <ul> <li> <p>Real: For sampling schemes that include a segment ending at 20 cm depth, SOC stock was calculated directly up to 20 cm. This method was also applied when the maximum soil sampling depth was less than 20 cm.</p> </li> <li> <p>Spline: When the sampling depth did not include 20 cm but extended beyond it, a spline interpolation was applied. This method employs all available cumulative values and their associated depths to estimate the SOC stock at 20 cm.</p> </li> </ul> </ul> <h3>Additional Observations</h3> <ul> <li> <p>For sites listed in rows 200-216, the GPS position represents the plot´s centroid, as no specific location was reported.</p> </li> <li> <p>For sites listed in rows 344-633, sampling was conducted at 0-7.5 cm, 7.5-15 cm, and 15-30 cm depth. In sites where the total depth was 15 cm, bulk density was measured only in the first layer (0-7.5 cm), and this value was also applied to the 7.5-15 cm layer. In sites where the total depth was 20 cm, bulk density was measured in all layers and calculated for 20 cm as a weighted average.</p> </li> <li> <p>For sites listed in rows 102-134, bulk density measurements were not taken directly. Instead, data were retrieved from the "SoilGrids" website (<a href="https://soilgrids.org/">https://soilgrids.org/</a>). Bulk density values for the 0-5 cm and 5-15 cm layers were downloaded, and a weighted average was calculated before determining the SOC stock.</p> </li> </ul> <h3> </h3> <h3>Funding:</h3> <p>Funded were provided by ANII (FSDA_1_2018_1_154817, Procesos Inductivos para generación de buenas prácticas agropecuarias: Compilación y análisis de bases de datos a nivel predial; FSA_1_2022_1_175272, Evaluación multiescalar del desempeño ambiental de sistemas agropecuarios con diferente nivel de intensificación a partir de indicadores derivados de sensores remotos); INIA (FPTA-515, Indicadores de sostenibilidad ambiental para el sector agropecuario de Uruguay basados en información derivada de sensores remotos y modelos biofísicos); and IDB (URUGUAY/UR-T1277, Adopción de prácticas Agroecológicas y Huella de Carbono en la Agricultura Uruguaya).</p> <p> </p>
globalbioticinteractions/AEC-DBCNet: Collaborative databasing of North American bee collections within a global informatics network project archive
<p>Data in this archive are from the <em>Collaborative databasing of North American bee collections within a global informatics network project</em>. Data was originally captured using Arthropod Easy Capture software developed at the American Museum of Natural History (AMNH), New York. Project lead investigators are John Ascher (Principal Investigator) and Jerome Rozen (Co-Principal Investigator) at the AMNH, and Douglas Yanega (Principal Investigator), University of California Riverside.</p> <p><strong>Please use this citation for this archive: </strong>John Ascher, Digital Bee Collections Network data archive from the C<em>ollaborative databasing of North American bee collections within a global informatics network project</em>. Version: 08 Mar 2016. https://doi.org/10.5281/zenodo.1436853</p> <p>This project was supported by the National Science Foundation grant <a href="https://nsf.gov/awardsearch/showAward?AWD_ID=0956388">DBI 0956388</a> and <a href="https://nsf.gov/awardsearch/showAward?AWD_ID=0956340">DBI 0956340</a></p> <p><strong>ABSTRACT</strong> Natural history collections contain millions of bee specimens documenting the geographic ranges, temporal occurrence patterns, and floral associations of the 20,000 described bee species. This project will digitize and consolidate specimen records from 10 bee collections across the United States. The investigators will make or verify species identifications, capture full label data, georeference and error-check localities, and upload this information to publicly accessible databases. Web-based tools will be used to capture data across collections efficiently, validate bee and plant names through automated comparison with taxonomic authority files, and synthesize data on species pages with images, digitized literature records, and other information about bees and their host plants. Data will be uploaded to the Global Biodiversity Information Facility and to Discover Life (www.discoverlife.org), a website that features customizable global maps for all global bee species and dynamic identification keys for North American species. To obtain information needed to conserve and manage pollinators, the investigators will work with ecologists to model geographic and temporal trends in bee populations in relation to environmental variables. Bees are the most important pollinators of the approximately 1/3 of crops that require animal pollination. Recent declines in honey bee populations highlight the need to understand better the roles of native bees in agricultural and natural systems. This project will help predict risks to bees and their pollination services from climate change, habitat loss, and other factors. The outreach program Bee Hunt (www.discoverlife.org/bee) will educate the public, including students in underserved communities, about bee diversity and the importance of pollination services. Using digital photography and rigorous research protocols, Bee Hunt will empower people at biological field stations, nature centers, parks, schools, and other sites to collect high-quality data to augment information from specimen records.</p>
Database of freshwater fish species of the Amazonia Region in Colombia: records from ichthyological collections.
<p>The database corresponds to an exhaustive review of freshwater from the Amazonia Region records from the catalogs of scientific reference collections from different institutions. </p>
Collection of chemical occurance data from NORMAN surface water database and other related files
<p>This data set collection includes the downloaded chemical occurrence data from the NORMAN database. It is related with the surface waters. Additionally, this collection also includes the chemical data from the NORMAN database and DSSTox.<br><br>Curated version of this dataset using the newly developed CleanGeoStreamR R package can be found in:<a href="https://zenodo.org/records/13799955"><em> Curated NORMAN Data</em></a>.<br><br>More details about the applied methods and the development of <strong>CleanGeoStreamR</strong> can be found in the following scientific paper: <a href="https://doi.org/10.1016/j.ecoinf.2025.103038"><em>Automated Curation of Spatial Metadata in Environmental Monitoring Data</em></a> (DOI: 10.1016/j.ecoinf.2025.103038).<br><br><br></p>
[OBSOLETE] OCTOPUS Database v.2.2: The CRN Denudation UOW (in preparation) collection [2024]
<p><strong>Database of unpublished cosmogenic Be-10 and Al-26 concentrations from modern river sediment and basin-averaged denudation rates inferred from these data.</strong> Ancillary spatial data includes: sample site location (point), basin outline (polygon), digital elevation model (raster), D8 flow direction and flow accumulation grids (raster), topographic gradient (raster), atmospheric pressure (raster), and cosmogenic nuclide production scaling factor and topographic shielding grids (raster). The vector spatial data uses the WGS84/Pseudo-Mercator (EPSG: 3857) projected coordinate reference system. The raster data uses the WGS86/UTM projected coordinate reference system, UTM zones depending on the extent and location of each data package. Sample metadata is comprehensive and includes all necessary information and input files for the recalculation of denudation rates using the CAIRN model (https://github.com/LSDtopotools/LSDTopoTools_CRNBasinwide). All denudation rates were recalculated and harmonised using CAIRN. The extent of the data covers Australia.</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.