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641 results for “data validation”
Code to generate figures 3 and 4 of: "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."
<p>Code to generate figures 3 and 4 of the manuscript titled "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."</p> <p> </p>
Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities"
<p>Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities", Applied Energy, 2019</p>
tobac-flow Validation data
<p>Validation results for the detection of cores and anvils using the tobac-flow method</p> <p>Validation was performed over 297 days in 2018. Other days were excluded due to missing ABI or GLM data. Validation was performed over a subset of the GOES-16 CONUS scan region.</p>
Training and validation data used to produce the pre-trained model for the TomoTwin paper.
<p>This datasets represents the training and validation data that was used to produce the pre-trained model for the TomoTwin paper. Please see 10.5281/zenodo.6637357 for the raw tomograms.</p>
Gaia Data Release 3: BP/RP split-epoch validation dataset
<p>This dataset includes mean BP/RP spectra for about 43.6 thousand sources for which two mean spectra per source were generated using only a random selection of the available epoch spectra.</p> <p>More details about this dataset are given in Appendix D in the paper "Gaia Data Release 3: Processing and validation of BP/RP low-resolution spectral data", De Angeli, F., et al. A&A (2022). Results obtained from this dataset are published in the same paper and in "Gaia Data Release 3: The Galaxy in your preferred colours. Synthetic photometry from Gaia low-resolution spectra", Gaia Collaboration, Montegriffo, P., et al. A&A (2022).</p>
Secondary Data: Measuring Person-centred Care in German Nursing Homes – Exploring Construct Validity of the Dementia Policy Questionnaire using Adjusted Multiple Correspondence Analysis
<p>This is the secondary data set and R-Code of R statistical software (version 4.0.4) to explore construct validity of the German Dementia Policy Questionnaire using Adjusted Multiple Correspondence Analysis.</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of prediction models" to be published in the journal Animal - Open Space.</p>
An SEM Approach to Validating the Psychological Model of Musical Groove (Data Set)
<p>Data set for the study "An SEM Approach to Validating the Psychological Model of Musical Groove"</p>
STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale (validation data)
<p>Raw and refined data for the validation tests performed in the paper "STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale":<br> - rallpack 1, analytical, STEPS3, STEPS4, mesh scaling with STEPS4. It is so simple that I provide only the raw data. The space saving using refined data is minimal</p> <p>- rallpack 3, STEPS 3, STEPS 4. Both raw and refined data are provided.</p> <p>- caburst, STEPS 3, STEPS 4. Both raw and refined data are provided.</p>
Radiation Belt Forecast Model and Framework (RBFMF) 10 Hour Hindcast Validation Data
<div><strong>Archived data for the manuscript “On the Performance of a Real-Time Electron Radiation Belt Specification Model” Staples et al., submitted to Space Weather, 2024.</strong></div> <div> </div> <div>Data in these files specify the radiation belt through phase space density (PSD) in adiabatic coordinate system. Simulated PSD is from the Radiation Belt Forecast Model and Framework (RBFMF) 10 hour hindcast, and measured PSD is from an intercalibrated multi-mission observatory (Van Allen Probes, GOES 13, 15, GPS, MMS, and THEMIS). For detailed description of the method used in the computation of this data, see sections 2 and 3 of the submitted manuscript.</div> <div> </div> <div>The THEMIS, Van Allen Probe, MMS, and GOES data used in computations is publicly available via http://cdaweb.gsfc.nasa.gov </div> <div>The GPS data is available via https://www.ngdc.noaa.gov/stp/space-weather/satellite-data/satellite-systems/gps/</div> <div> </div> <div>Data Preperation: </div> <div>Adam Kellerman, akellerman@atmos.ucla.edu </div> <div>Frances Staples, frances.staples@atmos.ucla.edu</div> <div> </div> <div>Support for this work was provided by NASA grants 80NSSC20K1402 and 80NSSC23K0096, and NSF grant 2149782.</div> <div> </div> <div><strong>'PSD_10hrHC_Jan2016-Oct2018.mat'</strong></div> <div>Matlab data file format.</div> <div>Data time period: January 2016 - October 2018. </div> <div> Variable Descriptions:</div> <div>time - Serial date.</div> <div>InvMu - 1st adiabatic invariant coordinate, mu.</div> <div>InvK - 2nd adiabatic invariant coordinate, k.</div> <div>lstar - 3rd adiabatic invariant coordinate, l*.</div> <div>psd_sim - 10 hour radiaiton belt hindcast. Simulated PSD has dimensions corresponding to (time,lstar,mu,k). </div> <div>psd_obs - PSD observed by multi-mission dataset, with dimensions matching the simulated PSD (time, lstar, mu, k). </div> <div> </div> <div><strong>'PSD_10hrHC_Mar2019-Dec2020.mat'</strong></div> <div>Matlab data file format.</div> <div>Data time period March 2019 - December 2020. </div> <div> <div>Variable Descriptions:</div> </div> <div>time - Serial date.</div> <div>InvMu - 1st adiabatic invariant coordinate, mu.</div> <div>InvK - 2nd adiabatic invariant coordinate, k.</div> <div>lstar - 3rd adiabatic invariant coordinate, l*.</div> <div>psd_sim - 10 hour radiaiton belt hindcast. Simulated PSD has dimensions corresponding to (time,lstar,mu,k). </div> <div>psd_gps - PSD observed by the GPS constellation, with dimensions matching the simulated PSD (time, lstar, mu, k). </div> <div> </div> <div><strong>'RBSP_beacondata_Jan2016-Oct2018.mat'</strong></div> <div>Matlab data file format.</div> <div>Data time period January 2016 - October 2018. </div> <div>Variable Descriptions:</div> <div>time - Serial date.</div> <div>InvMu - 1st adiabatic invariant coordinate, mu.</div> <div>InvK - 2nd adiabatic invariant coordinate, k.</div> <div>lstar - 3rd adiabatic invariant coordinate, l*. l* dimensions correpond to the dimensions of the 2nd invariant, K (time, K)</div> <div>psd - real time PSD observed from Van Allen Probe b (beacon data), with dimensions corresponding to (time,mu,k). </div> <div>psd_err - observed error of beacon PSD data (i.e. Beacon_PSD - FinalRBSP_PSD).</div> <div>psd_q - observed quotient of beacon PSD data (i.e., Beacon_PSD/FinalRBSP_PSD).</div> <div> </div> <div><strong>'README.txt'</strong></div> <div>Downloadable file descriptions. </div>
Synchronously recorded gait kinematic data with Inertial Measurement Units and a photogrammetry system for a validation assessment
<h3>Description</h3> <p>A gait database of 32 healthy adult subjects was built , volunteers were between 20 and 63 y.o. (33.64 ± 12.44) and 71.88% were females. Every individual underwent a barefoot walking test recorded simultaneously with Inertial Measurement Units (IMUs) and the photogrammetry system Vicon. The dataset contains the kinematic gait information of the hip, knee, and ankle joints in the three planes of motion: sagittal, frontal, and transversal. </p> <p>The signals recorded by the IMUs are referred to as I(t) and were captured with a sampling frequency of 50 Hz, and those recorded by the photogrammetry system are called V(t) and were captured with a sampling frequency of 100 Hz. To perform a comparative study of both systems, the V(t) signals must be resampled to 50 Hz. Then, the delay between the two signals must be corrected to align them. Finally, gait cycles can be extracted for each pair of trials following the data information provided, obtaining a pair of waveforms for each gait cycle [I(t), V(t)]. A total of 268 synchronous gait cycles [I(t), V(t)] can be recovered and analyzed in the three planes of motion per limb.</p> <h3>Data information</h3> <ul> <li><em>raw_data</em>: folder containing the 32 subjects raw kinematic signals recorded with IMUs (sampling frequency 50 Hz) and photogrammetry system (sampling frequency 100 Hz) synchronously.<br> <ul> <li>For IMUs records: <ul> <li>Z: sagittal plane.</li> <li>X: frontal plane.</li> <li>Y: transversal plane.</li> </ul> </li> <li>For photogrammetry system records: <ul> <li>X: sagittal plane.</li> <li>Y: frontal plane.</li> <li>Z: transversal plane.</li> </ul> </li> </ul> </li> </ul> <ul> <li><em>captures_information.xlsx</em>: table containing the delay correction and the samples corresponding to the events of the gait cycles. The delay correction is the number of samples for which each photogrammetry signal V(t), after being resampled to 50 Hz, must be moved to be completely aligned with its synchronous IMUs signal couple I(t). <ul> <li>If the delay is positive (+) the V(t) signal must be delayed by adding zeros at the beginning.</li> <li>If the delay is negative (-) the V(t) signal must be moved forward by removing zeros at the beginning.</li> </ul> </li> </ul>
Reference data set used to validate the hybrid cropland map at 500m (Fritz, S. 2024)
<p>This is a reference data set for validation of the hybrid cropland map at 500m resolution for the year 2019 (Fritz, 2024, map <a title="Hybrid cropland map (GLAD/WorldCereal)" href="../doi/10.5281/zenodo.10818823" target="_blank" rel="noopener">available here</a>)</p> <p>Sampling design: random whithin areas of improvement, where the WorldCereal map is performing better (less errors) than the GLAD cropland map 2019. </p> <p>Number of sample sites: 500</p> <p>Method of data collection: visual interpreation of various sources of information, including very high resolution images and photos. </p> <p><br>Tool for data collection: <a href="https://www.geo-wiki.org" target="_blank" rel="noopener">Geo-Wiki</a></p>
Data from: Geomorphological signatures of known hurricanes and validation of theoretical emplacement formulations: coastal boulder deposits on Cuban low-lying marine terraces
<p>Here we show the models made from SfM photogrammetry for 5 boulders found on the Cuban island emplaced by three hurricanes on the low marine terrace. To generate a 3D-scaled SfM point cloud of each studied boulder, we adapted an easy SfM workflow using Agisoft Metashape software (version 1.7.2). We surveyed each CBD obtaining pictures between 0.5 m - 3 m distance from the boulder and also climbing on the boulder to maximise the diversity of picture angles. The survey was carried over a time lapse of ~30 minutes and under cloudy conditions to avoid shadows created by direct sunlight. For scaling SfM results and assessing the quality of measurements, we used six wooden balls as photogrammetric targets. The diameter of the balls is 30 ± 0.2 x 10-3 m, as verified by repeated caliper measurements. We complemented these targets with two 70 x 10 cm wooden scales. All these targets were used as reference scale bars to correct the spherical distortion inherent to the camera optics and perform an auto-calibration. We further used these targets to check possible distortion after post processing and the generation of the resulting 3D model. In order to create a point cloud and a solid volume, we followed the software workflow: we first aligned the images to identify tiepoints, which were then used in bundle adjustment to compute the 3D dense clouds in a second step.</p>
Figure 3 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 3. The dependence of the speed wind at a height Figure 4. In-situ MS data breakdown scheme for a
Figure 5 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 5. The dependence of the correlation coefficient between in-situ wind speed at the WS and remote sensing data on the orientation angle of the main quadrants (a) and their position relative to the White Sea coastline (b).
Leaf and wood classification framework for terrestrial LiDAR point clouds: Simulated data validation dataset
<p>Set of 200 3D point clouds used in the validation of "Leaf and wood classification framework for terrestrial LiDAR point clouds". This dataset is a collection of point clouds simulated by a Monte-Carlo ray tracing (librat) using four 3D tree models from the fourth phase RAMI exercise (Widlowski et al, 2015).</p>
Leaf and wood classification framework for terrestrial LiDAR point clouds: Field data validation dataset
<p>Set of 10 3D point clouds used in the validation of "Leaf and wood classification framework for terrestrial LiDAR point clouds". This dataset is a collection of single trees scanned around the globe, from different biomes (both forest and urban areas), using the Riegl VZ-400 terrestrial laser scanner.</p>
Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial (Data for independent validation study)
<p>Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health relevant data from patients. However, to date, limited data and results have been published detailing real-world patient compliance, demonstrating accuracy in target indications or examining what novel insights and clinical value can be derived. Here we present novel, digital mobility data from two studies: an independent, non-interventional validation study with elderly, naturally slow walking subjects, and a global, multi-site phase IIb clinical trial involving patients with age-related muscle loss and slow walking speed (sarcopenia). Based on these data, we validate the accuracy of a novel algorithm for capturing in-clinic and real-world gait speed in frail, slow-walking adults. We demonstrate the feasibility of continuous monitoring with a wearable inertial sensor in elderly adults in real-world settings, and propose minimum thresholds for compliance required for robust capture of gait behaviors in this population. We also show how simple, inferred contextual information, describing the length of a given walking bout, can explain some of the variation in real-world gait speed, and use this information to demonstrate for the first time a relationship between in-clinic performance and real-world gait speed behavior. This work lays a foundation for exploration of the clinical relevance and value of such measures and is a first step in building a more complete chain of evidence between standardized physical performance assessment, real-world behavior, and subjective perceptions of mobility, independence and health.</p> <p>This dataset contains data collected during the independent validation study: derived data from raw accelerometry data, and summary performance data.</p> <p>The full dataset, including raw accelerometry data, is available here: <a href="https://mueller-et-al-2019.s3.amazonaws.com/index.html">https://mueller-et-al-2019.s3.amazonaws.com/index.html</a></p>
Fig. 6. Platypterygiine ophthalmosaurid Platypterygius hercynicus Kuhn, 1946 in New data on the ichthyosaur Platypterygius hercynicus and its implications for the validity of the genus
Fig. 6. Platypterygiine ophthalmosaurid Platypterygius hercynicus Kuhn, 1946, SMSS "SGS", holotype, from the late Aptian of Salzgitter, Germany. Skull roof of the type−specimen in dorsal view. A. Photograph. B. Interpretative drawing by Kolb and Sander (2009). C. Reinterpretation based on the nasal morphology of MHNH 2010.4 and on a cast of SMSS "SGS". As in MHNH 2010.4 (see Fig. 2), the nasal is markedly elongated anteriorly and wide posteriorly, overlapping the postfrontal extensively. The nasal is coloured grey. The foramen situated at the nasal−frontal suture is interpreted as the internasal foramen, and the holotype lacks the parietal foramen and most of the frontal. Illustrations borrowed from Kolb and Sander (2009) and used with the permission of both authors.
Fig. 5. Platypterygiine ophthalmosaurid Platypterygius hercynicus Kuhn, 1946, MHNH 2010.4 in New data on the ichthyosaur Platypterygius hercynicus and its implications for the validity of the genus
Fig. 5. Platypterygiine ophthalmosaurid Platypterygius hercynicus Kuhn, 1946, MHNH 2010.4, from the late Albian of Saint−Jouin−Bruneval, France. Left quadratojugal in medial (A) and lateral (B) views. Photographs (A1, B1), explantatory drawings (A2, B2). The quadratojugal possess an extensive lateral facet for the missing postorbital and squamosal, and a large processus quadratus.
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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.