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.
99
datasets available to search
ShareScore release 0.9.0
Dataset results
99 results for “High spatial resolution”
SESMG scenario-files of the study "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution"
<p>This dataset contains model scenario-files belonging to the publication "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution".</p> <p>The individual scenarios can be executed and evaluated with the "Spreadsheet Energy System Model Generator" (<a href="https://github.com/chrklemm/SESMG">SESMG</a>) <a href="https://github.com/chrklemm/SESMG/tree/v0.4.0rc1">v0.4.0rc1</a></p> <p>The respective file names indicate to which model run mentioned in the main study the scenario-files belong. For model runs for which no sepparate scenario file exists, the scenario "reference.xlsx" with adjusted SESMG settings was used.</p> <p> </p>
ASM-SS: The First Quasi-Global High Spatial Resolution Coastal Storm Surge Dataset Reconstructed from Tide Gauge Records
<p>The ASM-SS dataset is a high spatial resolution (every 10 km per node along the coastline), long-term (over 80 years from 1940 to 2020), quasi-global (within 45°S-45°N), hourly data-driven storm surge dataset. Each NetCDF file includes five parameters: longitude, latitude, nodes, time, and surge level. Longitude and latitude are the location information of nodes in degree; the unit of time is accumulated hours since 1900-01-01 00:00:00; surge levels are given in meters. Users can use longitude, latitude, and time as keywords to select surge levels at nodes of interest within a target period. </p>
High spatial resolution thickness changes of Pyrenean glaciers from 2011 to 2020
<p>Pyrenean glaciers are the largest in southern Europe. Because their very survival is threatened by climate change in the coming decades, it is urgently necessary to monitor their evolution. This dataset presents high spatial resolution observation of Pyrenean glaciers during the period 2011–2020. In 2011, the glacier surface was retrieved with an airborne lidar and in 2020 data were obtained with three different UAVs (fixed-wing or quad copters). Comparing these observations, glacier surface changes were computed for 17 out of the 24 remaining glaciers. Using high-resolution optical satellite imagery and field observations, glacier outline changes between 2011 and 2020 were also obtained. On average the glacierized area has shrunk by 23.2% and thickness has decreased on average by 6.3 m.</p>
High-resolution spatial multi-omics datasets
<p>Supplementary raw data. The raw microscopy data are not uploaded owing to their large size (6.8 Tb), but are available upon reasonable request (Long Cai: lcai@caltech.edu, Yodai Takei: ytakei@caltech.edu).</p> <p>These supplementary data contain additional files for RNA seqFISH+, DNA seqFISH+, and sequential immunofluorescence from cell culture and adult mouse cerebellum experiments.</p> <p>DNA seqFISH+ datasets (provided as a tar.gz folder for each replicate): Super-resolved DNA spot locations by DNA seqFISH+ along with sequential immunofluorescence intensity at the rounded voxel location.</p> <p>RNA seqFISH datasets (provided as a zip folder): Super-resolved mRNA or intron spot locations.</p> <p>Sequential immunofluorescence table (provided as a csv file): Mean voxel intensity of each immunofluorescence marker per nucleus for the adult mouse cerebellum datasets.</p> <p>Note that voxel sizes are 103 nm for x and y, and 250 nm for z in our experimental setting.</p> <p>Please find the uploaded readme.txt file for more details.</p>
High spatial resolution satellite images for glacier outlines
<p>Two RapidEye satellite images acquired on Sep 13, 2013 and Sep 17, 2018. The two satellite images have a high spatial resolution of 5 m × 5 m and were used to derive the outlines of the Parlung No. 94 Glacier in years 2013 and 2018, respectively. The GaoFen-7 (GF-7) satellite image with a high spatial resolution of 3 m × 3 m acquired on Feb 26, 2021 was used to derive the outlines of the Dongkemadi Glacier in 2021</p>
GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets for China from 1901 to 2020 generated by machine learning
<p>The high spatial resolution and century-long Standardized Precipitation Evapotranspiration Index (SPEI) dataset with a spatial resolution of 0.0083 degrees (~1 km) was spatially downscaled from the global SPEI data with a 0.5 degrees spatial resolution (https://spei.csic.es/database.html) based on machine learning integrated with high spatial resolution climatic and topographic variables. The 1-km SPEI datasets are across the land areas of China from January 1901 to December 2020, including 1-month, 3-month, 6-month and 12-month SPEIs. The unit of the data is 0.01. The dataset was evaluated using the root zone soil moisture and the historical drought events, and the evaluation indicated that the high spatial resolution SPEI dataset is reliable.</p> <p>Data Information: </p> <p>GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets over China from 1901 to 2020 generated by machine learning</p> <p>Publication: </p> <p><span>He, Q., Wang, M., Liu, K., & Wang, B. (2025). High-resolution Standardized Precipitation Evapotranspiration Index (SPEI) reveals trends in drought and vegetation water availability in China. <em>Geography and Sustainability</em>, <em>6</em>(2), 100228. https://doi.org/10.1016/j.geosus.2024.08.007</span></p> <p></p> <p>----------------------------------------------------data description---------------------------------------------</p> <p>This is a gridded dataset for the Standardized Precipitation Evapotranspiration Index (SPEI) at a spatial resolution of 1 km over the main terrestrial lands of China for each month during 1901-2020, which is generated using the Gaussian process regression (GPR) based on the Global SPEI database (https://spei.csic.es/database.html) integrated with high spatial resolution climatic and topographic variables. Four timescales of SPEI were generated: 1-month (SPEI-1), 3-month (SPEI-3), 6-month (SPEI-6) and 12-month (SPEI-12). The details are as follows:</p> <p>Region: China</p> <p>Temporal Extent: January 1901 to December 2020</p> <p>Spatial resolution: 0.0083° (~1 km)</p> <p>Temporal resolution: month</p> <p>Timescales: 1-month, 3-month, 6-month and 12-month</p> <p>Data format: GeoTIFF</p> <p>Unit: unitless (0.01)</p> <p>Geographic coordinate system: WGS 1984</p> <p>---------------------------------------------------dataset filename---------------------------------------------</p> <p>The file name specifically shows the data information.</p> <p>For example,</p> <p>“SPEI_1_2020_1.tif” means “1-month SPEI of January 2020”.</p> <p>“SPEI_3_2020_1.tif” means “3-month SPEI of January 2020”.</p> <p>All the file names are formatted in “SPEI_timescale_year_month”</p> <p>timescale: 1, 3, 6 and 12 indicate 1-month, 3-month, 6-month and 12-month, respectively</p> <p>year: from 1901 to 2020</p> <p>month: from 1 to 12</p> <p>--------------------------------------------------storage information-------------------------------------------</p> <p>The high-resolution SPEI dataset is stored in TIFF format using WGS 1984 coordinate system. The data type is int16 with a scale factor of 0.01. The nodata value is -32768. The dataset requires multiplication by 0.01 during application to obtain the actual value ranges.</p> <p>The data were compressed into .rar format every 10 years for each timescale SPEI.</p>
High-spatial-resolution monthly precipitation dataset over China during 1901–2017
<p>The dataset with 0.5 arcminute (~1 km) was spatially downscaled from CRU TS v4.02 based on Delta downscaling method, including monthly precipitation from 1901.1 to 2017.12. The dataset covers the main land area of China. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China.</p> <p>Another data download site is Loess plateau Scientific Data Center (http://loess.geodata.cn/). This is a Chinese website. This website publishes the updated histrorical dataset and future downscaled monthly precipitation under multiple SSP Scenarios and GCMs, with 1 km spatial resolution.</p> <p>/*************/ The dataset is updated yearly. Now, the period of the dataset is from 1901.1 to 2020.12.</p> <p>/*************/ The future 1km dataset from 2021-2100 is published.</p> <p><br> The data provider recommended the below publication as the reference.<br> Peng Shouzhang, Ding Yongxia, Liu Wenzhao, Li Zhi. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 2019, 11, 1931–1946, https://doi.org/10.5194/essd-11-1931-2019.</p>
Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
Open the record for dataset details and reuse information.
Data from: Organic electro-scattering antenna: Wireless and multisite probing of electrical potentials with high spatial resolution
Open the record for dataset details and reuse information.
Datasets of high spatial resolution scans of the airborne ultrasound field at the front, back and left side of an ultrasonic welding machine
<p>This dataset contains measuring data which are the result of investigations of the airborne ultrasound field of an ultrasonic welding machine. These investigations have been conducted within the scope of the EMPIR project 15HLT03: “Ears II - Metrology for modern hearing assessment and protecting public health from emerging noise sources”. In the context of “Ears II”, they served gaining knowledge for occupational safety and health. Here, the aim was to investigate the structure of the airborne ultrasound field at ultrasound related workplaces. Therefore, a reference workstation was set up in the laboratory of the Physikalisch-Technische Bundesanstalt (PTB). The airborne ultrasound field of a typical industrial source of airborne ultrasound (an ultrasonic welding machine) was measured with a scanning microphone system with a high spatial resolution.</p> <p>The dataset contains data of 6 measurements:</p> <ul> <li>At the back of the ultrasonic welding machine at a vertical surface with dimensions 168 cm x 150 cm (width x height)</li> <li>At the left side of the ultrasonic welding machine at a vertical surface with dimensions 168 cm x 150 cm (width x height)</li> <li>At the front of the ultrasonic welding machine at a horizontal surface with dimensions 168 cm x 75 cm (width x depth)</li> <li>At the front of the ultrasonic welding machine at a vertical surface with dimensions 168 cm x 150 cm (width x height) with a distance of 24 cm to the sonotrode</li> <li>At the front of the ultrasonic welding machine at a vertical surface with dimensions 168 cm x 150 cm (width x height) with a distance of 99 cm to the sonotrode</li> <li>At the front of the ultrasonic welding machine in a volume with dimensions 58 cm x 33 cm x 50 cm (width x depth x height) </li> </ul> <p>The measuring data is the square of the absolute value of the discrete Fourier transform of the signal voltage re 1 V<sup>2</sup> represented in the frequency domain, converted by fast Fourier transform, in the frequency range 0 – 100 kHz with a frequency resolution of 15.625 Hz. The data can be converted to sound pressure levels (SPL) using the calibration values published as a supplementary dataset.</p>
Supplementary Information to: Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high–resolution visible channel: Evaluation of candidate approaches with MODIS observations
<p>This repository contains the Python programmes and datasets used for</p> <p>the research described in the following paper:<br> https://doi.org/10.5194/amt-2019-334</p> <p>It has been prepared as supplementary information to the final paper.<br> <br> Note that the actual Cloud Property Retrieval (CPP) which would be<br> required for full reproducability of the results cannot be made<br> available by the authors, that the code included here is based on Python2, and that<br> paths to the dataset have to be adapted in the code.<br> <br> The repository consists of three separate parts/directories:<br> <br> * method: Python routines for generating the cloud property retrieval<br> input based on the Meteosat and MODIS data<br> <br> * analysis: Python routines for analysing the different experiments<br> from the CPP outputs, producing the figures and calculating the<br> comparison statistics<br> <br> * datasets: the various input and output datasets used for the<br> analyses of the paper</p>
High spatial resolution mapping identifies habitat characteristics of the invasive vine Antigonon leptopus on St. Eustatius (Lesser Antilles)
<p>On the Caribbean island of St. Eustatius, Coralita (<i>Antigonon leptopus</i>)<i> </i>is an aggressive invasive vine posing major biodiversity conservation concerns. The generation of distribution maps can address these conservation concerns by helping to elucidate the drivers of invasion. We test the use of support vector machines to map the distribution of Coralita on St. Eustatius at high spatial resolution and use this map to identify potential landscape and geomorphological factors associated with Coralita presence. This latter step was performed by comparing the actual distribution of Coralita patches to a random distribution of patches. To train the support vector machine algorithm, we used three vegetation indices and seven texture metrics derived from a 2014 WorldView-2 image. The resulting map shows that Coralita covered 3.18% of the island in 2014, corresponding to an area of 64 ha. The mapped distribution was highly accurate, with 93.2% overall accuracy (Coralita class producer's accuracy: 76.4%, user's accuracy: 86.2%). Using this classification map, we found that Coralita is not randomly distributed across the landscape, occurring significantly closer to roads and drainage channels, in areas with higher accumulated moisture, and on flatter slopes. Coralita was found more often than expected in grasslands, disturbed forest and urban areas, but was relatively rare in natural forest. These results highlight the ability of high spatial resolution data from sensors such as WorldView-2 to produce accurate invasive species, providing valuable information for predicting current and future spread risks and for early detection and removal plans.</p>
Data from: High resolution spatial mapping of human footprint across Antarctica and its implications for the strategic conservation of avifauna
Human footprint models allow visualization of human spatial pressure across the globe. Up until now, Antarctica has been omitted from global footprint models, due possibly to the lack of a permanent human population and poor accessibility to necessary datasets. Yet Antarctic ecosystems face increasing cumulative impacts from the expanding tourism industry and national Antarctic operator activities, the management of which could be improved with footprint assessment tools. Moreover, Antarctic ecosystem dynamics could be modelled to incorporate human drivers. Here we present the first model of estimated human footprint across predominantly ice-free areas of Antarctica. To facilitate integration into global models, the Antarctic model was created using methodologies applied elsewhere with land use, density and accessibility features incorporated. Results showed that human pressure is clustered predominantly in the Antarctic Peninsula, southern Victoria Land and several areas of East Antarctica. To demonstrate the practical application of the footprint model, it was used to investigate the potential threat to Antarctica's avifauna by local human activities. Relative footprint values were recorded for all 204 of Antarctica's Important Bird Areas (IBAs) identified by BirdLife International and the Scientific Committee on Antarctic Research (SCAR). Results indicated that formal protection of avifauna under the Antarctic Treaty System has been unsystematic and is lacking for penguin and flying bird species in some of the IBAs most vulnerable to human activity and impact. More generally, it is hoped that use of this human footprint model may help Antarctic Treaty Consultative Meeting policy makers in their decision making concerning avifauna protection and other issues including cumulative impacts, environmental monitoring, non-native species and terrestrial area protection.
High-spatial-resolution (0.0083° × 0.0083°) and long-term (1982 to 2010) monthly gridded wetland CH4 flux product for the Southeastern United States
<p>This dataset presents monthly gridded methane emissions from subtropical freshwater wetlands across the Southeastern United States spanning from 1982 to 2010, measured in nmol m-2 s-1. Each grid cell's methane flux prediction was adjusted based on the fractional wetland extent within that cell, utilizing data from the National Wetland Inventory (NWI) and the Wetland Area and Dynamics for Methane Modeling (WAD2M) product. The dataset includes mean monthly fluxes with no adjustment for wetland area (i.e., fluxes assuming hypothetical 100% wetland cover), as well as mean monthly fluxes adjusted for wetland area based on NWI or WAD2M, along with their respective standard deviations of the daily emissions for each month. Data are provided in NetCDF4 format.</p> <p>Lat: 25-40°N</p> <p>Lon: 95-75°W</p> <p>Period: 198201-201012 (for "CH4_Monthly_SEUS_unweighted.nc" and "CH4_Monthly_SEUS_NWI.nc") and 200001-201012 (for "CH4_Monthly_SEUS_WAD2M.nc")</p> <p>Temporal resolution: Monthly</p> <p>Spatial resolution: 0.0083° × 0.0083° (~1 km × 1 km)</p> <p>Unit: nmol m-2 s-1</p> <p>Fill value: -9999</p>
Supplementary Material for "High spatial resolution photogrammetry and LiDAR in the Cádiz Bay (SW, Spain): optimizing the application of UAV-techniques to salt marshes" article.
<p>Data presented in the study "High spatial resolution photogrammetry and LiDAR in the Cádiz Bay (SW, Spain): optimizing the application of UAV-techniques to salt marshes" </p>
Supplementary material for "Evaluating the Performance of High Spatial Resolution UAV-photogrammetry and UAV-LiDAR for Salt Marshes: the Cádiz Bay Study Case" article
<p>Data presented in the study "Evaluating the Performance of High Spatial Resolution UAV-photogrammetry and UAV-LiDAR for Salt Marshes: the Cádiz Bay Study Case".</p>
A high spatial resolution dataset for anthropogenic atmospheric mercury emissions in China during 1998-2014
<p>This database contains gridded atmospheric mercury emissions in 30 provinces of China by sectors from 1998 to 2014 at the resolution of 1 km×1 km. We distribute atmospheric mercury emissions in four sectors, i.e., agriculture, industry, service industry, and residences, based on China's land use data, enterprise data, road data, and population data. Gridded estimates of the total Hg (THg) and the three species, i.e., gaseous elemental Hg (Hg<sub>0</sub>), gaseous oxidized mercury (Hg<sub>II</sub>), and particulate-bound mercury (Hg<sub>p</sub>), are given separately.</p>
IMD New High Spatial Resolution (0.25X0.25 degree) Long Period (1901-2022) Daily Gridded Rainfall Data Set Over India: Matlab script for merging into single netcdf/mat file with datetime stamping
Open the record for dataset details and reuse information.
HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution
<p>This dataset contains 365 hyperspectral albedo maps of Earth with a temporal resolution of 1 day. The hyperspectral albedo maps are built from a 10-year average of the MODIS Surface Reflectance dataset (MCD43D42-48, version 6.1). Using a Principal Component Analysis (PCA) regression algorithm we combine different hyperspectral laboratory and in-situ measurements datasets of various dry soils, vegetation surfaces and mixure of both to reconstruct the albedo maps in the entire wavelength range from 400 to 2500 nm. The hyperspectral albedo maps have a spatial resolution of 0.25° in latitude and longitude.</p> <p>Additional hyperspectral albedo maps with a coarser spatial and spectral resolutions are available in the "Data sets" supplemetary material of "HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution" (Roccetti et al., 2024, https://doi.org/10.5194/egusphere-2024-167) or upon request to the corresponding author.</p>
Data from: Oscillayers: a dataset for the study of climatic oscillations over Plio-Pleistocene time scales at high spatial-temporal resolution
Motivation: In order to understand how species evolutionarily responded to Plio-Pleistocene climate oscillations (e.g. in terms of speciation, extinction, migration and adaptation), it is first important to have a good understanding of those past climate changes per se. This, however, is currently limited due to the lack of global-scale climatic datasets with high temporal resolution spanning the Plio-Pleistocene. To fill this gap, I here present Oscillayers, a global-scale and region-specific bioclim dataset, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc-minutes) and temporal (10 kyr time periods) resolution. This data set builds upon interpolated anomalies (Δ layers) between bioclim layers of the present and the Last Glacial Maximum (LGM) that are scaled relative to the Plio-Pleistocene global mean temperature curve, derived from benthic stable oxygen isotope ratios, to generate bioclim variables for 539 time periods. Evaluation of the scaled, interpolated estimates of palaeo-climates generated for the Holocene, Last Interglacial and Pliocene showed good agreement with independent General Circulation Models (GCMs) for respective time periods in terms of pattern correlation and absolute differences. Oscillayers thus provides a new tool for studying spatial-temporal patterns of evolutionary and ecological processes at high temporal and spatial resolution. Main types of variable contained: 19 bioclim variables for time periods throughout the Plio-Pleistocene. Input data and R script to recreate all 19 bioclim variables. Spatial location and grain: Global at 2.5 arc-minutes (4.65 x 4.65 = 21.62 km2 at the equator). Time period and grain: The last 5.4 million years. The grain is 10 kyr (= 539 time periods). Level of measurement: Data are for terrestrial climates (excluding Antarctica) taking sea level changes into account. Software format: All data are available as ASCII (ESRI) grid files.
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.