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278
datasets available to search
ShareScore release 0.9.0
Dataset results
278 results for “Validated dataset”
TITAM (Time Independent Tracking Algorithm for Medicanes) software validation dataset
<p>This dataset contains the output of four medicane simulations performed with Weather Research and Forecasting (WRF) Model in the <em>MAR</em> group (www.um.es/gmar), as well as an extract of ERA5 reanalysis data, used to validate TITAM (Pravia-Sarabia, Enrique, Montávez, Juan Pedro, Gómez-Navarro, Juan José, & Jiménez-Guerrero, Pedro. (2020, June 3). TITAM (Time Independent Tracking Algorithm for Medicanes) software (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.3874416).</p>
Data Set_Exploring the Molecular Mechanisms of Endothelial Dysfunction Affecting Myocardial Infarction by Integrating Multiple Datasets with In Vivo Experimental Validation
Open the record for dataset details and reuse information.
FunVIP: Fungal Validation and Identification Pipeline based on phylogenetic analysis - Validation dataset
<p>This is dataset and analysis to regenerate results published in Molecular Ecology Resources</p>
Dataset related to article "Apparent Diffusion Coefficient standardization: multi-center and multi-vendor validation study across 1.5T and 3T scanners in a diffusion MRI phantom"
<p>The dataset includes ADC measurements collected for MRI harmonisation across centers and vendors, performed as part of the multicenter RESPECT project, using the QIBA-NIST diffusion MRI phantom.</p> <p>The dataset includes the following sheets:</p> <p>"ADC" sheet: ADC measurements (averaged over 4 repeated measures) for each scanner, comprising both first and second acquisition and 3 imaging planes. </p> <p>"temperature" sheet: temperature measurement of the phantom for each acquisition session.</p> <p>"repeatability" sheet: ADC measurements of the 4 repeated measures for the first acquisition. Average values and CVs are also reported.</p>
WHUS2-CRv a global thin cloud removal dataset for Sentinel-2 images——Validation and testing parts
<p>The validation and testing parts of WHUS2-CRv dataset in which the paired cloud and cloud-free Sentinel-2 images are from different regions of the world. The types of land cover are rich and the acquisition dates of the experimental data cover a long time period (from 2015 to 2020) and all seasons.</p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1]J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373-389, Aug. 2020,http://doi.org/10.1016/j.isprsjprs.2020.06.021.</p> <p>[2]J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, http://doi.org/10.3390/rs13010157.</p> <p>[3]J. Li, Y. Zhang, Q. Sheng, Z. Wu, B. Wang, Z. Hu, G. Shen, M. Schmitt, M. Molinier, “Thin Cloud Removal Fusing Full Spectral and Spatial Features for Sentinel-2 Imagery,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 8759-8775, 2022, doi: 10.1109/JSTARS.2022.3211857.</p>
Phishing validation emails dataset
<p><strong>Description</strong>:<br>This dataset contains a collection of 2,000 emails, specifically curated for the purpose of validating machine learning models designed to differentiate between safe emails and phishing attempts. The dataset is a mix of real-world email samples and artificially generated emails, ensuring a comprehensive reflection of realistic email scenarios.</p> <p>Each entry in the dataset includes the full text of an email and a corresponding label that categorizes the email as either 'Safe Email' or 'Phishing Email.' This dataset is intended for use in validating the performance of models after they have been trained, providing a crucial step in ensuring the model's accuracy and reliability before deployment.<br><br></p> <p><strong>Dataset Structure</strong>:</p> <ul> <li><strong>Total Emails</strong>: 2,000</li> <li><strong>Email Types</strong>: <ul> <li>Safe Emails</li> <li>Phishing Emails</li> </ul> </li> <li><strong>Attributes</strong>: <ul> <li>Full text of the email</li> <li>Label indicating whether the email is safe or phishing</li> </ul> </li> </ul> <p><strong>Example Entries</strong>:</p> <ol> <li><strong>Email Text</strong>: "Dear Jordan, your subscription has been succes..." <ul> <li><strong>Email Type</strong>: Safe Email</li> </ul> </li> <li><strong>Email Text</strong>: "Congratulations! You've won a $3000 gift card...." <ul> <li><strong>Email Type</strong>: Phishing Email</li> <li> </li> </ul> </li> </ol> <p><strong><span>Acknowledgments</span></strong></p> <p>The authors would like to thank Sofia Tech Park and the Artificial intelligence and CAD systems laboratory for their assistance and support in conducting this research.</p>
Reliabılıty,Validity Of The Turkish Version Of The NIH-Minimal Dataset
ClinicalTrials.gov study NCT06049719. IPD Sharing: YES. Countries: 1. Publications: 0.
Validation of an Artificial Intelligence Enabled Diagnostic Support Software (ArtiQ.Spiro) in Primary Care Spirometry Datasets - a Retrospective Analysis
ClinicalTrials.gov study NCT05648227. IPD Sharing: NO. Countries: 1. Publications: 0.
Diagnosis of Kawasaki Disease in children using host RNA expression [Validation_HT12V3_Dataset]
GEO Series GSE73462. Homo sapiens. 147 samples. Type: Expression profiling by array.
Perturbation-informed signatures from crosswise integration of transcriptome and chromatin accessibility analyses predict susceptibility to candidate anticancer drugs (cisplatin validation dataset) [A
GEO Series GSE207607. Homo sapiens. 16 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
JPL SMAP Level 2B Near Real-time (2Hr Latency) CAP Sea Surface Salinity V5.0 Validated Dataset
This is the PI-produced JPL SMAP-SSS V5.0, level 2B NRT CAP, validated sea surface salinity (SSS) and extreme winds orbital/swath product from the NASA Soil Moisture Active Passive (SMAP) observatory available in near real-time with a latency of about 6 hours. It is based on the Combined Active-Passive (CAP) retrieval algorithm developed at JPL originally in the context of Aquarius/SAC-D and now extended to SMAP. JPL SMAP V5.0 SSS is based on the newly released SMAP V5 Level-1 Brightness Temperatures (TB). An enhanced calibration methodology has been applied to the brightness temperatures, which improves absolute radiometric calibration and reduces the biases between ascending and descending passes. The improved SMAP TB Level 1 TB will enhance the use of SMAP Level-1 data for other applications, such as sea surface salinity and winds. The JPL SMAP-SSS L2B CAP NRT product includes data for a range of parameters: derived SMAP sea surface salinity, SSS uncertainty and wind speed/direction data for extreme winds, brightness temperatures for each radiometer polarization, ancillary reference surface salinity, ice concentration, wind and wave height data, quality flags, and navigation data. Each data file covers one 98-minute orbit (15 files per day). Data begins on April 1,2015 and is ongoing, with a 6 hour latency in processing and availability. Observations are global in extent and provided at 25km swath grid with an approximate spatial resolution of 60 km.The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board Instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed for the surface roughness correction required for the surface salinity retrieval.
RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V5.3 Validated Dataset
The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V5.3 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval. <br><br> The evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B TA as input. The V6 L1B TA uses a lower TA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file is available in netCDF-4 file format. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 5.3 release is the smoothed salinity product with a spatial resolution of approximately 70 km.
RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8-Day Running Mean V5.3 Validated Dataset
The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8-Day Running Mean V5.3 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval. <br><br> The evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B TA as input. The V6 L1B TA uses a lower TA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file is available in netCDF-4 file format. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 5.3 release is the smoothed salinity product with a spatial resolution of approximately 70 km.
Perturbation-informed signatures from crosswise integration of transcriptome and chromatin accessibility analyses predict susceptibility to candidate anticancer drugs (cisplatin validation dataset)
GEO Series GSE207612. Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
A new classification of chromosome instability (CIN) phynotype, CIN-high and CIN-low (validation dataset)
GEO Series GSE34489. Homo sapiens. 33 samples. Type: Expression profiling by array.
The datasets for validation of cell deconvolution methods
<p>Many datasets for validation of cell deconvolution methods</p>
Validation dataset
<p>validation dataset</p>
Major datasets employed for the training and validation of RTMscore
<p>Major datasets employed for the training and validation of RTMscore.</p>
Digital Maturity Scale for Public Administration - scale validation dataset
<p>Dataset contains data collected during a pilot stage of a survey which was used to validate to scale for measuring the digital maturity in paublic administration.</p>
The training and validation dataset for ENSO-ASC model
<p>This repository contains the training and validation dataset of ENSO-ASC (10.5281/zenodo.5081794)</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.