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4,283 results for “Database”
A unified template for sediment source fingerprinting databases
<p>Over the last few years, the sediment source fingerprinting community has been engaged in promoting best practices to improve the design and the implementation of sediment fingerprinting techniques (<a href="https://doi.org/10.1007/s11368-022-03203-1">Evrard et al., 2022</a>). Data sharing is a key part of open science making research more reliable and accessible to the community. To move forward and improve data sharing, we propose these templates for databases and metadata.</p> <p>These templates include: common metadata for samples (soil, river flood deposit, sediment core...) description (name, IGSN, location, sampling date...), list and description of common properties (elemental geochemistry, organic matter, radionuclides…) used in sediment source fingerprinting studies. These templates are intended to evolve thanks to the participation of the community, as part of a collaborative project.</p> <p>In addition, the <strong>collectionneur </strong>R package was designed to help researchers and data managers maintain an up-to-date and well-organized database. is avalaible on <a href="https://github.com/tchalauxclergue/collectionneur"><strong>GitHub</strong> (https://github.com/tchalauxclergue/collectionneur)</a> and <a href="https://doi.org/10.5281/zenodo.15146958"><strong>Zenodo</strong> (https://doi.org/10.5281/zenodo.15146958)</a>. It facilitates the comparison and integration of new data entries into an existing database while keeping a detailed report of all modifications. All database formats are allowed, although it was initially designed for sediment source fingerprinting databases.</p> <p>Published databases following these templates are listed in the References section below. </p>
Database of water, agriculture and economic development in Huang-Huai-ai region of China
<p>The database of water, agriculture and economic development contains 61 prefecture-level cities in the Huang-Huai-Hai region from 2010 to 2019.</p> <p>Firstly, we summarize the city-level agricultural dataset from the Provincial Bureau of Statistics, which contains the annual agricultural output, total planting area, labor, fertilizer, and machinery of each prefecture-level city. </p> <p>Secondly, we collect agricultural output (total land value per hectare) as the output and four main types of inputs: labor, fertilizer, machinery, and agricultural water consumption.</p> <p>Thirdly, we also collect city-level unbalanced panel data from the Water Resources Bulletin database, which contains annual data on agricultural water consumption, groundwater supply, precipitation, and groundwater resources.</p>
Database on the History of Participation and Engagement
<p>The dataset presents a timeline and database of sustainability-related, innovative, and inclusive participation and engagement practices in Europe since the early days of digitalisation. The data reported here was assembled from Democratic Innovations collected from publicly accessible sources (Partecipedia, OECD's database of Representative Deliberate Processes and Institutions, Knowledge Network on Climate Assemblies (KNOCA), G1000, and the International Observatory on Participatory Democracy), with a specific focus on their contribution to social and environmental sustainability. By mapping the dynamics of implementation of Democratic Innovations across Europe, the dataset provides valuable insights into the potential for these innovations to foster more inclusive and resilient societies in line with social and environmental development goals. Each of the cases collected here was categorised on specific criteria, like geographical distribution, scales of governance, policy areas, citizen involvement, and digitalization.<br><br>INCITE-DEM is funded by the European Union (INCITE-DEM, GA nº 101094258). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or of the European Research Executive Agency (REA). Neither the European Union nor the granting authority (REA) can be held responsible for them</p>
Hydroelastic response of the scaled model of a floating offshore wind turbine platform in waves: HELOFOW Project Database
<p>This dataset contains the data measured during the <strong>HELOFOW </strong>model test campaign, performed at the Ocean and Hydrodynamic Engineering wave tank of Ecole Centrale Nantes (ECN): decay tests, regular wave tests and irregular waves tests. The preprocessed measured data is contained in MAT files.</p> <p>The model, the measurements and the tests are described in the appended Excel files. A Matlab(R) function is given as a short example to show how the MAT files are structured and how data may be handled for a plot. </p> <p>As stated in the reference paper (Leroy et al., <em>Ocean Engineering</em>, 2022):</p> <p>"As the size of floating wind turbines continues to increase, floating platforms reach dimensions that make their elastic and hydro-elastic behaviour significant. Several works in connection with the numerical modelling of the elastic behaviour of these wind turbines have been carried out but few validation data are available. This study focuses on the hydro-elastic response of a large floating wind turbine, in regular waves and severe sea-states. A new experimental wind turbine model has been designed to represent a 1:40 Froude-scaled spar platform carrying the DTU 10 MW turbine. The main challenge is here to reproduce a 1st bending mode frequency and hydrodynamic loads representative of a realistic large floating wind turbine. The platform model is made of a flexible backbone, reproducing the correct flexibility, and light floaters fixed on it provide the correctly scaled geometry. This experimental model is tested in various conditions including regular waves of several periods and steepness, and irregular waves of various intensity, including extreme 50-year return period conditions."</p> <p> </p> <p>This work was carried out within the framework of the WEAMEC, West Atlantic Marine Energy Community, and with funding from the Pays de la Loire Region and Europe (European Regional Development Fund). <br><br>HELOFOW project on <a href="https://www.weamec.fr/en/projects/helofow/">the WEAMEC website</a>. </p>
Cariban Lexical Database (CaLeD)
<p>This dataset contains a comprehensive collection of lexical items from various languages within the Carib linguistic family. It is structured to facilitate computational historical linguistics analysis, offering detailed information on language characteristics, word forms, and cognacy judgments. The data is curated to support research in linguistic typology, historical linguistics, and related fields.</p> <h4>Data Structure</h4> <p>The dataset is presented in a TSV (Tab-Separated Values) format, ensuring easy integration with common data analysis tools. Each lexical item in the dataset is detailed with multiple linguistic attributes, including phonological transcriptions, morphological analysis, and cognacy information. The following table summarizes the fields included in the dataset:</p> <table> <tbody> <tr> <th>Field Name</th> <th>Data Type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>ID</td> <td>string</td> <td>Unique identifier for each dataset entry.</td> </tr> <tr> <td>ID_lang</td> <td>string</td> <td>Unique identifier for the language within the dataset.</td> </tr> <tr> <td>Glottocode</td> <td>string</td> <td>Code uniquely identifying the language in the Glottolog database.</td> </tr> <tr> <td>Glottolog_Name</td> <td>string</td> <td>Name of the language as recorded in the Glottolog database.</td> </tr> <tr> <td>ISO639P3code</td> <td>string</td> <td>ISO 639-3 code for the language.</td> </tr> <tr> <td>ID_param</td> <td>string</td> <td>Unique identifier for the linguistic parameter or concept within the dataset.</td> </tr> <tr> <td>Concepticon_ID</td> <td>integer</td> <td>Identifier for the concept in the Concepticon database.</td> </tr> <tr> <td>Concepticon_Gloss</td> <td>string</td> <td>Gloss or definition of the concept from the Concepticon database.</td> </tr> <tr> <td>Value</td> <td>string</td> <td>Value of the linguistic data point, typically a word or phrase in the language.</td> </tr> <tr> <td>Form</td> <td>string</td> <td>Phonetic or phonological transcription of the linguistic data point.</td> </tr> <tr> <td>Segments</td> <td>string</td> <td>Further phonetic or phonological breakdown of the form.</td> </tr> <tr> <td>Source</td> <td>string</td> <td>Reference to the source or citation where the data was obtained.</td> </tr> <tr> <td>Morphemes</td> <td>string</td> <td>Morphological breakdown of the form.</td> </tr> <tr> <td>SimpleCognate</td> <td>integer</td> <td>Cognacy judgment, indicating whether the form is cognate with forms of the same meaning in related languages.</td> </tr> <tr> <td>PartialCognates</td> <td>string</td> <td>Partial cognacy coding, detailing the cognacy of individual segments or morphemes.</td> </tr> </tbody> </table> <h4>Intended Use</h4> <p>This dataset is intended for researchers and linguists specializing in the Carib linguistic family. It provides valuable insights into the lexical similarities and differences across the languages within this family, supporting studies on language evolution, relationships, and structure.</p> <h4>Additional Resources</h4> <ol> <li> <p><strong>Metadata for Validation</strong>: This dataset comes with comprehensive metadata following the Frictionless Data standard, ensuring that the data structure and types are accurately described for validation purposes. This metadata aids in maintaining the integrity and usability of the data across various computational platforms and research projects.</p> </li> <li> <p><strong>CLDF Version Available</strong>: For researchers utilizing the Cross-Linguistic Data Formats (CLDF), a version of this dataset is available in CLDF specifications. This version is provided as a zipped file, facilitating easier distribution and handling.</p> </li> </ol>
Data from 'Tracability of Forest Reproductive Material with the quality label 'Plant van Hier': A DNA database with genetic profiles of native autochthonous tree and shrub species of Flanders, Belgium'
<h2>Background</h2> <p>Indigenous trees and shrubs play an important role in multifunctional forest management. They form a significant part of the biodiversity in our forests. Forest reproductive material (FRM) of autochthonous Flemish origin is sold under the quality label ‘Plant van Hier’, a certification mark of the Agency for Nature and Forests. To ensure the provenance of the seedlings, we developed a DNA-database of genetic profiles of potential parent trees, using species-specific genetic markers. This database enables the traceability of FRM of the ‘Plant van Hier’ label throughout the entire production chain; from seed harvesting and cultivation to planting by the end user.</p> <p>This database contains the genetic profiles of almost all possible parent trees present within 27 Flemish autochthonous seed orchards of eight ecologically important tree and shrub species: <em>Carpinus betulus</em>, <em>Corylus avellana</em>, <em>Frangula alnus</em>, <em>Populus tremula</em>, <em>Sorbus aucuparia</em>, <em>Tilia cordata</em>, <em>Tilia platyphyllos,</em> and <em>Ulmus laevis</em>. The profiles were established using microsatellite markers (11 to 24 markers per species). New genetic markers were developed for <em>Carpinus betulus</em> and <em>Ulmus laevis</em>. PCR products were run on an ABI 3500 Genetic Analyser (Thermo Fisher Scientific).</p> <h2>Files</h2> <p>The files will be updated when new genotypes are added to the seed orchards. The current data files contain data from genotypes collected in the period 2018-2023. </p> <h3>Species_genotypes</h3> <p>These files contain the genetic fingerprints of the parent trees of autochthonous Flemish seed orchards. Missing data is indicated as ‘MD’. For <em>Carpinus betulus</em>, an octoploid species, the allelic phenotype is given instead of the genotype as the number of times that an allele occurs on a specific locus is not known.</p> <p>The next metadata is additionally given:<br>- Species: the Latin name of the species<br>- Seed_orchard: the name of the seed orchard in which the genotypes are located<br>- Code_seed_orchard: the code of the seed orchard in which the genotypes are located as given in the Register of Flemish Forest Reproductive Material (‘Register bosbouwkundig uitgangsmateriaal’; inbo.be)<br>- Genotype: the fieldname given to the genotype<br>- Origin: the location where the genotype was collected in Flanders, Belgium. Genotypes were collected from natural stands which are assumed to have an autochthonous origin. When the specific location is unknown, the location ‘Flanders’ is given. <br>- Year_sampled: the year in which the genotypes were sampled in the respective seed orchard for genetic analysis.</p> <h3>Species_binsets</h3> <p>These files contain the binsets and allele names that are used to score the alleles of the genotypes in the programme Geneious Prime 2019.3.2 (<a href="https://www.geneious.com">https://www.geneious.com</a>). For <em>Tilia platyphyllos </em>and <em>Tilia cordata</em>, the same binsets were used.</p>
Database of permacultural adoption responses in Mexicali, BC, Mexico. based on Circular Economy, Knowledge Management, and Sustainability policies
<p>Database documenting the perspectives of citizens in Mexicali, Baja California, Mexico, regarding the adoption of permaculture practices. The study is analyzed through the lenses of Knowledge Management, Circular Economy, and Sustainability Policies. The data was collected during the summer of 2024. </p>
HarP: Harmonized Prior river-lake database
<p><strong>Contact</strong>: Md Safat Sikder (mssikder@illinois.edu), Jida Wang (jidaw@illinois.edu)</p> <p> </p> <p><strong>Citation</strong></p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Crétaux, J.-F., and Pavelsky, T. M., 2024. HarP: Harmonized Prior river-lake database. <em>Zenodo</em>, <a href="https://doi.org/10.5281/zenodo.14205131">https://doi.org/10.5281/zenodo.14205131</a>.</p> <p>If you only use the PLD-TopoCat dataset, please cite the following paper:</p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. <em>Earth System Science Data</em>, 15, 3483-3511, <a href="https://doi.org/10.5194/essd-15-3483-2023">https://doi.org/10.5194/essd-15-3483-2023</a>.</p> <p> </p> <p><strong>Data description and components</strong></p> <p><strong>The Harmonized Prior river-lake database (HarP) for SWOT</strong> integrated the SWOT River Database (SWORD) (<em>Altenau et al.</em>, 2021) and the SWOT Prior Lake Database (PLD) (<em>Wang et al.</em>, 2023) into <strong>a geometrically (lake/river) explicit but topologically harmonized vector database</strong> to allow for coupled fluvial-lacustrine applications, including a synergistic use of both river and lake products from SWOT. </p> <p>In addition to the input river network (SWORD v16) and lake database (PLD v106), we used the MERIT Hydro v1.0.1 (<em>Yamazaki et al.</em>, 2019), a high-resolution (~90 m) global hydrography dataset, to develop this database.</p> <p>The SWORD-PLD harmonization process involves three major steps, with Step 3 being divided into three sub-steps. The processing chain is illustrated in the attached Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>", as well as in Section 2 of the product description document. The HarP database consists of the outputs from each of the steps. For convenience, the global landmass (excluding Antarctica) was partitioned to 68 Pfafstetter Level-2 basins/regions, with their IDs shown in Figure "<em>Pfaf2_basins.jpg</em>" attached.</p> <p> </p> <p>The HarP database consists of five datasets or components (outputs from each step), each with multiple features. The five datasets are described below, and more details are elaborated in the product description document.</p> <p><strong>1. Harmonized SWORD-PLD </strong>(file name "<em>Harmonized_SWORD_PLD</em>"): This is the fully harmonized SWORD-PLD dataset, <strong>the primary product of HarP </strong>(i.e., output of Step 3.3 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>"). This dataset couples SWORD and PLD into a geometrically segmented but topologically integrated dataset at the node, reach, and catchment scales (stored by three feature layers, respectively): </p> <p> (a) Harmonized feature nodes: Harmonized_feature_nodes_pfaf_xx<br> (b) Harmonized river network: Harmonized_river_network_pfaf_xx<br> (c) Harmonized feature catchments: Harmonized_feature_catchments_pfaf_xx<br> Note: ''pfaf_xx'' indicates the Pfafstetter Level-2 basin ID (shown in Fig. 'Pfaf2_basins.jpg').</p> <p>Figure "<em>HarP_example.jpg</em>", attached to this database, is an example of the fully harmonized SWORD-PLD dataset for the Ohio River Basin. The example shows three main features of the dataset: feature nodes (i.e., reach downstream ends, lake inlets, and lake outlets; see Fig. 3 in the product description document for definitions), river reaches (i.e., reaches characterized by SWORD alone, characterized by TopoCat alone, and shared by both SWORD and TopoCat), and catchments segmented by each of the feature nodes.</p> <p><strong>2. Intersected SWORD-PLD drainage configuration </strong>(file name "<em>Intersected_SWORD_PLD</em>"): This dataset is the intersected SWORD-PLD (prior river-lake) features (i.e., output of Step 2 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>"). This dataset was constructed independently from Step 1 and Step 3. In this dataset, the original geometries of SWORD and PLD are not altered, but instead, their geometric and drainage topological relationships are configured in the attribute tables. This dataset consists of three features:</p> <p> (a) Intersected reaches: Intersected_SWORD_reaches_pfaf_xx<br> (b) Intersected nodes: Intersected_SWORD_nodes_pfaf_xx<br> (c) Intersected lakes: Intersected_PLD_lakes_pfaf_xx</p> <p><strong>3. PLD-TopoCat </strong>(file name "<em>PLD_TopoCat</em>"): This dataset is the lake drainage topology and catchments (TopoCat) for PLD lakes (i.e., output of Step 1 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>"). PLD-TopoCat was developed to generate detailed lake drainage topology and connecting paths, which were later used to configure the off-SWORD-network PLD lakes into the tributaries that drain to SWORD. PLD-TopoCat was generated from PLD v106 and MERIT Hydro. Details of the developiong process and algorithm for TopoCat can be found at Sikder at al., (2023). PLD-TopoCat dataset contains six features:</p> <p> (a) Lake original polygon: PLD_lakes_pfaf_xx<br> (b) Lake raster polygon: Lake_raster_polygons_pfaf_xx<br> (c) Lake outlets: Lake_outlets_pfaf_xx<br> (d) Lake catchments: Lake_catchments_pfaf_xx<br> (e) Inter-lake reaches: Inter_lake_reaches_pfaf_xx<br> (f) Lake-network basins: Lake_network_basins_pfaf_xx<br> Note: full version of the PLD-TopoCat is available <a href="https://doi.org/10.5281/zenodo.14202301">here</a>.</p> <p><strong>4. SWORD-mirror network </strong>(file name "<em>SWORD_mirror</em>"): The SWORD-mirror network was constructed to facilitate the SWORD-TopoCat network merging process (i.e., output of Step 3.1 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>"). It is essentially <strong>a replica of SWORD except that the original SWORD reaches are geometrically modified to be aligned with the topological/hydrographic information depicted in MERIT Hydro</strong>. The SWORD-mirror network consists of four features:</p> <p> (a) SWORD-original reaches: SWORD_original_reaches_pfaf_xx<br> (b) SWORD-mirror prelim. reaches: SWORD_mirror_prelim_reaches_pfaf_xx<br> (c) SWORD-mirror reaches: SWORD_mirror_reaches_pfaf_xx<br> (d) SWORD-mirror reach catchments: SWORD_mirror_reach_catchments_pfaf_xx</p> <p><strong>5. Merged SWORD-mirror – TopoCat network </strong>(file name "<em>SWORD_TopoCat_merged</em>"): This dataset is the output of Step 3.2 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>". It is essentially the merged product of the inter-lake reaches (from Step 2) and SWORD-mirror reaches (from Step 3.1). The merged SWORD-mirror – TopoCat network consists of three features:</p> <p> (a) Merged SWORD-TopoCat reaches: SWORD_TopoCat_merged_reaches_pfaf_xx<br> (b) SWORD nodes at SWORD-TopoCat confluence: SWORD_TopoCat_confluence_nodes_pfaf_xx<br> (c) Reach catchments for merged network: SWORD_TopoCat_reach_catchments_pfaf_xx</p> <p>The attribute tables for each of the feature components are explained in Section 4 of the product description document. All files of HarP are available in both shapefile and geodatabase formats.</p> <p> </p> <p><strong>Disclaimer</strong><br>Authors of this dataset claim no responsibility or liability for any consequences related to the use, citation, or dissemination of HarP. For any quesitons, please contact Safat Sikder and Jida Wang.</p>
Cadenza Challenge (CAD2): databases for lyric intelligibility task
<h2>Cadenza</h2> <p>This is the training and validation data for the lyric intelligibility task from the <a href="https://cadenzachallenge.org/">Second Cadenza Machine Learning Challenge (CAD2).</a></p> <p>The Cadenza Challenges are improving music production and processing for people with a hearing loss. According to The World Health Organization, 430 million people worldwide have a disabling hearing loss. Studies show that not being able to understand lyrics is an important problem to tackle for those with hearing loss. Consequently, this task is about improving the intelligibility of lyrics when listening to pop/rock over headphones. But this needs to be done without losing too much audio quality - you can't improve intelligibility just by turning off the rest of the band! We will be using one metric for intelligibility and another metric for audio quality, and giving you different targets to explore the balance between these metrics.</p> <p>Please see the <a href="https://cadenzachallenge.org/">Cadenza website</a> for a full description of the data</p>
Tycho Region Database of impact craters >=21 meters on the Moon.
<p>This dataset presents the results of an innovative AI-driven lunar crater mapping project, focused exclusively on the Tycho region of the Moon at 21m/px (<strong>see Version V2 for the global catalog at 100m/px</strong>) , marking the first comprehensive application of artificial intelligence to detect, classify, and map lunar craters. Created through extensive work over a two-year period, this dataset leverages YOLOLens, a state-of-the-art deep learning model specifically optimized for high-resolution crater detection. YOLOLens, an innovative variant of the YOLO architecture, has been fine-tuned to handle the unique challenges of lunar surface imagery, delivering unparalleled accuracy in crater identification and localization. Detailed information on the model architecture and methodology can be found in relevant publications on:</p> <ol> <li>La Grassa, Riccardo, et al. <strong>"YOLOLens: A deep learning model based on super-resolution to enhance the crater detection of the planetary surfaces."</strong> Remote Sensing 15.5 (2023): 1171, https://doi.org/10.3390/rs15051171.</li> <li>La Grassa, R, et al. <strong>"LU5M812TGT: An AI-Powered global database of impact craters ≥0.4 km on the Moon"</strong>, ISPRS Journal of Photogrammetry and Remote Sensing, 2025, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2024.11.010.<strong><br></strong></li> </ol> <p><br>The dataset preparation process involved rigorous steps in preprocessing and post-processing to enhance the quality and usability of the data. Preprocessing techniques were employed to reduce noise and enhance contrast within the complex lunar landscape, while post-processing was used to refine the accuracy of crater boundaries and dimensions detected by the model. This approach facilitated a high-confidence dataset that stands as a valuable resource for the astronomical and AI research communities.</p> <p>The area analyzed in this dataset is defined by the following coordinates: longitude [-21.0000000001998046, 44.9997999998701630] and latitude [-50.9998000002697722, 39.0000000003997229].</p> <p>This dataset, which includes over 6.8 million craters at a resolution of 21m/px, is a valuable resource for both astrophysicists and AI researchers. It provides precisely labeled crater data, including coordinates, dimensions, and classifications, serving as an essential benchmark for comparative analyses, model validation, and advancements in lunar and planetary science.</p> <p>Notably, the global catalog created using the WAC imagery at 100m/px resolution is available in <strong>version V2</strong> of this repository. This global dataset complements the Tycho region-specific data by offering a broader perspective on lunar crater distribution.</p> <p>*********************************************************************🌕🌖🌗🌘🌑🌒🌓🌔🌕 **********************************************************************</p> <p>Release of a Tycho area at 21m/px of craters catalog with more than 6.8 million craters.</p> <ul> <li><em>File source csv</em></li> <li><em>Header: Longitude, Latitude, Diameter_w, Diameter_h, Confidence.</em></li> <li><em>The coordinates are absolute in the range of [-180, +180] of Longitude and [-90, +90] of Latitude. <br></em></li> <li><em>Diameters (km)</em></li> </ul> <p> </p>
RivFISH - An European database on fish species presence across river basins
<p>The RivFISH database aggregates the available data on freshwater-dependent fish presence in Europe, validated at the river basin level and considering taxonomical synonyms for species names, thus allowing for a maximization of data usage and robustness. This database also promotes interoperability with other datasets, including the IUCN Red List of Threatened Species, FishBase and the Catchment Characterisation and Modelling (CCM2) – River and Catchment Database v2.1. It is, as far as the authors know, the most up-to-date and comprehensive database on the presence of freshwater-dependent fish species for European river basins. The structure of the database is also prepared to deal with future alterations in species taxonomy, as well as new records of species occurrence in river basins.</p>
Oil and Gas Infrastructure Mapping (OGIM) database
<p>The Oil and Gas Infrastructure Mapping (OGIM) database is a global, spatially explicit, and granular dataset of oil and gas infrastructure. It is developed by Environmental Defense Fund (EDF) (<a href="https://www.edf.org/">www.edf.org</a>) and MethaneSAT, LLC (<a href="https://www.methanesat.org/">www.methanesat.org</a>), a wholly owned subsidiary of EDF. The OGIM database helps fill a crucial geospatial data need, by supporting the quantification and source characterization of oil and gas methane emissions. The database is developed via acquisition, analysis, curation, integration, and quality-assurance (performed at EDF) of publicly available geospatial data sources. These oil and gas facility datasets are reported by governments, industry, academics, and other non-government entities.</p> <p>OGIM is a collection of data tables within a GeoPackage. Each data table within the GeoPackage includes locations and facility attributes of oil and gas infrastructure types that are important sources of methane emissions, including: oil and gas production wells, offshore production platforms, natural gas compressor stations, oil and natural gas processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. OGIM v2.7 includes approximately 6.7 million features, including 4.5 million point locations of oil and gas wells and over 1.2 million kilometers of oil and gas pipelines.</p> <p>Please see the PDF document in the “Files” section of this page for more information about this version, including attribute column definitions, key changes since the previous version, and more. Full details on database development and related analytics can be found in the following Earth System Science Data (ESSD) journal paper. Please cite this paper when using any version of the database:</p> <p><span>Omara, M., Gautam, R., O'Brien, M., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D., Chulakadabba, A., Miller, C., Franklin, J., Wofsy, S., and Hamburg, S.: Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution, Earth Syst. Sci. Data Discuss., </span><a href="https://doi.org/10.5194/essd-15-3761-2023"><span>https://doi.org/10.5194/essd-15-3761-2023</span></a><span>, 2023.</span></p> <p>Important note: While the results section of this manuscript is specific to v1 of the OGIM, the methods described therein are the same methods used to develop and update v2.7. Additionally, while we describe our data sources in detail in the manuscript above, and include maps of all acquired datasets, this open-access version of the OGIM database does not include the locations of about 300 natural gas compressor stations in Russia. Future updates may include these locations when appropriate permissions to make them publicly accessible are obtained. </p> <p>OGIM v2.7 is based on public-domain datasets reported in February 2025 or prior. Each record in OGIM indicates a date (SRC_DATE) when the original source of the record was published or last updated. Some records may contain out-of-date information, for example, if a facility’s status has changed since we last visited a data source. We anticipate updating the OGIM database on a regular cadence and are continually including new public domain datasets as they become available.</p> <p>---</p> <p>Point of Contact at Environmental Defense Fund and MethaneSAT, LLC: Madeleine O’Brien (maobrien@methanesat.org) and Mark Omara (momara@edf.org).</p>
Database from: Developing a lateral topographic density model for Brazil.
<p>This dataset is part of the article entitled "DEVELOPING A LATERAL TOPOGRAPHIC DENSITY MODEL FOR BRAZIL".</p> <p>This dataset includes the topographic Lateral Topographic Density model for Brazil (LTDBrasil) and standard deviations (sdLTDBrasil), in Kg/m³, with 30 arc-seconds grid spacing.</p> <p>The files are in *tif and *.tfw format.</p> <p>Reference: Medeiros D.F., Marotta G.S., Yokoyama E., Franz I.B., Fuck R.A. 2021. Developing a lateral topographic density model for Brazil. Journal of South American Earth Sciences, v. 110, p. 103425. https://doi.org/10.1016/j.jsames.2021.103425</p>
Modern China Geospatial Database - Republican China Dataset
<p><strong>MCGD_Rep</strong> is a sample of spatial data for China in the first half of twentieth century (1900-1949). The data was extracted from the MCGD Main Dataset. It is based mostly on the list of <em>xian</em> (county) seats in 1931 [Source: Zang, Lihe 臧励龢, ed. Zhongguo gujin diming da cidian 中国古今地名大辞典. Shanghai 上海: Commercial Press, 1931], with the addition of some external data [Source: Crow Newspaper Directories]. By and large, it presents a list of the major locations in China between 1900 and 1949. It contains 1,977 entries with the following variables: name in Chinese, name in pinyin; name of the province in Chinese and in pinyin; latitude and longitude, and Name ID and Location ID.</p>
The Fano 3-fold database
<p><strong>The Fano 3-fold database</strong><br> <br> This is a dataset that relates to the graded (homogeneous coordinate) rings of possible algebraic varieties: complex Fano 3-folds with Fano index 1. Each entry in this dataset records the (anticanonical) Hilbert series of a possible Fano 3-fold <span class="math-tex">\(X\)</span>, along with the result of some analysis about how <span class="math-tex">\(X\)</span> may be (anticanonically) embedded in weighted projective space <span class="math-tex">\(\mathbb{P}(w_1,w_2,\ldots,w_s)\)</span>.</p> <p>For details, see the paper [BK22], which is a companion and update to the original paper [ABR02].</p> <p>If you make use of this data, please consider citing [BK22] and the DOI for this data:</p> <p>doi:10.5281/zenodo.5820338</p> <p>The data consists of two files in key:value format, "fano3.txt" and "matchmaker.txt". The files "fano3.sql" and "matchmaker.sql" contain the same data as the key:value files, but formatted ready for inserting in sqlite.</p> <p><em><strong>fano3.txt</strong></em></p> <p>This file contains data that relates to the graded (homogeneous coordinate) rings of possible algebraic varieties. For each entry, the essential characteristic data is the genus and basket; everything else follows (with the exception of the ID). Briefly, this essential data determines a power series, the Hilbert series, <span class="math-tex">\(\text{Hilb}(X,-K_X) = 1 + h_1t + h_2t^2 + \ldots\)</span> that can be written as a rational function of the form <span class="math-tex">\((\text{polynomial numerator in $t$}) / \prod_{i=1}^s(1-t^{w_i})\)</span>, where <span class="math-tex">\(w_1,w_2,\ldots,w_s\)</span> are positive integer weights.</p> <p>The data consists of 52646 entries. The 39550 stable entries (that is, with 'stable' equal to 'true') are assigned an ID 'id' in the range 1-39550. The 13096 unstable entries (that is, with 'stable' equal to 'false') are assigned an ID in the range 41515-54610. IDs in the range 39551-41514 are assigned to the higher index Fano varieties, and are not included in this dataset.</p> <p><strong>Example entry</strong><br> id: 1<br> weights: 5,6,7,...,16<br> has_elephant: false<br> genus: -2<br> h1: 0<br> h2: 0<br> ...<br> h10: 4<br> numerator: t^317 - t^300 - 6*t^299 - ... + 1<br> codimension: 24<br> basket: 1/2(1,1,1),1/2(1,1,1),1/3(1,1,2),...,1/5(1,2,3)<br> basket_size: 7<br> equation_degrees: 17,18,18,...,27<br> degree: 1/60<br> k3_rank: 19<br> bogomolov: -8/15<br> kawamata: 1429/60<br> stable: true</p> <p>(Some data truncated for readability.)</p> <p><strong>Brief description of an entry</strong><br> id: a unique integer ID for this entry<br> genus: <span class="math-tex">\(h^0(X,-K_X)-2\)</span><br> basket: multiset of quotient singularities <span class="math-tex">\(\frac{1}{r}(f,a,-a)\)</span><br> basket_size: number of elements in the 'basket'<br> k3_rank: <span class="math-tex">\(\sum(r-1)\)</span> taken over the 'basket'<br> kawamata: <span class="math-tex">\(\sum(r-\frac{1}{r})\)</span> taken over the 'basket'<br> bogomolov: sum of terms over 'basket' relating to stability (see [BK22])<br> stable: true if and only if 'bogolomov' <span class="math-tex">\(\le0\)</span><br> degree: anticanonical degree <span class="math-tex">\((-K_X)^3\)</span> of <span class="math-tex">\(X\)</span>, determined by above data (see [BK22])<br> h1,h2,...,h10: coefficients of <span class="math-tex">\(t,t^2,\ldots,t^{10}\)</span> in the Hilbert series <span class="math-tex">\(\text{Hilb}(X,-K_X)\)</span><br> weights: suggestion of weights <span class="math-tex">\(w_1,w_2,\ldots,w_s\)</span> for the anticanonical embedding <span class="math-tex">\(X\subset\mathbb{P}(w_1,w_2,\ldots,w_s)\)</span><br> numerator: polynomial such that the Hilbert series <span class="math-tex">\(\text{Hilb}(X,-K_X)\)</span> is given by the power series expansion of <span class="math-tex">\(\text{'numerator'} / \prod_{i=1}^s(1-t^{w_i})\)</span>, where the <span class="math-tex">\(w_i\)</span> in the denominator range over the 'weights'<br> codimension: the codimension of <span class="math-tex">\(X\)</span> in the suggested embedding, equal to <span class="math-tex">\(s - 4\)</span><br> has_elephant: true if and only if <span class="math-tex">\(h_1 > 0\)</span></p> <p><strong><em>matchmaker.txt</em></strong><br> <br> This file contains a set of pairs of IDs, in each case one from the canonical toric Fano classification [Kas10,toric] and one from "fano3.txt". The meaning is that the Hilbert series of the two agree, and this file contains all such agreeing pairs.</p> <p><strong>Example entry</strong><br> toric_id: 1<br> fano3_id: 27334</p> <p><strong>Brief description of an entry</strong><br> toric_id: integer ID in the range 1-674688, corresponding to an 'id' from canonical toric Fano dataset [Kas10,toric]<br> fano3_id: an integer ID in the range 1-39550 or 41515-54610, corresponding to an 'id' from "fano3.txt"</p> <p><br> <em><strong>fano3.sql </strong></em><strong>and <em>matchmaker.sql</em></strong><br> <br> The files "fano3.sql" and "matchmaker.sql" contain sqlite-formatted versions of the data described above, and can be imported into an sqlite database via, for example:</p> <pre><code class="language-bash">$ cat fano3.sql matchmaker.sql | sqlite3 fano3.db</code></pre> <p>This can then be easily queried. For example:</p> <pre><code class="language-bash">$ sqlite3 fano3.db > SELECT id FROM fano3 WHERE degree = 72 AND stable IS TRUE; 39550 > SELECT toric_id FROM fano3totoricf3c WHERE fano3_id = 39550; 547334 547377</code></pre> <p> </p> <p><strong>References</strong></p> <p>[ABR02] Selma Altinok, Gavin Brown, and Miles Reid, "Fano 3-folds, K3 surfaces and graded rings", in <em>Topology and geometry: commemorating SISTAG</em>, volume 314 of <em>Contemp. Math.</em>, pages 25-53. Amer. Math. Soc., Providence, RI, 2002.<br> [BK22] Gavin Brown and Alexander Kasprzyk, "Kawamata boundedness for Fano threefolds and the Graded Ring Database", 2022.<br> [Kas10] Alexander Kasprzyk, "Canonical toric Fano threefolds", <em>Canadian Journal of Mathematics</em>, 62(6), 1293-1309, 2010.<br> [toric] Alexander Kasprzyk, "The classification of toric canonical Fano 3-folds", <em>Zenodo</em>, doi:10.5281/zenodo.5866330</p> <p> </p>
Global Human Settlement Layer per zoom-level 18 Quadtree tile for selected countries as Spatialite database with OpenStreetMap building completeness assessment
<p>This Spatialite database contains the built-up area of the Global Human Settlement Layer (GHSL) per zoom-level 18 Quadtree tile. Additionally, it provides a comparison of the GHSL with buildings in OpenStreetMap: For each tile the built-up ratio between the building footprints and the GHSL is given and a binary completeness assessment (buildings complete, not complete) is provided for easy use. This dataset was created using the obmgapanalysis tool: https://git.gfz-potsdam.de/dynamicexposure/openbuildingmap/obmgapanalysis</p>
Zooplankton functional trait database for Canadian lakes
<p>This dataset contains functional traits of 102 crustacean zooplankton taxa sampled in 624 Canadian lakes (Pelagic sample), as well as 58 sub-fossil cladoceran taxa (Sediments sample) sampled in 101 lakes. Lakes were sampled across Canada as part of the NSERC Canadian LakePulse Network. The traits used are: feeding type (B(<em>Bosmina</em>)-filtration, C(<em>Chydorus</em>)-filtration, D(<em>Daphia</em>)-filtration, S(<em>Sidae</em>)-filtration, stationary suspension or raptorial), habitat (littoral, pelagic or intermediate) and trophic group (carnivore, herbivore, omnivore, or a combination of these). Pelagic species length of up to 10 individuals per taxon per lake were measured by BSA Environmental Services (Ohio, U.S.A.), and averaged for each taxon. Sediments sample lengths were either obtained from the literature (Demott & Kerfoot, 1982; Barnett et al., 2007; Griffiths et al., 2019), or from the Pelagic sample length data. Feeding type, habitat and trophic group functional traits were obtained from literature (Demott & Kerfoot, 1982; Barnett et al., 2007; Hébert et al., 2016; Griffiths et al., 2019).</p> <p>References</p> <p>Barnett, A. J., Finlay, K., & Beisner, B. (2007). Functional diversity of crustacean zooplankton communities: Towards a trait-based classification. <em>Freshwater Biology</em>, <em>52</em>(5), 796–813. https://doi.org/10.1111/j.1365-2427.2007.01733.x</p> <p>Demott, W. R., & Kerfoot, W. C. (1982). Competition among cladocerans: nature of the interaction between Bosmina and Daphnia. <em>Ecology</em>, <em>63</em>(6), 1949–1966. https://doi.org/10.2307/1940132</p> <p>Griffiths, K., Winegardner, A. K., Beisner, B. E., & Gregory-Eaves, I. (2019). Cladoceran assemblage changes across the Eastern United States as recorded in the sediments from the 2007 National Lakes Assessment, USA. <em>Ecological Indicators</em>, <em>96</em>, 368–382. 061</p> <p>Hébert, M.-P., Beisner, B. E., & Maranger, R. (2016). A compilation of quantitative functional traits for marine and freshwater crustacean zooplankton. Ecology. https://doi.org/10.1890/15-1275</p>
Soil visible–near infrared (vis–NIR) spectra for the Biomes of Australian Soil Environments (BASE) soil microbial diversity database
<p>Visible–near infrared spectra of 695 soil samples collected in the Biomes of Australian Soil Environments (BASE) soil microbial diversity project (Bissett et al., 2016). The spectra represent reflectance values from 2151 wavelengths that range from 350 nm to 2500 nm with a 1 nm interval. The dataset has unique sample identification numbers and the date of sampling, which can be related to the BASE (Australian Microbiome) database (https://data.bioplatforms.com/organization/australian-microbiome)</p>
Ramonda serbica de novo transcriptome database
<p>Ramonda serbica de novo transcriptome database translated into amino acid seq. Hydrated and desiccated leaf tissue</p>
PartialSpoof Database - Partially Spoofed Audio Dataset for Anti-spoofing
<p>All existing databases of spoofed speech contain attack data that is spoofed in its entirety. In practice, it is entirely plausible that successful attacks can be mounted with utterances that are only partially spoofed. By definition, partially-spoofed utterances contain a mix of both spoofed and bona fide segments, which will likely degrade the performance of countermeasures trained with entirely spoofed utterances. This hypothesis raises the obvious question: ‘Can we detect partially spoofed audio?’ This paper introduces a new database of partially-spoofed data, named <strong>PartialSpoof</strong>, to help address this question. This new database enables us to investigate and compare the performance of countermeasures on both utterance- and segmental- level labels. Experimental results using the utterance-level labels reveal that the reliability of countermeasures trained to detect fully-spoofed data is found to degrade substantially when tested with partially-spoofed data, whereas training on partially-spoofed data performs reliably in the case of both fully- and partially- spoofed utterances. Additional experiments using segmental-level labels show that spotting injected spoofed segments included in an utterance is a much more challenging task even if the latest countermeasure models are used.</p> <p> </p> <ul> <li><strong>!!!NEW!!! For detailed (bonafide/spoofing methods/nonspeech/concatenated parts) timestamps of PartialSpoof v1.3</strong> <ul> <li><a href="https://drive.google.com/drive/folders/1kKW3GBuooPkAl64Zyv6WPgICH5LZtnQR">Google Drive</a> </li> <li>The official version is under preparation. Please download this one if you urgently need it.</li> </ul> </li> <li>For fine-grained labels of PartialSpoof v1.2 <ul> <li>Arxiv: http://arxiv.org/abs/2204.05177</li> <li>PartialSpoof Database v1.2<strong> </strong>(including segmental-level labels in different temporal resolutions and timestamp labels)<strong>: This one</strong></li> </ul> </li> <li>For the multi-task version of PartialSpoof <strong>v1.1</strong> <ul> <li>Arxiv: https://arxiv.org/abs/2107.14132</li> <li>PartialSpoof Database v1.1 (including 0.16s segmental level labels): https://zenodo.org/record/5112031</li> </ul> </li> <li>For the initial version of PartialSpoof <strong>v1.0</strong> <ul> <li>Arxiv: https://arxiv.org/abs/2104.02518</li> <li>Samples: https://nii-yamagishilab.github.io/zlin-demo/IS2021/index.html</li> <li>PartialSpoof Database v1.0: https://zenodo.org/record/4817532</li> </ul> </li> </ul> <p>P.S.</p> <p>1. Compared to the <a href="../record/4817532#.YLO07S2l1hE">PartialSpoof_v1.0</a> and <a href="../record/5112031">PartialSpoof_v1.1</a>, only <strong>database_segment_labels_v1.2.tar.gz, database_vad.tar.gz, </strong> and<strong> README_v1.2</strong> are updated for version 1.2, you don't need to download other files if you already downloaded version1.0 or 1.1.</p> <p>2. File database_eval.tar.gz is a little large, if you cannot download it smoothly, you can download the split database_eval.tar.gz from <a href="../record/4817532#.YLO07S2l1hE">PartialSpoof_v1.0</a> </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.