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8,038 results for “validation”
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>
Forced degradation of five drug substances for meRgeION validation
<p>Drug substance is subjected to acid hydrolysis and oxidative stress against a baseline condition. The goal here is to profile and identify various degradation products of five APIs by setting stress conditions more severe than recommended storage, in order to further understand the underlying chemical mechanisms</p> <p>Non-targeted profiling in DDA mode was conducted for samples at Day 0 (1 sample) and Day 7 (3 samples for 3 conditions) on Orbitrap Fusion Lumos. Converted data files were submitted to MZMine for feature detection. Folder change of each feature under different conditions was calculated in excel. We then built a LC-MS/MS data processing pipeline in meRgeION that enables degradation product annotation by searching the spectral database <em>Drug+</em> (lib_drug_plus_matrix.RData) and mechanism understanding through FBMN. </p> <p> </p>
A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects: dataset
<p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>The enhanced mechanical behavior of polymer nanocomposites with spherical filler particles is attributed to the formation of matrix-filler interphases. The nano-scale leads to particularly high interphase volume fractions while rendering experimental investigations extremely difficult. Previously, we introduced a molecular dynamics-based interphase model capturing the crucial spatial profiles of elastic and inelastic properties inside the interphase. This contribution demonstrates that our model captures polymer nanocomposites’ essential characteristics reported from experiments. To this end, we thoroughly verify and validate the model before discussing the resulting local plastic strain distribution. Furthermore, we obtain a reinforcement in terms of the overall stiffness for smaller particles and higher filler contents, while the influence of particle spacing seems negligible, matching experimental observations in the literature. This paper proposes a methodology to unravel the underlying complex mechanical behavior of polymer nanocomposites and to translate the findings into engineering quantities accessible to a broader audience and technical applications.</p> </blockquote> <p><br> <br> <strong>Contact:</strong><br> Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong><br> Abaqus version R2018</p> <p><strong>License:</strong><br> Creative Commons Attribution 4.0 International<br> <br> <strong>Context:</strong><br> Data set supplementing journal paper:<br> [1] Ries, M.; Weber, F.; Possart, G.; Steinmann, P. & Pfaller, S., “A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects”, Composites Part A: Applied Science and Manufacturing, 2022, 107094.<br> This dataset contains the results presented in [1] and the necessary data to obtain those.</p> <p><br> <strong>Content:</strong></p> <p>simulation folder denotation (“-” used instead of decimal points):<br> distance_particles _ radius_particle _ thickness_ip _ num_ip _ length_box _ factor_el_length _ fraction_box_length _ switch_mat_ip</p> <p>with</p> <ul> <li> distance_particles: center distance of the nanoparticles in nm</li> <li> radius_particle: radius of the nanoparticles in nm</li> <li> thickness_ip: thickness of the interphase layers in nm</li> <li> num_ip: number of interphase layers</li> <li> length_box: box edge length in nm</li> <li> factor_el_length: factor scaling the element length on the arcs of the interphase layers (element length = factor_el_length * thickness_ip)</li> <li> fraction_box_length: matrix element length = length_box / fraction_box_length</li> <li> switch_mat_ip: if = 0: interphases are assigned their actual material properties, if = 1: interphases are assigned the material properties of the bulk</li> </ul> <p> <br> <br> each simulation folder contains the following file types:</p> <ul> <li> .cae: Abaqus model database, containing parts, meshes, loads, etc.</li> <li> .dat: Printed output from the analysis input file processor, as well as printed output of selected results written during the analysis</li> <li> .inp: Analysis input file</li> <li> .log: Log file, which contains start and end times for modules run by the current execution procedure</li> <li> .msg: Diagnostic or informative messages about the progress of the solution</li> <li> .odb: Output database containing all results data from an Abaqus analysis</li> <li> .sta: Status file with increment summaries</li> </ul> <p><strong>folder structure:</strong></p> <ul> <li>Standard_case:<br> simulation folders of the standard close (particle center distance: 5.1776 nm) and distant (particle center distance: 7.9481 nm) cases (particle radius: 2 nm, filler content 0.054 vol.%, number of interphase layers: 4, factor_el_length: 1.0) and further particle center distances</li> <li>Layers:<br> simulation folders with different numbers of interphase layers, i.e., different values for num_ip, based on the standard close and distant cases <ul> <li>Close_case</li> <li>Distant_case</li> </ul> </li> <li>Mesh:<br> simulation folders with different mesh qualities, i.e., different values for factor_el_length, based on the standard close and distant cases <ul> <li>Close_case</li> <li>Distant_case</li> </ul> </li> <li>Particle_size:<br> simulation folders with different particle sizes <ul> <li>2_nm: simulation folders with particle surface distance 2 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> <li>4_nm: simulation folders with particle surface distance 4 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> <li>8_nm: simulation folders with particle surface distance 8 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> </ul> </li> </ul>
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>
High-throughput metabolomics for the design and validation of a diauxic shift model
<p>Untargeted metabolomics on ten different regulatory strains in <em>Saccharomyces cerevisiae, </em>(BY4741). Samples were taken before and after the diauxic shift, to investigate regulatory consequences of gene deletions and their roles during the substantial metabolic reconfiguration that is the diauxic shift. The analysis of samples was performed on an Agilent UHPLC-qTOF-MS system which consisted of a 1290 II Infinity series UHPLC system with a 6550 UHD iFunnel accurate-mass qTOF spectrometer.</p> <p>Data-set used in: <a href="https://www.nature.com/articles/s41540-023-00274-9">High-throughput metabolomics for the design and validation of a diauxic shift model</a></p>
Validation of ERA5 v20190613 vs CGLS SWI 1km V1.0
QA4SM validation of soil moisture data: ERA5 v20190613 vs CGLS SWI 1km V1.0. URL: https://qa4sm.eu/ui/validation-result/f5b6c916-ebea-4762-87f9-677ac1263f35. Produced on QA4SM (https://qa4sm.eu)
Text-fig. 6. Correlation of the Cheringoma and Mazamba formations on the basis of benthic foraminiferans and mammals respectively. Identifications of foraminiferans are from Newton (1924) and Abrard (1928), and the ranges of foraminiferans are from Sella-Kiel et al. (1998). The time scale is from Gradstein et al. (2020). The distribution of Nummulites atacicus is included, but it is not known whether it is reworked from older deposits. If the identification is valid, it would support the thesis that there was a period of Ypresian deposition in the vicinity during which remains of the species were fossilised. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 6. Correlation of the Cheringoma and Mazamba formations on the basis of benthic foraminiferans and mammals respectively. Identifications of foraminiferans are from Newton (1924) and Abrard (1928), and the ranges of foraminiferans are from Sella-Kiel et al. (1998). The time scale is from Gradstein et al. (2020). The distribution of Nummulites atacicus is included, but it is not known whether it is reworked from older deposits. If the identification is valid, it would support the thesis that there was a period of Ypresian deposition in the vicinity during which remains of the species were fossilised.
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>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 5. Differences in Validation Criteria between Classical Engineering Sciences and the Field of Brain- Like Artificial Intelligence for Automation
<p>A usual validation procedure in classical fields of engineering and computer sciences as well as in Applied AI, which is currently the dominant AI research domain, is to analyze and implement different potential methods to solve a given problem and to then compare their performance. What is thus usually desired are comparable, quantifiable results. In comparison, the starting situation is<br> different in the field of Brain-Like AI (see Figure 5).</p>
Master curve BZN_valids_ER_1921-20
<div>Fractesus project. Fracture test mini-CT. Raw data JRQ. BZN. </div>
Documentation artifacts for conversational SRS in chatbots: a systematic review and a new meta-model proposal and validation
<p>Context: Chatbots are complex applications due to their capacity to engage and maintain a conversation with humans. However, the conversational-related requirements of chatbots are hard to elicit, document, and test. Another challenge is the documentation, since there are not so many directions on how to register and test subjective requirements. </p> <p>Methods: We followed systematic literature review (SLR) guidelines and identified 42 studies that address the artifacts used by practitioners to document conversational-related requirements in literature. We also investigated what conversational requirements are addressed in requirements documentation.</p> <p>Results: The main results indicate that UML diagrams, prototypes, tables of requirements, conversational flows, and scenarios are present in most chatbot documentation. Except for UML diagrams, those artifacts are used to document standard requirements or conversational requirements. In those artifacts, context-dependent behavior, assertivity, error handling, and human-like attitude are the most approached conversational requirements in the studies. In sequence, based on our findings, we proposed the conversational integrated map and validated it by conducting a 2-step questionnaire among software practitioners experience in requirements engineering and chatbot requirement's specification.</p> <p>Conclusion: Future studies should investigate if existing artifacts are enough to address all complex aspects of chatbots' specific conversational requirements or require further adaptation. Future studies should investigate specific SRS needs for different types of softwares.</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>
First Principles Validation of Energy Barriers in Ni75Al25
<p>The data from the paper - First Principles Validation of Energy Barriers in Ni<sub>75</sub>Al<sub>25</sub></p> <p>Read the read me for explanation of what is in each folder</p>
CEOS LPV DIRECT V2.1: A database of upscaled LAI, FAPAR and Fcover values for satellite biophysical product validation
<p>Ground references of high quality are needed to validate satellite-based products. The DIRECT V2.1 database compiles Leaf Area Index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR) and fraction of vegetation cover (FCover) averaged values over a 3 km x 3 km area. The ground data was upscaled using high spatial resolution imagery following CEOS WGCV LPV (so called CEOS LPV) LAI validation good practices (Fernandes et al., 2014) to properly account for the spatial heterogeneity of the site. Ground measurements performed during several international Cal/Val activities, including VALERI, BigFoot, SAFARI-2000, CCRS, Boston University, were compiled by S. Garrigues (Garrigues et al., 2008) in the DIRECT database, and later ingested in the CEOS LPV OLIVE tool (Weiss et al., 2014) for accuracy assessment.</p> <p>F. Camacho reviewed DIRECT to remove those sites without understory measurements (Camacho et al., 2013) and after that expanded the database adding the ImagineS network of sites (Camacho et al., 2021). DIRECT V2.1 is the last update including 44 new sites from China (Fang et al., 2019; Song et al., 2021) and 2 more sites from ESA FRM4Veg project (Brown et al., 2021).</p> <p>The CEOS LPV DIRECT V2.1 database constitutes a major effort of the international community to provide ground reference for the validation of satellite-based LAI and FAPAR ECVs, with a total of 176 sites around the world (7 main biome types) and 280 LAI values, 128 FAPAR and 122 FCOVER values covering the period from 2000 to 2021.</p> <p> </p> <p><strong><u>Data description</u></strong></p> <ul> <li>LAI, LAIeff, FAPAR and FCOVER upscaled values over 3 km x 3 km.</li> <li>LAI_NoUnderstory, refers to sites where only overstory was measured and thus are not recommended for accuracy assessment of satellite products.</li> </ul> <p><strong><u>File contents</u></strong></p> <p>A Header " Sites":</p> <p>General information for each site (coordinates, landcover, method, reference)</p> <p>For each variable:</p> <ul> <li># number of the site (site description in Sites)</li> <li>Lat_cen, latitude centre of 3km x 3km</li> <li>Lon_cen, longitude centre of 3km x 3km</li> <li>Site name, name of the site</li> <li>Date, MM/DD/YYYY</li> <li>Mean, average value over 3km x 3km</li> <li>Uncert, uncertainty over 3 km x 3km (STD)</li> </ul> <p> </p>
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>
De Jong Gierveld Loneliness Scale: validity, reliability and fairness in Peruvian adults
<p><span><strong><span>Abstract:</span></strong></span></p> <p><strong><span>Background: </span></strong><span>Loneliness, transient or long-lasting, constitutes one of the mental health problems of great public impact with peculiar characteristics. It can be defined as a subjective and unpleasant experience, in turn associated with other serious psychological symptoms. This variable in question has not been addressed in a timely manner, among other reasons due to the scarcity of instruments for specific populations. Although the De Jong Gierveld Loneliness Scale (</span><span>DJGLS), <span>based on Weiss' multidimensional model, has been adapted and validated in different contexts, it is still insufficient in Peru. Precisely, the objective was to determine the psychometric properties of the DJGLS, </span></span><span>its internal structure and factorial invariance.</span></p> <p><strong><span>Methods:</span></strong><span> An online survey of 1248 Peruvians between 18 and 70 years of age (M= 27.37, SD= 11.29) from all regions was used. The validation of the DJGLS was analyzed with Exploratory Factor Analysis </span><span>(EFA), <span>Confirmatory Factor </span>Analysis (CFA), <span>convergent validity, measurement invariance and internal consistency reliability.</span></span></p> <p><strong><span>Results: </span></strong><span>Psychometric properties were found with adequate values in its internal structure by means of the CFA, where it was found that the components of the scale are interrelated and the data matrix is factorizable. Here we present a model of two specific factors and one general factor, which is consistent with theory and has practical utility, revealing acceptable reliability values and invariance between sexes.</span></p> <p><strong><span>Conclusions:</span></strong><span> Adequate psychometric properties, which allow for a better data collection process in further related research, are revealed.</span></p> <p><span> </span></p> <p><strong><span>Keywords</span></strong><span>: loneliness; validity; reliability; fairness; adults.</span></p>
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>
An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation: datasets
<p>This data record contains datasets used in the study "An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation":</p> <ul> <li>The <code><span>final_design.ply</span></code> file contains the mesh corresponding to the final artefact design.</li> <li>The <code><span>material_measurements.nc</span></code> file contains goniophotometer records for the material reflectance.</li> <li>The <code><span>artefact_measurements.nc</span></code> file contains goniophotometer records for the artefact reflectance.</li> </ul>
Figure 15 in Linguimaera Pirlot, 1936 (Crustacea, Amphipoda, Melitidae), a valid genus
Figure 15. Linguimaera tias sp. nov., male, female (Victoria, Port Phillip Bay). P7´male' T in scale y = 1 mm; all other in scale x = 1 mm.
Figure 16 in Linguimaera Pirlot, 1936 (Crustacea, Amphipoda, Melitidae), a valid genus
Figure 16. Linguimaera sp. (south-western Bass Strait). Gn1, 2 small male, Gn2 large male, Gn1, 2 hyperadult male in scale x = 1 mm; Gn1´male hyperadult in scale y = 1 mm.
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.