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”
The Earth Surface System Scientific Data Thesaurus
<p>The Earth Surface System Scientific Data Thesaurus</p>
Data from: Abiotic legacies mediate plant-soil feedback during early vegetation succession on rare earth element mine tailings
<p>An increasing number of studies have shown how feedback interactions between plants and soil can influence primary and secondary succession. However, very little is known about the patterns and mechanisms of such plant-soil feedbacks on stressed mine tailings ecosystem, which can be severely contaminated by a range of toxic elements. </p> <p>In a two-phase plant-soil feedback experiment based on the rare earth element (REE) mine tailing soil, we investigated biotic (changes in bacterial and fungal community) and abiotic legacies (changes in chemical properties) of three pioneer grass species, and examined feedback effects of three grasses, two legumes and two woody plants with different root traits.</p> <p>Positive plant-soil feedback was found in Miscanthus sinensis, Paspalum thunbergii and Tephrosia candida, and neutral feedback was observed in other four plants. These effects corresponded with an increase of nutrients and total organic carbon, as well as a decrease of acidity and extractable aluminum and REEs. There were less signs of biotic changes in the conditioned tailings. </p> <p>The correlation analysis suggested a relationship between responses to soil legacies and root traits, as well as root economics spectrum. On the mine tailings, acquisitive species with higher specific root length appeared to have greater potential for positive feedback. </p> <p>Synthesis and application: Our study shows that early succession on contaminated REE mine tailings may lead to more positive plant-soil feedback than predicted based on results of non-contaminated soils, mainly due to the alleviation of abiotic stress in tailings. Therefore, the improvement of specific abiotic soil stress and the trait-based selection of acquisitive plants should be preferentially considered to promote the primary restoration of degraded land.</p>
Data and code for replication of: "Global warming and heat extremes to exacerbate inflationary pressures. M. Kotz, F. Kuik, E. Liz, C. Nickel. Nature Communications Earth & Environment (2023)."
<p>This repository contains secondary data and code necessary to reproduce the results of the manuscript:</p><p>Global warming and heat extremes to exacerbate inflationary pressures.</p><p>M. Kotz, F. Kuik, E. Liz, C. Nickel. Nature Communications Earth & Environment (2023).</p><p> </p><p>For further information please contact: maxkotz@pik-potsdam.de</p><p> </p><p>This document contains:</p><p>1. An outline of the data included in the repository.</p><p>2. An outline of the code included in the repository.</p><p> </p><p>See the README for further details.</p><p> </p><p>Credit and thanks go to Miles Parker, Chiara Osbat and Emanuele Franceschi for compiling the inflation data which is used in this study. Inflation data provided here has been anonymised (countries shuffled and names replaced by random letter combinations) to enable reproduction of our results, while limiting further use. Moreover, inflation in terms of the change in the logarithm of prices is included, whereas the level of price indices are excluded. For full inflation data please see the forthcoming publication by Miles Parker, Chiara Osbat,and Emanuele Franceschi (contact Miles.Parker@ecb.europa.eu for further enquiries into the raw inflation data).</p>
Data set: Al-Biruni Earth Radius Optimization with Deep Transfer Learning based Scene Image Classification on Remote Sensing Imagery
Open the record for dataset details and reuse information.
Oblisk Energy amplifier (projekt earth)
this oblisk sits in public parks. it send out and recieves energy from the universe. it can be found usually near lay lines where energy is at its most potent. it is made of solid copper which help balance neutrons of anyone who stands near by. its coated in 18k gold and at its base are some of the most powerful crystals the planet has to offer. Source: Objaverse 1.0 / Sketchfab
Earth Song - Sculpture
Earth Song - Sculpture Autor: Miloš Axman Year: 1986 Material: sandstone Place: Bellova, Libušina třída, (Brno-Kohoutovice). Photogrammetry from 248 photos. Source: Objaverse 1.0 / Sketchfab
Planet Earth at night
3d model of the planet earth at night, you can turn off the clouds layer. Source: Objaverse 1.0 / Sketchfab
Buddha Earth Touching Gesture
Buddha in the earth-touching gesture, Thailand, 1500, National Museum (Copenhagen, Denmark). Made with Memento Beta Source: Objaverse 1.0 / Sketchfab
Earth Geology
The CGMW Geological Map (© CGMW) is used as texture (https://www.geoexpro.com/articles/2018/07/3d-geological-map-of-the-world). Source: Objaverse 1.0 / Sketchfab
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.4. Excavation of earth-and-stone structure around the Kaliszany hoard (archaeological site no. 3)
<p>Photograph taken from a UAV showing excavations at archaeological site 3 in Kaliszany, Wągrowiec commune, Poland. It is a stone and earth structure in which a hoard of metal objects dating to the Late Bronze Age was discovered in 1943. The photo is from the 2022 survey, when the south-western part of the structure was explored. </p> <p>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Supplementary data for "Increasing risks of extreme salt intrusion events across European estuaries in a warming climate", published in Communications Earth & Environment
<p>This data repository contains python scripts and post-processed climate model and salt intrusion length data to reproduce figures in the paper below.</p> <p>====================</p> <p>Title: Increasing risks of extreme salt intrusion events across European estuaries in a warming climate (<a href="https://www.nature.com/articles/s43247-024-01225-w">Link to the full paper</a>)</p> <p>Author: Jiyong Lee, Bouke Biemond, Huib de Swart, and Henk A. Dijkstra</p> <p>Journal: Communications Earth & Environment</p> <p>Year: 2024</p> <p>Publisher: Nature</p> <p>====================</p>
Repository: Turbulent Fluxes and Evaporation/Sublimation Rates on Earth, Mars, Titan, and Exoplanets
<div>Repository: Turbulent Fluxes and Evaporation/Sublimation Rates on Earth, Mars, Titan, and Exoplanets</div> <div>Khuller & Clow (2024)</div> <div> </div> <div>Contents:</div> <div> </div> <div>1. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. turbulent fluxes measured by Fitzpatrick et al. (2017)</div> <div>a) Measured fluxes</div> <div>i) Measured_Abs_LE_Fitzpatrick: Absolute value of measured latent heat fluxes in W/m^2</div> <div>ii) Measured_Abs_SH_Fitzpatrick: Absolute value of measured sensible heat fluxes in W/m^2</div> <div>b) Modeled fluxes</div> <div>i) Modeled_DB_Abs_LE_Fitzpatrick: Absolute value of Dundas & Byrne (2010) modeled latent heat fluxes in W/m^2</div> <div>ii) Modeled_DB_Abs_SH_Fitzpatrick: Absolute value of Dundas & Byrne (2010) modeled sensible heat fluxes in W/m^2</div> <div>iii) Modeled_KC_Abs_LE_Fitzpatrick: Absolute value of Khuller & Clow modeled latent heat fluxes in W/m^2</div> <div>iv) Modeled_KC_Abs_SH_Fitzpatrick: Absolute value of Khuller & Clow modeled sensible heat fluxes in W/m^2</div> <div> </div> <div>2. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. turbulent fluxes measured by Fairall et al. (1996); Fairall et al. (2003)</div> <div>a) Measured fluxes</div> <div>i) Measured_Abs_LE_COARE: Absolute value of measured latent heat fluxes in W/m^2</div> <div>ii) Measured_Abs_SH_COARE: Absolute value of measured sensible heat fluxes in W/m^2</div> <div>b) Modeled luxes</div> <div>i) Modeled_DB_Abs_LE_COARE: Absolute value of Dundas & Byrne (2010) modeled latent heat fluxes in W/m^2</div> <div>ii) Modeled_DB_Abs_SH_COARE: Absolute value of Dundas & Byrne (2010) modeled sensible heat fluxes in W/m^2</div> <div>iii) Modeled_KC_Abs_LE_COARE: Absolute value of Khuller & Clow modeled latent heat fluxes in W/m^2</div> <div>iv) Modeled_KC_Abs_SH_COARE: Absolute value of Khuller & Clow modeled sensible heat fluxes in W/m^2</div> <div> </div> <div>3. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. ice sublimation rates measured by Clow et al. (1988)</div> <div>a) Measured sublimation rates</div> <div>i) Measured_dzdt_Clow: Measured sublimation rates in mm/day</div> <div> </div> <div>b) Modeled sublimation rates</div> <div>i) Modeled_DB_dzdt_Clow: Dundas & Byrne (2010) modeled sublimation rates in mm/day</div> <div>ii) Modeled_KC_dzdt_Clow: Khuller & Clow modeled sublimation rates in mm/day</div> <div> </div> <div>4. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. ice sublimation rate measured by Douglas & Mellon (2019)</div> <div>a) Roughness Lengths used</div> <div>i) z0_DM: Roughness lengths used in cm</div> <div> </div> <div>b) Modeled Sublimation Rates</div> <div>i) Modeled_DB_dzdt_u_001_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.01 m/s in mm/day</div> <div>ii) Modeled_DB_dzdt_u_005_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.05 m/s in mm/day</div> <div>iii) Modeled_DB_dzdt_u_010_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.10 m/s in mm/day</div> <div>iv) Modeled_DB_dzdt_u_015_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.15 m/s in mm/day</div> <div>v) Modeled_KC_dzdt_u_001_DM: Khuller & Clow modeled sublimation rates for u = 0.01 m/s in mm/day</div> <div>vi) Modeled_KC_dzdt_u_005_DM: Khuller & Clow modeled sublimation rates for u = 0.05 m/s in mm/day</div> <div>vii) Modeled_KC_dzdt_u_010_DM: Khuller & Clow modeled sublimation rates for u = 0.10 m/s in mm/day</div> <div>viii) Modeled_KC_dzdt_u_015_DM: Khuller & Clow modeled sublimation rates for u = 0.15 m/s in mm/day</div> <div> </div> <div>5. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. net ice sublimation inferred at Mars Phoenix landing site, by Smith et al. (2009)</div> <div>a) Parameters used</div> <div>i) z0_PHX: Roughness lengths used in cm</div> <div>ii) u_PHX: Wind speeds used in m/s</div> <div>iii) deltatheta1_PHX: Delta-surface temperatures used in K</div> <div> </div> <div>b) Modeled net sublimation</div> <div>i) Modeled_DB_deltatheta1_sensitivity_PHX: Dundas & Byrne (2010) modeled sublimation rates for varied delta-surface temperatures in cm</div> <div>ii) Modeled_DB_u_sensitivity_PHX: Dundas & Byrne (2010) modeled sublimation rates for varied wind speeds in cm</div> <div>iii) Modeled_DB_z0_sensitivity_PHX: Dundas & Byrne (2010) modeled sublimation rates for varied roughness lengths in cm</div> <div> </div> <div>6. Modeled vertical profiles of temperature and wind speed compared to Huygens probe measurements (Fulchignoni et al., 2005)</div> <div>i) Modeled_theta_Huygens_u_0_02: Temperature profile using 10-m wind speed of 0.02 m/s, in K</div> <div>ii) Modeled_theta_Huygens_u_1: Temperature profile using 10-m wind speed of 1 m/s, in K</div> <div>iii) Modeled_Z_Huygens: Altitude profile in m</div> <div> </div> <div>7. Modeled effect of various parameters on Mars ice sublimation rates</div> <div>a) Input data ranges</div> <div>i) Modeled_Mars_Theta2_warm: 3-m air temperatures for warm cases in K</div> <div>ii) Modeled_Mars_Theta2_cold: 3-m air temperatures for cold cases in K</div> <div>iii) Modeled_Mars_z0_range: Roughness lengths in cm</div> <div>iv) Modeled_Mars_P_range: Surface air pressure values in mbar</div> <div>v) Modeled_Mars_beta_range: Dimensionless gustiness parameters</div> <div> </div> <div>b) Modeled sublimation rates from Dundas & Byrne (2010) model</div> <div>i) Modeled_DB_dzdt_Mars_warm_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_DB_dzdt_Mars_warm_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_DB_dzdt_Mars_cold_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_DB_dzdt_Mars_cold_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>v) Modeled_DB_dzdt_Mars_warm_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vi) Modeled_DB_dzdt_Mars_cold_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vii) Modeled_DB_dzdt_Mars_warm_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>viii) Modeled_DB_dzdt_Mars_cold_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>xi) Modeled_DB_dzdt_Mars_warm_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div>x) Modeled_DB_dzdt_Mars_cold_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div> </div> <div>c) Modeled sublimation rates from Khuller & Clow model</div> <div>i) Modeled_KC_dzdt_Mars_warm_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_KC_dzdt_Mars_warm_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_KC_dzdt_Mars_cold_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_KC_dzdt_Mars_cold_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>v) Modeled_KC_dzdt_Mars_warm_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vi) Modeled_KC_dzdt_Mars_cold_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vii) Modeled_KC_dzdt_Mars_warm_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>viii) Modeled_KC_dzdt_Mars_cold_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>xi) Modeled_KC_dzdt_Mars_warm_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div>x) Modeled_KC_dzdt_Mars_cold_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div> </div> <div>d) Modeled latent heat fluxes from Dundas & Byrne (2010) model</div> <div>i) Modeled_DB_LE_Mars_warm_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_DB_LE_Mars_warm_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_DB_LE_Mars_cold_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_DB_LE_Mars_cold_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>v) Modeled_DB_LE_Mars_warm_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vi) Modeled_DB_LE_Mars_cold_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vii) Modeled_DB_LE_Mars_warm_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>viii) Modeled_DB_LE_Mars_cold_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>xi) Modeled_DB_LE_Mars_warm_beta_var: Latent heat flux in W/m^2 using range of beta values</div> <div>x) Modeled_DB_LE_Mars_cold_beta_var: Latent heat flux in W/m^2 using range of beta values</div> <div> </div> <div>e) Modeled latent heat fluxes from Khuller & Clow model</div> <div>i) Modeled_KC_LE_Mars_warm_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_KC_LE_Mars_warm_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_KC_LE_Mars_cold_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_KC_LE_Mars_cold_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>v) Modeled_KC_LE_Mars_warm_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vi) Modeled_KC_LE_Mars_cold_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vii) Modeled_KC_LE_Mars_warm_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>viii) Modeled_KC_LE_Mars_cold_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>xi) Modeled_KC_LE_Mars_warm_beta_var: Latent heat flux in W/m^2 using range of beta values</div> <div>x) Modeled_KC_LE_Mars_cold_beta_var: Latent heat flux in W/m^2 using range of beta values</div>
Oxygen driving hydrogen into the inner core: Implications for the Earth's core composition
<p>Dataset for "Oxygen driving hydrogen into the inner core: Implications for the Earth’s core composition"</p> <p>Including two sheets for raw data: Free Energies (21 records) and PVTX data (30 records).</p>
Data for: JWST COMPASS: NIRSpec/G395H Transmission Observations of the Super-Earth TOI-836b
<p>Data and models accompanying the publication "JWST COMPASS: NIRSpec/G395H Transmission Observations of the Super-Earth TOI-836b". Here we include:</p> <ul> <li>Data, ExoTiC-JEDI light curves and transmission spectrum of JWST NIRSpec/G395H transit observations</li> <li>Data, Eureka! light curves and transmission spectrum of JWST NIRSpec/G395H transit observations</li> <li>Models, PICASO models and data shown in Figure 5</li> </ul> <p>Manuscript DOI: [] and [paper link]</p>
Data for the research article "High-Temperature Deformation of Enstatite-Olivine Aggregates" published in JGR Solid Earth
<p>The data available in this repository is the original data presented in the research article: Bystricky, M., Lawlis, J., Mackwell, S., & Heidelbach, F. (2024). High-temperature deformation of enstatite-olivine aggregates, Journal of Geophysical Research: Solid Earth, 129, e2023JB027699. https://doi.org/10.1029/2023JB027699.</p> <p>Version v1: data at time of original submission (2023)</p> <p>Version v2: data at time of publication (2024)</p>
Response to Sea Surface Temperature and Primary Productivity to change in Earth's Orbit Eccentricity - Simulations
<p>This dataset contains ocean and ocean biogeochemistry outputs from modeling experiments with present-day geography and various Earth's orbit confiurations. The set of simulation targets the role of Eccentricity on the tropical ocean sea surface temperature and primary productivity (Beaufort & Sarr, 2024) . The simulations have been run using the IPSL-CM5A2 General Circulation Model (Sepulchre et al. 2020 - IPSL-CM5A2 – an Earth system model designed formulti-millennial climate simulations, GMD) and offline version of PISCESv2 model (Aumont et al., 2015 - PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, GMD). It includes 4 simulations. Data are monthly averages over the last 100 years of the simulations.</p> <p>Complementary outputs (4 simulations) can be found at https://www.seanoe.org/data/00728/84031/ (Beaufort et al., 2022)</p>
(Source: Google Earth) Figure 1. Area of Study. in Fish diversity of the Vatrak stream, Sabarmati River system, Rajasthan
(Source: Google Earth) Figure 1. Area of Study.
China Earth Observation Data Cube: The 30m Seamless Annual Leaf-On Landsat Composites from 1985 to 2024
<p>The <strong>30m seamless annual leaf-on Landsat composites from 1985 to 2024</strong> were generated using a comprehensive framework designed to ensure high-quality, consistent data across decades. Starting with preprocessed Level-2 surface reflectance images from multiple Landsat sensors, the dataset is restricted to the Leaf-On season, with rigorous cloud and shadow masking applied based on quality assessment bands. To maintain consistency across sensors, spectral harmonization is conducted, followed by annual composite generation using the medoid method to capture peak vegetation conditions. The resulting composites are structured into a spatially consistent data cube, facilitating efficient analysis and monitoring of vegetation dynamics over time.</p> <p>The band naming convention follows Landsat TM standards, with bands designated as <strong>Blue (B1), Green (B2), Red (B3), NIR (B4), SWIR1 (B5), and SWIR2 (B7)</strong>. Both qualitative and quantitative evaluations were conducted to validate the data quality. Here, we provide 2023 image data covering southwestern forest regions of China as a sample for testing. For access to the full dataset, please visit <strong>Google Earth Engine</strong> at <a href="https://code.earthengine.google.com/6d1ea26ff4463277840eaf6a2662763c">this link</a>, and <strong>Earth Engine App (<a target="_blank">Landsat Yearly Composite Viewer</a>)</strong> at <a href="https://ee-caiyt33-catcd.projects.earthengine.app/view/landsat-yearly-composite-viewer">this link</a>.</p> <p><strong><a target="_blank">The dataset has now been updated to include data up to 2024.</a></strong></p> <p><a target="_blank"><strong>Data citation:</strong> Cai, Y., Li, X., Zhu, P., Nie, S., Wang, C., Liu, X., & Chen, Y. (2025). China Earth Observation Data Cube: The 30m Seamless Annual Leaf-On Landsat Composites from 1985 to 2023. <em>Journal of Remote Sensing</em>. </a><a href="https://doi.org/10.34133/remotesensing.0698">DOI: 10.34133/remotesensing.0698</a></p> <p>For data-related inquiries, please contact Dr. Yaotong Cai at <a href="mailto:caiyt33@mail2.sysu.edu.cn">caiyt33@mail2.sysu.edu.cn</a>.</p>
Supplementary material for "Tracing emerging contaminants from the Baltic Sea and North Sea in fjord waters in southern Norway with rare earth elements as far-field tracers"
<p><span>Dataset presented and discussed in the manuscript of the research article “</span><span>Tracing emerging contaminants from the Baltic Sea and North Sea in fjord waters in southern Norway with rare earth elements as far-field tracers</span><span><span>” by Zocher et al. The manuscript will be submitted to <em>Environmental Pollution</em> and was prepared by the following authors: </span></span></p> <p> </p> <p><span><span>Anna-Lena Zocher (1), Tomasz Maciej Ciesielski (2,3), Stefania Piarulli (4), Julia Farkas (4) and Michael Bau (1). </span></span></p> <p><span> </span></p> <p><span><span>(1) School of Science, Constructor University, Bremen, Germany</span></span></p> <p><span><span>(2) Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway</span></span></p> <p><span><span>(3) </span></span><span><span>Department of Arctic Technology, The University Centre in Svalbard (UNIS), Longyearbyen, Norway</span></span></p> <p><span><span>(4) SINTEF Ocean, Trondheim, Norway</span></span></p> <p> </p> <p><span>This work was conducted within the ELEMENTARY project, and we appreciate funding from the Norwegian Research Council (grant No. 301236).</span></p>
Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean
<p>Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean and notebooks created for analyses and visualization.</p> <p>Configuration names:</p> <ul> <li>ne30_n</li> <li>ne30x4_n</li> <li>ne30x4_t</li> <li>ne30x8_t</li> </ul> <p>Variables at single level: PHIS,PRECC,PRECL,PS,TREFHT,LHFLX,SWCF,LWCF,TMQ,SNOWHLND</p> <p>Variables at pressure levels:U,V,OMEGA,RELHUM,Z3,Q</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.