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zenodo28/100

Global remote industrial heat sources dataset

Data content: Based on the VIIRS (Visible Infrared Imaging Radiometer Suite) sensor medium resolution 375mNPP-VIIRS active thermal anomaly data, field research, and other big data of the earth, we constructed the global continental region of high-energy-consuming industrial heat source product data set, totaling 25,544 data. After validation 23232 items are industrial heat source objects, and the recognition accuracy is 90.95%. The output format is shapefile. Time range of data:2012-2021 Spatial scope: Global continental area Projection method: WGS84 Volume of data: The total volume of data is about 3346kb. Type of data: Vector

opencc-by-4.0Aug 2023View details →

zenodo28/100

Dataset for "Impact of geostationary interferometric infrared sounder observations from long- and middle-wave bands on weather forecasts with a locally cloud-resolving global model"

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opencc-by-4.0Nov 2023View details →

zenodo28/100

A Global Lakes/Reservoirs Surface Extent Dataset (GLRSED): An integration of multi source data

Global lake/reservoir surface water extent is the basic input data for many studies. Although there are some datasets at present, there are problems such as incomplete or spatial inconsistency exist among them due to various reasons like different data sources and dynamic change characteristics of the surface water. Here, a new Global Lake/Reservoir Surface Extent Dataset (GLRSED) that contains spatial extent and basic attributes (e.g., name, area, lake type and source) of 2.17 million lakes/reservoirs was produced based on HydroLAKES, GRanD and OpenStreetMap. By spatially overlaying GLRSED with other auxiliary data, we identified lakes/reservoirs located in mountain, glacier and permafrost regions, etc. In addition, we calculated the Surface Water and Ocean Topography (SWOT) orbits passing through each lake/reservoir. The dataset could provide basic data for global lake/reservoir monitoring, as well as the study on the impact of human actions and climate changes on lake/reservoir freshwater availability, etc.

openodc-odblJan 2024View details →

zenodo28/100

U-Surf: a global 1km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling

High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth System Models (ESMs) and ultra-high-resolution urban climate modeling, particularly at large scales. Here, we present a first-of-its-kind 1km-resolution present-day (circa-2020) global continuous urban surface parameter dataset – U-Surf. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for developing dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet- and canopy-level. Our high-resolution U-Surf dataset significantly improves the representation of the urban land heterogeneity both within and across cities globally. U-Surf provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs, enables detailed city-to-city comparisons across the globe, and supports the next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf are also relevant as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to promote the research frontier on urban systems science, climate-sensitive urban design, and coupled human-Earth systems in the future. The complete list of parameters is presented in the table below. <table> <tbody> <tr> <td>Category</td> <td>Parameter</td> <td>Notes</td> </tr> <tr> <td>Radiative</td> <td>Roof | Impervious | Pervious canyon floor | Wall emissivity</td> <td> </td> </tr> <tr> <td> </td> <td>Roof | Impervious | Pervious canyon floor | Wall albedo</td> <td> </td> </tr> <tr> <td>Morphological</td> <td>Roof | Pervious fraction</td> <td>Roof fraction is w.r.t. urban horizontal surface, and pervious fraction is w.r.t. canyon floor (i.e. pervious and impervious canyon floor).</td> </tr> <tr> <td> </td> <td>Building height</td> <td>Unit: m; Height of wind in the canyon is simply set as half of the building height in CLMU.</td> </tr> <tr> <td> </td> <td>Canyon height-to-width ratio</td> <td> </td> </tr> <tr> <td> </td> <td>Urban percentage</td> <td> </td> </tr> <tr> <td>Thermal</td> <td>Roof | Wall thickness</td> <td>Unit: m</td> </tr> <tr> <td> </td> <td>Roof | Impervious canyon floor | Wall thermal conductivity</td> <td>Unit: W/m*K</td> </tr> <tr> <td> </td> <td>Roof | Impervious canyon floor | Wall volumetric heat capacity</td> <td>Unit: J/m^3*K</td> </tr> <tr> <td> </td> <td>Number of impervious canyon floor layer</td> <td> </td> </tr> <tr> <td> </td> <td>Minimum | Maximum interior building temperature</td> <td>Unit: K</td> </tr> <tr> <td> </td> <td>Air conditioning adoption rate</td> <td> </td> </tr> </tbody> </table>   Radiative and morphological parameters are presented in the format of both .tif and .nc to accommodate different needs for the urban climate modeling community. Thermal parameters adapted from CLMU are available in a single .nc file. A CESM-compatiable surface dataset and a time-variant urban dataset (including P_AC and T_BUILDING_MAX; Li et al., 2024) at standard resolution (0.9375°x1.25°) are included for direct simulation use. Note that the urban percentage used to create the surface dataset comes from the PCT_URBAN parameter calculated in U-Surf, but users can input their own urban extent data to generate a customized surface dataset. The raw 1-km data can be easily aggregated/regridded to other resolution as needed.   Version 1.1 updates: 1. Fill part of the data gaps in Asia.  2. Change the aggregation method of some parameters to be facet-area weighted in the 1deg surfdata.

opencc-by-sa-4.0Jul 2024View details →

zenodo28/100

Global Biotic Interactions: Elton Dataset Cache for Hurlbert Lab's Avian Diet Database

Global Biotic Interactions: Elton Dataset Cache The intended use of this archive/cache is to allow for offline-enabled access versions of existing species interaction datasets. The program "Elton" (https://doi.org/10.5281/zenodo.998263) was used to populate the content of elton-datasets.tar.gz . The same program can be used to extract information from the cache archive also. Global Biotic Interactions (https://globalbioticinteractions.org, https://doi.org/10.1016/j.ecoinf.2014.08.005) also uses these archives to create derived species interaction data archives, search indexes and APIs. Please note that due to size considerations, offline-enabled access to an elton dataset cache of iNaturalist interaction data has been excluded from this publications and moved into a separate Zenodo publication at https://doi.org/10.5281/zenodo.3950546 . Contents -------- README: this file elton-datasets.tar.gz: versioned archive with species interaction datasets elton-datasets.tar.sha256: content signature of elton-datasets.tar elton-datasets.tsv: list of included datasets elton.jar: commandline program to help access the species interaction datasets Usage ----- To install, extract elton-datasets.tar.gz into a directory of choice using: tar xfz elton-dataset.tar.gz To use, download elton.jar included this publication and execute the following to get a list of available datasets: java -Xmx4G -jar elton.jar datasets on a system that has java v8+ installed. If all goes well, you should be able to regenerate the included file elton-dataset.tsv . For more information on how to use elton.jar, execute: java -jar elton.jar usage or visit https://github.com/globalbioticinteractions/elton for more available commands. Alternatively, without using Elton, you can access the data by inspecting the access.tsv files in the various directories of the datasets directory. When using these datasets in a publication or product, please cite the *original* data providers and publications. You can find the citations in the data. Included datasets: hurlbertlab/dietdatabase    Allen Hurlbert. 2017. 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hash://sha256/f8b493abe3ac9faff6be9812884425d5b28f05acff3cb1d8f7aed5b8f6847334 hash://sha256/4c228e246e18cb0c217631cbcfb70909dd95cdfe105bbac581669a9324de103e hash://sha256/735f9f213e24c5e0883206ee2ba5fc2b6233f65052a8ea6146fcdd5dd561ddf6 hash://sha256/a933391fca958d34f273eac4be5a1d0c3072e8b7bfc76396dfddc33f8b3e583a

opencc-zeroNov 2021View details →

zenodo28/100

GGWS-PCNN: A global gridded wind speed dataset (1973/01-2021/12; Ongoing Update)

Profile of the dataset <ul> <li>The GGWS-PCNN is a global gridded monthly dataset of 10-m wind speed based on an artificial intelligence algorithm (the partial convolutional neural network), observations from weather stations (the HadISD dataset), and 34 climate models from CMIP6.</li> <li>It has a resolution of 1.25° × 2.5° (latitude × longitude).  We will update this dataset as soon as the new HadISD version is accessible.</li> <li>For more details about the dataset and its reconstructed processes, please see our paper "An artificial intelligence reconstruction of global gridded surface winds" published in the Science Bulletin.</li> </ul> Notice <ul> <li>The HadISD discovered an issue in the wind data after 2013. So in their version 3.3.0.202201p and later, they fixed this issue. Find the website <a href="https://www.metoffice.gov.uk/hadobs/hadisd/">Met Office Hadley Centre observations datasets</a> for more details.</li> <li>Due to the limitations of existing AI algorithms in reconstructing data with many missing values, our product has a small number of outliers (e.g. wind speeds less than zero or very high), most of which are located in the Antarctic region. We recommend you remove these outliers before using this dataset.</li> </ul> Reference Lihong Zhou, Haofeng Liu, Xin Jiang, et al. (2022). <a href="https://www.researchgate.net/publication/363806982_An_artificial_intelligence_reconstruction_of_global_gridded_surface_winds">An artificial intelligence reconstruction of global gridded surface winds</a>. Science Bulletin.

opencc-by-4.0Sep 2022View details →

zenodo28/100

OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping

Project Page <a href="https://open-earth-map.org/">https://open-earth-map.org/</a> Paper <a href="https://arxiv.org/abs/2210.10732">https://arxiv.org/abs/2210.10732</a> Overview OpenEarthMap is a benchmark dataset for global high-resolution land cover mapping. OpenEarthMap consists of 5000 aerial and satellite images with manually annotated 8-class land cover labels and 2.2 million segments at a 0.25-0.5m ground sampling distance, covering 97 regions from 44 countries across 6 continents. OpenEarthMap fosters research including but not limited to semantic segmentation and domain adaptation. Land cover mapping models trained on OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications. Reference <pre><code>@inproceedings{xia_2023_openearthmap, title = {OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping}, author = {Junshi Xia and Naoto Yokoya and Bruno Adriano and Clifford Broni-Bediako}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {6254-6264} }</code></pre> License Label data of OpenEarthMap are provided under the same license as the original RGB images, which varies with each source dataset. For more details, please see the attribution of source data <a href="https://open-earth-map.org/attribution.html">here</a>. Label data for regions where the original RGB images are in the public domain or where the license is not explicitly stated are licensed under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0</a> International License. Note for xBD data The RGB images of xBD dataset are not included in the OpenEarthMap dataset. Please download the xBD RGB images from <a href="https://xview2.org/dataset">https://xview2.org/dataset</a> and add them to the corresponding folders. The "xbd_files.csv" contains information about how to prepare the xBD RGB images and add them to the corresponding folders. Code Sample code to add the xBD RGB images to the distributed OpenEarthMap dataset and to train baseline models is available <a href="https://github.com/bao18/open_earth_map">here</a>. Leaderboard Performance on the test set can be evaluated on the <a href="https://codalab.lisn.upsaclay.fr/competitions/9121">Codalab webpage</a>.

opencc-by-4.0Oct 2022View details →

zenodo28/100

A dataset of global lake-level simulations and reconstructions since the Last Glacial Maximum

Open the record for dataset details and reuse information.

opencc-by-4.0May 2024View details →

zenodo28/100

Picturing global substorm dynamics in the magnetotail using low-altitude ELFIN measurements and data mining-based magnetic field reconstructions (SST19 Reconstruction Output Dataset for Review)

The dataset containing SST19 reconstruction output data accompanying the GRL submission for "Picturing global substorm dynamics in the magnetotail using low-altitude ELFIN measurements and data mining-based magnetic field reconstructions"

openmit-licenseJun 2024View details →

zenodo28/100

Dataset: Falcon's Beyond Global, Inc. (FBYD) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

zenodo28/100

Dataset for the Manuscript "Tropical Precipitation and Marine Eco-System Response to Early Indian Ocean Dipole under Global Warming"

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →

zenodo28/100

Subset of CPTAC CCRCC Global TMT Dataset

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →

zenodo28/100

A global gross primary productivity dataset of sunlit and shaded leaves via combining two-leaf light use efficiency model with random forest from 2002 to 2020

The TL-CRF model generated a global 0.05´0.05° product for eight-day gross primary productivity (GPP) of sunlit and shaded canopies from 2002 to 2020 by embedding the random forest (RF) submodule into the two-leaf light use efficiency (TL-LUE) model while considering the seasonal differences in the clumping index. The RF technique was used to integrate various environmental stress factors including meteorological, hydrological, soil properties, and elevation, thereby improving the overall scale of the complex environmental conditions to the maximum LUE. Eight-day GPP was then aggregated into monthly, seasonal, and annual GPP. This novel GPP product could support further research on spatial and temporal patterns of the carbon cycle and its association with climate change.

opencc-by-4.0Aug 2024View details →

zenodo28/100

A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics

Motivation Increasing heat stress due to climate change poses significant risks to human health and can lead to widespread social and economic consequences. Evaluating these impacts requires reliable datasets of heat stress projections.  Data Record CMIP6 We present a global dataset projecting future dry-bulb, wet-bulb, and wet-bulb globe temperatures under 1-4°C global warming scenarios (at 0.5°C intervals) relative to the preindustrial era, using outputs from 16 CMIP6 global climate models (GCMs) (Table 1). All variables were retrieved from the historical and SSP585 scenarios which were selected to maximize the warming signal. Wet-bulb and wet-bulb globe temperature are calculated using the Davies-Jones[1] and Liljegren[2]  approach respectively. The dataset was bias-corrected against ERA5 reanalysis by incorporating the GCM-simulated climate change signal onto the ERA5 baseline (1950-1976) at a 3-hourly frequency. It therefore includes a 27-year sample for each GCM under each warming target. The data is provided at a fine spatial resolution of 0.25° x 0.25° and a temporal resolution of 3 hours, and is stored in a self-describing NetCDF format. Filenames follow the pattern "VAR_bias_corrected_3hr_GCM_XC_yyyy.nc", where: <ul> <li> "VAR" represents the variable (Ta, Tw, WBGT for dry-bulb, wet-bulb, and wet-bulb globe temperature, respectively), </li> <li> "GCM" denotes the CMIP6 GCM name, </li> <li> "X" indicates the warming target compared to the preindustrial period, </li> <li> "yyyy" represents the year index (0001-0027) of the 27-year sample </li> </ul> Table 1 CMIP6 GCMs used for generating the dataset for Ta, Tw and WBGT. <div> <table> <tbody> <tr> <td> GCM </td> <td> Realization </td> <td> GCM grid spacing </td> <td> Ta </td> <td> Tw </td> <td> WBGT </td> </tr> <tr> <td> ACCESS-CM2 </td> <td> r1i1p1f1 </td> <td> 1.25ox1.875o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> BCC-CSM2-MR </td> <td> r1i1p1f1 </td> <td> 1.1ox1.125o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> CanESM5 </td> <td> r1i1p2f1 </td> <td> 2.8ox2.8o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> CMCC-CM2-SR5 </td> <td> r1i1p1f1 </td> <td> 0.94ox1.25o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> CMCC-ESM2 </td> <td> r1i1p1f1 </td> <td> 0.94ox1.25o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> CNRM-CM6-1 </td> <td> r1i1p1f2 </td> <td> 1.4ox1.4o </td> <td> ✓ </td> <td> ✓ </td> <td> </td> </tr> <tr> <td> EC-Earth3 </td> <td> r1i1p1f1 </td> <td> 0.7ox0.7o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> GFDL-ESM4 </td> <td> r1i1p1f1 </td> <td> 1.0ox1.25o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> HadGEM3-GC31-LL </td> <td> r1i1p1f3 </td> <td> 1.25ox1.875o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> HadGEM3-GC31-MM </td> <td> r1i1p1f3 </td> <td> 0.55ox0.83o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> KACE-1-0-G </td> <td> r1i1p1f1 </td> <td> 1.25ox1.875o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> KIOST-ESM </td> <td> r1i1p1f1 </td> <td> 1.9ox1.9o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> MIROC-ES2L </td> <td> r1i1p1f2 </td> <td> 2.8ox2.8o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> MIROC6 </td> <td> r1i1p1f1 </td> <td> 1.4ox1.4o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> MPI-ESM1-2-HR </td> <td> r1i1p1f1 </td> <td> 0.93ox0.93o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> <tr> <td> MPI-ESM1-2-LR </td> <td> r1i1p1f1 </td> <td> 1.85ox1.875o </td> <td> ✓ </td> <td> ✓ </td> <td> ✓ </td> </tr> </tbody> </table> </div> ERA5 We also provide hourly Tw and WBGT derived from ERA5 reanalysis during 1950-2023 to enable analyses of heat stress changes from historical period to a warmer climate.   Data Access An inventory of the dataset is available in this repository. The complete dataset, approximately 57 TB in size, is freely accessible via Purdue Fortress' long-term archive through Globus. The bias-corrected CMIP6 dataset is available at <a href="https://transfer.rcac.purdue.edu/file-manager?origin_id=6538f53a-1ea7-4c13-a0cf-10478190b901&origin_path=%2F">Globus Link1</a>, and the ERA5 dataset is available at <a href="https://transfer.rcac.purdue.edu/file-manager?destination_id=63242aea-d3e0-4aa4-9372-0e19dd0c6539&destination_path=%2F">Globus Link2</a>. After clicking the link, users may be prompted to log in with a Purdue institutional Globus account. You can switch to your institutional account, or log in via a personal Globus ID, Gmail, GitHub handle, or ORCID ID. Alternatively, the dataset can be accessed by searching for the universally unique identifier (UUID)—"6538f53a-1ea7-4c13-a0cf-10478190b901" for CMIP6, and “63242aea-d3e0-4aa4-9372-0e19dd0c6539” for ERA5 dataset—in Globus. Dataset Validation We validate the bias-correction method and show that it significantly enhances the GCMs' accuracy in reproducing both the annual average and the full range of quantiles for all metrics within an ERA5 reference climate state. This dataset is expected to support future research on projected changes in mean and extreme heat stress and the assessment of related health and socio-economic impacts. For a detailed introduction to the dataset and its validation, please refer to our data descriptor currently under review at Scientific Data. We will update this information upon publication.

opencc-by-nc-4.0Sep 2024View details →

zenodo28/100

Geological dataset used in Duan et al. Global subduction redox cycle drives mantle long-term oxidation

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2024View details →

zenodo28/100

NDUI+: A fused DMSP-VIIRS based multidecadal, high-resolution global normalized difference urban index (NDUI) dataset

M. Singh and S. Ghosh are equal contributors to this work and are designated as co-first authors

opencc-by-4.0Oct 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record

Find research datasets worth reusing

Dataset results

Global remote industrial heat sources dataset

Dataset for "Impact of geostationary interferometric infrared sounder observations from long- and middle-wave bands on weather forecasts with a locally cloud-resolving global model"

A Global Lakes/Reservoirs Surface Extent Dataset (GLRSED): An integration of multi source data

U-Surf: a global 1km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling

Global Biotic Interactions: Elton Dataset Cache for Hurlbert Lab's Avian Diet Database

GGWS-PCNN: A global gridded wind speed dataset (1973/01-2021/12; Ongoing Update)

OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping

A dataset of global lake-level simulations and reconstructions since the Last Glacial Maximum

Picturing global substorm dynamics in the magnetotail using low-altitude ELFIN measurements and data mining-based magnetic field reconstructions (SST19 Reconstruction Output Dataset for Review)

Dataset: Falcon's Beyond Global, Inc. (FBYD) Stock Performance

Dataset: Global X Aging Population ETF (AGNG) Stock Performance

Dataset: Global X Clean Water ETF (AQWA) Stock Performance

Dataset: iShares MSCI Global Gold Miners ETF (RING) Stock Performance

Dataset: Global X Hydrogen ETF (HYDR) Stock Performance

Dataset for the Manuscript "Tropical Precipitation and Marine Eco-System Response to Early Indian Ocean Dipole under Global Warming"

Subset of CPTAC CCRCC Global TMT Dataset

A global gross primary productivity dataset of sunlit and shaded leaves via combining two-leaf light use efficiency model with random forest from 2002 to 2020

A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics

Geological dataset used in Duan et al. Global subduction redox cycle drives mantle long-term oxidation

NDUI+: A fused DMSP-VIIRS based multidecadal, high-resolution global normalized difference urban index (NDUI) dataset

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Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro