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708 results for “Global dataset”
Datasets used to train the models in "Deep learning for denoising High-Rate Global Navigation Satellite System data."
<p>Datasets used to train the models in "Deep learning for denoising High-Rate Global Navigation Satellite System data." Additional information can be found at https://github.com/amtseismo/hrgnss_denoising.</p>
Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Heart Electromechanics Model Using Gaussian Processes Emulators - Training Datasets
<p>This database contains all training datasets for the Gaussian processes emulators (GPEs) trained in the study entitled "Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators", submitted to PLOS Computational Biology.</p> <p>Every folder contains two csv files:</p> <p>- parameters.csv: the rows are the samples and the columns represent the parameters that were varied in the analysis</p> <p>- outputs.csv: the rows are the samples and the columns represent the values for the output features simulated for each sample</p> <p>In ventricular_cell_model, there are four folders:</p> <p>- ionic: ToR-ORd model samples used to train GPEs to predict the ventricular calcium transient features</p> <p>- contraction_isometric_stretch1.0: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with no strain (or stretch 1.0).</p> <p>- contraction_isometric_stretch1.1: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with 0.1 strain (or stretch 1.1).</p> <p>- contraction_isotonic: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isotonic.</p> <p>The folder atrial_contraction_model follows the same structure, but the ionic model was Courtemanche, used to represent an atrial rather than ventricular calcium transient.</p> <p>The folder tissue_electrophysiology contains the training dataset for the GPEs to predict total atrial and ventricular activation times with an Eikonal model.</p> <p>The folder passive_mechanics contains the training dataset for the GPEs to predict inflated volumes and mean atrial and ventricular fiber strains for a passive inflation.</p> <p>The folder CircAdapt contains the training dataset for the GPEs to predict four-chamber pressure and volume features with the CircAdapt ODE model.</p> <p>Finally, the folder fourchamber contains the samples generated with a 3D-0D four-chamber electromechanics model to predict pressure and volume biomarkers for cardiac function.</p> <p>The details about the model can be found in the original publication.</p>
Clinical Trials_Original dataset. Rising Pharmaceutical Innovation in the Global South.
<p>This is a supplementary document of the research reports from the "Research Collaboration on Technology, Equity, and the Right to Health", between the Global Health Centre (GHC) at the Geneva Graduate Institute in Switzerland, the James P. Grant School of Public Health at BRAC University in Bangladesh, and the Universidad de los Andes (ANDES) in Colombia, supported by the Open Society University Network (OSUN). For more information please refer to: Knowledge Portal on Innovation and Access to Medicines - https://www.knowledgeportalia.org/. </p>
Global dataset for "Global leaf-trait mapping based on optimality theory "
<p>This repository contains Global data used for “<em>Global leaf-trait mapping based on optimality theory</em><strong>” </strong>published in GEB.</p> <ol> <li>Global_Maps_SLA represents climatology of published Global SLA used for comparison (details products see table 1 and figure 4).</li> <li>Global_Maps_Na represents climatology of published Global Narea used for comparison (details see table 1 and figure 4).</li> <li>Global_Maps_Nmass represents climatology of published Global Nmass used for comparison (details see table 1 and figure 4).</li> <li>TS_SLA is simulated time-series of <em>SLA</em> based on optimality theories from 1992 to 2015</li> <li>TS_Na is simulated time-series of <em>Narea </em>based on optimality theories from 1992 to 2015</li> <li>TS_Nmass is simulated time-series of <em>Nmass </em>based on optimality theories<em> </em> from 1992 to 2015</li> <li>TS_LMA_decidudous is simulated time-series of deciduous <em>LMA</em> based on optimality theories from 1982 to 2016</li> <li>TS_LMA_evergreen is simulated time-series of evergreen <em>LMA</em> based on optimality theories from 1982 to 2016</li> <li>TS_Vcmax25 is simulated time-series of Vcmax25 based on optimality theories from 1982 to 2016</li> </ol>
A high-resolution global land daily drought index dataset during 1979–2022
<p>A global daily drought index dataset named as daily evapotranspiration deficit index (DEDI) is constructed using daily actual evapotranspiration and potential evapotranspiration data provided by European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The DEDI dataset has a spatial grid resolution of 0.25°×0.25° and covers global land areas for the period 1979 to 2022. The DEDI dataset can be a good index for assessing the dry and wet severity in terms of spatial patterns and temporal evolutions when compared to other available daily drought indices. Moreover, the DEDI dataset is also demonstrated to have advantages in detecting ecological or agricultural droughts. The DEDI dataset also appears reasonable and promising in facilitating drought monitoring and early warning from a daily perspective.</p><p>This dataset accompanies the following publication: Zhang, X., Duan, J., Cherubini, F. et al. A global daily evapotranspiration deficit index dataset for quantifying drought severity from 1979 to 2022. Sci Data 10, 824 (2023). https://doi.org/10.1038/s41597-023-02756-1</p>
Dataset - Mumme et al. 2023 Global Change Biology
<p>Dataset used in Mumme et al. (2023) Wherever I may roam – Human activity alters movements of red deer (<em>Cervus elaphus</em>) and elk (<em>Cervus canadensis</em>) across two continents. For full list of funding information, please see the acknowledgement section of the original article (<a href="https://doi.org/10.1111/gcb.16769">https://doi.org/10.1111/gcb.16769</a>).</p>
Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets - training datasets
<p>Sample datasets for the <strong>Case Studies</strong> section of the <em> Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets </em>web book (<a href="https://isprs-gis-sdg.readthedocs.io">https://isprs-gis-sdg.readthedocs.io</a>)</p>
Global datasets to evaluate a multi-sensor approach for observation of floods
<p><strong>1. Overview</strong></p> <p>This repository contains datasets used to evaluate potential improvements to flood detectability afforded by combining data collected by Landsat, Sentinel-2, and Sentinel-1 for the first time globally. The datasets were produced as part of the manuscript "A multi-sensor approach for increased measurements of floods and their societal impacts from space" which is currently in review.</p> <p><strong>2. Dataset Descriptions</strong></p> <p>There are two datasets included here.</p> <p><strong>(a) A global grid of revisit periods of Landsat, Sentinel-1, Sentinel-2 Satellites and their combination </strong>[GlobalMedianRevisits.zip]</p> <p>A global dataset of revisit periods of individual satellites and their combination based on a 0.5-degree resolution grid.<br> Revisit periods are defined as the time between two consecutive observations of a particular point on the surface, for the satellite missions Landsat, Sentinel-2 and Sentinel-1. The grid was created using ArcMap 10.8.1 and intersections of the grid were used to create points. For each individual point, average revisit times (i.e., to account for irregular revisits, downlink issues) were calculated for each individual satellite and the composite of the three satellites. Averaged revisit times for each of these points were calculated based on the number of image tiles that intersected a particular grid point with more than a 30-minute time difference between each other acquired between 01 Jan 2016 and 31 Dec 2020.<br> The following equation is used to calculate revisit periods:</p> <p>Average revisit time for a grid point = (Number of days between 01 Jan 2016 and 31 Dec 2020 (1827)) / (Total Number of Images captured)</p> <p>Only revisits occurring between 82.5 N and 55 S of land grid points are considered; Antarctica is omitted from analysis. For satellite missions that consist of two spacecraft orbiting simultaneously (Sentinel-1 A/B, and Sentinel-2 A/B), images acquired by both satellites were used in average revisit period calculation for a given grid point. Sum totals of image tiles of all three missions are used to calculate composite point-based revisit times.</p> <p><strong>(b) Average revisit periods of satellites for flood records in the DFO database </strong>[FloodInfo.zip]</p> <p>Average Revisit Times of Landsat, Sentinel-1, Sentinel-2 and their ensemble are calculated for 5130 flood records in the Dartmouth Flood Observatory's (DFO) flood record database. These were appended to the already existing attributes of the database.</p>
Supporting Dataset for "Reducing a tropical cyclone weak-intensity bias in a global numerical weather prediction system"
<p>This archive supports the submission of "Reducing a tropical cyclone weak intensity bias in a global numerical weather prediction system" to Monthly Weather Review. It contains model configurations, the software used to create ensemble perturbations, the software used to compute the diagnostics discussed in the text, and the software use to plot figures.</p> <p>After downloading, the contents can be extracted using:</p> <blockquote> <p>tar -xzf idealtc1_archive-1.tgz</p> </blockquote>
TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation
<p><strong>TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation</strong></p> <p> </p> <p><strong>Description:</strong></p> <p>The global monthly GOME-2A SIF dataset (2007–2021) with correction of temporal degradation. The corrected global GOME-2 SIF dataset can be obtained in two types. The daily level2 dataset is provided in hdf5 format(compressed in the zip files named "{Year}{Quater}.zip"). The name of the hdf5 files was SIF_daily_YYYYMMDD.h5, YYYY, MM, and DD represent the year, month, and date, respectively. The level3 datasets which were aggregated monthly from the level2 dataset, have a spatial resolution of 0.5°and were saved in TIFF format in chronological order from 2007 to 2021 (compressed in the file "Level3.zip"). The name of the files was SIFpar_evi_monthly _YYYYMM.tif, where SIF was product type, par, and evi represented upscaled parameters, monthly represented temporal scale, YYYY and MM was the year and month, respectively. The SIF output was stored in the hdf5 files along with other variables of interest for further processing and visualization. See the appendix for the structure of the hdf5 file.</p> <p> </p> <p><strong>cloud_fraction</strong><strong>[float]</strong>:</p> <p>Description: Effective cloud fraction derived from GOME-2 Level1B product.</p> <p>Units: none</p> <p><strong>latitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center latitude.</p> <p>Units: degrees N</p> <p><strong>longitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center longitude.</p> <p>Units: degrees E</p> <p><strong>latitude_bounds</strong><strong>[float]</strong>:</p> <p>Description: Latitude of the boundary corners for each pixel.</p> <p>Units: degrees N</p> <p><strong>longitude _bounds</strong><strong>[float]</strong>:</p> <p>Description: Longitude of the boundary corners.</p> <p>Units: degrees E</p> <p><strong>SIF_740</strong><strong>[float]</strong>:</p> <p>Description: SIF signal at 740nm retrieved using the 735–758 nm fitting window.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>SIF_daily</strong><strong> [float]</strong>:</p> <p>Description: SIF signal at 740nm with correction of day-length.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>Sigma_i</strong><strong>[float]</strong>:</p> <p>Description: The squre of single retrieval error of SIF_740.</p> <p>Units: (mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>)<sup>2</sup></p> <p><strong>Solar_zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar zenith angle.</p> <p>Units: degrees</p> <p><strong>Solar_azimuth_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar azimuth angle.</p> <p>Units: degrees</p> <p><strong>Viewing _zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Viewing zenith angle.</p> <p>Units: degrees</p> <p><strong>Viewing_azimuth_angle</strong><strong>[float]</strong>:</p> <p>Description: Viewing azimuth angle.</p> <p>Units: degrees</p> <p><strong>chi2</strong><strong>[float]</strong>:</p> <p>Description: The reduced chi-square value calculated based on the the fitting residuals.</p> <p>Units: None</p> <p><strong>Rad_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average radiance within the 735~758 nm window</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>ps_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within at around 680 nm ().</p> <p>Units: None</p> <p><strong>ps_red</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within the 665~680 nm window</p> <p>Units: None</p> <p><strong>NDVI</strong><strong>[float]</strong>:</p> <p>Description: Calculated by the TOA reflectance at red band (around 680 nm) and near-infrared band (around 780nm).</p> <p>Units: None</p> <p><strong>QA</strong><strong>[int]</strong>:</p> <p>Description: Quality_flag.</p> <p>0= Bad (ineffective original data)</p> <p>1= Good (passed all quality-filtering criteria)</p> <p>2= Good and the cloud fraction is lower than 0.3</p> <p>Units:None</p>
TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation
<p><strong>TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation</strong></p> <p> </p> <p><strong>Description:</strong></p> <p>The global monthly GOME-2A SIF dataset (2007–2021) with correction of temporal degradation. The corrected global GOME-2 SIF dataset can be obtained in two types. The daily level2 dataset is provided in hdf5 format(compressed in the zip files named "{Year}{Quater}.zip"). The name of the hdf5 files was SIF_daily_YYYYMMDD.h5, YYYY, MM, and DD represent the year, month, and date, respectively. The level3 datasets which were aggregated monthly from the level2 dataset, have a spatial resolution of 0.5°and were saved in TIFF format in chronological order from 2007 to 2021 (compressed in the file "Level3.zip"). The name of the files was SIFpar_evi_monthly _YYYYMM.tif, where SIF was product type, par, and evi represented upscaled parameters, monthly represented temporal scale, YYYY and MM was the year and month, respectively. The SIF output was stored in the hdf5 files along with other variables of interest for further processing and visualization. See the appendix for the structure of the hdf5 file.</p> <p> </p> <p><strong>cloud_fraction</strong><strong>[float]</strong>:</p> <p>Description: Effective cloud fraction derived from GOME-2 Level1B product.</p> <p>Units: none</p> <p><strong>latitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center latitude.</p> <p>Units: degrees N</p> <p><strong>longitude</strong><strong>[float]</strong>:</p> <p>Description: Pixel center longitude.</p> <p>Units: degrees E</p> <p><strong>latitude_bounds</strong><strong>[float]</strong>:</p> <p>Description: Latitude of the boundary corners for each pixel.</p> <p>Units: degrees N</p> <p><strong>longitude _bounds</strong><strong>[float]</strong>:</p> <p>Description: Longitude of the boundary corners.</p> <p>Units: degrees E</p> <p><strong>SIF_740</strong><strong>[float]</strong>:</p> <p>Description: SIF signal at 740nm retrieved using the 735–758 nm fitting window.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>SIF_daily</strong><strong> [float]</strong>:</p> <p>Description: SIF signal at 740nm with correction of day-length.</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>Sigma_i</strong><strong>[float]</strong>:</p> <p>Description: The squre of single retrieval error of SIF_740.</p> <p>Units: (mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>)<sup>2</sup></p> <p><strong>Solar_zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar zenith angle.</p> <p>Units: degrees</p> <p><strong>Solar_azimuth_angle</strong><strong> [float]</strong>:</p> <p>Description: Solar azimuth angle.</p> <p>Units: degrees</p> <p><strong>Viewing _zenith_angle</strong><strong> [float]</strong>:</p> <p>Description: Viewing zenith angle.</p> <p>Units: degrees</p> <p><strong>Viewing_azimuth_angle</strong><strong>[float]</strong>:</p> <p>Description: Viewing azimuth angle.</p> <p>Units: degrees</p> <p><strong>chi2</strong><strong>[float]</strong>:</p> <p>Description: The reduced chi-square value calculated based on the the fitting residuals.</p> <p>Units: None</p> <p><strong>Rad_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average radiance within the 735~758 nm window</p> <p>Units: mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup></p> <p><strong>ps_NIR</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within at around 780 nm.</p> <p>Units: None</p> <p><strong>ps_red</strong><strong>[float]</strong>:</p> <p>Description: The average reflectance within the 665~680 nm window</p> <p>Units: None</p> <p><strong>NDVI</strong><strong>[float]</strong>:</p> <p>Description: Calculated by the TOA reflectance at red band (around 680 nm) and near-infrared band (around 780nm).</p> <p>Units: None</p>
mapspamc_db: a database with global spatial datasets to support the implementation of the mapspamc R package.
<p>This repository contains the mapspamc database (mapspamc_db), a collection of global spatial datasets to support the implementation of the <a href="https://github.com/michielvandijk/mapspamc">mapspamc</a> R package. The database also includes subnational crop statistics and matching country shapefiles for several country examples. For more information on how to use the mapspamc package in combination with mapspamc_db, see the <a href="https://michielvandijk.github.io/mapspamc/">mapspamc documentation</a>. Detailed information on the contents of mapspam_db, such as the sources of information and pre-processing is described in the mapspamc_db documentation (pdf file) that is part of the repository.</p>
Subset of Global Sub-Daily Rainfall (GSDR) dataset
<p>A subset of the Global Sub-Daily Rainfall (GSDR) dataset, including the countries marked as 'open' in Table A2 of the accompanying paper (DIO: 10.1175/JCLI-D-18-0143.1).</p> <p>The dataset includes archive data, raw data, quality control flags and quality controlled data and raw data. Example quality control files (with headers), raw data processing scripts and INTENSE Python code are also included.</p>
Dataset for article Rodriguez et al "Assessing global urban CO2 removal"
<p>This is a complementary dataset associated with the following publication: Rodriguez et al. "Assessing global urban CO2 removal" <em>Nature Cities.</em></p>
Global Human Presence Intensity Dataset (2017)
<h1>Overview</h1> <ul> <li>Understanding global patterns of human presence is crucial for monitoring anthropogenic pressures on ecological integrity, optimizing tourism management, and informing decision-making in various domains. However, existing datasets on human presence are insufficient and primarily limited to local scales.</li> <li>We combined massive geotagged microblogs and multi-covariates to infer the global human presence in 2017 at a fine spatial resolution of 0.01 degrees. Specifically, we proposed a Human Presence Indicator (HPI) to quantify human presence. It categorizes the intensity of human presence at a location in four levels based on year-long statistics of geotagged data. HPI-0, HPI-1, HPI-2, and HPI-3 represent no human presence, occasional human presence, frequent human presence, and sustained human presence, respectively.</li> <li>The model achieved a macro-F1 score of 0.72 on a test set comprising over 1.9 million grids in China and 0.84 on the manually labeled samples available worldwide. Cross-validation with external datasets, including geotagged social media data from X and global human settlement and population data, corroborated the model's effectiveness.</li> </ul> <h1>Data Directory Contents</h1> <p>The complete dataset is organized into four main components, each available as a separate compressed <code>.zip</code> file for download:</p> <ul> <li> <p><strong>Gridded HPI Data:</strong> Contains the primary data product showing global human presence intensity (on a 0-3 scale) at a 0.01-degree resolution in GeoTIFF format.</p> </li> <li> <p><strong>Covariate Layers:</strong> Contains all 76 predictor variables used for model training, resampled to the same 0.01-degree resolution.</p> </li> <li> <p><strong>Validation Data:</strong> Contains the manually labeled samples that were used to assess model performance.</p> </li> <li> <p><strong>Model Files:</strong> Contains the final trained random forest model in <code>.joblib</code> format, ready for use by Python practitioners.</p> </li> </ul> <h1>Citation</h1> <p>Please cite both the dataset and the related article when using these data:</p> <ul> <li>Luo, P., Yi, J., Du, Y., Huang, S., Wang, N., Tu, W., Hu, D., & Wei, H. (2025). Global Human Presence Intensity Dataset (2017) (1.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16499251</li> <li> <p><span lang="EN-US">Luo, P., Yi, J., Du, Y., Huang, S., Wang, N., Tu, W., Hu, D., & Wei, H. (2025). Mapping global human presence for nature conservation using geotagged social media data. Biological Conservation, 311, 111404. https://doi.org/10.1016/j.biocon.2025.111404</span></p> </li> </ul>
StoichLife: A global dataset of plant and animal elemental content
Open the record for dataset details and reuse information.
A global dataset of soil particulate organic carbon
Open the record for dataset details and reuse information.
Global dataset of plant diversity and the spatial variability of grassland biomass from NutNet
While there is strong evidence of diversity effects on temporal variability of productivity, whether this mechanism extends to variability across space remains elusive. Here, we present data from Nutrien Network (www.nutnet.org) that were used to determine the relationship between diferent scales of plant diversity and spatial variability of productivity in 83 grasslands worldwide, and to quantify the effect of experimentally increased spatial heterogeneity in environmental conditions on this relationship. There are two data sets, one for the pre-treatment (observational_data.csv) data, and other for the experimentally increased heterogeneity (increased heterogeneity.csv). In these data sets, study sites contained at least three replicates that originated from blocks each composed of ten 5 m × 5 m plots. In addition, pre-treatment data has a subset of sites in where soil conditions where measured (observational_data_soil.csv) and a version in which data for each site are sumarized and site-level climatic variables obtained from WorldClim (www.worldclim.org) are added. (observational_data_site_climate.csv). If you need any clarification or further information, please contact us.
Dataset for 'Rotational dependence of turbulent transport coefficients in global convective dynamo simulations of solar-like stars'
<p>For moderate and slow rotation, magnetic activity of solar-like stars is observed to strongly depend on rotation, while for rapid rotation, only a very weak or no dependency is detected. These observations do not yet have a solid explanation in terms of dynamo theory. To work towards such an explanation, we numerically investigated the rotational dependency of dynamo drivers in solar-like stars, that is, stars that have a convective envelope of similar thickness as in the Sun. We ran semi-global convection simulations of stars with rotation rates from 0 to 30 times the solar value, corresponding to Coriolis numbers, Co, of 0 to 110. We measured the turbulent transport coefficients describing the magnetic field evolution with the help of the test-field method, and compared with the dynamo effect arising from the differential rotation, self-consistently generated in the models. The trace of the <strong><span class="math-tex">\(\alpha\)</span></strong> tensor increases for moderate rotation rates with Co<sup>0.5</sup> and levels off for rapid rotation. This behavior is in agreement with the kinetic <span class="math-tex">\(\alpha\)</span> based on the kinetic helicity, if one takes into account the decrease of the convective scale with increasing rotation. The <strong><span class="math-tex">\(\alpha\)</span></strong> tensor becomes highly anisotropic for Co > 1, <span class="math-tex">\(\alpha_{rr}\)</span> dominates for moderate rotation (1<Co<10), and <span class="math-tex">\(\alpha_{\phi\phi}\)</span> for rapid rotation (Co > 10). The effective meridional flow, taking into account the turbulent pumping effects, is markedly different from the actual meridional circulation profile. Hence, the turbulent pumping effect is dominating the meridional transport of the magnetic field. Taking all dynamo effects into account, we find three distinct regimes. For slow rotation, the <span class="math-tex">\(\alpha\)</span> and Rädler effects are dominating in presence of anti-solar differential rotation. For moderate rotation, <span class="math-tex">\(\alpha\)</span> and <span class="math-tex">\(\Omega\)</span> effects are dominant, indicative of <span class="math-tex">\(\alpha\Omega\)</span> or <span class="math-tex">\(\alpha^2\Omega\)</span> dynamos in operation, producing equatorward-migrating dynamo waves with the qualitatively solar-like rotation profile. For rapid rotation, an <span class="math-tex">\(\alpha^2\)</span> mechanism, with an influence from the Rädler effect, appears to be the most probable driver of the dynamo. Our study reveals the presence of a large variety of dynamo effects beyond the classical <span class="math-tex">\(\alpha\Omega\)</span> mechanism, which need to be investigated further to fully understand the dynamos of solar-like stars. The highly anisotropic <strong><span class="math-tex">\(\alpha\)</span></strong> tensor might be the primary reason for the change of axisymmetric to non-axisymmetric dynamo solutions in the moderate rotation regime.</p> <p>For the full article see <a href="https://arxiv.org/abs/1910.06776">https://arxiv.org/abs/1910.06776</a></p>
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (2/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020).</p> <p>data270rdc-my34.tar.xz: for Ls=270-300 (48 Sols)</p> <p>data300rdc-my34.tar.xz: for Ls=300-330 (51 Sols)</p> <p>data330rdc-my34.tar.xz: for Ls=330-360 (56 Sols)</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.