Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,477
datasets available to search
ShareScore release 0.9.0
Dataset results
1,477 results for “earth”
Original data for: Probing luminescence of rare earth ions in natural pink fluorites using Raman microscopes
<p>Data for Publication:</p> <p>Probing luminescence of rare earth ions in natural pink fluorites using Raman microscopes<br> Hans Hagemann, Sareh Ayoubipour, Teresa Delgado, Cédric Schnyder, Edwin Gnos</p> <p>Journal of Raman Spectroscopy, 2022;53:1464–1470</p> <p>https://doi.org/10.1002/jrs.6383</p> <p> </p>
Computational Prediction and Experimental Realisation of Earth Abundant Transparent Conducting Oxide Ga-doped ZnSb2O6 - computational datasets
<p>Additional computational data for our publication on Ga-doped ZnSb2O6. This repository contains two files related to the defect calculations ("new-defect-corrections.json" and "aide-plotter"), which contain data concerning the charge correction schemes and total energies of all defect species, and the charge transition levels (which can be used to repeat the SCFL analysis), respectively. The other two files are the AMSET output files, which can be used to plot all the calculated charge transport data used in this study. Documentation for plotting using these files is provided at https://github.com/hackingmaterials/amset.</p> <p>Contact:</p> <p>joe.willis.15@ucl.ac.uk; d.scanlon@ucl.ac.uk</p>
Earth-abundant photocatalyst for H2 generation from NH3 with light-emitting diode illumination
<p>This repository contains supplementary data for "Earth-abundant photocatalyst for H2 generation from NH3 with light-emitting diode illumination". </p>
Material for manuscript submitted to Earth and Space Science "Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin"
<p>Configuration files for AROME Indian Ocean, NEMO and OASIS which are necessary to reproduce the results in the publication :</p> <p>Corale, L; Malardel S. , Bielli S. and M-N Bouin (2022) Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin. <em>Earth and Space Science.</em></p>
Tutorial to explore riverbank erosion along Jamuna River, Bangladesh, with the Google Earth Engine
<p>This short tutorial shows the different features and data that are available in a tool that allows you to explore riverbank erosion in Bangladesh. The tool has been developed by: Freihardt & Frey (under submission): Assessing riverbank erosion in Bangladesh using time series of Sentinel-1 radar imagery in the Google Earth Engine.</p> <p>The tool can be accessed via this link: <a href="https://code.earthengine.google.com/3ea8f1fd5d771accc621550d744a914e?hideCode=true">https://code.earthengine.google.com/3ea8f1fd5d771accc621550d744a914e?hideCode=true</a></p> <p>To access the video tutorial without downloading it: <a href="https://youtu.be/_b9AAPDw7Wk">youtu.be/_b9AAPDw7Wk</a></p>
An experimental investigation on the water retention behaviour of a silty soil for the computation of the lateral earth thrust on a retaining wall.
<p>The dataset is linked with the conference paper available open access with the same title. In the excel file, each spreadsheet reports the data of the figures available in the manuscript.</p>
🌏 Planet Earth
Earth Animation + Moon and Hubble Telescope. 2048 PNG Textures. Enjoy! Source: Objaverse 1.0 / Sketchfab
Benefits of Ontologies in Earth System Science
<p>The exponential growth of data due to technological developments along with an increased recognition of research data as relevant research output during the last decades substantiates fundamental challenges in terms of interoperability, reproducibility and reuse of scientific information. Being cross-disciplinary at its core, research in Earth System Science comprises divergent domains such as Paleontology, Marine Science, Atmospheric Sciences and Molecular Biology in addition to different types of data such as observation and simulation data. Within the various disciplines, distinct methods and terms for indexing, cataloguing, describing and finding scientific data have been developed, resulting in several controlled Vocabularies, Taxonomies and Thesauri. However, given the semantic heterogeneity across scientific domains, effective utilisation and (re)use of data is impeded while the importance of enhanced and improved interoperability across research areas will increase even further, considering the global impact of Climate Change to literally all aspects of everyday life. There is thus a clear need to harmonise practices around the development and usage of semantics in representing and describing information and knowledge. For a beneficial usage of semantic artefacts, sustainability is the key: any kind of terminology service must be maintained to guarantee that terms and relations are offered in a persistent way. But if they are, Vocabularies, Taxonomies, Thesauri and Ontologies can serve as a driving force for improved visibility and findability of research output within and across different research areas. Why Ontologies matter, what they are, and how they can be used will be depicted on our Poster in an easy-to-understand way.</p> <p> </p>
Changes Monitoring in Hongjiannao Lake from 1987-2023 using Google Earth Engine and Analysis of Climatic and Anthropogenic Forces (Climatic Data)
<p>This dataset presents temporal (1987 to 2023) climatic data for the weather station near Hongjiannao Lake.</p>
Projections of Earth's technosphere: Worldbuilding pipelines
<p>This repository contains the ten worldbuilding pipeline documents from the paper titled "Projections of Earth’s technosphere: Scenario modeling, worldbuilding, and overview of remotely detectable technosignatures" by Haqq-Misra, Profitiliotis, and Kopparapu (2025, <em>Technological Forecasting and Social Change</em>, 218: 124194, <a href="https://doi.org/10.1016/j.techfore.2025.124194" target="_blank" rel="noopener">doi:10.1016/j.techfore.2025.124194</a>).</p> <p>For more information about this project, visit <a href="https://futures.bmsis.org/" target="_blank" rel="noopener">futures.bmsis.org</a>.</p>
Nowcast of Aerospace Ionizing Radiation System (NAIRAS) simulation of the effect of the 2024-05-11 coronal mass ejection and solar particle event on Earth's atmosphere
<p>The effect of the CME on the cutoff rigidity and the dose at different altitude as computed by NAIRAS.</p> <p>The neutron monitor data from OULU and the DSCOVR data for solar wind density and speed are put as a reference for when the Forbush decrease happens and when the CME arrives.</p> <p> </p> <p>The version 2 added files with shorter lead time before the CME arrival and bigger labels</p>
Trajectories of backtracked passive particles for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"
<p>This collection hosts the trajectories of bactracked passive particles using Ocean Parcels v2.0 (The Parcels v2.0 Lagrangian framework: new field interpolation schemes. Delandmeter, P and E van Sebille (2019), <em>Geoscientific Model Development</em>, <em>12</em>, 3571–3584) and ocean surface velocity fields from the output of: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea". </p> <p>trajectories_2009.tar: trajectories for particles released between 2009-01-01 and 2009-12-31</p> <p>trajectories_2010.tar: trajectories for particles released between 2010-01-01 and 2010-12-31 (as shown in "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea")</p> <p>trajectories_2011.tar: trajectories for particles released between 2011-01-01 and 2011-12-31</p> <p>release_sites.csv: release sites for each release date.</p> <p>ocean_parcels_backtrack_VIB_carib12.py : python script to run ocean parcels to generate the trajectories published here.</p>
Sensing Reflector Height Variation on Seconds-Scale Using Low Earth Orbit Interferometric Reflectometry (LEO-IR)
<p><span>The simulated data (SNR, elevation and azimuth angles etc.) used in this study.</span></p> <p><span>File name format: arc number for GNSS (or LEO) <span>constellation</span> '_' PRN '_' 'seg'+arc number for a GNSS (or LEO) satellite</span></p> <p><span>File content format: 1[year] 2[month] 3[day] 4[hour] 5[minute] 6[seconds] 7[elevation angle] 8[azimuth angle] 9[chang rate of elevation angle] 10[SNR] 11[rh_ref] 12[rhdot_ref] 13[t_seconds]</span></p> <p> </p>
Dataset related to article 'The Collaborative Seismic Earth Model: Generation 2'
<p>This repository holds 23 earthquake simulations conducted using the second generation of the Collaborative Seismic Earth Model (CSEM2, Noe et al., submitted), serving as benchmark simulations for reproducibility. The simulations are performed with the spectral-element wave propagation solver Salvus (Afanasiev et al., 2019) on event-adapted meshes (Thrastarson et al., 2020). Accompanying waveform data from the stations can be downloaded from public FDSN services and accessed via tools such as the ObsPy mass downloader (Krischer et al., 2015). Information about the earthquakes and receivers is provided in the event files. The source-time function, a filtered heaviside for 50 - 160 s, is included in 'stf.h5' for global simulations and for 20 - 160 s for regional simulations, respectively.</p> <p>The event and waveform information to create Figure 4 in the manuscript are the following:</p> <p>a) <a href="../api/records/11047927/draft/files/GCMT_event_LAKE_TANGANYIKA_REGION_Mag_6.0_2017-2-24-0.h5/content" target="_blank" rel="noopener noreferrer">GCMT_event_LAKE_TANGANYIKA_REGION_Mag_6.0_2017-2-24-0.h5</a>, stf 50 - 160 s</p> <p>b) <a href="../api/records/11047927/draft/files/GCMT_event_CENTRAL_CALIFORNIA_Mag_5.6_2020-6-4-1.h5/content" target="_blank" rel="noopener noreferrer">GCMT_event_CENTRAL_CALIFORNIA_Mag_5.6_2020-6-4-1.h5</a>, stf 20 - 160 s</p> <p>c) <a href="../api/records/11047927/draft/files/GCMT_event_CHILE-ARGENTINA_BORDER_REGION_Mag_5.7_2018-6-21-16.h5/content" target="_blank" rel="noopener noreferrer">GCMT_event_CHILE-ARGENTINA_BORDER_REGION_Mag_5.7_2018-6-21-16.h5</a>, stf 20 - 160 s</p> <p>Simulations for b&c are performed on smaller domains and on regular cubed-sphere meshes.</p>
Forest Fire Dataset for Peninsular Malaysia (2001-2023) Extracted from Multiple-Source Remote Sensing Data using Google Earth Engine
<ul> <li>Dataset: Forest Fire data</li> <li>Time Period: 2001 to 2023</li> <li>Location: Peninsular Malaysia</li> <li>Historical Fire Source: MCD64A1 and FIRMS Hotspots</li> <li>Fire Factors Extracted: Global Remote Sensing Data from GEE</li> </ul> <p>The framework extraction process can be reffered from the following publication:</p> <ul> <li>Framework to Create Inventory Dataset for Disaster Behavior Analysis Using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis</li> <li>Journal: <em>Forests</em> <strong>2024</strong>, <em>15</em>(6), 923;</li> <li><a href="https://doi.org/10.3390/f15060923">https://doi.org/10.3390/f15060923</a></li> <li>The variables name such as AET (actual evapotranspiration) can be found from the article.</li> </ul> <p>Access the framework code from: </p> <ul> <li><a href="https://github.com/chewyeejian/GEE_FrameworkForestFireDataset">https://github.com/chewyeejian/GEE_FrameworkForestFireDataset</a></li> </ul> <p>The time sequence in the variable indicate whether it's a monthly data / yearly accumulated data / seasonal data, example:</p> <ul> <li>200101_aet (Year 2001, Month 01, value for aet (actual evapotranspiration)</li> <li>2001_aet_DJF (Average of December, January, February)</li> <li>2001_aet_MAM (Seasonal Average of March, April, May)</li> <li>2001_aet_JJA (Seasonal Average of June, July, August)</li> <li>2001_aet_SON (Seasonal Average of September, October, November)</li> <li>2001_aet_annual (Annual average of 2001)</li> </ul>
Dataset and Model Files for Full Waveform Inversion Seismic Earth Model WUS324
<p><strong>Dataset and Model Files for Full Waveform Inversion Seismic Earth Model WUS324</strong></p> <p> </p> <p>Arthur Rodgers</p> <p><em>Geophysical Monitoring Program, Lawrence Livermore National Laboratory, Livermore CA 94551, USA</em>; and</p> <p><em>Department of Earth Sciences, Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland</em></p> <p> </p> <p>rodgers7@llnl.gov</p> <p> </p> <p>10.5281/zenodo.11619519</p> <p> </p> <p><strong>Summary</strong></p> <p>This data set includes the metadata and model for the three-dimensional (3D) seismic Earth model WUS324 (Rodgers et al., 2024). This model describes seismic wavespeeds, density and attenuation for the 3D volume spanning the surface to 400 km depth, latitudes from Mexico to Canada (28 to 52) and longitudes from the Pacific Ocean to the Great Plains (-132 to -100). The metadata tabulates the earthquakes and seismic networks and stations used in the creation and validation of 3D seismic Earth model WUS324.</p> <p> </p> <p>The WUS324 model is provided in NetCDF format (readable by for example, <em>xarray</em>, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with <em>ParaView</em> (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with <em>Salvus</em> (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>).</p> <p><strong> </strong></p> <p><strong>References</strong></p> <p>Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, Geophys. J. Int., 216(3), 1675–1692, doi: 10.1093/gji/ggy469</p> <p>Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. The Visualization Handbook, 717(8). https://doi.org/10.1016/b978-012387582-2/50038-1</p> <p>Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 5(1). https://doi.org/10.5334/jors.148</p> <p>Rodgers, A., C. Doody and A. Fichtner (2024). WUS324: Converged Full Waveform Inversion Improves Waveform Fits While Imaging Crustal and Upper Mantle Structure in the Western United States, manuscript in preparation.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work was initiated under Laboratory Directed Research and Development project 20-ERD-008 at Lawrence Livermore National Laboratory (LLNL) and continued with support from the National Nuclear Security Administration Ground-based Nuclear Detonation Detection program. AR is grateful to the Eidgenössische Technische Hochschule, Zürich for support as an Academic Guest and to LLNL for Professional Research and Teaching Leave. This work was performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344. LLNL-MI-865269.</p> <p> </p> <p> </p> <p>Files contained in this data set.</p> <table> <tbody> <tr> <td> <p><strong>Filename</strong></p> </td> <td> <p><strong>Description </strong></p> </td> </tr> <tr> <td> <p>WUS324_all_events_project.csv</p> </td> <td> <p>Table of all 216 events considered during the creation of WUS324. This table includes the origin date and time, location and moment tensor parameters.</p> </td> </tr> <tr> <td> <p>WUS324_all_networks.txt</p> </td> <td> <p>Table of all seismic networks and that contributed to WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_inversion_events.txt</p> </td> <td> <p>Table of the 126 event names used in the inversions that created WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_inversion_all_paths.csv</p> </td> <td> <p>Table of all event-station paths that contributed to the creation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_validation_events.txt</p> </td> <td> <p>Table of the 65 event names used in the validation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_validation_all_paths.csv</p> </td> <td> <p>Table of all event-station paths that contributed to the validation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_16sec.h5</p> </td> <td> <p>WUS324 model for simulating waveforms with minimum period of 16 seconds in Salvus HDF5 format.</p> </td> </tr> <tr> <td> <p>WUS324_16sec.xdmf</p> </td> <td> <pre>Auxiliary file for WUS324_16sec.h5, used to import model into Paraview.</pre> </td> </tr> <tr> <td> <p>WUS324.nc</p> </td> <td> <pre>WUS324 model in netCDF format following the metadata standards of the Incorporated Research Institutions for Seismology Earth Model Collaboratory</pre> </td> </tr> </tbody> </table>
Detection of an Earth-sized exoplanet orbiting the nearby ultracool dwarf star SPECULOOS-3
<p>Light curves and spectra of SPECULOOS-3 used in the research work presented in the Nature Astronomy article "Detection of an Earth-sized exoplanet orbiting the nearby ultracool dwarf star SPECULOOS-3"</p>
Code and Data Supplement for Using feature importance as exploratory data analysis tool on earth system models
<p>This contains:</p> <ul> <li>Code for all analyses in</li> <li>E3SM data</li> </ul> <p>For the paper Using <em>feature importance as exploratory data analysis tool on earth system models.</em></p>
Data for "Uncertainties too large to predict tipping times of major Earth system components from historical data"
<p>All data needed to reproduce the figures from the Science Advances manuscript "Uncertainties too large to predict tipping times of major Earth system components from historical data" by Ben-Yami et al.</p> <p>Figs12SamplePathData.zip and Fig3SamplePathData.zip includes the generated synthetic timeseries of the conceptual models in Figs 1-3.</p> <p>The .txt files are the different AMOC observational time series, with the file names structured as dataset_fingerprint.txt. C18 and C18_2GMT are the subpolar gyre SSTs minus one times and twice the global mean SSTs, respectively. The dipole fingerprint is as defined in the text. The first column is the date, and the second column the fingeprint values. The values are monthly means and thus the value for the day of the month in the date is not significant.</p>
Supplementary material for "Rare earth elements and yttrium (REY) in fjord waters: Comparison between seawater in the Trondheimfjord (Norway), its local riverine REY sources and the North Atlantic"
<p><span>Dataset presented and discussed in the research article “</span><span>Rare earth elements and yttrium (REY) in fjord waters: Comparison between seawater in the Trondheimfjord (Norway), its local riverine REY sources and the North Atlantic<span><span>” by Zocher et al. The article is published in <em>Geochimica et Cosmochimica Acta</em> and was prepared by the following authors: Anna-Lena Zocher (1), Tomasz Maciej Ciesielski (2,3), Stefania Piarulli (4), Julia Farkas (4) and Michael Bau (1). </span></span></span></p> <p> </p> <p>(1) School of Science, Constructor University, Bremen, Germany</p> <p>(2) Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway</p> <p>(3) Department of Arctic Technology, The University Centre in Svalbard (UNIS), Longyearbyen, Norway</p> <p>(4) SINTEF Ocean, Trondheim, Norway</p> <p> </p> <p><span><span>Table A1 includes the general information and data for all stations, Table A2 the more detailed salinity data for stations Nidelva A, B and C. </span></span></p> <p> </p> <p><span><span>Reference research article:</span></span></p> <p><span><span> </span></span></p> <p><span><span>Zocher, A.-L., Ciesielski, T. M., Piarulli, S., Farkas, J., & Bau, M. (2024). </span></span><span><span>Rare earth elements and yttrium (REY) in fjord waters: Comparison between seawater in the Trondheimfjord (Norway), its local riverine REY sources and the North Atlantic. Geochimica et Cosmochimica Acta.</span></span></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.