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.
8,038
datasets available to search
ShareScore release 0.7.1
Dataset results
8,038 results for “validation”
Z/γ + jets event samples at next-to-leading order QCD at 13 TeV (ATLAS validation)
<p>Z/γ plus multi-jet event samples at parton level in HDF5 event format</p> <p><span class="math-tex">\(\sqrt{s}=13~TeV\)</span></p> <p><span class="math-tex">\(\mu_R=\frac{1}{2}\left(m_{\perp,Z}+\sum_{\rm jets}p_{\perp,j}\right)\\ \mu_F=\frac{1}{N_{\rm jet}}\left(m_{\perp,Z}+\sum_{\rm jets}p_{\perp,j}\right)\)</span></p> <p>Generated with <a href="https://gitlab.com/hpcgen/me">Sherpa</a> using the attached setup files</p> <p>Files can be filtered and merged using the <a href="https://gitlab.com/shoeche/lheh5-reader">tools provided on GitLab</a></p>
Data for: A catalytic model for SARS-CoV-2 reinfections: Performing simulation-based validation and extending the model to include nth infections
<p>For code and more details see: </p> <ul> <li><code>inf_for_sbv.RDS</code> - simluated timeseries of primary infections used in the simulation-based validation of reinfections. </li> <li><code>inf_for_sbv_third.RDS</code> - simluated timeseries of primary infections used in the simulation-based validation of third infections. </li> <li><code>3_posterior_90_null_correctdata.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) when not considering a second lambda parameter (to third infections)</li> <li><code>3_posterior_90_null_l2_correctdata.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) when considering a second lambda parameter (to third infections)</li> <li><code>3_sim_90_null_correctdata.RDS</code> - simulation results when not considering a second lambda parameter for third infections (as used in the manuscript)</li> <li><code>3_sim_90_null_l2_correctdata.RDS</code> - simulation results when considering a second lambda parameter for third infections (as used in the manuscript)</li> </ul> <p> </p>
Computationally defined and in vitro validated putative genomic safe harbour loci for transgene expression in human cells
<p>Selection of the target site is an inherent question for any project aiming for directed transgene integration. Genomic safe harbour (GSH) loci have been proposed as safe sites in the human genome for transgene integration. Although several sites have been characterised for transgene integration in the literature, most of these do not meet criteria set out for a GSH, and the limited set that do have not been characterised extensively. Here, we conducted a computational analysis using publicly available data to identify 25 unique putative GSH loci that reside in active chromosomal compartments. We validated stable transgene expression and minimal disruption of the native transcriptome in three GSH sites <em>in vitro</em> using human embryonic stem cells (hESCs) and their differentiated progeny. Furthermore, for easily targeted transgene expression, we have engineered constitutive landing pad expression constructs into the three validated GSH in hESCs.</p>
choderalab/geometry-benchmark-espaloma: Small molecule geometry benchmark dataset to validate espaloma-0.3
<p>This is a collection of preprocessed QM and MM optimized structures needed to perform the small molecule geometry benchmark study, described in the <strong>espaloma-0.3</strong> paper:</p> <p>Kenichiro Takaba, Iván Pulido, Pavan Kumar Behara, Mike Henry, Hugo MacDermott Opeskin, John D. Chodera, Yuanqing Wang. "Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond" (<a href="https://arxiv.org/abs/2307.07085">arXiv:2307.07085</a>)</p> <p>This benchmark study calculates and compares the RMSD, TFD, and ddE metrics for a specified set of MM force fields. The initial optimized structures were sourced from the <a href="https://github.com/openforcefield/qca-dataset-submission/tree/master/submissions/2021-06-04-OpenFF-Industry-Benchmark-Season-1-v1.1">OpenFF Industry Benchmark Season 1 v1.1</a> dataset, which is available through <a href="https://qcarchive.molssi.org/">QCArchive</a>. More details about the preprocessing steps is available at <a href="https://github.com/choderalab/geometry-benchmark-espaloma/tree/main/qc-opt-geo">https://github.com/choderalab/geometry-benchmark-espaloma/tree/main/qc-opt-geo</a>.</p> <ul> <li><strong>02-chunks.tar.gz</strong>: QM optimized structures chunked into small file sizes.</li> <li><strong>02-outputs-openff-2.0.0-espaloma-0.3.0rc1.tar.gz</strong>: MM optimized structures using openff-2.0.0 and espaloma-0.3.0rc1 force field (former release candidate of espaloma-0.3)</li> <li><strong>02-outputs-gaff2.11.tar.gz</strong>: MM optimized structures using gaff-2.11 force field</li> <li><strong>02-outputs-espaloma-0.3.0rc6.tar.gz</strong>: MM optimized structures using espaloma-0.3.0rc6 (espaloma-0.3) force field</li> <li><strong>02-outputs-openff-2.1.0.tar.gz</strong>: MM optimized structures using openff-2.1.0 force field</li> </ul>
Data set for model validation in "Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model"
<p>This upload contains the data set for model validation in the manuscript "Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model".</p>
Collocated model and observation datasets for the validation of the Copernicus Mediterranean Sea Waves Analysis and Forecast for the period 2018-2020.
<p>The collocated values are used for skill evaluation of the Mediterranean Sea Waves Analysis and Forecast system for a three-year-long period (Korres et al., 2022). The list of datasets includes:</p> <ol> <li>The collocated model (analysis) – buoy values for significant wave height Hs (Insitu_Hs.mat)</li> <li>The collocated model (analysis) – buoy values for spectral moments (0,2) wave period Tm (Insitu_Tm.mat)</li> <li>The collocated model (first – guess) – satellite values for significant wave height Hs (Satellite_Hs.mat)</li> <li>The collocated model – satellite values for wind speed U10 (Satellite_U10.mat)</li> </ol> <p>Each .mat file contains a header for the variables included. Collocations can be used to estimate standard quality metrics (e.g. scatter index, bias, root-mean-squared-difference). Procedures to produce these model-observation collocated datasets and determine the overall skill assessment are described in detail in Ravdas et al. (2018) and Oikonomou et al. (2022). The buoy (in-situ) measurements are obtained from the product INSITU_GLO_WAV_DISCRETE_MY_013_045 (EU Copernicus Marine Service Product, 2022a), and associated variables contain a quality flag (“time_qc”, “position_qc”, “buoy_Hs_qc”, “buoy_Tm_qc”) (de Alfonso et al., 2022a,b). In addition, the model first-guess significant wave height and the wind speed forcing (Hersbach et al., 2023) are collocated with available satellite observations (EU Copernicus Marine Service Product, 2022b) over the entire model domain.</p> <p>References</p> <p>de Alfonso, M., Manzano, F., and Gallardo, A. (2022a): EU Copernicus Marine Service Quality Information Document for the In Situ TAC Product, INSITU_GLO_WAV_DISCRETE_MY_013_045, Issue 5.0, Mercator Ocean International, <a href="https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-045.pdf">https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-045.pdf</a></p> <p>de Alfonso, M., Manzano, F., Gallardo, A., and In Situ TAC (2022b): EU Copernicus Marine Service Product User Manual for Multi-Year WAVE In Situ Product, INSITU_GLO_WAV_DISCRETE_MY_013_045, Issue 2.0, Mercator Ocean International, <a href="https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-045.pdf">https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-045.pdf</a></p> <p>EU Copernicus Marine Service Product (2022a): Multi-Year WAVE In Situ Product, Mercator Ocean International, [dataset], <a href="https://doi.org/10.17882/70345">https://doi.org/10.17882/70345</a></p> <p>EU Copernicus Marine Service Product (2022b): Global Ocean L 3 Significant Wave Height From Reprocessed Satellite Measurements, Mercator Ocean International, [dataset], <a href="https://doi.org/10.48670/moi-00176">https://doi.org/10.48670/moi-00176</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed on 26-09-2023)</p> <p>Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2022): Mediterranean Sea Waves Analysis and Forecast (CMEMS MED-Waves, MEDWAΜ4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). <a href="https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4">https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4</a></p> <p>Oikonomou, C., Denaxa D., and Korres, G. (2022): EU Copernicus Marine Service Quality Information Document for the Mediterranean Sea Waves Reanalysis, MEDSEA_ANALYSISFORECAST_WAV_006_017, Issue 2.2, Mercator Ocean International, <a href="https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-MED-QUID-006-017.pdf">https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-MED-QUID-006-017.pdf</a>.</p> <p>Ravdas, M., Zacharioudaki, A., and Korres, G. (2018): Implementation and validation of a new operational wave forecasting system of the Mediterranean Monitoring and Forecasting Centre in the framework of the Copernicus Marine Environment Monitoring Service, Nat. Hazards Earth Syst. Sci., 18, 2675–2695, <a href="https://doi.org/10.5194/nhess-18-2675-2018">https://doi.org/10.5194/nhess-18-2675-2018</a></p> <p> </p>
Supplementary Materials: ImpactX Modeling of Benchmark Tests for Space Charge Validation
<p>Supplementary materials (aka data artifact or data archive) for our HB2023 publication: " ImpactX Modeling of Benchmark Tests for Space Charge Validation" (Paper ID: THBP44).</p> <p>This work was supported by the Director, Office of Science of the U.S. Department of Energy under Contracts No. DE-AC02-05CH11231 and DE-AC02-07CH11359. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.</p>
Images of plants at harvest timepoint of Mini5SynCom Screen and Validation Experiments
<p>pictures of plants at harvest timepoint (14 days post infection) of all experiments and data shown in publication titled "Identifying microbiota community patterns important for plant protection using machine learning in synthetic community experiments" by B. Emmenegger, J. Massoni, C.M. Pestalozzi, M. Bortfeld-Miller, B.A. Maier and J.A. Vorholt.</p> <p>The picture name is unique to be able to link to data presented in supplemental data 1.</p>
Experimental Validation of Automated OMA and Mode Tracking for Structural Health Monitoring of Transmission Towers
<p>This dataset is part of the Smart Tower Project and is intended to accompany academic publications entitled "Experimental Validation of Automated OMA and Mode Tracking for Structural Health Monitoring of Transmission Towers". It provides valuable data under high wind excitation conditions and is beneficial for researchers in the field of structural dynamics and Structural Health Monitoring (SHM). The dataset includes high-resolution accelerometer data and estimated modal parameters, along with part of the code script used for analysis.</p> <p>Dataset Components:</p> <ol> <li> <p><strong>Raw Accelerometer Data (April Month)</strong>: High-resolution time-series accelerometer data recorded during April, under high wind excitation conditions. The data is sampled at a rate of 250 Hz. The data is organized in dd/YYYYmmdd_HHMMSS.tdms</p> <ul> <li><strong>Format</strong>: TDMS</li> </ul> </li> <li> <p><strong>Modal Parameters</strong>: Resonance frequencies estimated over the entire monitoring period from 29/03/2023 to 25/06/2023.</p> <ul> <li><strong>Format</strong>: Parquet</li> </ul> </li> </ol>
Validated CFD Model for Multimode Gasoline Compression Ignition Engine
<p>A validated CFD model for the multimode combustion engine developed under the DOE funded project DE-EE0008478 (Co-optimized Mixed-Mode Engine and Fuel Demonstrator for Improved Fuel Economy while Meeting Emissions Requirements). It incorporates advanced physics-based fuel surrogate models for thermophysical properties and reaction kinetics. The combustion modes include spark ignition (SI), low temperature combustion (LTC), and compression ignition (CI). The real fuel model was validated for RON60, RON70, RON80, RON90, and two biofuel blends. The validation cases can be found in <a href="https://doi.org/10.2172/1887341">https://doi.org/10.2172/1887341</a>. </p>
Examination of the status of Phyciodes tharos distincta Bauer, 1975, confirming it as a valid subspecies.
<p>The subspecific status of <i>Phyciodes tharos distincta</i> is reexamined. A specimen series from southern Arizona reveals that <i>distincta </i>shows a distinct, unique phenotype dissimilar from eastern North American nominotypical <i>P. t. tharos</i>. Subspecies <i>distincta </i>occupies a very limited range in extreme southeastern California, southern Arizona, and northwestern Mexico. A lectotype is designated.</p>
Initial Chronic Human Validation Study: Subcutaneous Implantable Cardioverter Defibrillator (S-ICD) System
ClinicalTrials.gov study NCT00853645. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study for Validation of Standardized Questionnaires on Depression and Investigation of the Frequency of Depression in Rheumatoid Arthritis (RA) Participants
ClinicalTrials.gov study NCT02485483. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Valproate in Dementia (VALID)
ClinicalTrials.gov study NCT00071721. IPD Sharing: Not stated. Countries: 1. Publications: 4.
The LAVA (Lateral Flow Antigen Validation and Applicability) Study for COVID-19
ClinicalTrials.gov study NCT04629157. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinical Validation of a Molecular Test for Ciprofloxacin-Susceptibility in Neisseria Gonorrhoeae
ClinicalTrials.gov study NCT02961751. IPD Sharing: Not stated. Countries: 1. Publications: 1.
External Validation of the CLOVER Score for Detecting Occult Cancer in Venous Thromboembolism Patients
ClinicalTrials.gov study NCT07310693. IPD Sharing: YES. Countries: 1. Publications: 1.
Validation of Falls Decision Rule to Exclude Intracranial Bleeding in Geriatric Fall Patients
ClinicalTrials.gov study NCT06525727. IPD Sharing: NO. Countries: 1. Publications: 2.
Development and Validation of the Client Centered Occupational Therapy Service Model
ClinicalTrials.gov study NCT04465422. IPD Sharing: NO. Countries: 1. Publications: 4.
Validation of the Hospital Asthma Severity Score (HASS)
ClinicalTrials.gov study NCT02782065. IPD Sharing: NO. Countries: 1. Publications: 1.
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.