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12,072 results for “Global”
Observed phenological indicators and environmental drivers at global change experiments at the Jornada Basin LTER site, 2014-2020
This dataset contains plant phenological data extracted from phenocams installed at a global exchange experiment involving Chihuahuan desert plant communities at the Jornada Basin LTER site in southern New Mexico, U.S.A. Cycles of plant growth, termed phenology, are tightly linked to environmental controls, and our overarching objective in this study is to determine if temperature or precipitation are relatively more important for determining shrub and grass greenup date (start of season) and senescence date (end of season). At these camera locations, we experimentally manipulated incoming precipitation at the Jornada Basin LTER for over a decade and recorded plant leaf phenology at the daily scale for seven years using phenocams. The data here are derived from raw "phenocam" camera data collected at two ongoing studies at the Jornada Basin LTER site, one studying ecosystem responses to long term changes in water and nitrogen availability, and one studying plant productivity and partitioning responses to water availability and herbivory (studies 349 and 456, respectively). Phenocams at the sites have collected images since 2014, and basic color and greenness data extracted from those images are available in a companion dataset on EDI (knb-lter-jrn.210574001). This dataset includes the derived annual and quarterly phenological indices and greenness indices for each plot monitored by phenocams, and temperature and precipitation variables aggregated to the same frequency. The dataset also includes R code and input files used to generate these derived data. See Currier and Sala 2022 for more details. This study is ongoing.
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.
Indicative distribution maps for Ecosystem Functional Groups - Level 3 of IUCN Global Ecosystem Typology
<p>This dataset includes the current version of the indicative distribution maps and profiles for <strong>Ecosystem Functional Groups</strong> - Level 3 of IUCN Global Ecosystem Typology (v2.1). Please refer to Keith <em>et al.</em> (2020) and Keith et al. (2022).</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes for each functional group of ecosystems to enable any ecosystem type to be assigned to a group.</p> <p>Maps are indicative of global distribution patterns and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Most maps were prepared using a coarse-scale template (e.g. ecoregions), but some were compiled from higher resolution spatial data where available (see details in profiles). Higher resolution mapping is planned in future publications.</p> <p>We emphasise that spatial representation of Ecosystem Functional Groups does not follow higher-order groupings described in respective ecoregion classifications. Consequently, when Ecosystem Functional Groups are aggregated into<strong> functional biomes</strong> (Level 2 of the Global Ecosystem Typology), spatial patterns may differ from those of biogeographic biomes. Differences reflect the distinctions between functional and biogeographic interpretations of the term, “biome”.</p>
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 dataset of nitrogen fixation rates across inland and coastal waters based on a coordinated synthesis effort
Biological nitrogen fixation converts inert di-nitrogen gas into bioavailable nitrogen and can be an important source of bioavailable nitrogen to organisms. This dataset synthesizes the aquatic nitrogen fixation rate measurements across inland and coastal waters. Data were derived from papers and datasets published by April 2022 and include rates measured using the acetylene reduction assay (ARA), 15N2 labeling, or the N2/Ar technique. The dataset is comprised of 4793 nitrogen fixation rates measurements from 267 studies, and is structured into four tables: 1) a reference table with sources from which data were extracted, 2) a rates table with nitrogen fixation rates that includes habitat, substrate, geographic coordinates, and method of measuring N2 fixation rates, 3) a table with supporting environmental and chemical data for a subset of the rate measurements when data were available, and 4) a data dictionary with definitions for each variable in each data table. This dataset was compiled and curated by the NSF-funded Aquatic Nitrogen Fixation Research Coordination Network (award number 2015825).
Global eutrophication and antibiotic resistance genes dataset for "Coupling mechanisms between cyanobacteria and antibiotic resistance genes in freshwater ecosystems"
This dataset compiles global records of cyanobacteria, antibiotic resistance genes (ARGs), and associated water quality parameters to support research on freshwater ecosystem dynamics. It includes 990 metagenomes, 16,648 chlorophyll-a (Chl-a) records, and over 90 documented cases of ARGs–cyanobacteria co-occurrence under comparable spatiotemporal conditions. The dataset covers the years 2000–2024 and provides both raw measurements and harmonized tables for cross-study comparisons. Data were extracted from previously published literature and public repositories, with references to source publications included. This archive is intended to facilitate reproducible analyses, enable large-scale meta-studies, and support further exploration of microbial interactions in freshwater systems.
MCR LTER: Coral Reef: Dead coral skeletons impair key recovery processes following coral bleaching; data for Kopecky et al., 2024 Global Change Biology
The data included in this data package were collected on the North shore of Moorea, French Polynesia, from 2015-2023 to explore how dead coral skeletons (e.g,, left after coral bleaching events) influence critical processes tied to coral reef resilience. Together, these various datasets were used for analyses in the manuscript entitled "Changing disturbance regimes, material legacies, and stabilizing feedbacks: dead coral skeletons impair key recovery processes following coral bleaching", published in Global Change Biology. These data are in support of a publication Kopecky et al. (2024) Global Change Biology, and were a part of the thesis of K. Kopecky. The manuscript title and author list are as follows: Changing disturbance regimes, material legacies, and stabilizing feedbacks: dead coral skeletons impair key recovery processes following coral bleaching. Kai Kopecky, Russell J. Schmitt, Sally J. Holbrook. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 22-24354 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2024). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
A global map of terrestrial habitat types
<p>We provide a global spatially explicit characterization of 47 (version 001) terrestrial habitat types, as defined in the International Union for Conservation of Nature (IUCN) habitat classification scheme, which is widely used in ecological analyses, including for assessing species’ Area of Habitat. We produced this novel habitat map by creating a global decision tree that intersects the best currently available global data on land cover, climate and land use. The maps broaden our understanding of habitats globally, assist in constructing area of habitat (AOH) refinements and are relevant for broad-scale ecological studies and future IUCN Red List assessments. We hope that these data and outlined framework will spur further development of biodiversity-relevant habitat maps at global scales. An interactive interface helping to navigate the map can be found at on the Naturemap website ( https://explorer.naturemap.earth/map).</p> <p>Provided is the code to recreate the map (to made available soon), the global composite image at native -100m Copernicus resolution for level 1 and level 2 and layers of aggregated fractional cover (unit: [0-1] * 1000) at 1km for level 1 and level 2.</p> <p>Starting with version 004 there changemasks for the years 2016, 2017, 2018 and 2019 are supplied. Changemasks for the composite masks show the changed grid cells and their new values with earlier years being nested in later years, e.g. using the changemask for 2019 includes all changes up to 2019. For the fractional cover estimates at ~1km resolution, new fractional cover changemasks are supplied as subtraction (before - after) between the previous and current year (unit range: [-1 to 1] * 1000).</p> <p>We highlight that only changes in land cover are considered since most of the ancillary layers (e.g. pasture, forest management, climate, etc...) are static and thus not all changes in habitats can be found. We therefore recommend end users to continue using the 2015 dataset unless specific habitat updates to habitat are needed.</p> <p><strong>Citation:</strong></p> <p>Please cite the published paper and state the used version of the habitat map</p> <p>Jung, M., Dahal, P.R., Butchart, S.H.M., Donald, P.F., De Lamo, X., Lesiv, M., Kapos, V., Rondinini, C., Visconti, P., (2020). A global map of terrestrial habitat types. Sci. Data 7, 256. <a href="https://doi.org/10.1038/s41597-020-00599-8">https://doi.org/10.1038/s41597-020-00599-8</a></p>
Global monthly catches from tuna surface fisheries by 1° grid (1958-2023) (FIRMS level 0)
<p>We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries that use fishing gears set at the water's surface. This dataset was created by harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1958-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO), we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP), facilitating the seamless integration of data into the dataset.</p> <p>Geo-referenced catch data from tuna surface fisheries are reported in either the number of fish or live-weight equivalent (metric tonnes), with some strata providing catches in both units. The catches primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. The data are stratified by year, month, fishing fleet, fishing gear, fishing mode, 1° grid area of longitude and latitude, and taxon.</p> <p>The dataset encompasses 42 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 14 species of tunas, 9 species of billfish, 4 species of Spanish mackerels, 2 species of bonitos, and wahoo. Despite uncertainties and incomplete data due to under-reporting, the dataset also includes reported catches for 12 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries.</p> <p>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries using surrounding nets, gillnets, entangling nets, and pole-and-lines from over 70 fishing fleets across 69 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than six decades.</p>
Global annual catches from tuna fisheries (1918-2023) (FIRMS level 0)
<p>We constructed the most comprehensive dataset of nominal catches from global tuna fisheries by compiling and harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1918-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO),we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP),facilitating the seamless integration of data into the dataset.<br><br>Nominal catch data are expressed in live-weight equivalent (metric tonnes) and primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. In recent years,data from fisheries in the Atlantic and Western-Central Pacific Oceans have partially included amounts of fish discarded dead. The data are stratified by year,fishing fleet,fishing gear,large spatial area,and taxon.<br><br>The dataset encompasses 50 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 15 species of tunas,10 species of billfish,8 species of Spanish mackerels,2 species of bonitos,and wahoo. In 2023,the global catch for these species was estimated to exceed 6.4 million metric tonnes. Despite uncertainties and incomplete data due to under-reporting,the dataset also includes reported catches for 14 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries. The total reported catch of these elasmobranch species was approximately 154,000 metric tonnes in 2023.<br><br>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries from over 161 fishing fleets across 159 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than seven decades.</p>
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>
Update of: The Global Fire Atlas of individual fire size, duration, speed and direction
<p>This is an updated and extended record of the Global Fire Atlas introduced by Andela et al. (2019). Input data (burned area and land cover products) are updated to the MODIS Collection 6.1 (the original version featured in Andela et al. (2019) was based on collection 6.0 burned area and collection 5.1 land cover products, respectively). The timeseries is extended to cover the period 2002 to August 2024.</p> <h2><strong>Methodological Notes:</strong></h2> <p>The method employed to create the dataset precisely follows the approach described by Andela et al. (2019).</p> <p>The input burned area product is MCD64A1 Collection 6.1. It is described by Giglio et al. (2018) and available at: https://lpdaac.usgs.gov/products/mcd64a1v061/. </p> <p>The input land cover product is MCD12Q1 Collection 6.1. It is described by Sulla-Menashe et al. (2019) and available at: https://lpdaac.usgs.gov/products/mcd12q1v061/. </p> <p>Note that while the methods have remained the same compared to Andela et al. (2019), we do observe small differences between the Global Fire Atlas products originating from differences between the MCD64A1 collection 6.1 burned area data used here and the collection 6 data used in the original product. In addition, we observe more substantial differences in the dominant land cover class associated with each fire due to the differences between the MCD12Q1 collection 6.1 data used here and collection 5.1 data used in the original product. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. For each MODIS tile, the fire season is defined as the twelve months centred on the month with peak burned area (see Andela et al., 2019). For example, for a MODIS tile with peak burned area in December, the 2023 fire season would be defined as the period from July 2023 to June 2024, with the current record ending in August 2024. This is particularly relevant in the Southern extratropics and the northern hemisphere subtropics, where the fire seasons often span the new year. The local definition of the fire season is based on climatological peak in burned area as described by Andela et al. (2019).</p> <p>Here we extended the time-series to include the fire season of 2002, and extended the time-series until February 2025.</p> <h2> </h2> <h2><strong>Usage Notes:</strong></h2> <h3><strong>Incomplete Observations for the Latest Fire Seasons:</strong></h3> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. As such, the time-series can be incomplete for the latest fire season (e.g. the "2024 fire season") and also for the penultimate fire season (e.g. the "2023 fire season") due to the way that fire seasons are defined (see above). For example, if the month with peak burned area for a tile is December, then full data covering the 2023 fire season in that tile are not available until midway through the 2024 calendar year. This contrasts with the original dataset from Andela et al. (2019), which only included the data for entire fire seasons between 2003 and 2016. </p> <h3><strong>Observational Outages:</strong></h3> <p>For the purpose of time-series analysis, we note that the 2002 product may have been affected by outages of Terra-MODIS (most notably, June 15 2001 - July 3 2001 and March 19 2002 - March 28 2002), which affects the burn date estimates and Global Fire Atlas product. Following the launch of Aqua-MODIS in May 2002 burn date estimates are more reliable as estimated from both MODIS sensors onboard Terra and Aqua. </p> <h3><strong>File Naming Convention:</strong></h3> <p>GFA_v<em>{time-stamp}</em>_<em>{data-type}</em>_<em>{fire_season}</em>.<em>{file_type}</em></p> <p><em>{time-stamp}</em><strong> </strong>= Date that code was run.</p> <p><em>{data-type}</em><strong> </strong>= “ignitions” or “perimeters” for vector files; “day_of_burn”, “direction”, “fire_line”, or “speed” for raster files.</p> <p><em>{fire_season} </em>= the locally-defined fire season in which the fire was ignited (see more below).</p> <p><em>{file_type} </em>= ".shp" for vector files; ".tif" for raster files. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. Hence the file GFA_v20240409_perimeters_2003.shp can include fires from the 2003 fire season that ignited in the calendar years 2002 or 2004. </p> <h3>Coordinate systems (Map Projections):</h3> <p>Vector data are provided on the WGS84 projection.</p> <p>Raster data are provided on the MODIS sinusoidal projection used in NASA tiled products. The WKT string defining this projection is:</p> <pre><code>'PROJCS["unnamed",GEOGCS["Unknown datum based upon the custom spheroid",DATUM["Not_specified_based_on_custom_spheroid",SPHEROID["Custom spheroid",6371007.181,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Sinusoidal"],PARAMETER["longitude_of_center",0],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]'</code></pre> <p> </p> <h2><strong>Data Layers:</strong></h2> <p><em><strong>Table 1: Overview of the Global Fire Atlas data layers. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire, while the underlying 500 m gridded layers reflect the day-to-day behavior of the individual fires. In addition, we provide aggregated monthly summary layers at a 0.25° resolution for regional and global analyses.</p> <table> <tbody> <tr> <td>File name</td> <td>Content</td> </tr> <tr> <td>SHP_ignitions.zip</td> <td>Shapefiles of ignition locations with attribute tables (see Table 2)</td> </tr> <tr> <td>SHP_perimeters.zip</td> <td>Shapefiles of final fire perimeters with attribute tables (see Table 2)</td> </tr> <tr> <td>GeoTIFF_direction.zip</td> <td>500 m resolution daily gridded data on direction of spread (8 classes)</td> </tr> <tr> <td>GeoTIFF_day_of_burn.zip</td> <td>500 m resolution daily gridded data on day of burn (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_speed.zip</td> <td>500 m resolution daily gridded data on speed (km/day)</td> </tr> <tr> <td>GeoTIFF_fire_line.zip</td> <td>500 m resolution daily gridded data on the fire line (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_monthly_summaries.zip</td> <td>Aggregated 0.25° resolution monthly summary layers. These files include the sum of ignitions, average size (km2), average duration (days), average daily fire line (km), average daily fire expansion (km2/day), average speed (km/day), and dominant direction of spread (8 classes). </td> </tr> </tbody> </table> <p> </p> <p><em><strong>Table 2: Overview of the Global Fire Atlas shapefile attribute tables. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire.</p> <table> <tbody> <tr> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>lat, lon</td> <td>Coordinates of ignition location (°)</td> </tr> <tr> <td>size</td> <td>Fire size (km2)</td> </tr> <tr> <td>perimeter</td> <td>Fire perimeter (km)</td> </tr> <tr> <td>start_date, start_DOY</td> <td>Start date (yyyy-mm-dd), start day of year (1-366)</td> </tr> <tr> <td>end_date, end_DOY</td> <td>End date (yyyy-mm-dd), end day of year (1-366)</td> </tr> <tr> <td>duration</td> <td>Duration (days)</td> </tr> <tr> <td>fire_line</td> <td>Average length of daily fire line (km)</td> </tr> <tr> <td>spread</td> <td>Average daily fire growth (km2/day)</td> </tr> <tr> <td>speed</td> <td>Average speed (km/day)</td> </tr> <tr> <td>direction, direc_frac</td> <td>Dominant direction of spread (N, NE, E, SE, S, SW, W, NW) and associated fraction</td> </tr> <tr> <td>MODIS_tile</td> <td>MODIS tile id</td> </tr> <tr> <td>landcover, landc_frac</td> <td>MCD12Q1 dominant land cover class and fraction (UMD classification), provided for 2002-2023</td> </tr> <tr> <td>GFED_regio</td> <td>GFED region (van der Werf et al., 2017; available at https://www.globalfiredata.org/)</td> </tr> </tbody> </table> <p> </p> <p> </p>
GlobalHighPM₂.₅: Global Daily Seamless 1 km Ground-Level PM₂.₅ Dataset over Land (2017–Present)
<p>GlobalHighPM<sub>2.5</sub> is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>This dataset contains input data, analysis codes, and generated dataset used for the following article. If you use the GlobalHighPM<sub>2.5</sub> dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. <a href="https://weijing-rs.github.io/publications/Wei_et_al-NC-2023.pdf" target="_blank" rel="noopener">First close insight into global daily gapless 1 km PM<sub>2.5</sub> pollution, variability, and health impact</a>. <em>Nature Communications</em>, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3</p> </li> </ul> <p><strong>Input Data</strong></p> <p>Relevant raw data for each figure (compiled into a single sheet within an Excel document) in the manuscript.</p> <p><strong>Code</strong></p> <p>Relevant Python scripts for replicating and ploting the analysis results in the manuscript, as well as codes for converting data formats.</p> <p><strong>Generated Dataset</strong></p> <p>Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM<sub>2.5</sub> dataset over land from 2017 to the present. This dataset exhibits high quality, with cross-validation coefficients of determination (CV-R<sup>2</sup>) of 0.91, 0.97, and 0.98, and root-mean-square errors (RMSEs) of 9.20, 4.15, and 2.77 µg m<sup>-3</sup> on the daily, monthly, and annual bases, respectively.</p> <p><strong>Due to data volume limitations, </strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2022 </strong>is accessible at: <strong><a href="../records/10795661">GlobalHighPM2.5 (2022)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2021 </strong>is accessible at: <strong><a href="../records/10398385">GlobalHighPM2.5 (2021)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2020 </strong>is accessible at: <strong><a href="../records/10402639">GlobalHighPM2.5 (2020)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2019 </strong>is accessible at: <strong><a href="../records/10402723">GlobalHighPM2.5 (2019)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2018 </strong>is accessible at: <strong><a href="../records/10402824">GlobalHighPM2.5 (2018)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2017 </strong>is accessible at: <strong><a href="../records/10403497">GlobalHighPM2.5 (2017)</a></strong></p> <p> continuously updated...</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
Pre-processed (in Detectron2 and YOLO format) planetary images and boulder labels collected during the BOULDERING Marie Skłodowska-Curie Global fellowship
<p>This database contains 4976 planetary images of boulder fields located on Earth, Mars and Moon. The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024. The data was already splitted into train, validation and test datasets, but feel free to re-organize the labels at your convenience. </p> <p>For each image, all of the boulder outlines within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a>). This dataset differs from the previous dataset included along with the manuscript <a href="https://zenodo.org/records/8171052">https://zenodo.org/records/8171052</a>, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). In addition, the boulder outlines were also pre-processed so that it can be ingested directly in YOLOv8.</p> <p>A description of what is what is given in the README.txt file (in addition in how to load the custom datasets in Detectron2 and YOLO). Most of the other files are mostly self-explanatory. Please see previous dataset or manuscript for more information. If you want to have more information about specific lunar and martian planetary images, the IDs of the images are still available in the name of the file. Use this ID to find more information (e.g., M121118602_00875_image.png, ID M121118602 ca be used on https://pilot.wr.usgs.gov/). I will also upload the raw data from which this pre-processed dataset was generated (see <a href="https://zenodo.org/records/14250970">https://zenodo.org/records/14250970</a>).</p> <p>Thanks to this database, you can easily train a Detectron2 Mask R-CNN or YOLO instance segmentation models to automatically detect boulders. </p> <p><strong>How to cite:</strong></p> <p>Please refer to the "how to cite" section of the readme file of <a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth.</a></p> <p><strong>Structure:</strong></p> <pre><code>. └── boulder2024/ ├── jupyter-notebooks/ │ └── REGISTERING_BOULDER_DATASET_IN_DETECTRON2.ipynb ├── test/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── train/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── validation/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── detectron2_inst_seg_boulder_dataset.json ├── README.txt ├── yolo_inst_seg_boulder_dataset.yaml</code></pre> <p> </p> <pre><code>detectron2_inst_seg_boulder_dataset.json</code></pre> <p>is a json file containing the masks as expected by Detectron2 (see <a href="https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html">https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html</a> for more information on the format). In order to use this custom dataset, you need to register the dataset before using it in the training. There is an example how to do that in the jupyter-notebooks folder. You need to have detectron2, and all of its depedencies installed. </p> <pre><code>yolo_inst_seg_boulder_dataset.yaml</code></pre> <p>can be used as it is, however you need to update the paths in the .yaml file, to the test, train and validation folders. More information about the YOLO format can be found here (<a href="https://docs.ultralytics.com/datasets/segment/">https://docs.ultralytics.com/datasets/segment/</a>).</p>
SM2RAIN-ASCAT (2007-2021) global daily satellite rainfall including aggregated values and trend parameters as 10km resolution GeoTIFFs
<p>This is a GeoTIFF version of the <a href="http://hydrology.irpi.cnr.it/download-area/sm2rain-data-sets/">SM2RAIN-ASCAT (2007-2021): global daily satellite rainfall from ASCAT soil moisture</a> data set v1.1 (Brocca et al. 2019). Conversion steps are available <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/SM2RAIN"><strong>here</strong></a>. Few important notes:</p> <ul> <li>Daily values are stored as integers, whereas in the NetCDF the dataset is rounded to one decimal place.</li> <li>The NetCDF has also a Quality Flag for a better and more informed use of the data (here omitted).</li> <li>P05, P50 and P95 indicate quantiles derived per pixel.</li> </ul> <p>Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <strong><a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/input_layers/MOD13Q1/03-data-access.ipynb">python tutorial</a></strong>. Basically models are fitted for <strong>each pixel</strong> and the model parameters are saved as images.</p> <p>Monthly averages and s.d. of precipitation are available in the files:</p> <ul> <li>clm_precipitation_sm2rain.*_m_10km_s0..0cm_2007..2021_v1.5.tif = monthly precipitation in mm,</li> <li>clm_precipitation_sm2rain.*_sd.10_10km_s0..0cm_2007..2021_v1.5.tif = standard deviation of precipitation in mm * 10 per month (multiplied by 10 so Integers can be used),</li> </ul> <p>Downscaled monthly averages (1 km) are also available (<a href="https://doi.org/10.5281/zenodo.1435912">https://doi.org/10.5281/zenodo.1435912</a>).</p> <p>To cite this data set please refer to the <strong><a href="https://doi.org/10.5281/zenodo.2591214">original copy</a></strong> of the data set.</p> <ul> <li>Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). <strong><a href="https://doi.org/10.5194/essd-11-1583-2019">SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations</a></strong>. Earth Syst. Sci. Data, 11, 1583–1601. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a></li> </ul>
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
Global monthly percentage of vegetation cover (MODIS FCover MODV1A product: America, Pacific)
<p>Monthly Global FCover product generated from MODIS data. Dataset represent monthly gap-filled FCover estimates the period 2000-2015 over Pacific and America. FCover was estimated using linear spectral mixture analysis and interpolated using empirical orthogonal functions algorithm to take advantage of all non-missing available pixels in both the spatial and temporal dimensions to gap-fill missing satellite observations. The global product of vegetation cover (as percentage of cover) based on MODIS images with monthly variation can be used as a critical support for several indicators related to ecologically based modelling.</p>
A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice (Supplementary Data)
<p><strong>This is the supplemental material for:</strong></p> <p>Brooks, H.L., Miner, K.R., Kreutz, K.J., Winski, D.A., (in review). A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice. </p> <p><strong>Purpose:</strong></p> <p>This systematic literature review contextualizes current data availability and examines spatial and temporal gaps in the long-range transported Pb analyses (concentration and isotope ratios) in ice and snow samples. Additionally, we note areas of needed community improvement. It is our hope that researchers will also benefit from a queryable set of references, allowing for quick access to the records appropriate to address multiple research questions. </p> <p><strong>Available Files:</strong></p> <p><em><strong>Table A1:</strong></em> Metadata for Pb records -- Individual sample sites</p> <p><em><strong>Table A2:</strong></em> Metadata for Pb records -- Transect sample sites</p> <p><em><strong>Table A3:</strong></em> Records grouped into 23 regions</p> <p><em><strong>Supplement_fig_25Aug2024: </strong></em>Additional figures supporting main manuscript</p> <p><em><strong>Supplement_method_25Aug2024: </strong></em>Methodology used for the systematic literature review</p> <p><em><strong>Supplement_citations_25Aug2024:</strong></em> Citations for all records included in the systematic literature review</p> <p><em><strong>citations_export.bib:</strong></em> Export of all systematic literature review citation data as bibtex format. Easy import to citation managers (Zotero, Mendley, Endnote, etc)</p> <p><em><strong>indexedReferences.csv:</strong></em> CSV dump of citations_export.bib indexed with citation keys used in TableA.3</p> <p><em><strong>tables.RDS: </strong></em>TableA.1, TableA.2, and indexed References formatted for easy import into R</p> <p><em><strong>tables.sqlite: </strong></em>TableA.1, TableA.2, and indexed References formatted for SQL queries in SQLite</p> <p><em><strong>readme_tables_sqlite.md:</strong></em> Examples of SQLite queries</p> <p> </p> <p><strong>Systematic Literature Review Methodology:</strong></p> <p>To address the current spatial and temporal distribution of long-range transported Pb deposited in the cryosphere (snow-pits and ice cores), we completed a systematic literature review, following the methodology outlined by Booth et al (2016). We completed an “exhaustive coverage [search], citing all relevant literature" (Booth et al., 2016), using the search terms “Lead (Pb) isotopes and concentration in surface snow, snow pits, and ice cores”. We performed an initial comprehensive literature search on these search terms on Web of Science Collection databases in September 2020 and May 2023. Records evaluated for relevance using the title and abstract. Removal of clearly off-topic papers (e.g., the chemistry of penguin feces) gathered in the search due to the dual meaning of “lead” reduced the paper count to 326 titles. The full text of the remaining publications was evaluated with clear explicit criteria for inclusion and exclusion, based on the following criteria.</p> <ol> <li> <ol> <li>Only studies examining long-traveled background atmospheric lead signals were considered. All point source pollution studies examining the localized effects of traffic, road salt, mines, industry, power plants, human activity at base camp stations, etc, were excluded. An exception was made for samples which were taken at sufficient depths in the analyzed record to predate the pollution source or where wind trajectory did not transport pollution to the collection site regardless of close geographic proximity.</li> <li> <p>Only studies of natural, undisturbed snowpacks and ice cores were examined. Studies which sampled snow from urban structures were excluded. Point source studies of emissions detail the localized effects of traffic, road salt, mines, industry, power plants, and human activity at base camp stations. While meaningful for understanding the direct emissions from various sources and developing new technology aimed at reducing source emissions, point source emission studies do not contribute to the understanding of regional and global signals. Additionally, studies examining the volcanic signal in snow following major modern eruptions were excluded, as this was classified as disturbed snow.</p> </li> <li>Studies must specify the sampling localities by providing a minimum of latitude and longitude. Where sampling locations are only referenced by colloquial names, the distance from point source pollution cannot be verified. Therefore, such studies were excluded.</li> <li> <p>Records of <sup>210</sup>Pb in snow and ice were excluded. <sup>210</sup>Pb is useful for establishing chronology in young snow and ice due to its small half life (~ 22.3 years). But it is not useful for consideration of old records and the source constraint of <sup>210</sup>Pb into the atmosphere is poorly constrained over time (Nijampurkar & Clausen, 1990). Therefore, it cannot be considered in conjunction with Pb isotopes and concentrations. Records of <sup>210</sup>Pb in snow and ice were excluded.</p> </li> <li> <p>Pb isotopes and concentrations taken from cryoconites (soil-like composites of dust, industrial soot, and microbial mats of photosynthetic bacteria) were excluded from this literature review. Cryoconites are important to glacial systems as they alter the albedo of the glacier surface, and therefore affect the glacier melt rate (Fountain et al., 2004). However, they must be considered separately from surface snow, snow pits, and ice cores due to the drastic differences in formation and biologic nature.</p> </li> <li> <p>The publication must be available to the author (<em>e.g.,</em> through the University Library, from collaborators)</p> </li> </ol> </li> </ol> <p>To ensure that the literature search conducted on the Web of Science was robust and complete, citations were checked to ensure inclusion in the literature search results and included when missing. Publications were indexed into Table A.1 and Table A.2. Following the completion of publication indexing, Table A.1 and Table A.2 were evaluated against the 23 regions (Table A.3) -- 20 from RGI 7.0 (RGI 7.0 Consortium, 2023) and 3 author defined regions -- to identify areas/papers that may have been missed in the initial search. Areas with few or no results were searched again using Google Scholar and Web of Science.</p> <p>Based on these searches, we sought to understand the current spatial and temporal coverage of these records, shed light on gaps in the previous research and make recommendations on mitigating these gaps going forward. We used tables and graphics, included in the main text and the supplement, to summarize the characteristics of the compiled records. In the main text, we discuss the limitations and gaps within the current long-range transported Pb literature, and recommend paths to mitigate these gaps. Finally, in the main text, we illustrate an example of how researchers can query this record compilation, allowing for quick access to the records appropriate to address their research questions.</p> <p><strong>Methodology Bibliography:</strong></p> <p>Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a successful literature review (Second edition). Sage.</p> <p>Fountain, A. G., Tranter, M., Nylen, T. H., Lewis, K. J., & Mueller, D. R. (2004). Evolution of cryoconite holes and their contribution to meltwater runoff from glaciers in the McMurdo dry valleys, Antarctica. Journal of Glaciology, 50(168), 35–45. https://doi.org/10.3189/172756504781830312</p> <p>Nijampurkar, V. N., & Clausen, H. B. (1990). A century old record of lead-210 fallout on the greenland ice sheet. Tellus Series B Chemical and Physical Meteorology, 42(1), 29–38. https://doi.org/10.1034/j.1600-0889.1990.00005.</p> <p>RGI 7.0 Consortium. (2023). Randolph glacier inventory—A dataset of global glacier outlines, version 7.0. (Version 7.0) [Dataset]. NSIDC: National Snow and Ice Data Center. https://doi.org/doi:10.5067/f6jmovy5navz</p>
Global Coastal Transect System (GCTS)
<p>Cross-shore coastal transects are essential to coastal monitoring, offering a consistent reference line to measure coastal change, while providing a robust foundation to map coastal characteristics and derive coastal statistics thereof. The Global Coastal Transect System consists of more than 11 million cross-shore coastal transects uniformly spaced at 100-m intervals alongshore, for all OpenStreetMap coastlines that are longer than 5 kilometers.</p> <p>While the data is available here for download, we highly recommend direct access via the cloud. For latest usage instructions please see the tutorials at https://github.com/TUDelft-CITG/coastpy. The dataset is extensively described in Calkoen, F. R., Luijendijk, A. P., Vos, K., Kras, E., & Baart, F. (2025). Enabling coastal analytics at planetary scale. <em>Environmental Modelling & Software</em>, <em>183</em>, 106257; please cite this paper when the data is used. </p>
Annual time series of global VIIRS nighttime lights for 2000-2024 at 500-m spatial resolution extrapolated using logistic regression
<p>The <a href="https://eogdata.mines.edu/products/vnl/"><strong>Annual Visible Night Light (VNL) V2</strong></a> (VIIRS) images at 500-m spatial resolution for the period 2012 to 2024 (Elvidge et al., 2021) have been used to extrapolate the values backwards for years 2000–2011. This was done by fitting a logistic regression (per pixel) and then predicting the values for the previous years (see nightlights_stack_500m.R). After consistent time-series have been produced, I also derived the difference between year 2024 and year 2000 (nightlights.difference_viirs.v21_m_500m_s_2000_2024_go_epsg4326_v20230318.tif): this shows average rate of change for the 25 years period. Use with caution: extrapolation of values can lead to artifacts. For most of the land surface, however, it appears that the growth of night lights follows exponential growth function and hence nights in the past can be represented accurately by fitting decay / logistic regression function.</p> <p>Original values from the Annual VNL V2 product have been converted from 0–200 to 0–2000 scale and are available as Cloud-Optimized GeoTIFFs.</p> <p>Principal components (PC1, PC2, PC3, PC4) were derived using SAGA GIS (sums-of-squares-and-cross-products matrix) method. The first PC1 usually matches the long-term mean value, PC2 matches the 1st derivation in values. File "nightlights_dmsp.v10_m_1km_s_19920101_20241231_go_epsg4326_v20251006.tif" contains 33 years 1992 to 2024, but at 1 km resolution.</p> <p>To cite the Annual VNL V2, please use:</p> <ul> <li>Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). <a href="https://doi.org/10.3390/rs13050922">Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019</a>. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922</li> </ul> <p>Historic night light images (1 km resolution) are also available from <a href="https://doi.org/10.6084/m9.figshare.9828827.v10">Figshare</a>:</p> <ul> <li>Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). <a href="https://doi.org/10.1038/s41597-020-0510-y">A harmonized global nighttime light dataset 1992–2018</a>. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y</li> </ul>
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.