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225 results for “Global database”
GRiMeDB: a comprehensive global database of methane concentrations and fluxes in fluvial ecosystems with supporting physical and chemical information
The Global River Methane Database (GriMeDB) is a compilation of measurements of CH4 concentrations and fluxes for flowing water environments derived from publications, reports, data repositories, and other outlets between 1973 and 2021. Assembly of GRiMeDB was motivated by the goal of having a centralized, standardized resource to facilitate further studies of CH4 pattern and process in flowing water systems, upscaling efforts, and identification of tendencies in when, where, and how CH4 has been sampled in streams and rivers across the world. Thus, CH4 data are supported by concurrent observations (as available) of aquatic CO2, N2O, temperature, conductivity, pH, dissolved oxygen, nitrogen, phosphorus, organic carbon, and discharge, along with site data (latitude, longitude, elevation, and [as available]: stream order, elevation, channel slope, catchment size, and codes for distinct or disturbed channel types). GRiMeDB includes over 24,000 records of CH4 concentration and greater than 8,000 flux measurements from over 5,000 unique sites, most of which are resolved to the daily time scale.
glenglat: Global englacial temperature database
<p>Open-access database of englacial temperature measurements compiled from data submissions and published literature. It is developed on <a href="https://github.com/mjacqu/glenglat">GitHub</a> and published to <a href="https://doi.org/10.5281/zenodo.11516611">Zenodo</a>. This version (1.0.0) of the dataset is described in the following publication:</p> <blockquote> <p>Mylène Jacquemart, Ethan Welty, Marcus Gastaldello, and Guillem Carcanade (2025). glenglat: A database of global englacial temperatures. Earth System Science Data 17(4): 1627–1666. <a href="https://doi.org/10.5194/essd-17-1627-2025">https://doi.org/10.5194/essd-17-1627-2025</a></p> </blockquote> <h2>Dataset structure</h2> <p>The dataset adheres to the Frictionless Data <a href="https://specs.frictionlessdata.io/tabular-data-package">Tabular Data Package</a> specification. The metadata in <code>datapackage.json</code> describes, in detail, the contents of the tabular data files in the <code>data</code> folder:</p> <ul> <li><code>source.csv</code>: Description of each data source (either a personal communication or the reference to a published study).</li> <li><code>borehole.csv</code>: Description of each borehole (location, elevation, etc), linked to <code>source.csv</code> via <code>source_id</code> and less formally via source identifiers in <code>notes</code>.</li> <li><code>profile.csv</code>: Description of each profile (date, etc), linked to <code>borehole.csv</code> via <code>borehole_id</code> and to <code>source.csv</code> via <code>source_id</code> and less formally via source identifiers in <code>notes</code>.</li> <li><code>measurement.csv</code>: Description of each measurement (depth and temperature), linked to <code>profile.csv</code> via <code>borehole_id</code> and <code>profile_id</code>.</li> </ul> <p>For boreholes with many profiles (e.g. from automated loggers), pairs of <code>profile.csv</code> and <code>measurement.csv</code> are stored separately in subfolders of <code>data</code> named <code>{source.id}-{glacier}</code>, where <code>glacier</code> is a simplified and kebab-cased version of the glacier name (e.g. <code>flowers2022-little-kluane</code>).</p> <h3>Supporting information</h3> <p>The folder <code>sources</code>, available on <a href="https://github.com/mjacqu/glenglat">GitHub</a> but omitted from dataset releases on <a href="https://doi.org/10.5281/zenodo.11516611">Zenodo</a>, contains subfolders (with names matching column <code>source.id</code>) with files that document how and from where the data was extracted.</p> <h2>Tables</h2> <p>Jump to: <a href="#source"><code>source</code></a> · <a href="#borehole"><code>borehole</code></a> · <a href="#profile"><code>profile</code></a> · <a href="#measurement"><code>measurement</code></a></p> <h3><a name="source"></a><code>source</code></h3> <p>Sources of information considered in the compilation of this database. Column names and categorical values closely follow the Citation Style Language (CSL) 1.0.2 specification. Names of people in non-Latin scripts are followed by a latinization in square brackets (e.g. В. С. Загороднов [V. S. Zagorodnov]) and non-English titles are followed by a translation in square brackets. The family name of Latin-script names is wrapped in curly braces when it is not the last word of the name (e.g. Emmanuel {Le Meur}, e.g. {Duan} Keqin) or the name ends in two or more unabbreviated words (e.g. Jon Ove {Hagen}). The family name of a Chinese name (and of the latinization) is wrapped in curly braces when it is not the first character.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>id</code> (required)</td> <td>string</td> <td>Unique identifier constructed from the first author's lowercase, latinized, family name and the publication year, followed as needed by a lowercase letter to ensure uniqueness (e.g. Загороднов 1981 → zagorodnov1981a).</td> </tr> <tr> <td><code>author</code></td> <td>string</td> <td>Author names (optionally followed by their ORCID or contact email in parentheses) as a pipe-delimited list.</td> </tr> <tr> <td><code>year</code> (required)</td> <td>year</td> <td>Year of publication.</td> </tr> <tr> <td><code>type</code> (required)</td> <td>string</td> <td>Item type.<br>- article-journal: Journal article<br>- book: Book (if the entire book is relevant)<br>- chapter: Book section<br>- document: Document not fitting into any other category<br>- dataset: Collection of data<br>- map: Geographic map<br>- paper-conference: Paper published in conference proceedings<br>- personal-communication: Personal communication between individuals<br>- speech: Presentation (talk, poster) at a conference<br>- report: Report distributed by an institution<br>- thesis-phd: Doctor of Philosophy (PhD) thesis<br>- thesis-msc: Master of Science (MSc) thesis<br>- webpage: Website or page on a website</td> </tr> <tr> <td><code>title</code> (required)</td> <td>string</td> <td>Item title.</td> </tr> <tr> <td><code>url</code></td> <td>string</td> <td>URL (DOI if available).</td> </tr> <tr> <td><code>language</code> (required)</td> <td>string</td> <td>Language as ISO 639-1 two-letter language code.<br>- da: Danish<br>- de: German<br>- en: English<br>- es: Spanish<br>- fr: French<br>- ja: Japanese<br>- ko: Korean<br>- ru: Russian<br>- sv: Swedish<br>- zh: Chinese</td> </tr> <tr> <td><code>container_title</code></td> <td>string</td> <td>Title of the container (e.g. journal, book).</td> </tr> <tr> <td><code>volume</code></td> <td>integer</td> <td>Volume number of the item or container.</td> </tr> <tr> <td><code>issue</code></td> <td>string</td> <td>Issue number (e.g. 1) or range (e.g. 1-2) of the item or container, with an optional letter prefix (e.g. F1) or part number (e.g. 75pt2).</td> </tr> <tr> <td><code>page</code></td> <td>string</td> <td>Page number (e.g. 1) or range (e.g. 1-2) of the item in the container, with an optional letter prefix (e.g. S1).</td> </tr> <tr> <td><code>version</code></td> <td>string</td> <td>Version number (e.g. 1.0) of the item.</td> </tr> <tr> <td><code>editor</code></td> <td>string</td> <td>Editor names (e.g. of the containing book) as a pipe-delimited list.</td> </tr> <tr> <td><code>collection_title</code></td> <td>string</td> <td>Title of the collection (e.g. book series).</td> </tr> <tr> <td><code>collection_number</code></td> <td>string</td> <td>Number (e.g. 1) or range (e.g. 1-2) in the collection (e.g. book series volume).</td> </tr> <tr> <td><code>publisher</code></td> <td>string</td> <td>Publisher name.</td> </tr> </tbody> </table> <h3><a name="borehole"></a><code>borehole</code></h3> <p>Metadata about each borehole.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>id</code> (required)</td> <td>integer</td> <td>Unique identifier.</td> </tr> <tr> <td><code>source_id</code> (required)</td> <td>string</td> <td>Identifier of the source of the earliest temperature measurements. This is also the source of the borehole attributes unless otherwise stated in <code>notes</code>.</td> </tr> <tr> <td><code>glacier_name</code> (required)</td> <td>string</td> <td>Glacier or ice cap name (as reported).</td> </tr> <tr> <td><code>glims_id</code></td> <td>string</td> <td>Global Land Ice Measurements from Space (GLIMS) glacier identifier.</td> </tr> <tr> <td><code>location_origin</code> (required)</td> <td>string</td> <td>Origin of location (<code>latitude</code>, <code>longitude</code>).<br>- submitted: Provided in data submission<br>- published: Reported as coordinates in original publication<br>- digitized: Digitized from published map with complete axes<br>- estimated: Estimated from published plot by comparing to a map (e.g. Google Maps, CalTopo)<br>- guessed: Estimated with difficulty, for example by comparing <code>elevation</code> to a map (e.g. Google Maps, CalTopo)</td> </tr> <tr> <td><code>latitude</code> (required)</td> <td>number [degree]</td> <td>Latitude (EPSG 4326).</td> </tr> <tr> <td><code>longitude</code> (required)</td> <td>number [degree]</td> <td>Longitude (EPSG 4326).</td> </tr> <tr> <td><code>elevation_origin</code> (required)</td> <td>string</td> <td>Origin of elevation (<code>elevation</code>).<br>- submitted: Provided in data submission<br>- published: Reported as number in original publication<br>- digitized: Digitized from published plot with complete axes<br>- estimated: Estimated from elevation contours in published map<br>- guessed: Estimated with difficulty, for example by comparing location (<code>latitude</code>, <code>longitude</code>) to a map of contemporary elevations (e.g. CalTopo, Google Maps)</td> </tr> <tr> <td><code>elevation</code> (required)</td> <td>number [m]</td> <td>Elevation above sea level.</td> </tr> <tr> <td><code>mass_balance_area</code></td> <td>string</td> <td>Mass balance area.<br>- ablation: Ablation area<br>- equilibrium: Near the equilibrium line<br>- accumulation: Accumulation area</td> </tr> <tr> <td><code>label</code></td> <td>string</td> <td>Borehole name (e.g. as labeled on a plot).</td> </tr> <tr> <td><code>date_min</code></td> <td>date (%Y-%m-%d)</td> <td>Begin date of drilling, or if not known precisely, the first possible date (e.g. 2019 → 2019-01-01).</td> </tr> <tr> <td><code>date_max</code></td> <td>date (%Y-%m-%d)</td> <td>End date of drilling, or if not known precisely, the last possible date (e.g. 2019 → 2019-12-31).</td> </tr> <tr> <td><code>drill_method</code></td> <td>string</td> <td>Drilling method.<br>- mechanical: Push, percussion, rotary<br>- thermal: Hot point, electrothermal, steam<br>- combined: Mechanical and thermal</td> </tr> <tr> <td><code>ice_depth</code></td> <td>number [m]</td> <td>Starting depth of continuous ice. Infinity (INF) indicates that only snow, firn, or intermittent ice was reached.</td> </tr> <tr> <td><code>depth</code></td> <td>number [m]</td> <td>Total borehole depth (not including drilling in the underlying bed).</td> </tr> <tr> <td><code>to_bed</code></td> <td>boolean</td> <td>Whether the borehole reached the glacier bed.</td> </tr> <tr> <td><code>temperature_uncertainty</code></td> <td>number [°C]</td> <td>Estimated temperature uncertainty (as reported).</td> </tr> <tr> <td><code>notes</code></td> <td>string</td> <td>Additional remarks about the study site, the borehole, or the measurements therein as a pipe-delimited list. Sources are referenced by <code>source.id</code>. Quality concerns are prefixed with '[flag]'.</td> </tr> <tr> <td><code>curator</code></td> <td>string</td> <td>Names of people who added the data to the database, as a pipe-delimited list.</td> </tr> <tr> <td><code>investigators</code></td> <td>string</td> <td>Names of people and/or agencies who performed the work, as a pipe-delimited list. Each entry is in the format 'person (agency; ...) {notes}', where only person or one agency is required. Person and agency may contain a latinized form in square brackets.</td> </tr> <tr> <td><code>funding</code></td> <td>string</td> <td>Funding sources as a pipe-delimited list. Each entry is in the format 'funder [rorid] > award [number] url', where only funder is required and rorid is the funder's ROR (https://ror.org) ID (e.g. 01jtrvx49).</td> </tr> </tbody> </table> <h3><a name="profile"></a><code>profile</code></h3> <p>Date and time of each measurement profile.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>borehole_id</code> (required)</td> <td>integer</td> <td>Borehole identifier.</td> </tr> <tr> <td><code>id</code> (required)</td> <td>integer</td> <td>Borehole profile identifier (starting from 1 for each borehole).</td> </tr> <tr> <td><code>source_id</code> (required)</td> <td>string</td> <td>Source identifier.</td> </tr> <tr> <td><code>measurement_origin</code> (required)</td> <td>string</td> <td>Origin of measurements (<code>measurement.depth</code>, <code>measurement.temperature</code>).<br>- submitted: Provided as numbers in data submission<br>- published: Numbers read from original publication<br>- digitized-discrete: Digitized with Plot Digitizer from discrete points of depth versus temperature<br>- digitized-continuous: Digitized with Plot Digitizer from a continuous data source (e.g. line plot of depth versus temperature)</td> </tr> <tr> <td><code>date_min</code></td> <td>date (%Y-%m-%d)</td> <td>Measurement date, or if not known precisely, the first possible date (e.g. 2019 → 2019-01-01).</td> </tr> <tr> <td><code>date_max</code> (required)</td> <td>date (%Y-%m-%d)</td> <td>Measurement date, or if not known precisely, the last possible date (e.g. 2019 → 2019-12-31).</td> </tr> <tr> <td><code>time</code></td> <td>time (%H:%M:%S)</td> <td>Measurement time.</td> </tr> <tr> <td><code>utc_offset</code></td> <td>number [h]</td> <td>Time offset relative to Coordinated Universal Time (UTC).</td> </tr> <tr> <td><code>equilibrium</code></td> <td>string</td> <td>Whether and how reported temperatures equilibrated following drilling.<br>- true: Equilibrium was measured<br>- estimated: Equilibrium was estimated (typically by extrapolation)<br>- false: Equilibrium was not reached</td> </tr> <tr> <td><code>notes</code></td> <td>string</td> <td>Additional remarks about the profile or the measurements therein as a pipe-delimited list. Sources are referenced by <code>source.id</code>. Quality concerns are prefixed with '[flag]'.</td> </tr> </tbody> </table> <h3><a name="measurement"></a><code>measurement</code></h3> <p>Temperature measurements with depth.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>borehole_id</code> (required)</td> <td>integer</td> <td>Borehole identifier.</td> </tr> <tr> <td><code>profile_id</code> (required)</td> <td>integer</td> <td>Borehole profile identifier.</td> </tr> <tr> <td><code>depth</code> (required)</td> <td>number [m]</td> <td>Depth below the glacier surface.</td> </tr> <tr> <td><code>temperature</code> (required)</td> <td>number [°C]</td> <td>Temperature.</td> </tr> </tbody> </table>
Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from various reanalysis datasets
<h1>Dataset Description</h1> <p>Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from several reanalysis datasets. This dataset is the result of an extension of the Jenkinson-Collison circulation type classification to the entire globe, including a modification of its original formulation for the southern hemisphere.</p> <p>A modified version of the IPCC-AR6 Reference Regions that excludes the intertropical range where the method is not applicable is also included, as used in the reference paper for global assessment.</p> <p>Further details in <a href="https://doi.org/10.1007/s00382-022-06658-7" target="_blank" rel="noopener">https://doi.org/10.1007/s00382-022-06658-7 </a></p> <h2>Note for version 1.1.0</h2> <p>This version corrects an issue in the previous release, which was incorrectly labeled as <em>version 0.1</em>. That version was incomplete due to the omission of previously existing files, and should be considered <strong>incomplete</strong>. Version 1.1.0 restores all original files alongside the newly added one, ensuring the dataset is now complete and consistent. We apologize for any inconvenience this may have caused and appreciate your understanding.</p>
A Comprehensive Global Aquatic N2O Emission Database (GANED): Unravelling N2O Emission Patterns from Different Water Bodies, 1980-2023
The Global Aquatic Nitrous Oxide Emission Database (GANED) is a comprehensive synthesis of empirical observations of N2O concentration measurements and flux records, spanning the period 1980-2023. GANED advances N2O research by providing the first global systematic emission mechanisms among the different aquatic system types, including rivers, streams, estuaries, reservoirs, ponds, lakes, open seas and coastal areas. The N2O data in GANED is further interconnected with biogeochemical metadata on dissolved oxygen, dissolved organic carbon, ammonium, nitrate, nitrite, total nitrogen, water temperature, salinity and pH, along with site data (latitude, longitude, codes of channel type, depth, surface area, elevation). The dataset explains the discrepancy that emission of N2O in aquatic bodies is determined mainly by substrate availability, and not by climatic factors, and reveals the systematic biases of concentration-only measurements, which can result in an underestimation of fluxes in effluent water of dynamically changing aquatic waters. Consequently, GANED constitutes a crucial transition “where” emissions occur to understanding “why” they differ across systems, and thus enabling targeted mitigation interventions. GANED includes 5130 records of N2O concentration and 7386 flux measurements from 3,002 unique sites, most of which are resolved to the daily time scale.
A Global database of methane concentrations and atmospheric fluxes for streams and rivers
This dataset, referred to as MethDB, is a collation of publicly available values of methane (CH4) concentrations and atmospheric fluxes for world streams and rivers, along with supporting information on location, geographic, physical, and chemical conditions of the study sites. The data set is composed of four linked tables, corresponding to the data sources (Papers_MethDB), the study sites (Sites_MethDB), concentrations (Concentrations_MethDB), and influx/efflux rates (Fluxes_MethDB). Information was extracted from journal articles, government reports, book chapters, and similar sources that were acquired before 15 September 2015. Concentrations and fluxes were converted to a standard unit (micromoles per liter for concentration and millimoles per square meter per day for flux) and both the author-reported and converted data are included in the database. MethDB was assembled as part of a larger synthesis effort on stream and river CH4 dynamics, and assembled data were used to identify large-scale patterns and potential drivers of fluvial CH4 and to generate an updated global-scale estimate of CH4 emissions from world rivers.
Global Violent Deaths (GVD) database 2004-2021, 2023 update, version 1.0
<p>The <a href="https://www.smallarmssurvey.org/database/global-violent-deaths-gvd">Global Violent Deaths (GVD) database</a> integrates indicators on the major causes of lethal interpersonal and communal violence—intentional and unintentional homicides, killings in legal interventions, and direct conflict deaths—and combines them in a single violent deaths indicator. These indicators are also reported in a disaggregated format by the sex of the victim and perpetration mechanism, namely firearm killings. The GVD database tracks this information across 222 countries and territories worldwide yearly from 2004 and reports both crude counts and rates per 100,000 population. The input data is retrieved from reliable sources, such as governments, national and international organizations, trusted non-governmental organizations, and verified media outlets. Missing data points are estimated using the methods described in this document.</p> <p>The GVD database is updated annually by the <a href="https://www.smallarmssurvey.org/">Small Arms Survey</a>, an associated programme of the Geneva Graduate Institute, which strengthens the capacity of governments and practitioners to reduce illicit arms flows and armed violence. This is done through three mutually reinforcing activities: the generation of policy-relevant knowledge, the development of authoritative resources and tools, and the provision of training and other services. The GVD database benefits from financial support from governments and organizations, and notably its core donors, who are publicly disclosed <a href="https://www.smallarmssurvey.org/who-we-are/funding-and-finance">online</a>. The Small Arms Survey follows rigorous procedures to ensure that the input data, the applied methods, and the results are of reasonable quality. If the user encounters apparent errors, they should contact us via email at <a href="mailto:media@smallarmssurvey.org">media@smallarmssurvey.org</a>.</p> <p>Regions, sub-regions, countries, and territories are defined based on the classification system used by the UN Statistical Division (2013 revision), except for Kosovo, England and Wales, Northern Ireland, and Scotland. The names and designations reported in the database do not imply any sort of endorsement by the Small Arms Survey.</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>
Global database of Coastal Characteristics (GCC)
<p>This dataset present a Global database of Coastal Characteristics (GCC) with 80 indicators spanning the</p> <ul> <li>geophysical,</li> <li>hydrometeorological and</li> <li>socioeconomic</li> </ul> <p>environment, at a high alongshore resolution of 1 km and provided at ~730,000 points along the global ice-free coastline. The latest freely available global datasets and a global high-resolution transect system are used to derive these indicators.</p> <p>The geophysical indicators include coastal slopes and elevation maxima, land-use, presence of vegetation or sandy beaches.The hydro-meteorological indicators involve water level, wave conditions and meteorological conditions (rain and temperature). Additionally, the socioeconomic indices are related to population, GDP and presence of critical infrastructure (roads, railways, ports and airports).</p> <p>The indicators are provided in three comma-separated values (CSV) files, one for each group:</p> <ul> <li>GCC_geophysical.csv</li> <li>GCC_hydrometeorological.csv</li> <li>GCC_socioeconomic.csv</li> </ul> <p>Information for each individual indicator, including its name, long name (description), units and type, are provided in the meta_data.yml file.</p>
The FORCIS database: A global census of planktonic Foraminifera from ocean waters
<p>The FORCIS (Foraminifera Response to Climatic Stress) database is a synthesis grouping datasets on living planktonic foraminifera. We assembled foraminiferal diversity and distribution data in the global oceans from 1910 until 2018, curating published and unpublished datasets. This database includes data collected using plankton tows, continuous plankton recorder, sediment traps and plankton pump from the global ocean.</p> <p>The FORCIS database version 01 is composed of 5 files (“.csv” format). All data coming from different sampling devices were put into separate “.csv” files. Only the data of the CPR from the Southern Hemisphere have been separated from the Northern Hemisphere CPR data as the data structure is not the same (species counts resolved vs. binned total counts, respectively). </p> <p>Apart from the file of CPR data from the Northern Hemisphere that contains only metadata and binned total counts, all the remaining four files contain 4 blocks:</p> <ul> <li> <p>Block 1: metadata (from column 1 to 71)</p> </li> <li> <p>Block 2: original counts (from column 72 to 274)</p> </li> <li> <p>Block 3: generated counts based on the validated taxonomy (from column 275 to 331). We added “_VT” to each species name to distinguish it from other taxonomy levels. E.g. “g_bulloides” became “g_bulloides_VT”. The number of species counted per subsample is also reported in the column “number_of_species_counted_VT”</p> </li> <li> <p>Block 4: generated counts based on the lumped taxonomy (from column 332 to 379). In this case, we added “_LT” to each species name. E.g. “n_dutertrei” became “n_dutertrei_VT”. We also calculated the number of species counted per subsample and reported it in the column “number_of_species_counted_LT”</p> </li> </ul> <p>Foraminifera abundance data counts are reported in different categories in the blocks 1,2 and 3 and described in the table below:</p> <table> <tbody> <tr> <td> <p><strong>count_type</strong></p> </td> <td> <p><strong>unit</strong></p> </td> </tr> <tr> <td> <p>Absolute</p> </td> <td> <p>ind/m3</p> </td> </tr> <tr> <td> <p>Relative</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Raw</p> </td> <td> <p>number of individuals</p> </td> </tr> <tr> <td> <p>Fluxes</p> </td> <td> <p>ind/m2/day</p> </td> </tr> <tr> <td> <p>Bin_Absolute</p> </td> <td> <p>ind/m3</p> </td> </tr> <tr> <td> <p>Bin_Relative</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Bin_Raw</p> </td> <td> <p>number of individuals</p> </td> </tr> <tr> <td> <p>Bin_Fluxes</p> </td> <td> <p>ind/m2/day</p> </td> </tr> </tbody> </table> <p> </p> <p>For more details about the FORCIS database column description, please check the data descriptor paper <strong>Chaabane et al. (2023) (https://doi.org/10.1038/s41597-023-02264-2).</strong></p> <p>The database is kept open for any new entries and the updated version will be released in csv format. The labels of updated versions of the released “.csv” files will contain the date of their publication and versioning number.</p>
Emissions Database for Global Atmospheric Research, version v4.3.2 part I Greenhouse gases
<p>The Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2, partim Greenhouse gases compiles anthropogenic emissions data for CO2, CH4 and N2O based on international statistics and emission factors. The version v4.3.2 of the EDGAR emission inventory provides global estimates, broken down to IPCC-relevant source-sector levels, from 1970 (the year of EU’s first Air Quality Directive) to 2012 (the end year of the first commitment period of the Kyoto Protocol (KP)). Strengths of EDGAR v4.3.2 include global geo-coverage (226 countries), continuity in time, and comprehensiveness in activities. Emission sources of the multiple gases include all human activities except the land-use, land-use change and forestry sector and are compiled following a bottom-up and IPCC-compliant approach. The dataset provides in addition to the complete timeseries 1970-2012 also annual and global gridmaps of 0.1 degree by 0.1 degree resolution for each source-sector and each year. For 2010 also 12 monthly gridmaps per source-sector are provided.</p>
GeoDAR-TopoCat: Drainage topology and catchment database (TopoCat) for Georeferenced global Dams And Reservoirs (GeoDAR)
<p><strong>Contact</strong>: Md Safat Sikder (msikder@ksu.edu), Jida Wang (jidawang@ksu.edu; gdbruins@ucla.edu)</p> <p> </p> <p><strong>Data description</strong></p> <p>This data can be considered a supplement to the Georeferenced global Dams And Reservoirs (GeoDAR) dataset (doi:10.5281/zenodo.6163413). </p> <p>Here in GeoDAR-TopoCat, the method of TopoCat (doi:10.5281/zenodo.7420810) has been applied on GeoDAR reservoirs in order to construct the drainage topology and catchments for global reservoirs.</p> <p>To avoid ambiguity, please refer to this version of GeoDAR-TopoCat as “<strong>GeoDAR-TopoCat v1.1-1.0</strong>”, where “1.1” specifies the version of GeoDAR reservoirs, whose drainage topology and catchments are constructed using the method in version “1.0” of TopoCat.</p> <p> </p> <p><strong>Relevant datasets</strong></p> <ul> <li>The original GeoDAR v1.1 dataset without topology can be accessed here: doi:10.5281/zenodo.6163413.</li> <li>The TopoCat v1.0 dataset, originally developed based on HydroLAKES v1.0, can be accessed here: doi:10.5281/zenodo.7420810.</li> </ul> <p> </p> <p><strong>Attribute description</strong></p> <p>Description of the attributes of GeoDAR-TopoCat is the same as those of TopoCat v1.0. The unique ID of each GeoDAR reservoir is specified in “id_v11” (consistent with the GeoDAR dataset). Please refer to the attributes of TopoCat and GeoDAR for more details.</p> <p> </p> <p><strong>Data and code availability</strong></p> <p>All datasets are available under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license (<a href="https://creativecommons.org/licenses/by/4.0">https://creativecommons.org/licenses/by/4.0</a>).</p> <p>Please refer to GeoDAR and TopoCat datasets for other details and disclaimers.</p> <p> </p> <p><strong>Citation</strong></p> <p>We request anyone who uses GeoDAR-TopoCat to cite <strong>both GeoDAR and TopoCat papers</strong>:</p> <p>Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, M. S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs database for bridging attributes and geolocations. Earth System Science Data, 14, 1869-1899, 2022, <a href="https://doi.org/10.5194/essd-14-1869-2022">https://doi.org/10.5194/essd-14-1869-2022</a>.</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. Earth System Science Data Discussion, in review, <a href="https://doi.org/10.5194/essd-2022-433">https://doi.org/10.5194/essd-2022-433</a>.</p>
CoCO2 global emission point source database
<p>This dataset contains a global emission catalogue of CO2 and co-emitted species (NOx, SO2, CO, CH4) from thermal power plants for the year 2018. The dataset contains annual emission information for individual thermal power plants at their exact geographical location. Each facility is linked to a specific temporal (i.e., monthly, day-of-the-week and hourly) and vertical distribution profile to derive spatial- and temporal-resolved emissions for modelling efforts. The dataset was produced as part of the CoCO2 project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 958927.</p>
Global N2O Database version 1.0
Agriculture is the primary source of the powerful greenhouse gas (GHG) nitrous oxide (N2O) and an important source of GHG emissions. Due to sampling limitations, N2O measurements have traditionally been sparse; with research studies that often have less than 50 sampled days within a year. Nitrous oxide emissions are highly variable and short-lived peak emission periods may contribute more than 50% to annual emissions. Gap filling around these peaks, if measured at all, can result in poor estimations under the standard practice using linear interpolation. Improved gap filling methods that reflect covariate data will likely reduce uncertainty and improve annual N2O estimates. The Global N2O Database was created to serve as a repository for these datasets as well as become a resource for publicly available data and analytical advances. These datasets have been joined in data sheets that use the same formatting, allowing for easy access and comparison of data sets. We hope that this data availability will lead to improvements in N2O understanding and mitigation.
Global Transmission Database
<p>The Global Transmission Database (GTD) consists of comprehensive data regarding existing and planned cross-border transmission capacities globally collated from public sources. The dataset is oriented towards representing entry level capacity data (MW) that can be used in energy system models and other computational tools. Transmission capacities are provided at a country-to-country basis in addition to regional level data for a sample of larger countries (Australia, Brazil, Canada, China, India, Indonesia, Japan, Philippines, Russian Federation, United States, Vietnam). Capacities are provided for land-based transmission pathways as well as for subsea pathways. Refer to the accompanying paper for details on the applied methodology as well as a file by file description of the repository content.</p>
A global database of bird nest traits
<p>The reproductive success of birds is closely tied to the characteristics of their nests. It is crucial to understand the distribution of nest traits across phylogenetic and geographic dimensions to gain insight into bird evolution and adaptation. Despite the extensive historical documentation on breeding behavior, a structured dataset describing bird nest characteristics has been lacking. To address this gap, we have compiled a comprehensive dataset that characterizes three ecologically and evolutionarily significant nest traits—site, structure, and attachment—for 9,248 bird species, representing all 36 orders and 241 out of the 244 families. By defining seven sites, seven structures, and four attachment types, we have systematically classified the nests of each species using information from text descriptions, photos, and videos sourced from online databases and literature. This nest traits dataset serves as a valuable addition to the existing body of morphological and ecological trait data for bird species, providing a useful resource for a wide range of avian macroecological and macroevolutionary research.</p>
Global Plantation Forest Carbon database
<p>This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (<a href="https://forc-db.github.io/">https://forc-db.github.io/</a>) and GROA databases (https://github.com/forc-db/GROA).</p> <p>When using these data, please cite:</p> <p>Bukoski, J.J., Cook-Patton, S.C., Melikov, C., Ban, H., Liu, J.C., Harris, N., Goldman, E., and Potts, M.D. 2022. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests. <em>Nature Communications</em> 13(4206). doi: 10.1038/s41467-022-31380-7</p>
Global arboreal pollen database for Termination V (404-433 kyr BP)
<p>This dataset is a global compilation of arboreal pollen records covering Termination V (~404-433 kyr BP).<br> 1) Arboreal pollen proxy (Arboreal pollen (AP) or fern spores) percentages were computed for 19 pollen records covering large portions of Termination V (404-433 kyr BP) at a global scale (Sheet 1-19)<br> 2) The compiled arboreal pollen proxy percentages were resampled with a 2-kyr timestep from 405 to 433 kyr BP (Sheet 20)<br> 3) The resampled arboreal pollen proxy percentages were classified in four forest groups defined according to their main bioclimatic specificity (Eurosiberian-Boreoamerican (ES-BA), Subtropical-North, Tropical, Subtropical-South) (Sheet 21). The Eurosiberian-Boreoamerican group (ES-BA), as defined by Braun-Blanquet (1930), covers boreal and temperate forests. Therefore, both Tibetan Plateau pollen records (Heqing and ZB13-C2) are counted in the ES-BA group, the high altitude compensating for their relatively low latitudes. Although Tenaghi Philippon and Lake Ohrid records are on the geographical boundary with the Mediterranean region, they are also classified in the ES-BA group for they rather reflect a temperate forest cover. The Northern Hemisphere Subtropical group (Subtropical-N) encompasses warm temperate forests of the Northern Hemisphere and therefore extends beyond the geographical boundaries of the Mediterranean region (Mediterranean sensu lato). The Tropical group (T) refers to tropical and afromontane forests. Finally, the Southern Hemisphere Subtropical group (Subtropical-S) only includes the MD96-2048 record which is at the limit between tropical and subtropical forests (Table 2). More information on the forest groups is provided in the associated publication: <a href="https://doi.org/10.5194/cp-2021-143">https://doi.org/10.5194/cp-2021-14</a></p>
Global Reservoir Geometry Database
<p>This is a global-scale reservoir storage-area-depth dataset including 6,824 major reservoirs. For each reservoir, the storage-area-depth relationships were derived from an optimal geometric shape selected iteratively from five possible regular geometric shapes that minimizes the error of total storage and surface area estimation. This algorithm has been applied to 6,800 reservoirs included in the Global Reservoir and Dam database (GRanD). The relative error between the estimated and observed total storage is no more than 5% and 50% for 66% and 99% of all GRanD reservoirs, respectively. More importantly, the storage-depth profiles derived from the approximated reservoir geometry compared well with remote sensing based estimation at 40 major reservoirs from previous studies, and ground-truth measurements for 34 reservoirs in the United States and China.</p>
Global whole-rock geochemical database compilation
<p>Dataset associated with the submitted publication - Global whole-rock geochemical database compilation</p>
A Global Database of Historic and Real-time Flood Events based on Social Media
<p>Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10.000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE are detected. All results are publicly available on <a href="http://www.globalfloodmonitor.org">www.globalfloodmonitor.org</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.