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1,477 results for “earth”
Dataset supporting publication: "Development and Validation of Analytical Solutions for Earth Basket (Spiral) Heat Exchangers"
<p>Dataset supporting publication: “Development and Validation of Analytical Solutions for Earth Basket (Spiral) Heat Exchangers” (publication available for download:<a href="https://zenodo.org/record/7274096#.Y2JpLnbMJPY">GEOFIT Zenodo</a>).</p> <p>This paper presents an analytical solution and its validation for earth basket (vertical spiral) ground heat<br> exchangers. The model, based on the well known Finite Line Source Equation, accounts for the heat exchanger pipe diameter and seasonally varying near surface temperature. For computational efficiency the standard approach of using G-functions has been implemented as well. The analytical model is validated based on laboratory experiments and extensive CFD analysis.</p>
Borehole pressure and temperature data, barometric pressure and temperature data, solid Earth tidal strain, for OmanDP site BA1, 4/18 to 1/19
<p>Borehole (relative) pressure and temperature data for OmanDP site BA1: holes BA1B and BA1D from 4/18 to 1/19.</p> <p>Barometric pressure and temperature data for OmanDP site BA1 from 4/18 to 1/19.</p> <p>Earth tidal strain for the OmanDP site BA1: estimates from PyGTide software from 4/18 to 9/20.</p>
Response of the Snowball Earth Climate to Orbital Forcing at High CO2 Level
<p>CAM3&CLM3 simulation results that support the findings of this study are archived here.</p>
Earth Food Security: Use Case Service
<p>This use case service consists in a water availability index computed based on soil status from Earth Observation. For the computation of water availability, long-time series of Soil Moisture is used to establish climatology over West Africa.</p>
Dataset to manuscript "The role of weathering on morphology and rates of escarpment retreat of the rift margin of Madagascar" by Wang et al. (2023) submitted to Journal of Geophysical Research-Earth Surface.
<p>The dataset includes the relevant raw chemical element content data and the chemical weathering condition analysis of river sediment samples of Madagascar. The dataset is a supplement to the manuscript "The role of weathering on morphology and rates of escarpment retreat of the rift margin of Madagascar", by Wang et al. (2023) submitted to the Journal of Geophysical Research-Earth Surface. Commercially sensitive data is hidden but is available by request directly to the corresponding author. The data should <strong>NOT</strong> be used commercially.</p> <p>A MATLAB code for the weathering indices calculation is open-access on GitHub (https://github.com/yanyanwangesd/chemical_weathering). Please contact the corresponding author for more info or technical support on using the code. </p>
Output files for Carbon storage in the Earth's deep interior implied by carbonate-silicate-metal melt miscibility
<p>Density and speciation output files for Carbon storage in the Earth's deep interior implied by carbonate-silicate-metal melt miscibility.</p>
Nimble code and dataset for: Estimating true density in large, alpine herbivores using Google Earth imagery
<p>This nimble code will estimate elk density from the count data (DO_data_list.RData) and covariates (DO_constants_list.RData). Data and covariates include: a 'y' matrix of double observer counts with 3315 rows (1 for each 250 m square plot) and 3 columns for the observer 1's exclusive counts, observer 2's exclusive counts, and the count of elk detected by both observers. The double-observe protocol was only employed in 372 random cells. 'n' is a vector of total counts – the total number of uniquely detected elk in each plot. 'obs1tot' is a vector of the total count of elk by just observer 1, who counted elk in all 3315 cells. There are two dummy indicator variables that are all 1's ('obs1constraint' and 'obs2constraint') to constrain unobserved but estimated counts to sum to expected totals. For example, observer 1 was the only observer in 2943 plots. This single count would be the equivalent of the sum of the exclusive observer 1 counts and the joint observer 1 and 2 counts in a double-observer protocol. Additionally, the sum of observer 2s exclusive counts and observer 1's total counts must equal the total count of unique individuals in a cell. The covariates 'meadow' and 'deadfall' are the additive log-ratio transformations of the proportion of open meadow and burned forest, respectively, in each 250-m plot. The 'nonforest' covariate is the number of hectares in each plot that was not conifer forest. 'M' is the total number of plots. 'firstsearch' is an indicator variable, indicating which plots were part of the first search area in the alpine >2750 m above sea level. 'domethod' is an indicator variable, indicating which plots were searched using the double-observer protocol. </p>
Figure data for paper "The first ground level enhancement seen on three planetary surfaces: Earth, Moon and Mars"
<p>Here we provide the data used for generating Figures 1-4 as published in the paper "The first ground level enhancement seen on three planetary surfaces: Earth, Moon and Mars" </p>
Model data and code supporting "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "
<p>Model data and analysis code supporting the Geoscientific Model Development manuscript "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "</p> <p> </p> <p> </p>
Datasets for "Trapdoor fault activation: a step towards caldera collapse at Sierra Negra, Galápagos, Ecuador", Journal of Geophysical Research: Solid Earth
<p>The following files were used in the analysis from "Trapdoor fault activation: a step towards caldera collapse at Sierra Negra, Galápagos, Ecuador", <em>Journal of Geophysical Research: Solid Earth</em>:</p> <p><strong>alos2_csk/alos2_sm1_dsc_20180504_20180713/:</strong> Includes DEM used in processing of the ALOS-2 SM1 descending interferogram spanning 4 May 2018–13 July 2018 (dem.2alks_2rlks.crop.*); geocoded SAR offsets in pixels (range resolution=1.43 m/pixel; azimuth resolution=2.01 m/pixel; denseOffsets.bil.2alks_2rlks.geo*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.2alks_2rlks.geo.*); geocoded, unwrapped interferometric phase (filt_topophase.unw.2alks_2rlks.geo.*); geocoded incidence and heading angle for the interferogram (los.rdr.2alks_2rlks.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/alos2_sm3_asc_20180114_20180701/:</strong> Includes DEM used in processing of the ALOS-2 SM3 ascending interferogram spanning 14 January 2018–1 July 2018 (dem.crop.*); geocoded, unwrapped interferometric phase (filt_topophase.unw.geo.*); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/alos2_wd1_dsc_147_180518_180629/</strong>: Includes DEM used in processing of the ALOS-2 WD1 descending interferogram spanning 18 May 2018–29 June 2018 (crop.dem.*); geocoded, unwrapped interferometric phase (filt_180629-180518_2rlks_14alks.unw.geo.*) ; geocoded incidence and heading angle for the interferogram (180629-180518_2rlks_14alks.los.geo.*); geocoded coherence for the interferogram (180629-180518_2rlks_14alks.cor.geo.*); geocoded mask for the interferogram (filt_topophase.unw.masked.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_asc_20180617_20180703/:</strong> Includes DEM used in processing of the COSMO-SkyMed ascending interferogram spanning 17 June 2018–3 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.54 m/pixel; azimuth resolution=2.48 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_asc_20180703_20180719/</strong>: Includes DEM used in processing of the COSMO-SkyMed ascending interferogram spanning 3 July 2018–19 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.54 m/pixel; azimuth resolution=2.48 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_dsc_20180618_20180704/:</strong> Includes DEM used in processing of the COSMO-SkyMed descending interferogram spanning 18 June 2018–4 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.70 m/pixel; azimuth resolution=2.45 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_dsc_20180704_20180720/: </strong>Includes DEM used in processing of the COSMO-SkyMed descending interferogram spanning 4 July 2018–20 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.70 m/pixel; azimuth resolution=2.45 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>S1.zip</strong>: Unwrapped, geocoded interferometric phase in meters for Sentinel-1 ascending and descending interferograms, spanning time periods of interest. </p> <p><strong>S1_20180630_20180706_asc_mask_nan_ref.grd: </strong>Unwrapped, geocoded, and masked interferometric phase for Sentinel-1 ascending interferogram spanning 30 June 2018–6 July 2018.</p> <p><strong>S1_20180701_20180707_desc_mask_nan_ref.grd: </strong>Unwrapped, geocoded, and masked interferometric phase for Sentinel-1 descending interferogram spanning 1 July 2018–7 July 2018.</p> <p><strong>tandemx12m_crop.grd</strong>: TanDEM-X 12 meter DEM in meters.</p> <p><strong>pleaides_tandemx12m_diff.grd</strong>: Difference between the TanDEM-X 12 meter DEM and the Pléiades-derived DEM, computed from images on 29 October 2018 and 6 December 2019.</p> <p><strong>trapdoorFaultSlip.zip</strong>: Discretized trapdoor fault patch dip-slip modeled to fit deformation from Sentinel-1 ascending interferograms, estimated using the Classic Slip Inversion software.</p> <p><strong>trapdoorFaultTraces.zip</strong>: Caldera and trapdoor fault traces, derived from <em>Bell et al. 2021</em>.</p> <p><strong>SN14_tilt_10s_2018-19.txt</strong>: Text filt containing date-time (sampled at 10 s, in matplotlib date-time number format), N-S tilt and E-W tilt. Tilt values can be converted to microradians by multiplying by a factor of 0.00129. Tilt data obtained from authors of <em>Bell et al. 2021</em>. For use of this dataset, please cite <a href="https://doi.org/10.1038/s41467-021-21596-4">https://doi.org/10.1038/s41467-021-21596-4</a>.</p>
Long-term soil erosion monitoring in China using the RUSLE model based on Google Earth Engine
<p>This dataset contains soil erosion maps in China at 500m resolution from 2010 to 2020. The dataset is stored in GeoTif format with the unit of t/(km<sup>2 </sup>· a).</p>
SCALE-WIN22 cruise track, stations, sea-ice edge, and SAR images on Google Earth
<p>This Google Earth KMZ file contains the cruise track of the SCALE-WIN22 expedition, the description of activities on each station, the daily ice-edge location, and the quick-view SAR images. This is an interactive description of the cruise report:</p> <p>Vichi, M. (2023). SCALE-WIN22 Cruise Report. Zenodo. <a href="https://doi.org/10.5281/zenodo.7901529">https://doi.org/10.5281/zenodo.7901529</a></p> <p>The layers are separated in single folders that can be activated or deactivated. All data have a time stamp and a time span. By moving the time slider on the upper left (and adjusting the time interval), it is possible to follow the ship and display the corresponding SAR images and sea-ice edge. All the images are displayed for a time duration of one day. The exact time and name of the image is found in the object description.</p> <p>Legend:</p> <ul> <li>White line: ship track</li> <li>Red pin: station (with the main activities: refer to the report for more informations)</li> <li>Yellow pin: ship location at the time of the COSMO-SkyMed image</li> <li>Green pin: ship location at the time of the Terrasar-X image</li> <li>Red thick line: sea-ice edge from AMSR2 (green shading is the uncertainty)</li> </ul> <p>Quick-view SAR images are courtesy of DLR and ASI. Sea-ice edge location was produced by SAWS.</p> <p>The code to generate the KML file is available on github: <a href="https://github.com/mvichi/SCALE">https://github.com/mvichi/SCALE</a></p>
Datasets for Russell & Jones, Transport and eruption of mantle xenoliths creates a lagging problem, Communications Earth and Environment
<p>Example datasets for Russell & Jones, Transport and eruption of mantle xenoliths creates a lagging problem, Communications Earth and Environment </p>
Simulations for manuscript Unravelling the forcing and feedbacks contributing to Pliocene Arctic warming in EC-Earth simulations
<p>Nine PlioMIP2 experiments were conducted to investigate the mid-Pliocene Arctic climate. These experiments considered three CO2 levels (280, 400 and 560 ppm), modern or Pliocene ice sheet conditions in Greenland and Antarctica and either prescribed or dynamic vegetation. Simulations were performed by the EC-Earth3-LR Veg climate model with a horizontal resolution of ~1.125°. The dataset contains selected output data from the simulations. Simulations were run for 1000 years. The last 200 years were used for analysis after the model reached an equilibrium as measured by global surface air temperature trend less than 0.05 K per century. </p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Power et al. (2023).</p>
Data for: Improvements in the Land and Crop Modeling over Flooded Rice Fields by Incorporating the Shallow Paddy Water (Submitting to the Journal of Advances in Modeling Earth Systems)
<p>We incorporated the shallow paddy surface water layer into the Noah-MP land surface model to improve its performance of surface heat fluxes over flooded rice paddies. Field measurements from two crop sites, i.e., SAITO (early rice) and SAGA (late rice), were used to initialize and evaluate the modified Noah-MP model (Maruyama, 2021). Additionally, we investigated the roles of some key parameters in the land and crop modeling. </p> <p>Note that, all numerical experiments in this study were conducted at the field scale using the offline version of Noah-MP (Niu et al., 2011) running within the High-Resolution Land Data Assimilation System (HRLDAS v3.9; Chen et al., 2007). Please refer to the official HRLDAS/Noah-MP unified Github repository (<a href="https://github.com/NCAR/hrldas">https://github.com/NCAR/hrldas-release</a>) for the original model codes.</p> <p>The related model code modifications and model outputs were included in this dataset. Surface observations for the nearest AMeDAS or meteorological observatory stations were obtained from the Japan Meteorological Agency website (<a href="https://www.jma.go.jp/jma/indexe.html">https://www.jma.go.jp/jma/indexe.html</a>), and were also provided in this dataset.</p> <p> </p> <p>References</p> <p>Chen, F., Manning, K. W., LeMone, M. A., Trier, S. B., Alfieri, J. G., Roberts, R. D., et al. (2007). Description and evaluation of the characteristics of the NCAR high‐resolution land data assimilation system. <em>Journal of Applied Meteorology and Climatology</em>, 46(6), 694-713. <a href="https://doi.org/10.1175/JAM2463.1">https://doi.org/10.1175/JAM2463.1</a></p> <p>Maruyama, A. (2021). Data for: Coupling land surface and crop models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies (version 2) [Data set]. Mendeley Data. <a href="https://doi.org/10.17632/tv23z95r5g.2">https://doi.org/10.17632/tv23z95r5g.2</a></p> <p>Niu, G., Yang, Z., Mitchell, K., Chen, F., Ek, M., Barlage, M., et al. (2011). The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. <em>Journal of Geophysical Research, </em>116, D12109. <a href="https://doi.org/10.1029/2010JD015139">https://doi.org/10.1029/2010JD015139</a></p>
Supplementary data for the study "Unveiling the rheological control of magmatic systems on volcano deformation: the interplay of poroviscoelastic magma-mush and thermo-viscoelastic crust ", submitted in the Journal of Geophysical Research: Solid Earth
<p>Supplementary data for the study "Unveiling the rheological control of magmatic systems on volcano deformation: the interplay of poroviscoelastic magma-mush and thermo-viscoelastic crust ", submitted in the Journal of Geophysical Research: Solid Earth</p>
Monthly outputs of Today's Earth-Global
<p>This database includes Today's Earth-Global output, which includes monthly river discharge, snow depth, and soil moisture.</p> <p>Please find the details from <a href="https://www.eorc.jaxa.jp/water/documents.html?1">Today's Earth</a>.</p>
Supplementary Information for "Analytical electron microscopy study of the composition of BaHfO₃ nanoparticles in REBCO films: The influence of rare-earth ionic radii and REBCO composition"
<p>Supplementary information containing image and spectroscopy data, and data-processing notebooks for the article "<strong>Analytical electron microscopy study of the composition of BaHfO<sub>3</sub> nanoparticles in REBCO films: The influence of rare-earth ionic radii and REBCO composition</strong>".</p> <p>Article (open access): <a href="https://doi.org/10.1039/D3MA00447C">https://doi.org/10.1039/D3MA00447C</a></p> <p>The data is sorted in folders for each figure, and the <em>Notes.pdf</em> file contains additional information about the different file formats and processing steps.</p> <p>If you have questions, you can contact Lukas Grünewald at the E-Mail listed on the ORCID page: <a href="https://orcid.org/0000-0002-5898-0713">https://orcid.org/0000-0002-5898-0713</a></p>
Table 3 for the Study: "Correlation Study: Triggering and Magnitude of Earthquakes in Italy (≥M4.3) in Relation to the Positions and Gravitational Forces of the Sun, Moon, and Planets Relative to Earth."
<p><strong>Study of σFR gravitational forces exerted by the angular distances of all 9 S.S. celestial bodies toward Earth: L'Aquila Earthquake, 06.04.2009, Italy, M6.1 for Section 2.9.</strong></p><p>In case of display problems or missing data, the file <a href="https://zenodo.org/api/files/30eebe1b-d3a1-406a-975c-23ad8abe143f/Table_3_FR_L'Aquila_2009.xlsx?versionId=c778a73b-60c6-4504-a178-8edb1471e98c">Table_3_FR_L'Aquila_2009.xlsx</a> on Drive available for consultation is this one: <a href="https://docs.google.com/spreadsheets/d/1QteARypgGETNkbLB_O0VNWfmrYa0saFWvX0z1EacDEo/edit?usp=sharing">https://docs.google.com/spreadsheets/d/1QteARypgGETNkbLB_O0VNWfmrYa0saFWvX0z1EacDEo/edit?usp=sharing</a></p><p>The URL of the Calculation Excel sheet that allows calculation of the resulting gravitational force σFR in the month of the 2009 L'Aquila earthquake, M6.1 (n. 85), in the study <i>"</i>Correlation Study: Triggering and Magnitude of Earthquakes in Italy (≥M4.3) in Relation to the Positions and Gravitational Forces of the Sun, Moon, and Planets Relative to Earth<i>", </i>Section 2.9.</p>
Modeling liquid transport in the Earth's mantle as two-phase flow: Effect of an enforced positive porosity on liquid flow and mass conservation
<p>These data files include the raw data for the figures shown in Lee et al. titled as 'Modeling liquid transport in the Earth’s mantle as two-phase flow: Effect of an enforced positive porosity on liquid flow and mass conservation'. <br> </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.