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.
21,983
datasets available to search
ShareScore release 0.9.0
Dataset results
21,983 results for “UNITE”
Dataset of Reconfigurable Metamaterial Processing Units that Solve Arbitrary Linear Calculus Equations
<p>Dataset of <em>Reconfigurable Metamaterial Processing Units that Solve Arbitrary Linear Calculus Equations</em></p>
Techno-economics of Geothermal Power in the Contiguous United States Under Baseload and Flexible Operations (Dataset)
<p>This dataset was created as part of a project that is undergoing journal peer-review. Here is the abstract:</p> <p> </p> <p>As geothermal technology continues to gain momentum, it becomes increasingly essential for stakeholders to assess the economic feasibility of geothermal power projects when integrated into electricity markets. This study aimed to evaluate the techno-economic viability of identified and undiscovered hydrothermal systems across the contiguous United States. Multiple data and methods were developed and integrated to model the lifecycle techno-economics of geothermal projects at hourly resolution. Additionally, flexible operations through wellhead throttling were evaluated to estimate the potential improvement compared to business-as-usual power plant operations. A total of 3,632 and 17,789 MWe of identified and undiscovered hydrothermal resources, respectively, were found to be economically competitive. Multiple sites were profitable under a typical 70 USD/MWh power purchase agreement, with return on investment close to 400%. However, operating in the open power market yielded non-profitable projects across the majority of sites. Opting for flexible dispatch resulted in an improved economic performance. Particularly, the most profitable site had nearly 20\% relative improvement in return on investment upon switching from baseload to flexible dispatch.</p>
FIGURE 9 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 9. Left elytron of female Medusapyga alsea, showing fixed setae. Scale bar 100 µm.
Multi-Source Precipitation Data Fusion Across Continental United States
<p><strong><span>Dataset Description</span></strong><span>: This dataset supports our research presented in the paper "<em>A deep learning-based framework for multi-source precipitation fusion</em>" by Gavahi et al., (2023) published in <em>Remote Sensing of Environment</em>. The study introduces a novel deep learning architecture for merging and downscaling multiple precipitation products, aiming to enhance quantitative precipitation estimation (QPE) accuracy. The developed model, the Precipitation Data Fusion Network (PDFN), integrates 3D-CNN and ConvLSTM layers to capture the inherent spatiotemporal dependencies of precipitation data. The results indicate significant improvements in error statistics.</span></p> <p><span>The dataset includes merged daily precipitation estimations using the PDFN model. The data cover the Continental United States (CONUS) and are provided at a spatial resolution of 0.05 degrees. Temporal coverage spans from January 1, 2015, to April 30, 2024. The coordinate reference system used is WGS1984.</span></p> <p><strong><span>Note</span></strong><span>: Since the PERSIANN-CDR dataset is only available until the end of 2023, in this dataset, we used PDIR-Now instead to ensure the dataset's continuity and completeness. In the original paper, we used PERSIANN-CDR, but here we used PDIR-Now to extend the dataset to cover the period until April 30, 2024.</span></p> <p><strong><span>Usage Notes</span></strong><span>: This dataset is intended for use in applications such as land surface modeling, flood forecasting, drought monitoring and prediction. Users are requested to cite the associated paper when utilizing the dataset for academic or research purposes. </span></p> <p><strong><span>Related Publications</span></strong><span>: For further details on the methodology and applications of this dataset, refer to the paper "Gavahi, K., E. Foroumandi, and H. Moradkhani (2023), A deep learning-based framework for multi-source precipitation fusion, Remote Sensing of Environment, doi:10.1016/j.rse.2023.113723"</span></p>
Fig. 5 in New distributional records of the genus Tetragonoderus Dejean, 1829 from the United States of America, including an updated key to species (Coleoptera: Carabidae)
Fig. 5 – Tetragonoderus pallidus Horn, 1869: a, map of distribution; b, habitus.
Fig. 4 in New distributional records of the genus Tetragonoderus Dejean, 1829 from the United States of America, including an updated key to species (Coleoptera: Carabidae)
Fig. 4 – Tetragonoderus latipennis LeConte, 1874: a, map of distribution; b, habitus.
Fig. 1 in New distributional records of the genus Tetragonoderus Dejean, 1829 from the United States of America, including an updated key to species (Coleoptera: Carabidae)
Fig. 1 – Tetragonoderus fasciatus (Haldeman, 1843): a, map of distribution; b, habitus.
Fig. 3 in New distributional records of the genus Tetragonoderus Dejean, 1829 from the United States of America, including an updated key to species (Coleoptera: Carabidae)
Fig. 3 – Tetragonoderus laevigatus Chaudoir, 1876: a, map of distribution; b, habitus.
Fig. 2 in New distributional records of the genus Tetragonoderus Dejean, 1829 from the United States of America, including an updated key to species (Coleoptera: Carabidae)
Fig. 2 – Tetragonoderus intersectus (Germar, 1824): a, map of distribution; b, habitus.
Fig. 1 in Evaluating categories of resistance in soybean genotypes from the United States and Brazil to Aphis glycines (Hemiptera: Aphididae)
Fig. 1. Damage level scale (1 to 5); feeding damage caused by Aphis glycines on soybean leaves.
Material stock map of the United Kingdom and the Republic of Ireland
<p>Understanding the size and spatial distribution of material stocks is crucial for sustainable resource management and climate change mitigation. This study presents high-resolution maps of buildings and mobility infrastructure stocks for the United Kingdom (UK) and the Republic of Ireland (IRL) at 10 m, combining satellite-based Earth observations, OpenStreetMaps, and material intensities research. Stocks in the UK and IRL amount to 19.8 Gigatons or 279 tons/cap, predominantly aggregate, concrete and bricks, as well as various metals and timber. Building stocks per capita are surprisingly similar across medium to high population density, with only the lowest population densities having substantially larger per capita stocks. Infrastructure stocks per capita decrease with higher population density. Interestingly, for a given building stock within an area, infrastructure stocks are substantially larger in IRL than in the UK. These maps can provide useful insights for sustainable urban planning and advancing a circular economy.</p> <p>This dataset features a detailed map of material stocks in the United Kingdom and the Republic of Ireland on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.</p> <p><strong>Spatial extent</strong><br>This dataset covers the whole British Isles. Due to processing reasons, the dataset is internally structured into the Island of Ireland, and the Island of Great Britain.</p> <p><strong>Temporal extent</strong><br>The map is representative for ca. 2018.</p> <p><strong>Data format</strong><br>The data are organized by nations. Within each nation, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p> <p>Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).</p> <p>Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.</p> <ul> <li>t at 10m x 10m</li> <li>kt at 100m x 100m</li> <li>Mt at 1km x 1km</li> <li>Gt at 10km x 10km</li> </ul> <p>For each nation, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. </p> <p>Additionally, the grand total mass per nation is tabulated for each island in <em>mass_grand_total_t_10m2.tif.csv</em>. County code and the ID in this table can be related via zones_name_pop<em>.csv</em>.</p> <p><strong>Material layers</strong><br>Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). </p> <p><strong>Further information</strong><br>For further information, please see the publication.<br>Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Publication</strong></p> <p>D. Wiedenhofer, F. Schug, H. Gauch, M. Lanau, M. Drewniok, A. Baumgart, D. Virág, H. Watt, A. Cabrera Serrenho, D. Densley Tingley, H. Haberl, D. Frantz (2024): Mapping material stocks of buildings and mobility infrastructure in the United Kingdom and the Republic of Ireland. <em>Resources, Conservation and Recycling</em> 206, 107630. <a href="https://doi.org/10.1016/j.resconrec.2024.107630">https://doi.org/10.1016/j.resconrec.2024.107630</a></p> <p><strong>Funding</strong><br>This research was primarly funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p> <p><strong>Acknowledgments</strong><br>We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC, and Wolfgang Wagner for granting access to preprocessed Sentinel-1 data.</p>
A comparison of the present growth pace of the United States infrastructure with the needs of the next decade (2025-2050)
<p><span>The US population relies on crumbling infrastructural facilities needing continual upkeep. As economic activity accelerates with purchasing power projected at more than $34.102 trillion (about $100,000 per person) compared to $16.7 trillion (about $51,000 per person) today, with the US population experiencing rapid growth with a projected population of over 438 million by 2050 compared to 350 million today, infrastructural development will be significantly affected. This study considers how human interactions and recent developments (the Covid-19 outbreak and the development of artificial intelligence) will bring about change in the future demand for infrastructure in the US by 2045.</span></p>
The Impact of Structural Modification on Electrochromic and Electroluminescent Properties of D-A-D Benzothiadiazole Derivatives with a Fluorene Linker and (Bi)Thiophene Units
Open the record for dataset details and reuse information.
Binary data file used for analysis_common_envelope unit testing in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** this file is automatically downloaded as part of the Phantom github actions tests **</p> <p>This is an example snapshot from a Phantom simulation of a common envelope interaction, taken from the paper by <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.3181G">González-Bolívar et al. (2022)</a>. It is posted here primarily in order to perform unit and regression testing on the <a href="https://github.com/danieljprice/phantom/blob/master/src/utils/analysis_common_envelope.f90">analysis_common_envelope</a> module in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code (<a href="http://adsabs.harvard.edu/abs/2018PASA...35...31P">Price et al. 2018</a>).</p>
Towards improving short-term predictions of fine particulate matter over the United States via assimilation of satellite aerosol optical depth retrievals
<p>This dataset contains paired modeled and observed values of different trace gases and aerosol species over the CONUS for the period of 15 July to 14 August 2014 for the three different data assimilation experiments (BKG, MET_BE and MET+EMIS_BE) described in the paper. </p>
Effect of meteorological variability on fine particulate matter simulations over the contiguous United States
<p>Supporting data files for the paper titled "Effect of meteorological variability on fine particulate matter simulations over the contiguous United States" submitted to JGR-Atmosphere for publication.</p>
Diagnostic Evaluation of Large-domain Hydrologic Models calibrated across the Contiguous United States
<p>Data repository for: Rakovec, O., Mizukami, N., Kumar, R., Newman, A., Thober, S., Wood, A. W., et al. ( 2019). Diagnostic evaluation of large‐domain hydrologic models calibrated across the contiguous United States. <em>Journal of Geophysical Research: Atmospheres</em>, 2019; 124: 13991–14007. <a href="https://doi.org/10.1029/2019JD030767">https://doi.org/10.1029/2019JD030767</a></p> <p>If you use this dataset in scientific publication, the aforementioned publication needs to be acknowledged.</p> <p><strong>rakovec_JGRA_2019.tar.gz </strong>refers to the mHM model simulations</p> <p><strong>Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</strong> refers to a dataset published earlier in Mizukami et al. (2017, doi: 10.1002/2017WR020401)</p> <p>#########################################################################</p> <p>## BASIN-WISE DISCHARGE SIMULATIONS:</p> <p>#########################################################################</p> <p><strong>(1) VIC model: CONUS-wide runs based on the Mizukami et al. 2017 WRR paper</strong></p> <p>stored under: Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</p> <p>meaning, 1 parameter set applied across all basins.</p> <ul> <li>Calibration period: ./$BASIN_ID/output/hcdn_calib_case04_rgn0.txt</li> <li>Validation period: ./$BASIN_ID/output/hcdn_vali_case04_rgn0.txt</li> </ul> <p>Note the time stamp is missing in the VIC files, and should be following for</p> <ul> <li>calibration period: init_date="1999-10-01"</li> <li>validation periods: init_date="1989-10-01"</li> </ul> <p>Finally, headers are missing for the VIC files, should be:</p> <ul> <li>qsim is first column</li> <li>qobs is second column</li> </ul> <p><strong>(2) VIC model: onsite calibrations </strong></p> <p>stored under: Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</p> <p>meaning, each basin has different parameter set </p> <ul> <li>Calibration period: ./$BASIN_ID/output/hcdn_calib_case04.txt</li> <li>Validation period: ./$BASIN_ID/output/hcdn_vali_case04.txt</li> </ul> <p><strong>(3) mHM model: CONUS-wide runs based on Rakovec et al. 2019 JGR-A</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>meaning, 1 parameter set applied across all basins.</p> <ul> <li>Calibration period: ./mHM_basins/$BASIN_ID/calib_001/output_calibMB_eval/daily_discharge.out</li> <li>Validation period: ./mHM_basins/$BASIN_ID/calib_001/output_validMB_eval/daily_discharge.out</li> </ul> <p><strong>(4) mHM model: onsite calibrations </strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>meaning, each basin has different parameter set </p> <ul> <li>Calibration period: ./mHM_basins/$BASIN_ID/calib_001/output/daily_discharge.out</li> <li>Validation period: ./mHM_basins/$BASIN_ID/calib_001/output_valid/daily_discharge.out</li> </ul> <p><strong>(5) mHM model: default parameter set from EU/Germany</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>the prior parameter set taken from the develop git branch of mhm</p> <p>Note that the calibration and validation periods are in one file:</p> <ul> <li>./mHM_basins/$BASIN_ID/def_000/output/daily_discharge.out </li> </ul> <p>#########################################################################</p> <p>## CONUS-WISE FLUXES,STATES,PARAMETERS</p> <p>#########################################################################</p> <p><strong>(1) mHM model: CONUS-wide runs based on Rakovec et al. 2019 JGR-A</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <ul> <li>Fluxes/states: mHM_entire_domain/calib_001/output/mHM_Fluxes_States.nc (monthly time step: 1950-2010)</li> <li>Model parameters (from the restart file): mHM_entire_domain/calib_001/output/mHM_restart_001.nc</li> </ul> <p> </p>
Data for Patterns of Settlement Following Forced Migration: The Case of Bosnians in the United States
<p>This repository contains the raw data used in Patterns of Settlement Following Forced Migration: The Case of Bosnians in the United States. These data were downloaded in February 2018 from the U.S. Census Bureau’s American Community Survey website at https://www.census.gov/programs-surveys/acs. The code used to analyze these data and intermediate datasets derived from them is available in GitHub at https://github.com/JohnPalmer/bosnian_settlement.</p>
Text-fig. 2 Cluster diagram of the considered acritarch assemblages. Paired group, Jaccard measure. in Overview Of The Stratigraphy And Initial Quantitative Biogeographical Results From The Devonian Of The Albergaria-A-Velha Unit (Ossa-Morena Zone, W Portugal)
Text-fig. 2 Cluster diagram of the considered acritarch assemblages. Paired group, Jaccard measure.
Fig. 3 in Potential pest bark and ambrosia beetles from Cuba not present in the continental United States
Fig. 3. Dorsal and lateral view of female Ips calligraphus interstitialis. Scale bar: 1 mm.
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.