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.
7,195
datasets available to search
ShareScore release 0.9.0
Dataset results
7,195 results for “SET”
Date set for the manuscript: The Role of Sediment Gas Storage in the Methane Dynamics of a Shallow Freshwater Reservoir
<p>The dataset supports the findings of the manuscript entitled: Linking Sediment Gas Storage to the Methane Dynamics in a Shallow Freshwater Reservoir. The manuscript presents results based on in-situ monitoring at the main pre-dam of the Wupper reservoir. The reservoir is located in Germany and the monitoring was conducted from March 2020 to November 2021. The main goal of the monitoring was to measure the spatial variability of methane fluxes and its dynamics in the reservoir. The methane budget of the reservoir was then analysed in combination with estimates of the amount of free gas stored in the sediment matrix, which were derived from acoustic observations. The findings are discussed in the manuscript.</p> <p>Therefore, the dataset provided as an xlsx file includes separate sheets for:</p> <ul> <li>Sediment measurements of potential methane production (PMP), loss on ignition (LOI), dissolved methane (CH<sub>4</sub>) in porewater, carbon and nitrogen content.</li> <li>Gas content in the sediment estimated from maximum acoustic backscatter for 2020 and 2021 for the reservoir spatial grid.</li> <li>Monthly time-series of methane fluxes (oxidation, diffusion, ebullition, and potential flux at the sediment water interface - PSWI) for each monitoring location and for the reservoir (average of all monitoring locations) with the inclusion of degassing at the dam overflow and advective methane transport (Net-export) by inflow and outflow.</li> <li>Time series of daily mean values of ebullition and environmental parameters monitored in the reservoir.</li> </ul> <p>Explanations and units are provided in column labels. NaN refers to missing data.</p> <p>In this version (Version 2), the calculation of the PSWI has been corrected. It is now calculated as the vertically integrated potential methane production over the top 30 cm layer of the sediment.</p>
ptychographic_diffraction_image_sets_20211026
<p>121 spiral scans with 963 images each</p> <p>using a medipix3 detector with 55 um pixelsize and at 1.55 m downstream of the samples</p> <p>step size is 50 nm per scan with 0.4 s exposure time</p> <p>beam position is included in positions.csv</p> <p> </p>
Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for 24 hours lead time
<p>Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for 24 hours lead time in the years 2015-2020. The data set is provided in .Rdata format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>). <br> <br> For more information about the data set see: <a href="https://github.com/jobstdavid/paper_gamvinereg">https://github.com/jobstdavid/paper_gamvinereg</a></p>
Data Set For Efficient Calculation of Dispersion Energy for Multireference Systems with Cholesky Decomposition. Application to Excited-state Interactions
<p>Data Set to Accompany:</p> <p>"Efficient Calculation of Dispersion Energy for Multireference Systems with Cholesky Decomposition. Application to Excited-state Interactions"</p>
CPT-1 whole-proteome feature matrices (no-EVE set)
<p><strong>Cross-protein transfer learning for variant effect prediction</strong></p> <p>This repository contains the feature matrices for CPT-1 to make variant effect prediction on 15,557 human proteins NOT in the EVE set (<a href="https://www.nature.com/articles/s41586-021-04043-8">Frazer et al., 2021</a>), initially released with the manuscript "Cross-protein transfer learning substantially improves zero-shot prediction of disease variant effects".</p> <p> </p> <p><strong>Citation</strong></p> <p>Jagota, M.*, Ye, C.*, Albors, C., Rastogi, R., Koehl, A., Ioannidis, N., and Song, Y.S.†<br> "Cross-protein transfer learning substantially improves zero-shot prediction of disease variant effects", bioRxiv (2022)</p> <p>*These authors contributed equally to this work.<br> †To whom correspondence should be addressed: <a href="mailto:yss@berkeley.edu">yss@berkeley.edu</a></p> <p>DOI: <a href="https://doi.org/10.1101/2022.11.15.516532">https://doi.org/10.1101/2022.11.15.516532</a></p>
Demontration Activities Data Set on the performances of the Photocatalytic pilot plant In Demosite 4 (Galeb, Omis, Croatia) Deliverable 5.6
<p>Raw data (TOC and emerging contaminants time profiles) concernig the validation experiment of the photocatalytic pilot plant for tertiary water treatment deployed in ProjectO demosite 4. The data are presented in D5.6 and pertain three months of demonstration activities</p>
Data set on firm characteristic and cash holding in Nigeria
<p>This data set is for research on firm characteristics and cash holding. The data comprise panel data for variables such as return on asset, asset tangibility, leverage, capital expenditure, dividend, growth, cash, firm size, firm age and foreign ownership, for 600 firm-year. </p>
WorldFloods extended data set
<p><strong>"Global Flood Extent Segmentation in Optical Satellite Images"</strong> article data.</p><p>This data set is the extended version of the <strong>WorldFloods </strong>dataset released by<a href="https://www.nature.com/articles/s41598-021-86650-z"> Mateo-Garcia et al. (2021)</a>. We filtered low-quality floodmaps, extended the period of coverage to include flood events up to 2023, and manually fixed the labels of several flood maps. The resulting dataset has 509 flood extent maps from 144 different flood events.</p><p>The flood extent masks were visually inspected, and manually corrected when necessary, in order to provide reliable data to train supervised ML algorithms for flood extent segmentation. Here we provide the <strong>floodmaps.zip </strong>with vectorized reference masks, containing polygons of flood water, permanent water, clouds, and area of interest for each flood map.</p><p>Additionally, the <strong>metadatas.zip </strong>contains all the necessary information to download corresponding Sentinel-2 images, as well as the location of each flood event and activation code (according to Copernicus EMS, UNOSAT, or GLOFMIR conventions).</p><p>Portalés-Julià, E., Mateo-García, G., Purcell, C., & Gómez-Chova, L. Global flood extent segmentation in optical satellite images. <i>Sci Rep</i> <strong>13</strong>, 20316 (2023). https://doi.org/10.1038/s41598-023-47595-7</p><p>This dataset is released under a Creative Commons non-commercial license (https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt) </p><p>The development of this dataset has been supported by the Spanish Ministry of Science and Innovation project PID2019-109026RB-I00 (MINECO-ERDF MCIN/AEI/10.13039/501100011033).</p>
Data set for manuscript 'Quantifying geomorphically effective floods using satellite observations of river mobility'
<p>Data underlying the plots / used in the modelling work for the paper 'Quantifying geomorphically effective floods using satellite observations of river mobility', submitted to <em>Geophysical Review Letters.</em></p>
assessment of aldehydes to PTR-MS m/z 69 in indoor air measurements - data set
<ul> <li>contact: Lisa Ernle (lisa.ernle@mpic.de), Nijing Wang (nijing.wang@mpic.de), Jonathan Williams (jonathan.williams@mpic.de)</li> <li>instruments: fast GC-MS SOFIA (MPIC), PTR-ToF-MS 8000 (Ionicon)</li> <li>merged dataset</li> <li>calibrated with VOC standard gas mix (Apel-Riemer Environmental Inc., Colorado, USA)</li> <li>units (filename): <ul> <li>normalized counts per second [ncps] (20210426_p_ncps.txt, bar_mean.txt, bar_std.txt)</li> <li>parts per billion [ppb] (all_sub_20210426_ppb.txt)</li> </ul> </li> <li>for information concerning updated versions, please see ReadMe.txt</li> </ul>
TEAMx-PC22 (TEAMx pre-campaign 2022) – DWD Doppler wind lidar data set (SLXR172)
<p>This dataset contains data measured by DWD with a Doppler Wind Lidar SLXR172 during the TEAMx pre-campaign 2022. More details about TEAMx can be found at <a href="http://www.teamx-programme.org/">http://www.teamx-programme.org</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Measurement location and time period </strong></p> <p>Measurements with the SLXR172 were collected at the site of Brannenburg (47.741547 N / 12.122187 E / 456 m MSL) between 15.June – 19 October 2022.</p> <p><strong>2. Measurement setup</strong></p> <p>During the measurement period, two different scanning modes were applied:</p> <p>15. June - 26. July 2022 and 13. August – 19. October 2022 (VAD_CSM).</p> <ul> <li><strong>VAD (velocity-azimuth display) scans in </strong><strong>continuous scanning mode</strong><strong> : </strong>These scans were conducted at an elevation angle of 35°. Azimuth angle interval of the CSM data sampling was about 1.1°. </li> </ul> <p>27.July – 12. August 2022 (VAD_RHI)</p> <ul> <li><strong>VAD scans in step-stare mode: </strong>Step-stare scans were conducted at an elevation angle of 35° and with azimuth steps of 15°.</li> <li><strong>RHI (</strong><strong>range-height indicator) scans into the Inn Valley</strong>; The RHI scans were performed for 10 azimuth angles from 151° to 160° and covered elevation angles from 3° to 51°.</li> </ul> <p> </p> <p><strong><em>3. Data processing, corrections and filter</em></strong></p> <p>For <strong><em>VAD scans in continuous scanning mode</em></strong> the processed wind fields are provided. The data have not been corrected. The data can be filtered using the parameters R<sup>2 </sup>(coefficient of determination), CN (condition number) and NVRAD (number of radial velocities) as described in Päschke (2015):</p> <p>R<sup>2</sup>> 0.95 and CN<10 and NVRAD>12 </p> <p>Please note that in the postprocessing of the VAD CSM scans, the R<sup>2</sup> filter criterion was set to R<sup>2</sup>>0 in order to include all data and therefore might also include scans where the assumptions of homogeneity are not fulfilled. The parameter qwind is therefore not meaningful due to this configuration and should not be used to filter the data. We recommend the use of the above criterion from Päschke.</p> <p>For scans from the <strong><em>VAD scans in step stare mode</em></strong> as well as the <strong><em>RHI scans</em></strong> the raw data files are provided. They have not been corrected nor filtered.</p> <p><strong>4. Data file structure</strong></p> <p>The data are provided in NetCDF format. File names contain date and time information in UTC. The following wildcard characters are used in the file examples below: yyyy - year; mm - month, dd - day; HH - hour, MM - minute, `SS` - second. Files are sorted in monthly folders.</p> <p>The data are provided in two zip-files.</p> <ul> <li>VAD_CSM contains the processed wind fields from 15. June - 26. July 2022 and from 13. August – 19. October 2022)</li> <li>VAD+RHI the raw data files for 27.July – 12. August 2022.</li> </ul> <p>Raw data files of the VAD CSM scans can be provided upon request.</p> <p><strong>5. Contact</strong></p> <p>Contact Katrin.sedlmeier(at)dwd.de.at for any questions regarding the data set.</p> <p><strong>6. References</strong></p> <p>Päschke, E., Leinweber, R., and Lehmann, V.: An assessment of the performance of a 1.5 μm Doppler lidar for operational vertical wind profiling based on a 1-year trial, Atmos. Meas. Tech., 8, 2251–2266, https://doi.org/10.5194/amt-8-2251-2015, 2015.</p>
Data set for the article "Synchronous replication initiation of multiple origins"
<p>This data set contains the data of the submitted article "Synchronous replication initiation of multiple origins". The data was generated using simulations in python that are linked below. Experiments indicate that E. coli initiates DNA replication at multiple origins synchronously in fast growth conditions. We study by mathematical modelling under what conditions replication is initiated synchronously.</p>
University Helpdesk Support Data Set
<p>This repository contains the data set presented in the publication "Identifying NFRs conflicts using quality ontologies" by Al Balushi et al. [1]. The data set has been extracted from the manuscript for easier reuse.</p> <p>[1] Al Balushi, T., Khod, O., Sampaio, P. R. F., Patel, M., Manchester, B. S. W., Corcho, O., & Loucopoulos, P. (2008). Identifying NFRs conflicts using quality ontologies. <em>SEKE 2008</em>, 929.</p>
Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for five different lead times
<p>Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for the lead times 24, 48, 72, 96 and 120 hours in the years 2015-2020. The data set is provided in .RData format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>). <br> <br> For more information about the data set see: <a href="https://github.com/jobstdavid/paper_tsEMOS">https://github.com/jobstdavid/paper_tsEMOS</a></p>
Data sets for "On new two-dimensional UHF radar observations of equatorial spread F at the Jicamarca Radio Observatory" by Rodrigues et al.
<p>AMISR-14 data set used in the study entitled "On new two-dimensional UHF radar observations of equatorial spread F at the Jicamarca Radio Observatory" by Rodrigues et al. and published by Earth, Planets and Space, doi: 10.1186/s40623-023-01876-7.</p>
Data Sets: Experimental Assessment of Laser Scarecrows for Reducing Avian Damage to Sweet Corn
<p>This archive contains 2 data sets and a word doc with metadata and code for statistical analyses used within the manuscript entitled</p> <p><strong>Experimental Assessment of Laser Scarecrows for Reducing Avian Damage to Sweet Corn</strong></p> <p>by</p> <p>Sean T. Manz, Kathryn E. Sieving, Rebecca N. Brown, Page E. Klug, Bryan M. Kluever</p> <p>in press at Pest Management Science as of September 2023.</p>
Plane waves versus correlation-consistent basis sets: A comparison of MP2 non-covalent interaction energies in the complete basis set limit
<p>Supporting data and analysis scripts for the work</p> <p><a href="https://doi.org/10.26434/chemrxiv-2023-203z9">Plane waves versus correlation-consistent basis sets: A comparison of MP2 non-covalent interaction energies in the complete basis set limit</a></p>
(EMPIR 19ENG06 HEFMAG) Data sets of measurements of magnetic loss and complex permeability on amorphous and nanocrystalline samples up to the MHz range
<p>We measured the magnetic losses and the complex permeability of amorphous and nanocrystalline ribbons from DC to 1 GHz by combined application of fluxmetric and transmission line methods. Two transverse field annealed Co-based amorphous alloys, ~13 µm and ~25 µm tick and two nanocrystalline Finemet type alloys, ~13 µm and ~20 µm tick, endowed with defined transverse magnetic anisotropy, were characterized. </p>
H2020 ENODISE Analytical Data Set configuration C ECL
<p>Preliminary wake-interaction noise and steady-loading noise estimates for a contrarotating propeller system, using analytical modeling.</p>
Test data set for CRIMAC-RAW-To-Svf-TSf
<p>Test data set for https://github.com/CRIMAC-WP4-Machine-learning/CRIMAC-Raw-To-Svf-TSf, accompanying code to the paper "Quantitative processing of broadband data as implemented in a scientific splitbeam echosounder" submitted to Ecology and Evolution (Wiley). This is the Simrad EK80 raw files collected by the Norwegian Institute of Marine Research and used in the paper, mainly through the subset json files in https://github.com/CRIMAC-WP4-Machine-learning/CRIMAC-Raw-To-Svf-TSf/tree/main/Data. To reproduce echogram figures the files Zenodo-hosted files IMR-D20210507-T074652-Svf.raw and IMR-D20211215-T143432-TSf.raw must be downloaded. This research is a part of the CRIMAC - Centre for research-based innovation in marine acoustic abundance estimation and backscatter classification funded by the Research Council of Norway (grant no. 309512).</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.