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1,477 results for “earth”
Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints
<p>This dataset consists of a collection of self-consistent radial seismic Earth models. The models are derived by inverting a large set of normal-mode centre-frequencies, quality factors, and geodetic data, including mass, moment of inertia, and tidal response.</p><p>The dataset is accompanied by a research paper titled "Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints" (DOI: <a href="https://doi.org/10.1093/gji/ggad254">10.1093/gji/ggad254</a>). The paper presents the methodology and findings related to the development of the models.</p><p>This version (V0.2) of the dataset replaces the previous version (V0.1). </p><p><strong>Dataset Details</strong></p><p>The dataset includes confidence intervals (CIs) for these parameters at 25%, 50%, and 75% levels that are representative of the uncertainty of the sampled model parameters. For example, the files <a href="https://zenodo.org/api/files/fa9f19f0-bf6a-41a8-8ca7-489a5b1b9c49/screm-25p-high.dat?versionId=b3b349c2-0a0b-48cc-b1f3-90137b8d1dcb">screm-25p-high.dat</a> and <a href="https://zenodo.org/api/files/fa9f19f0-bf6a-41a8-8ca7-489a5b1b9c49/screm-25p-low.dat?versionId=81db34f2-29f4-48aa-95c4-bb73b050a47f">screm-25p-low.dat</a> contain the upper and lower bound of the 25% CI sampled model range. A python plotting script is available, which plots the CIs.</p><p>The dataset files are provided in comma-separated values (CSV) format, containing the following columns:</p><ol><li><strong>Radius (km)</strong>: Radial distance from the center of the Earth.</li><li><strong>Depths (km)</strong>: Depth from the surface of the Earth.</li><li><strong>Density (g/cm³)</strong>: Radial density structure.</li><li><strong>P-wave velocity (vp) (km/s)</strong>: Radial compressional (P) wave velocity structure</li><li><strong>S-wave velocity (vs) (km/s)</strong>: Radial shear (S) wave velocity structure.</li><li><strong>Qkappa</strong>: Radial bulk attenuation structure.</li><li><strong>Qmu</strong>: Radial shear wave attenuation structure.</li><li><strong>Bulk Modulus (K) (GPa)</strong>: Radial bulk modulus structure.</li><li><strong>Shear Modulus (Mu) (GPa)</strong>: Radial shear modulus structure.</li><li><strong>Pressure (GPa)</strong>: Radial pressure profile.</li><li><strong>Temperature (K)</strong>: Radial geothermal profile.</li></ol><p><strong>Citation</strong></p><p>If you use this dataset in your research or refer to the models, please cite the following paper:</p><p><strong>Title:</strong> Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints<br><strong>Authors:</strong> J. Kemper, A. Khan, G. Helffrich, M. van Driel, D. Giardini<br><strong>Journal:</strong> Geophysical Journal International<br><strong>Year: </strong>2023<br><strong>DOI:</strong> <a href="https://doi.org/10.1093/gji/ggad254">10.1093/gji/ggad254</a></p><p>Please also acknowledge the dataset by providing a link to the Zenodo repository and its DOI.</p><p>Bibtex:<br>@article{Kemper_etal23,<br>author = {Kemper, J and Khan, A and Helffrich, G and van Driel, M and Giardini, D},<br>title = "{Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints}",<br>journal = {Geophysical Journal International},<br>pages = {ggad254},<br>year = {2023},<br>month = {06},<br>issn = {0956-540X},<br>doi = {10.1093/gji/ggad254},<br>url = {https://doi.org/10.1093/gji/ggad254},<br>} </p><p> </p>
Return on Investment Metrics for Data Repositories in Earth and Environmental Sciences
Despite a growing recognition of the importance of data to the economy and to science, investment in repositories to manage and disseminate that data in easily accessible and understandable ways is scarce. Keeping repository services active and up-to-date for a long time period is difficult due to this funding situation. As a result, repositories must continually provide proof of their value, their Return on Investment (ROI) to their sponsors; yet doing so has always been difficult, problematic and not always successful. In this work, an analysis of approaches for assessing the ROI of several scientific data repositories has identified various techniques that repositories use to report on the impact and value of their data products and services. A survey of selected repositories rated the set of metrics identified and rated each by its importance as well as the ease with which the metric could be measured. The discussion is broken down into considerations for calculating costs, perceived value of repositories and suggested metrics that would allow a repository to calculate an ROI. The authors, representatives of environmental data repositories, concluded that easily obtainable data use metrics, such as data downloads, etc., have limited value while more informative analyses would require additional resources.
Earth-scattering likelihoods: Likelihood and p-value tables for reconstructing the local Dark Matter Density
<p>Tables of likelihoods, p-values and best-fits associated with the EarthScatterLikelihood code - <a href="https://github.com/bradkav/EarthScatterLikelihood">https://github.com/bradkav/EarthScatterLikelihood</a> - released alongside the paper "<em>Measuring the local Dark Matter density in the laboratory</em>" (<a href="https://arxiv.org/abs/2004.01621">arXiv:2004.01621</a>).</p> <p>Examples for how to load the files are given in 'EarthScatterLikelihood/plotting'. Simply extract the folders into 'EarthScatterLikelihood/results' in https://github.com/bradkav/EarthScatterLikelihood. </p>
Hubei STEC Data through CORS stations for DOY 059 and 061 of the year 2018 which used in (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City, manuscript submitted to Earth and Space Science Journal AGU)
<p>Manuscript submitted to Earth and Space Science AGU entitled with <br> (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City)<br> by: Mohamed Freeshah, Xiaohong Zhang, Xiaodong Ren, Jun Chen, and Zhibo Zhao</p> <p>The STEC data inside two compressed folders named as stec059 and stec061, respectively.<br> The STEC file name has the CORS station name for the first forth letters and next three numbers epresent the Day of the year.<br> For example:<br> ES010590.18STEC<br> ES01 is the station name<br> 059 is the day of year (DOY), 2018</p>
Spherical harmonic model of the shape of Earth's Moon: MoonTopo2600p
<p><em><strong>THIS MODEL IS SUPERSEDED BY <a href="../doi/10.5281/zenodo.10796953">Spherical harmonic models of the shape of the Moon (principal axis coordinate system)</a>.</strong></em></p> <p> </p> <p><strong>MoonTopo2600p.shape</strong> is a spherical harmonic model of the shape of Earth's Moon in a principal axis coordinate system. This model makes use of 4-pi normalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The description of how this spherical harmonic model was constructed can be found in Wieczorek (2015).</p>
Data, materials, methods and codes for publication Reis et al., "Understanding the stickiness of commodity supply chains is key to improving their sustainability", 2020, One Earth.
<p>Explanation of the data and code used for the article: "Understanding the stickiness of commodity supply chains is key to improving their sustainability", 2020, One Earth.</p> <p> The file: "StickinessAnalysisCompleteGeneric_CleanUpdate18-5-2020.R" is the R code/ script containing all the data preparation and the stickiness analysis.</p> <p>The file: "BRAZIL_SOY_V2.3_WITH_DOMESTIC_TRADERS.csv" contains the raw data of Brazil's soy exports and domestic consumption from trase.earth. This dataset can also be obtained from trase.earth in the latest version.</p> <p>The file: "NodesCiS.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: A. logistics hubs supplying traders, and D. traders supplying countries. They are put together in the same table because they are all "sending" relationships.</p> <p>The file: "NodesCiR.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: C. traders sourcing from logistics hubs, and E. countries sourcing from traders. They are put together in the same table because they are all "receiving" relationships.</p> <p>The file "NodesCiSMunCountry.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: B. logistics hubs supplying countries (directly not passing through traders). This is separate in another table because it is a direct sending relationship from LHs to countries.</p> <p>The file "NodesCiRMunCountry.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: F. countries sourcing from logistics hubs (directly not passing through traders). This is separate in another table because it is a direct receiving relationship from LHs to countries.</p> <p>The file: "NodesWPiS.csv" is a table with the WPi (stickiness on flows) measured for the types of supply chain relationships: A. logistics hubs supplying traders, and D. traders supplying countries. They are put together in the same table because they are all "sending" relationships.</p> <p>The file: "NodesWPiR.csv" is a table with the Ci (stickiness on flows) measured for the types of supply chain relationships: C. traders sourcing from logistics hubs, and E. countries sourcing from traders. They are put together in the same table because they are all "receiving" relationships.</p> <p>The file "NodesWPiSMunCountry.csv" is a table with the Ci (stickiness on flows) measured for the types of supply chain relationships: B. logistics hubs supplying countries (directly not passing through traders). This is separate in another table because it is a direct sending relationship from LHs to countries.</p> <p>The file "NodesWPiRMunCountry.csv" is a table with the Ci (stickiness on flows) measured for the types of supply chain relationships: F. countries sourcing from logistics hubs (directly not passing through traders). This is separate in another table because it is a direct receiving relationship from LHs to countries.</p>
Reflectance and emission spectra of Earth-like planets orbiting red giant stars
<p>Spectra of red giant stellar hosts and reflectance/emission planetary spectra as described in Kozakis & Kaltenegger (2020). File names and content are explained in 0readme.txt. Please email theakozakis@gmail.com with any questions.</p> <p> </p>
Source Data for Manuscript: "Retrievals Applied To A Decision Tree Framework Can Characterize Earth-like Exoplanet Analogs"
<p>This dataset accompanies the manuscript entitled: "Retrievals Applied To A Decision Tree Framework Can Characterize Earth-like Exoplanet Analogs", which was accepted for publication in the Planetary Science Journal. Included are the source files for all figures included in the paper.</p>
CLaMS results used for age of air analysis in the report of assessment of the ESA Earth Explorer candidate mission CAIRT
<p>Results of the Chemical Lagrangian Model of the Stratosphere (CLaMS) used for age of air analysis in the report of assessment of the ESA Earth Explorer candidate mission CAIRT. Days of results: 2011-01-01, 2011-04-01, 2011-07-01, 2011-10-01 and 2019-09-23. Included trace gases: SF6, CFC-11, CFC-12, HCFC-22, N2O and CH4. The results also include the clock tracer BA, which can be converted into the precise mean age of or air of the model environment with the attached python script 'AOA2age_years.py'.</p>
data from "Fractal properties of isolines at varying altitude revealing different dominant geological processes on Earth"
<p>The file contains the data used to produce Fig.4 for the paper "Fractal properties of isolines at varying altitude revealing</p><p>different dominant geological processes on Earth", by Andrea Baldassarri, Marco Montuori, Olga Prieto-Ballesteros,</p><p>and Susanna C. Manrubia, Journal of Geophysical Research: PlanetsVolume 113, Issue E9, https://doi.org/10.1029/2007JE003066</p>
Figure 3 in Terrestrial Ecoregions of the World: A New Map of Life on Earth
Figure 3. The relative richness of terrestrial mammal species by ecoregion is depicted. Warmer colors denote ecoregions containing richer assemblages.
Figure 2 in Terrestrial Ecoregions of the World: A New Map of Life on Earth
Figure 2. The map of terrestrial ecoregions of the world recognizes 867 distinct units, roughly a fourfold increase in biogeographic discrimination over that of the 193 units of Udvardy (1975). Maps of freshwater and marine ecoregions are similarly needed for conservation planning.
Figure 1. The ecoregions are categorized within 14 in Terrestrial Ecoregions of the World: A New Map of Life on Earth
Figure 1. The ecoregions are categorized within 14 biomes and eight biogeographic realms to facilitate representation analyses.
Figure 4 in Terrestrial Ecoregions of the World: A New Map of Life on Earth
Figure 4. The level of species endemism for terrestrial mammals shows different patterns than that of richness. Warmer colors denote ecoregions containing more endemic species.
Data from: Deep learning-assisted near-Earth asteroid tracking in astronomical images
<p>This repository is the data release of our paper <em>Deep learning-assisted near-Earth asteroid tracking in astronomical images</em>. There are two categories in this repository:</p> <ul> <li>Simulated training dataset for training the star segmentation network. </li> </ul> <p>The dataset consists of two folders: image (grayscale images) and mask (binary images). The size of each image is 256*256.</p> <ul> <li>Example data for testing asteroid tracking algorithm.<br><br></li> </ul> <p>If you find this work useful, please cite our paper:</p> <div> <div>@article{du2024ASR,</div> <div>title = {Deep learning-assisted near-Earth asteroid tracking in astronomical images},</div> <div>journal = {Advances in Space Research},</div> <div>volume = {73},</div> <div>number = {10},</div> <div>pages = {5349-5362},</div> <div>year = {2024},</div> <div>issn = {0273-1177},</div> <div>doi = {https://doi.org/10.1016/j.asr.2024.02.048},</div> <div>url = {https://www.sciencedirect.com/science/article/pii/S0273117724001911},</div> <div>author = {Zhenhong Du and Hai Jiang and Xu Yang and Hao-Wen Cheng and Jing Liu},</div> <div>keywords = {Near-Earth asteroid, Deep learning, Convolutional neural network, Faint object extraction, Moving object linking},</div> <div>}</div> </div>
Data for: Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid-ammonia nucleation
<p>Model dataset variables produced from the IFS and TM5 modules in EC-Earth3 version 3.3.4. which contains the control case and three experiments with the NPF lookup table. This paper is published at EGUshpere by journal: Geoscientific Model Development.</p> <p>The files contain:</p> <p>Compressed tar file of NetCDF data from IFS output for all four simulations. All IFS data have been averaged to monthly means from 6-hourly grib datasets. The post-process bash script which contains the function for the CDN and cloud effective radius weighted average towards cloud_time is found in the supplemented zendo link.</p> <p>NetCDF files from TM5 general output for each simulation. </p>
A seven-Earth-radius helium-burning star inside a 20.5-min detached binary
<p><strong>2024-02-19 updated for including the full MESA inlists of evolutionary models.</strong></p> <p><strong>MESA_inlist.zip</strong> -<strong> </strong>the full MESA inlists of evolutionary models (including evolutions of single stars and binaries).</p> <p>_____________________________________________________________________________________________________________________</p> <p>This record contains the data of spectra, photometries and models used for the paper <em><strong>A seven-Earth-radius helium-burning star inside a 20.5-min detached binary</strong>,<a href="https://www.nature.com/articles/s41550-023-02188-2"> click here</a></em></p> <p><strong>J0526_fchart.png</strong> - the finder chart of J0526;</p> <p><strong>J0526_Keck_[0-5].spec </strong> (in Keck_ori_spectra.zip)<strong> </strong>- Keck I/LRIS spectra of J0526;</p> <p><strong>J0526_GTC_[0-6].spec </strong> (in GTC_ori_spectra.zip)<strong> </strong>- GTC/OSIRIS spectra of J0526;</p> <p><strong>J0526_GTC_syn_[0-6].spec </strong> (in GTC_spectra_models.zip)<strong> </strong>- GTC/OSIRIS spectra and their best-fit TLUSTY/Synspec spectra;</p> <p><strong>J0526_RV_bary_corrected.dat</strong> - the barycenter-corrected radial velocities derived from LRIS and OSIRIS spectra;</p> <p><strong>J0526_SED.dat</strong> - the photometric fluxes in the broad-band SED and corresponding synthetic fluxes from the best-fit model spectrum;</p> <p><strong>ZTF_J0526_[r,g].dat </strong>(in light_curves.zip) - ZTF <em>r</em>-/<em>g</em>-band light cuvrs of J0526, the raw data were provided by NASA/IPAC Infrared Science Archive (<a href="https://irsa.ipac.caltech.edu/">https://irsa.ipac.caltech.edu</a> );</p> <p><strong>Lijiang_J0526_[r,g].dat </strong>(in light_curves.zip) - LJT <em>r</em>-/<em>g</em>-band light cuvrs of J0526;</p> <p><strong>TMTS_J0526_L.dat </strong>(in light_curves.zip) - LJT <em>L</em>-band light cuvrs of J0526;</p> <p><strong>CHeB_c[0.32,0.33,0.34]+H1e-6.track</strong> (in MESA_models.zip) - the evolutionary tracks for core helium-burning stars with He-core mass of [0.32,0.33,0.34] M<sub>sun</sub> and H-envelope mass of 1e-6 M<sub>sun</sub>;</p> <p><strong>ZAHeMS_H0_sequence.dat</strong> (in MESA_models.zip) - the zero-age He-star sequence (without H envelope);</p> <p><strong>ZAHeMS_H1e-6_sequence.dat</strong> (in MESA_models.zip) - the zero-age He-star sequence (with H-envelope mass of 1e-6 M_sun );</p> <p><strong>binary_0.33+0.735.dat </strong> (in MESA_models.zip) - the binary evolutionary track for a core helium-burning star (M<sub>core</sub>=0.33 M_sun, M<sub>env</sub>=1e-6 M_sun ) and a CO WD (M=0.735 M_sun);</p> <p><strong>binary_0.36+0.735.dat </strong> (in MESA_models.zip) - the binary evolutionary track for a core helium-burning star (M<sub>core</sub>=0.36 M_sun, M<sub>env</sub>=1e-6 M_sun ) and a CO WD (M=0.735 M_sun);</p> <p><strong>J0526_[SED, RV, lc]_fit_corner.png</strong> - the corner plots for [SED, RV, light curve] MCMC fit.</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2011_2015)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.