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.
5,155
datasets available to search
ShareScore release 0.9.0
Dataset results
5,155 results for “resolution”
Aladdin: High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D Cine MRI
<p>The uploaded files include high-resolution 3D images of the left atrium from 18 individuals—10 healthy volunteers and 8 patients with various cardiovascular diseases—along with their corresponding left atrium segmentation maps. Additionally, a deformation atlas based on the 10 healthy cases is also provided.</p> <p>For more information, visit: <a href="https://github.com/cgalaz01/aladdin_cmr_la" target="_new" rel="noopener">https://github.com/cgalaz01/aladdin_cmr_la</a></p>
Transwell-Based Microfluidic Platform for High-Resolution Imaging of Airway Tissues
<div> <div> <div> <div> <p>This dataset contains original data collected during our study on the development and characterization of a transwell-based microfluidic platform designed for high-resolution imaging of airway tissues. It includes quantitative measurements of various features of live airway epithelium tissues, such as Trans-Epithelial Electrical Resistance (TEER), Cilia Beating Frequency (CBF), LDH Release (a cytotoxicity assay), tissue thickness, and cell number. Additionally, the dataset provides results from computational simulations modeling the shear stress in the microchannel generated by perfusion.</p> </div> </div> </div> </div>
Collapse of a freestanding rock pillar at Matterhorn Hörnligrat: daily high-resolution images show kinematic precursor
<p>Animated time series of manually selected pictures highlights the visible displacement of a freestanding rock pillar at Matterhorn Hörnligrat two weeks prior the collapse. Data used are available under https://doi.pangaea.de/10.1594/PANGAEA.967586 (Weber et al., 2024).</p>
High-resolution chain transform fault bathymetry from the PI-LAB experiment
<p>Bathymetry data from PI-LAB, MGL1602 (<a href="https://doi.org/10.1029/2018JB015982">https://doi.org/10.1029/2018JB015982</a>).</p> <p>Format: Lat/Lon/Depth [m]</p> <p>For reference, please cite: Harmon, N., Rychert, C., Agius, M., Tharimena, S., Le Bas, T., Kendall, J. M., & Constable, S. (2018). Marine geophysical investigation of the Chain Fracture Zone in the equatorial Atlantic from the PI‐LAB experiment. <em>Journal of Geophysical Research: Solid Earth</em>, <em>123</em>(12), 11-016</p>
High-resolution figures of Buchner et al. 2024
<p>High-resolution figures of Buchner et al. 2024 "New data indicate larger decline of morphological diversity in split-footed lacewing larvae than previously estimated" in Insects (MDPI).</p>
Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS
<p>InSAR Line-of-Sight (LOS) velocities and their associated uncertainties in the southeastern Tibetan Plateau, along with the strain rate fields.</p> <p><br>Citations:</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS. Geophysical Research Letters.</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13731812</p>
DCP-MTL: Vectorization of Agricultural Cultivation Field Parcels via Boundary-Parcel Multi-Task Learning Network in Ultra-High-Resolution Remote Sensing Images
<p><span>This paper introduces the first UHR UAV dataset specifically for CFP, designed to evaluate the performance of the proposed model in identifying these parcels. </span><span>The dataset offers ultra-high spatial resolution, various field parcel types, and broad geographic coverage. </span><span>Figure 8 </span><span>shows </span><span>the spatial distribution of the study data. Jilin Province is the primary region for training and evaluating the model, while Hebei, Henan, Anhui, Zhejiang, and Hainan are auxiliary regions for testing the model's transferability. </span></p>
PAPILA: High resolution inventory of atmospheric emissions in Latin America.
<p><strong>Brief description</strong></p> <p>PAPILA dataset is a collection of annual emission inventories of reactive gases (CO, NMVOC, SO2, NH3, NOx), Greenhouse gases (CO2, CH4), and particles (PM25, PM10, BC, OC) from anthropogenic sources in South America, for the period 2014–2020. Here is presented PAPILA version 2.0, The first version of this inventory is available <a href="https://data.mendeley.com/datasets/btf2mz4fhf/3">here.</a></p> <p>PAPILA is the first AEI from anthropogenic sources covering the continental SA region, which combines local available information with a global database in a proper and rigorous way. For this purpose, global datasets were used as a basis, enriching it with locally developed inventories available in the literature until 2023 for Argentina, Chile, Colombia, Mexico and Ecuador.</p> <p>The Dataset consider emissions from 13 sectors which are organized and denominated inspired mostly on the nomenclature given by CAMS: thermal power plants (ENE); road transportation (TRO); non-road transportation (TNR); fugitive emissions (FEF); industries, including fuel consumption in manufacturing industries and construction industrial processes, (IND); solvents (SLV); refineries (REF); agricultural soils (AGS); agricultural livestock (AGL); domestic and international navigation (SHP); solid waste disposal (including solid waste, wastewater, and incineration) (SWD); open biomass burning (OBB); residential , commercial and other sectors (RCO)</p> <p>To consult the main methodological considerations , review the methodological document available for download</p> <p>This inventory should contribute to the design of policies that seek to mitigate climate change and improve air quality by providing policy makers, stakeholders and scientists with qualified scientific spatial explicit emission information</p> <p><strong>Metadata</strong></p> <p>The inventories are presented as netCDF4 files, one for each year and specie, gridded in WGS84 projection (lon-lat) with a spatial resolution of 0.1∘ × 0.1∘ covering the domain 32–120∘ W and 34∘ N–58∘ S.</p> <p>Each file contains 14 variables corresponding to the emissions in Tg yr−1 from the13 sectors estimated and the sum of all categories (SUM)</p>
PSTN-High resolution images
<p>PSTN-High resolution images</p>
Experimental data for the article "High Resolution Rovibrational Spectroscopy of the ν6 and ν3+v7 Bands of H2CCCH+"
<p> data measured with COLTRAP apparatus 6th December - 11th December 2023<br> column 1 (x-axis) is frequency in cm-1, accuracy ~0.001 cm-1, precision ~0.0002 cm-1<br> column 2 (y-axis) are ion counts on mass 39u, integer<br><br> The data given here are original data with 14 concatenated data files.<br> Some spurious data points have been commented out by using "#"<br><br></p>
Coherent structures of a hydrothermal buoyant plume in the near-field obtained through LES at ultra-high resolution.
<p>Three-dimensional coherent structures identified by iso-surfaces of lambda2 = -1 (a) and lambda2 = -10^(-2) (b) using the method by Jeong and Hussain (1995) for a buoyant forced plume (Gamma0 = 1.14, where Gamma is the flux balance parameter defined by B. Morton and Middleton, 1973). The colormap represents the absolute temperature anomaly. The time interval in the video corresponds directly to the simulation time of the plume.</p> <p>This is an output of a Large Eddy Simulation (LES) performed using the <a href="http://basilisk.fr/">Basilsk</a> code, an adaptive mesh refinement (AMR) code featuring a second-order accurate finite-volume solver for the Navier–Stokes equations. These equations are solved in their three-dimensional Boussinesq form for an incompressible fluid. The plume modeled here represents a typical hydrothermal vent, with source conditions set to 300 °C for temperature, 0.7 m/s for velocity, and a vent radius of 2.8 cm.</p> <div> <div> <div>Source :</div> <div>Jeong J, Hussain F. On the identification of a vortex. <em>Journal of Fluid Mechanics</em>. 1995;285:69-94. doi:10.1017/S0022112095000462 <div>MORTON, B. R. et MIDDLETON, Jason. Scale diagrams for forced plumes. <em>Journal of Fluid Mechanics</em>, 1973, vol. 58, no 1, p. 165-176.</div> </div> </div> </div>
Global soil water content (volumetric m³/m³) for 10kPa, 33kPa and 1500kPa suctions predicted at 4 depths (0, 30, 60, and 100 cm) at 1km resolution
<p>Volumetric soil water content (m³/m³) at 10 kPa, 33 kPa, and 1500 kPa suctions was predicted at four depths (0, 30, 60, and 100 cm) with a spatial resolution of 1 km. The maps of van Genuchten (vG) parameters from Gupta et al. (2022) were used to calculate the soil water content at these pressures, following Equation 1 from Gupta et al. (2022).</p> <p>References:</p> <ol> <li>Gupta, Surya, Papritz, Andreas, Lehmann, Peter, Hengl, Tomislav, Bonetti, Sara, & Or, Dani. (2022). Global maps of soil water characteristics parameters developed using the random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution [Data set]. https://doi.org/10.5281/zenodo.6343570</li> </ol>
High-resolution spectral characterization of YSES 1 system with VLT/CRIRES+
<p>This repository contains the extracted spectra of YSES 1 and its super-Jovian companions b and c observed with the high-resolution spectrograph VLT/CRIRES+ (R~100,000). The paper from <a href="https://doi.org/10.3847/1538-3881/ad7ea9">Zhang et al. (2024)</a> provides more details of the data analyses. </p>
Updated 30 m resolution global annual burned area map
<p>Updated 30 m resolution global annual burned area maps (GABAM) of 2014-2021 are released for free download.The annual burned area map is defined as <strong>spatial extent of fires that occurs within a whole year and not of fires that occurred in previous years.</strong>GABAM was generated via an automated pipeline based on Google Earth Engine (GEE), using all the available Landsat images on GEE platform. The product was projected in a Geographic (Lat/Long) projection at 0.00025° (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10°×10° tiles spanning the range 180°W–180°E and 80°N–60°S.<br>contacts: zhangzhaoming@aircas.ac.cn/zhangzm@radi.ac.cn</p>
DRET-DNA PAINT: using Dark dyes for fast super-resolution imaging
Open the record for dataset details and reuse information.
Data and code for: Cellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons
<p>Data and accompanying analysis code to generate main figures from "Cellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons" in PNAS.</p> <p> </p> <p> </p>
Raw images, video, and data file for the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials"
<p>This dataset includes the raw images and data file in the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials", specifically:</p> <ul> <li>Raw images for the optimized print with the PEGDA-glycerol-water resin (Figure 2 & Figure S2)</li> <li>Raw images for the optimized print with the PEGDA-glycerol-LB resin (Figure 2)</li> <li>Raw images for the optimized print with the BSA-PEGDA-water resin (Figure 3)</li> <li>Raw images and video for the spontaneous capillary flow of LB media in a PEGDA-glycerol-LB microfluidic chip (Figure 4)</li> <li>Raw data for the UV-vis spectrum of LB media (Figure S4)</li> </ul>
Groundwater Potential Zones of Khyber Pakhtunkhwa 2024 at 10 m Spatial Resolution
<p>This study presents the <strong>Groundwater Potential Zones (GWPZ)</strong> map for the Khyber Pakhtunkhwa province of Pakistan, developed at a high spatial resolution of <strong>10 meters</strong>. The mapping was performed using a <strong>Multi-Criteria Decision Analysis (MCDA)</strong> approach, specifically the <strong>Analytic Hierarchy Process (AHP)</strong>, integrating seven key spatial layers to identify potential groundwater zones. These layers include <strong>Rainfall, </strong><strong>Landcover, </strong><strong>Slope, </strong><strong>Drainage Density</strong>, <strong>Geology</strong>, <strong>Soil Texture</strong>, and <strong>Lineament Density.</strong></p> <p>The GWPZ map was validated with field data collected from <strong>300 random sample points</strong> measuring the <strong>depth to the water table</strong>. An <strong>Area Under the Curve (AUC)</strong> analysis of the validation results showed an accuracy of <strong>70%</strong>, indicating a reliable identification of potential groundwater zones. This GWPZ map provides valuable insights for water resource management, enabling targeted exploration and sustainable utilization of groundwater resources in the study area.</p>
High-resolution (250x250m) gridded daily mean wind speed dataset for Austria spanning from 1961 to 2023
<p>Overview:</p> <p><strong>Resolution</strong>: 250x250 m<br><strong>Projection</strong>: EPSG 31287, Austria Lambert<br><strong>Extent</strong>: Austria <br><strong>Period </strong>: 1961-2023<br><strong>Format:</strong> NetCDF</p> <p>Application:</p> <p>High-resolution gridded climate data derived from in-situ observations play a crucial role in global and regional climatology. The data are a valuable input for further climate impact studies, particularly in ecological and energy modelling, and can be subsequently used for wind power potential analysis. Moreover, policymakers can make informed decisions based on accurate climate information derived from this dataset, enhancing the effectiveness of climate-related policies and interventions. Additionally, the data can be used for model evaluation and bias adjustment.</p> <p>Methods:</p> <p>1. Data homogenization</p> <p>Breaks in the station data time series were detected and corrected using the Standard Normal Homogeneity Test (SNHT), which identifies where the mean changes the most. If this change exceeds a certain threshold, the time series is adjusted by aligning the statistical distribution of values before the change to those after the change, assuming the most recent time series is correct. This adjustment is achieved using the Quantile Mapping (QM) approach.</p> <p>2. Spatial interpolation</p> <p>All methods were tested in a nested 10-fold cross-validation (CV) scheme. This means that 10 % of the stations are left out (outer loop), and the other 90 % are used for training the model (inner loop). The outer loop is solely used for validating the model, whereas the inner loop serves for model optimization.</p> <p>A two-stage approach was applied. Initially, a background field - the climatology for each month - was calculated using Random Forest Regression (RFR) with a defined set of predictors. Subsequently, model residuals were spatially interpolated using the 3D Inverse Distance Weighting (3D IDW) method. Differences between daily values and corresponding monthly climatologies were also interpolated using 3D IDW. The final daily mean wind speed field is calculated by adding the monthly climatology fields to the interpolated daily residuals.</p>
Data files for "Flash development at sub-pixel resolution with the Geostationary Lightning Mapper"
<p>This depository includes the data files used to produce results in the paper "Resolving sub-pixel flash development with the Geostationary Lightning Mapper."</p> <p>Each lightning mapping array (LMA) file in OKLMA.tar is a text file. Each Earth Networks Total Lightning Network (ENTLN) file in ENTLN.tar is a csv file. Each Geostationary Lightning Mapper (GLM) file in GOES16_GLM.tar is a netcdf file.</p> <p>Oklahoma lightning mapping array data is available from the NOAA National Severe Storms Laboratory website: https://data.nssl.noaa.gov/thredds/catalog/WRDD/OKLMA/catalog.html.</p> <p>Geostationary Operational Environmental Satellite (GOES) data can be accessed through several links found at https://goes-r.nsstc.nasa.gov/home/.</p> <p>We thank Dr. Jeff Lapierre and Earth Networks for free use of ENTLN data for scientific purposes.</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.