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.
1,956
datasets available to search
ShareScore release 0.9.0
Dataset results
1,956 results for “test data”
Tissue-engineered oral epithelial barrier for dental material testing: towards establishing in vitro biomimetic models - Underlying data
<p>Underlying CT data of "<strong>Tissue-engineered oral epithelial barrier for dental material testing: towards establishing <em>in vitro </em>biomimetic models</strong>"<br><a href="https://doi.org/10.1089/ten.tec.2024.0154">https://doi.org/10.1089/ten.tec.2024.0154</a></p> <p>Foteini Machla a, Paraskevi Kyriaki Monou b, c, Chrysanthi Bekiari d, Dimitrios Andreadis e Evangelia Kofidou d, , Emmanouel Panteris f, Orestis L. Katsamenis g, h, Maria Kokoti a, Petros Koidis a, Imad About i, Dimitrios Fatourosb, c, Athina Bakopoulou a</p> <p>a Department of Prosthodontics, Tissue Engineering Core Unit, School of Dentistry, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece</p> <p>b Department of Pharmaceutical Technology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece</p> <p>c Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki 57001, Greece</p> <p>d Laboratory of Anatomy and Histology, Veterinary School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece</p> <p>e Department of Oral Medicine/Pathology, School of Dentistry, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece</p> <p>f Department of Botany, School of Biology, Faculty of Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece</p> <p>g μ-VIS X-ray Imaging Centre, Faculty of Engineering and the Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom</p> <p>h Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom UK</p> <p>i Centre National de la Recherche Scientifique, Institute of Movement Sciences, Aix Marseille University, Marseille 13385, France</p> <p> </p> <p><strong>Measurement of ΤΕΟΕ thickness</strong></p> <p>X-ray computed micro-tomography (μCT) as employed to examine the microstructure of the paraffin-embedded tissue engineered oral epithelium (TEOE), enabling comprehensive 3D assessment of thickness using volumetric analysis (cf. supplementary for imaging parameters) (11). Imaging was conducted using an isotropic voxel-edge of 6.0 μm. Local Thickness was carried out in 3D using the “Volume Thickness Map” tool within Dragonfly software (cf. supplementary), allowing for the visualization and quantification of the spatial distribution and variability of tissue thickness.</p> <p>Imaging was conducted at the University of Southampton’s μ-VIS X-ray Imaging Centre ( https://muvis.org ) / 3D X-ray Histology facility, utilizing a customised μCT scanner optimisedfor intricate histological analyses(1), based on Nikon’s XTH225ST system (Nikon Metrology, UK). Operating parameters were set at 80 kVp / 86 μA (6.88 W), with a source-to-object distance of 37.5 mm and a source-to-detector distance of 937.4 mm, resulting in a magnification factor of 25x and an isotropic voxel-edge of 6.0 μm. Imaging acquisition involved the collection of 4001 projections using a 2850 x 2850 dexels detector, by averaging 4 frames per projection, with an exposure time of 500 ms per projection.</p> <p>Visualisation and analysis of the reconstructed dataset was done using Dragonfly software (Comet Technologies Canada Inc.; software accessible at http://www.theobjects.com/dragonfly). Assessment of Local Thickness was carried out in 3D using the “Volume Thickness Map” tool within Dragonfly software, following segmentation of the tissue layer. Local thickness analysis allowed for the visualization and quantification of the spatial distribution and variability of thickness within the tissue engineered oral epithelium (TEOE).</p> <p>Visual representation of local thickness histograms was employed to elucidate the distribution of thickness throughout the TEOE. These histograms effectively illustrate the number of voxels associated with specific cross-sectional thickness, offering both a graphical depiction of the variation in thickness across the tissue sample, and a quantitative measure of the average thickness of the specimen.</p> <p>It's worth noting, the volumetric and non-destructive nature of the technique enabled whole-block imaging, which proved crucial in addressing challenges arising from tissue sample shrinkage. This shrinkage, a consequence of dehydration during the fixation process, can occur in some cases and lead led to the specimen wrapping. While wrapping is not a common occurrence, this analysis method allowed for the evaluation of challenging-shaped specimens, such as the wrapped one presented in Figure 4. Unlike conventional 2D methods such as classical histology, which rely on the angle of slicing and encounter limitations when dealing with non-perfectly perpendicular slicing, μCT-based XRH enables analysis of all specimens, including those with complex shapes.</p> <p> </p>
Score matching for differential abundance testing of compositional high-throughput sequencing data - data repository
<p>Data repository for "Score matching for differential abundance testing<br>of compositional high-throughput sequencing data" (<a href="https://github.com/bio-datascience/cosmoDA">github</a>)</p> <p>To use, clone the repository, then download the zip file and unpack it in the main directory of the repository.</p>
Synthetic TEST FLIM 6D Data Adapted to 5D Modulo
Open the record for dataset details and reuse information.
Data from: A non-parametric maximum test for the Behrens–Fisher problem
Non-normality and heteroscedasticity are common in applications. For the comparison of two samples in the non-parametric Behrens–Fisher problem, different tests have been proposed, but no single test can be recommended for all situations. Here, we propose combining two tests, the Welch t test based on ranks and the Brunner–Munzel test, within a maximum test. Simulation studies indicate that this maximum test, performed as a permutation test, controls the type I error rate and stabilizes the power. That is, it has good power characteristics for a variety of distributions, and also for unbalanced sample sizes. Compared to the single tests, the maximum test shows acceptable type I error control.
Data from: Development and usability testing of an audit and feedback tool for anesthesiologists
Background: We describe the creation and evaluation of a personal audit & feedback (A&F) tool for anesthesiologists. Methods: A survey aimed at capturing barriers for personal improvement efforts and feedback preferences was administered to attending anesthesiologists. The results informed the design and implementation of 4 dashboards that display information on individual practice characteristics as well as comparative performance on several quality metrics. The dashboards' usability was then tested using the human-centered design framework. Results: Anesthesiologists listed lack of information on current practice as the main barrier for improvement. Regarding usability, participants gave the dashboards an average score of 3.8 (scale 1-5) on consistency, learnability, and information organization, and performed the assigned tasks well, with an average score of 89% (range, 79-100%). Conclusions: We describe the design, implementation and usability testing of an innovative tool that utilizes data derived from the EHR system to provide A&F to anesthesiology providers.
Data from: Testing sensory evidence against mnemonic templates
Most perceptual decisions require comparisons between current input and an internal template. Classic studies propose that templates are encoded in sustained activity of sensory neurons. However, stimulus encoding is itself dynamic, tracing a complex trajectory through activity space. Which part of this trajectory is pre-activated to reflect the template? Here we recorded magneto- and electroencephalography during a visual target-detection task, and used pattern analyses to decode template, stimulus, and decision-variable representation. Our findings ran counter to the dominant model of sustained pre-activation. Instead, template information emerged transiently around stimulus onset and quickly subsided. Cross-generalization between stimulus and template coding, indicating a shared neural representation, occurred only briefly. Our results are compatible with the proposal that template representation relies on a matched filter, transforming input into task-appropriate output. This proposal was consistent with a signed difference response at the perceptual decision stage, which can be explained by a simple neural model.
Dataset comparing different skewness methods tested in "Quantifying and comparing radiation damage in the Protein Data Bank"
<p>Dataset comparing five different skewness metrics (including the Bnet metric) across 23 radiation damage datasets in the publication "Quantifying and comparing radiation damage in the Protein Data Bank"</p>
Test Landsat data of RaBET
<p>A test dataset of Landsat images from collection 1 level 2 and collection 2 level 1 in the area of MLRA 41</p>
Database of extreme events, test cases selection and available data, Deliverable 5.1 – ECFAS Project (GA 101004211), www.ecfas.eu
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p> <p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p> <p><em><strong>Reference literature:</strong></em></p> <p><strong>Souto-Ceccon, P. E., Montes, J., Duo, E., Ciavola, P., Fernández-Montblanc, T., and Armaroli, C.: A European database of resources on coastal storm impacts, Earth Syst. Sci. Data, 17, 1041–1054, <a href="https://doi.org/10.5194/essd-17-1041-2025">https://doi.org/10.5194/essd-17-1041-2025</a>, 2025.</strong></p> <p><strong>Description of the product</strong></p> <p>Deliverable 5.1 is a comprehensive inventory of extreme coastal events that produced flooding at different locations along the European coastline. It includes the collection and identification of events, locations and available information on the test cases.</p> <p>The purpose of the ECFAS database is to provide a source of information on extreme coastal events and locations that experienced coastal flooding, considering both hazard and impact aspects. Thus, the database collects events, sites and available information to support further investigation on specific test cases. Test cases are defined here as specific sites where an extreme event that generated flooding and damage occurred. The time frame considered for the analysis is between 2010 and 2020 in order to use recent satellite imagery with good resolution and including, if possible, overlap with Sentinel missions.</p> <p>The product includes three files: 1) an Excel file with the inventory; 2) an accompanying report that includes the guidelines to use the inventory, other relevant information on the method used to implement the inventory and the sources of information; 3) the test cases polygons in .geojson format.</p> <p>This ECFAS Database is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the database are licensed under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p> <p>This <strong>Report</strong> is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p> <p> </p> <p><em><strong>Disclaimer:</strong></em></p> <p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p> <p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p> <p> </p>
Data generated for 'Early stage of Erythrocyte Sedimentation Rate test: Fracture of a high-volume-fraction gel'
<p>This dataset has been generated for the manuscript entitled "Early stage of Erythrocyte Sedimentation Rate test: Fracture of a high-volume-fraction gel".</p>
Data for the paper "A Multi-station Meteor Monitoring (M³) System. I. Design and Testing"
<p>This repository contains following data:</p> <p>1.Meteor observation data:</p> <p>JSON files of observation reports, including time, location of station, meteor ECI coordinates etc.</p> <ul> <li>folder: observations/HH: JSON files of station A</li> <li>folder: observations/YQ: JSON files of station A</li> </ul> <p>2.Meteoroid orbit data:</p> <p>Orbit data generated by WMPL (https://github.com/wmpg/WesternMeteorPyLib) for 473 meteoroids. Each folder contains reports and charts for success runs, and error massages if unsuccessful.</p>
Influence of Anthropometric Data, Shoulder Complex Muscle Strength, and Abdominal-lumbar Endurance in Closed Kinetic Chain Upper Extremity Stability Test
ClinicalTrials.gov study NCT05291273. IPD Sharing: YES. Countries: 1. Publications: 0.
Normative Data of Dynamic Gait Index and 5 Time Sit to Stand Test Among Elderly Population
ClinicalTrials.gov study NCT05651412. IPD Sharing: NO. Countries: 1. Publications: 0.
The Construction of a Chinese Picture Naming Test and Its Preliminary Normative Data
ClinicalTrials.gov study NCT00713232. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Testing the Implementation of a Toolbox to Optimize Data Collection and Data Quality of the National Medical Quality Indicators in Long-term Care Facilities: a Pilot Study. (NIP-Q-UPGRADE Subaim 1.8)
ClinicalTrials.gov study NCT06848725. IPD Sharing: NO. Countries: 1. Publications: 0.
Global Real World Data in Patients With Advanced Thyroid Cancer on Standard of Care and Specialized Interventions- Registry of Oncologic Outcomes With Testing and Treatment.
ClinicalTrials.gov study NCT06507878. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Metabolic Testing Data and Education on Attitudes and Beliefs Related to Carbohydrate Intake in Adolescent Female Athletes
ClinicalTrials.gov study NCT06837376. IPD Sharing: NO. Countries: 1. Publications: 0.
A Three-phase Study That Will Compare the ECG Data Recorded Using the Test Device With the Data Recorded by a Reference Device, Evaluate the ECG Signal Quality of the Test Device Over a 10-day Simulat
ClinicalTrials.gov study NCT07200232. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of a Test Algorithm for Use to Analyze ECG Data Collected From a Test Device of a 24-hour Simulated Use Period
ClinicalTrials.gov study NCT07188129. IPD Sharing: NO. Countries: 1. Publications: 0.
Developing and Testing a Social Network Data Capture Tool to Improve Partner Services: a Preliminary Pilot Implementation
ClinicalTrials.gov study NCT06659003. IPD Sharing: NO. Countries: 1. Publications: 0.
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.