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.
641
datasets available to search
ShareScore release 0.9.0
Dataset results
641 results for “data validation”
Data Sets for Evaluation of the Psychometric Properties and Validity of the German Version of the Process Model of Emotion Regulation Scale (PMERQ)
<p>Data files relate to an investigation of the psychometric properties of the German Version of the Process Model of Emotion Regulation Scale (PMERQ). Data set 1 (pmerq_1) contains information regarding the age, gender, ethnicity, and educational status of participants. In addition, responses to the 45 items of the initial translation of the 10-scale PMERQ are included. Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the revised translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the 16-item German Interpersonal Emotion Regulation Questionnaire (IERQ), the 10-item German Emotion Regulation Questionnaire (ERQ), , the German version of the 10-item Big Five Inventory-10 (BFI-10), the 4-item German version of the Patient Health Questionnaire-4 (PHQ-4), the German version of the Satisfaction with Life Scale (SWLS), and the 17-item German Social Desirability Scale-17 (SES-17). Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the readability-improved translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the German version of the Satisfaction with Life Scale (SWLS) and the German version of the 9-items UCLA Loneliness Scale (UCLA).</p>
In situ data for GLEAM4 validation
<p>In situ validation data from eddy covariance stations used in the validation of GLEAM4, as described in "GLEAM4: global land evaporation dataset at 0.1° resolution from 1980 to near present" by Miralles et al. (2025) (preprint doi: <a href="https://doi.org/10.21203/rs.3.rs-5488631/v1">https://doi.org/10.21203/rs.3.rs-5488631/v1</a>). This data is used in <a href="https://github.com/h-cel/GLEAM4">https://github.com/h-cel/GLEAM4</a> repository. </p> <p>Data sources for the in situ data</p> <ul> <li>FLXUNET LA Thuile, FLUXNET2015 and FLUXNET-CH4: <a href="https://fluxnet.org/data/">https://fluxnet.org/data/</a></li> <li>AmerifFlux: <a href="https://ameriflux.lbl.gov/data/aboutdata/">https://ameriflux.lbl.gov/data/aboutdata/</a></li> <li>ICOS: <a href="https://www.icos-cp.eu/data-products">https://www.icos-cp.eu/data-products </a></li> <li>EFDC: <a href="https://www.europe-fluxdata.eu/home/data/data-policy">https://www.europe-fluxdata.eu/home/data/data-policy </a></li> </ul> <p>Data sources for the model data:</p> <ul> <li>GLEAM4 (v4.2a with data assimilation) and GLEAM v3.8 data: <a href="https://www.gleam.eu/">https://www.gleam.eu/</a> </li> <li>ERA5-Land data from the Climate Data Store: <a href="https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=download">https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=download</a></li> <li>FLUXCOM data: <a href="https://www.fluxcom.org/EF-Download/" target="_blank" rel="noopener">https://www.fluxcom.org/EF-Download/</a></li> </ul>
Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities: validation cohort meta data and parsed TCR repertoire data
<p>Meta data corresponding the the validation cohort for the paper, "Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities" by Magdalena L Russell, Aisha Souquette, David M Levine, Stefan A Schattgen, E Kaitlynn Allen, Guillermina Kuan, Noah Simon, Angel Balmaseda, Aubree Gordon, Paul G Thomas, Frederick A Matsen IV, and Philip Bradley. These meta data include: </p> <p>(1) SNP genotypes for the two SNPs which overlap with the discovery cohort<br> - (nicaragua_snp_genotypes_ints.tsv) -- SNP genotypes as integers<br> - (nicaragua_snp_genotypes_strings.tsv) -- SNP genotypes as allele strings <br> (2) the ancestry PCs for each individual in the validation cohort (nicaragua_snp_ancestry_PCA.tsv)<br> (3) a file including IMGT genes and sequences used for parsing TCRB repertoire data (human_vj_allele_cdr3_nucseqs.tsv)<br> (4) a file including IMGT genes used for parsing TCRA repertoire data (human_vj_alleles_alpha.tsv)<br> (5) Parsed TCRA repertoire data (nicaragua_parsed_TCRA.tgz)<br> (6) Parsed TCRB repertoire data (nicaragua_parsed_TCRB.tgz) </p> <p><strong>Corresponding raw validation cohort TCR repertoire data is available here:</strong> https://www. ncbi.nlm.nih.gov/bioproject/PRJNA762269 (The BioProject database, accession number: PRJNA762269)</p> <p><strong>Software tools designed to work with these data are available here:</strong> https://github.com/phbradley/tcr-gwas</p>
Data and Code for: Validating ATLAS: a regional-scale high-throughput tracking system
<p>Data and code for the manuscript: <strong>Validating ATLAS: a regional-scale high-throughput tracking system</strong></p>
Raw Data for the article: Complete intra-laboratory validation of a LAL assay for bacterial endotoxin determination in EBV-specific cytotoxic T lymphocytes
<p>Endotoxin content is a critical factor that affects the safety of biological pharmaceutical products. International pharmacopoeias describe several reference methods to determine endotoxin levels in advanced therapy medicinal product (ATMP) preparations. Administration of ATMPs must be done as rapidly as possible to ensure complete viability and potency of the cellular product. To evaluate the endotoxin content in the shortest time possible, we chose to validate an alternative method based on the use of the Charles River Portable Testing System (PTS) and FDA-approved cartridges, compliant with the requirements of the European Pharmacopoeia and providing results in <20 min. Here, we describe a unique and complete validation approach for instrument, personnel, and analytical method for assessment of endotoxins in ATMP matrices. The PTS system provides high sensitivity and fast quantitative results and uses less raw material and accessories compared with compendial methods. It is also less time consuming and less prone to operator variability. Our validation approach is suitable for a validated laboratory with trained personnel capable of conducting the ATMP release tests, and with very low intra-laboratory variability, and meets the criteria required for an alternative approach to endotoxin detection for in-process and product-release testing of ATMPs.</p>
In-vitro Major Arterial Cardiovascular Simulator: Benchmark Data Set for in-silico Model Validation
<p><strong>Background</strong><br> <br> The data described here supplements the paper "In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation" (to be submitted). It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper M. Wisotzki, A. Mair, P. Schlett, B. Lindner, M. Oberhardt, S. Bernhard, In Vitro Major Arterial Cardiovascular Simulator to Generate Benchmark Data Sets for In Silico Model Validation (2022), Data 7(11), DOI: 10.3390/data7110145 and the Zenodo doi when using this dataset.</p> <p><strong>General description / Dataset Structure</strong></p> <p>Each mat-File describes a different stenosis degree at the popliteal artery of the in-vitro simulator MACSim (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.</p> <p>The different stenosis degrees for each degree are:<br> ScenarioI: 100 % Area fraction (no stenosis)<br> ScenarioII: 37,5 % Area fraction<br> ScenarioIII: 23,4 % Area fraction<br> ScenarioIV: 6,56 % Area fraction</p> <p><strong>Data fields for each file</strong></p> <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>name of the scenario according to the paper, corresponds to filename</td> </tr> <tr> <td>configuration</td> <td>parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table> <p> </p> <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the paper (node numbering, not sensor numbers) or in the software SISCA (https://gitlab.com/agbernhard.lse.thm/sisca) in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor, unit mmHg for type 'p' and ml/s for type 'q' </td> </tr> <tr> <td>anatomicalPosition</td> <td> <p>name of the corresponding anatomical position</p> </td> </tr> </tbody> </table>
SIAC validation data
<p>This is the SIAC validation over AERONET and RadCalNet sites for S2 and L8. It contains the product IDs for both S2 and L8, the ground estimations of atmospheric parameters and surface reflectance from AERONET and RadCalNet individually.</p>
Code and data for "Discovery and validation of tissue-specific DNA methylation as noninvasive diagnostic markers for colorectal cancer".
<p>Code and data for "<strong>Discovery and validation of tissue-specific DNA methylation as noninvasive diagnostic markers for colorectal cancer</strong>".</p> <ul> <li> <p>The publicly available datasets supporting the conclusions of this article are available in the Gene Expression Omnibus repository (<a href="https://www.ncbi.nlm.nih.gov/geo/">https://www.ncbi.nlm.nih.gov/geo/</a>) and UCSC Xena Browser (TCGA, <a href="https://xena.ucsc.edu/">https://xena.ucsc.edu/</a>).</p> </li> </ul>
Data from the paper "Learning to clusterize urban areas: two competitive approaches and an empirical validation"
<p>Data for urban clustering used in the paper "Learning to clusterize urban areas: two competitive approaches and an empirical validation". We release two datasets for urban clustering based on data acquired in Santiago de Chile. The first dataset is computed at the level of urban blocks. The second dataset is computed at the level of individuals using a uniform sample of Santiago inhabitants. Both datasets comprises features based on social characteristics (e.g., SES), land use, and aesthetic visual perception of the city. The features of each data unit (blocks or individuals) are provided using row packing (each row is a data unit) in CSV files. We release PCA (Principal Components Analysis) features for both datasets.</p>
Training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series
<p>This dataset contains training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series. The data have been derived from Sentinel-5P total column carbon monoxide observations, using the offline processing stream.</p> <p><strong>Preprocessing</strong></p> <p>The following operations have been applied on the original S5P imagery:</p> <ol> <li>Images have been resampled to 0.1 by 0.1 degree spatial resolution</li> <li>Pixels with quality assessment value less than or equal to 0.5 have been set to NA</li> <li>Images have been aggregated by day of observation</li> <li>Images have been cropped to -60 to 60 degrees latitude</li> <li>Images have been devided into spatiotemporal blocks of size 128 x 128 pixels and 16 days</li> </ol> <p>Imagery has been recorded between 2021-01-01 and 2021-11-25. Notice that both the training and the validation blocks have been randomly sampled from all available blocks.</p> <p><br> <strong>Data Format and Naming Conventions</strong></p> <p>Input and output data blocks are stored as GeoTIFF files, where bands represent time. Notice the following file naming conventions:</p> <ul> <li>Files starting with <em>X</em> represent input measurements for training, where artificial gaps have been added.</li> <li>Files starting with <em>Y</em> represent true measurements without artificially added gaps (but still containing gaps in many cases).</li> <li>Binary masks of input data where all pixels with valid measurements are 1 and others 0 are stored in files whose name starts with <em>MASK</em></li> <li>Files starting with <em>VALMASK</em> contain a binary mask where only pixels that are available in Y but not in X are 1. The latter is used for validation on artificially removed pixels only.</li> </ul> <p>Numbers in filenames encode spatial and temporal block indexes.</p> <p>In addition, the dataset contains prediction of the validation blocks from different models in the `predictions` directory. The subfolders contain output from different models:</p> <ul> <li>mean refers to simple block-wise mean predictions.</li> <li>timeseries refers to simple linear time series interpolation.</li> <li>gapfill refers to the method proposed in [1].</li> <li>stmra refers to the method proposed in [2].</li> <li>STpconv refers to predictions passed on an artificial neural netowork with three-dimensional partial convolutions.</li> </ul> <p><strong>References</strong></p> <p>[1] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., & Furrer, R. (2018). Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2841-2853.</p> <p>[2] Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 38, 100465.</p>
Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test [DATA]
<p>Data and metadata for the publication "Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test", published in Earth and Space Science.</p>
Data from: Hindcast-validated species distribution models reveal future vulnerabilities of mangroves and salt marsh species
<p>Rapid climate change threatens biodiversity via habitat loss, range shifts, increases in invasive species, novel species interactions, and other unforeseen changes. Coastal and estuarine species are especially vulnerable to the impacts of climate change due to sea level rise and may be severely impacted in the next several decades. Species distribution modeling can project the potential future distributions of species under scenarios of climate change using bioclimatic data and georeferenced occurrence data. However, models projecting suitable habitat into the future are impossible to ground truth. One solution is to develop species distribution models for the present and project them to periods in the recent past where distributions are known to test model performance before making projections into the future. Here, we develop models using abiotic environmental variables to quantify the current suitable habitat available to eight Neotropical coastal species: four mangrove species and four salt marsh species. Using a novel model validation approach that leverages newly available monthly climatic data from 1960-2018, we project these niche models into two time periods in the recent past (i.e., within the past half-century) when either mangrove or salt marsh dominance was documented via other data sources. Models were hindcast-validated and then used to project the suitable habitat of all species at four time periods in the future under a model of climate change. For all future time periods, the projected suitable habitat of mangrove species decreased, and suitable habitat declined more severely in salt marsh species.</p>
Research data for "Indirect learning and physically guided validation of interatomic potential models"
<p>This dataset contains structural data, potential parameter files, and data shown in the plots for the publication "Indirect learning and physically guided validation of interatomic potential models". Details of the contents can be found in README.txt.</p>
Data from: Development and validation of targeted environmental DNA (eDNA) metabarcoding for early detection of 69 invasive fishes and aquatic invertebrates
<p>Invasive species are of concern due to their impacts on ecosystems and economies, but they pose significant control challenges. Environmental DNA (eDNA) is a powerful tool in the detection of aquatic organisms at low densities due to high sensitivity and ease of collection. Aquatic eDNA analyses have increased worldwide and are generally either applied to a few target species (quantitative PCR) or for broad taxonomic applications (metabarcoding). Here we describe the development and testing of a hybrid approach that utilized high sensitivity PCR primer sets and high-throughput sequencing (HTS), referred to as <em>targeted metabarcoding</em>, to detect 69 fishes and invertebrates. We identified target species based on reports of globally important invasive species and developed two independent PCR primers for each species (CO1 and a second mtDNA region). We assessed sensitivity and eDNA interference for all 138 primers (2 per species, 69 species) using standard end-point PCR and tested them on 10 eDNA samples spiked with various amounts of one or more of the target species' DNA. The sensitivity of the 138 primer sets ranged between 1.5×10<sup>-5</sup> and 2.64 ng template DNA (mean = 0.069 ng). Primers were also tested for interference effects using plankton eDNA to simulate field conditions. The inclusion of interfering plankton DNA reduced the sensitivity for most primer sets by one or more orders of magnitude (range 0 to 3). Overall, our targeted metabarcoding resulted in the detection of ~ 98% of species in the DNA spiked samples, and, perhaps more importantly, the HTS read count was positively related to the quantity of spiked DNA (P < 0.002). We envision this technique being particularly useful for the early detection of species at low population densities; however, there are diverse applications of targeted metabarcoding for monitoring aquatic community composition and quantifying ecosystem change and health.</p>
Data reported in development and cross-validation of a veterans mental health risk factor screen
<p>Background. VA primary care patients are routinely screened for current symptoms of PTSD, depression, and alcohol disorders, but many who screen positive do not engage in care. In addition to stigma about mental disorders and a high value on autonomy, some veterans may not seek care because of uncertainty about whether they need treatment to recover. A screen for mental health risk could provide an alternative motivation for patients to engage in care.</p> <p>Results. Twelve items assessing dissociation, emotional lability, life stress, and moral injury correctly classified 86% of those who later had elevated PTSD and/or depression symptoms (sensitivity) and 75% of those whose later symptoms were not elevated (specificity). Performance was also very good for 110 veterans who identified as members of ethnic/racial minorities.</p> <p>Conclusions. Mental health status was prospectively predicted in VA primary care patients with high accuracy using a screen that is brief, easy to administer, score, and interpret, and fits well into VA's integrated primary care. When care is readily accessible, appealing to veterans, and not perceived as stigmatizing, information about mental health risk may result in higher rates of engagement than information about current mental disorder status.</p>
Validation of transpulmonary thermodilution variables in hemodynamically stable patients with heart diseases - Individual patient data
<p>This dataset contains individual subject data for hemodynamic measurements assessed in the present study.</p>
Assessing Pairwise Ecological Association Inference using a novel Ecological Network Inference Simulation-Validation Framework - Data Repository
<p>Accompanying data for manuscript entitled "<span>A novel Network Inference Simulation-Validation Framework for Assessment of Ecological Network Inference Performance</span>" whose submission is imminent.</p>
Baltic Sea Region Land Cover Plus - Training and Validation data
<p>Training and validation data used in creating Baltic Sea Region Land Cover Plus (BSRLC+) maps: <a href="https://doi.org/10.5281/zenodo.10653871" target="_blank" rel="noopener">Dataset link</a></p> <ul> <li><strong>landcover_training_data_2006_2018.gpkg</strong>: Points data of consistent land cover from 2006 to 2018</li> <li><strong>crop_training_data_{year}.gpkg</strong>: Points data of crop types derived from <a href="https://doi.org/10.1038/s41597-023-02517-0">EuroCrop dataset </a>in particular year (2019, 2021, 2023)</li> <li><strong>landcover_validation_{year}.gpkg</strong>: Points data of validation data derived from <a href="https://doi.org/10.1038/s41597-020-00675-z">LUCAS points </a>in particular year (2009, 2012, 2015, 2018)</li> <li><strong>Metadata.pdf</strong>: Information of land cover code in each dataset</li> </ul> <p>Version notes:</p> <p>Version 2: Correcting the validation data 2018 and Metadata file</p> <p>Version 1: Original upload</p>
Data S6. Cell Size Validation Dataset
<p>This dataset contains the input values to compute the 2010 MTI in different cell sizes options, and the final MTI values. </p>
How can we biochemically validate protein function predictions with the Ras GTPase family? - Associated data
<p>This is the data that accompanies the pub "<a href="https://doi.org/10.57844/arcadia-74ad-345f">How can we biochemically validate ProteinCartography with the Ras GTPase family?</a>" It's part of a group of pubs focused on validating ProtienCartography that begins with "<a href="https://doi.org/10.57844/arcadia-cae9-96c4">A strategy to validate protein functions <em>in vitro</em></a><a href="https://doi.org/10.57844/arcadia-cae9-96c4">." </a></p> <p>For this repository, we ran ProteinCartography <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.5.0">v0.5.0</a> using human HRas and KRas as our inputs for a single run (UniProt ID: <a href="https://www.uniprot.org/uniprotkb/P01112/entry">P01112</a> and <a href="https://www.uniprot.org/uniprotkb/P01116/entry">P01116</a>). We asked for 3,000 Foldseek hits and 7,000 BLAST hits for a total of 10,000 structures. The updated configuration file is in the zipped folder in this repository. Also included in the zipped folder are the inputs, structures of all hits, and all ProteinCartography results. </p> <p>Finally, we created a custom overlay for the protein map using this <a href="https://github.com/Arcadia-Science/2023-actin-embedding/blob/main/notebooks/3_plotting_overlays.ipynb">notebook</a> and the manually annotated TSV file in this repository, where we denoted which group of substrates a protein is predicted to act on based on its annotation from UniProt.</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.