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8,038 results for “validation”
Experimental Validation video for paper "Safe Multimodal Communication in Human-Robot Collaboration"
<p>Experimental Validatio video for paper Safe Multimodal Communication in Human-Robot Collaboration.</p> <p>The video shows a collaborative manipulator executing a pick-and-place task communication with the user via a multimodal fusion architecture that fuses gesture and voice. The experiment compares a safe and an unsafe interaction and highlight the differences.</p>
Validating NISAR's cropland mapping approach and the USDA/NASS Cropland Data Layer against ground truth data in a fragmented urban agricultural region
<p>Field data used in manuscript:</p> <p>1 shapefile containing the ROI outline for which Sentinel-1 data was cropped</p> <p>1 shapefile containing the 93 fields investigated with their names, types and sizes as attributes </p> <p>8 annual csv data for active fields, consisting of 3 harvest dates and 5 planting dates. This list is after translating data from Farmlogic Report (not conducive to analysis in the format) and filtering for fields greater 1 acre. The study lateron further screened to use only fields greater than 2 acreas (0.81 hectares). </p> <p> </p>
Enhancing Microservice Reusability in MSA through the Reusable Microservices Framework: Development, Validation, and Evaluation
<p>The data of the paper: Enhancing Microservice Reusability in MSA through the Reusable Microservices Framework: Development, Validation, and Evaluation</p>
ISOLDE model and validation statistics to support: Guanine-containing ssDNA and RNA induce dimeric and tetrameric SAMHD1 in cryo-EM and binding studies
<p>These files provide the pdb atom coordinates and structural validation of the ISOLDE structural model (Fig. 6) contained in the manuscript "Guanine-containing ssDNA and RNA induce dimeric and tetrameric SAMHD1 in cryo-EM and binding studies" </p>
Validation of C3S SM combined v202212 vs C3S SM combined v202012 vs ERA5-Land v20190904
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ERA5-Land v20190904. URL: https://qa4sm.eu/ui/validation-result/80beb102-f5f4-4b1a-94f4-57008df9e322. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v202212 vs C3S SM combined v202012 vs ERA5-Land v20190904
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ERA5-Land v20190904. URL: https://qa4sm.eu/ui/validation-result/aaf66b58-ce30-4754-9349-8c48dcf81595. Produced on QA4SM (https://qa4sm.eu)
Synthetic and measured emission spectra for testing and validation of MEC-BP
<p>The data contain synthetic and measured (spark discharge) emission spectra in order to test and validate the results of the so called multi-element combinatory Boltzmann plot method. This is an OES-based approach to deduce the number concentration ratio of two elements present in a spark discharge plasma employed for binary NP generation in the gas phase. It is aimed to provide a tool for investigating the evolution of the concentration ratio corresponding to the ablated electrode materials in spark-based NP generators under real operational conditions. The method is based on the construction of a Boltzmann plot for the spectral line intensity ratios at every combination. The produced plots (the so-called multi-element combinatory Boltzmann plots, MEC-BPs) are directly related to the LTE plasma temperature and the number concentration ratio of the neutral atoms. The total concentration ratio – including ions – is calculated from a simple plasma model, without requiring further measurements.</p> <p>The python project in which the method is implemented can be found here: https://pypi.org/project/spark-mec-bp/0.1.0/</p>
Data for the publication: "Development and In-Vivo Validation of a Portable Phosphorescence Lifetime-Based Fiber-Optic Oxygen Sensor"
<p>This data set contains all raw data for the publication “Development and In-Vivo Validation of a Portable Phosphorescence Lifetime-Based Fiber-Optic Oxygen Sensor”:</p> <p>- Raw sensor data</p> <p>- Python scripts</p> <p>- particle photon scripts</p> <p>- CAD Drawings</p> <p>- PCB Designs</p>
Is my model fit for purpose? Validating a population model for predicting freshwater fish responses to flow management
<p>Models based on ecological processes ("process-explicit models") are often used to predict ecosystem responses to environmental changes or management scenarios. However, models are imperfect and need to be validated, ideally by testing their assumptions and outputs against independent empirical data sets. Examples of validation of process-explicit models are rare. Recently, stochastic population models have been developed to predict the likely responses (over 10-120 years) of a riverine fish (golden perch, Macquaria ambigua) to flow management in the Murray-Darling Basin (MDB) in eastern Australia, one of the world's most regulated river basins. Declines of golden perch (and other species) are a direct consequence of altered hydrology, and managers require information to predict how fish will respond to possible future hydrological conditions to guide the substantial investments in flow management. Here, we use two independent field data sets to validate our population model. We compared model predictions to observed trends to ask: (1) how do predicted population sizes and growth rates compare to observed data? (2) does the correlation between predicted and observed population sizes and growth rates vary among populations? (3) does the correlation between predicted and observed population sizes and growth rates vary across observed hydrological conditions? and (4) how do modelled and observed fish movement rates compare? We found reasonable correlations between fish population sizes and growth rates as predicted by the model and observed in independent data sets for several populations (Aim 1) but the strength of these correlations varied among populations (Aim 2) and hydrological conditions (Aim 3). Predicted and observed fish movement rates were strongly correlated (Aim 4). Population models are frequently used in conservation decision-making but are rarely validated. We demonstrate that: (1) validation can identify model strengths and weaknesses; (2) observed data sets often have inherent limitations that can preclude robust validations; (3) validation is likely be more common if appropriate observed data sets are available; and (4) validation should consider the purpose of modelling. Wider consideration of these messages would contribute to more critical examinations of models so they can be most appropriately used in conservation decision-making.</p>
Data for validation and norming of the Danish KIDSCREEN-10 child/adolescent version in a national representative sample of school pupils in grades five through eight
<p>Data for for analyses in Measuring child and adolescent well-being in Denmark: validation and norming of the Danish KIDSCREEN-10 child/adolescent version in a national representative sample of school pupils in grades five through eight. Contains the following variables:</p> <p>Variables Kid1 to Kid10 are the KISDCREEN 10 items. Category labels are the orginal from the KIDSCREEN consortium (Danish version). Items Kid3 and Kid4 are reversed coded according to the KIDSCREEN coding manual. Valus have been recoded from 1-5 to 0-4 for the purpose of item analyses.</p> <p>Language (spoken in the home): 1 = Danish, 2 = other</p> <p>School: 1 = public, 2 = private</p> <p>Sex: 1 = boy, 2 = girl</p> <p>Grade (level): 1 = 5th grade, 2 = 6th grade, 3 = 7th grade, 4 = 8th grade</p> <p>Sample (random): 1 to 7</p>
Validation of the EQ-5D-3L in Patients with Severe COVID-19 One Year After Hospital Discharge
<p>This is a prospective cohort study database aimed at validating the EQ-5D-3L in patients with COVID-19 who underwent invasive mechanical ventilation, assessing their quality of life one year after hospital discharge.</p>
PrisonLIFE project - Adaptation, translation equivalence and content validity of the MQPL survey in Serbian
<p>This dataset was created as part of the PrisonLIFE project, funded by the Science Fund of the Republic of Serbia under Grant No. 7750249.</p> <p>This dataset includes information about the adaptation process, translations, and content validity assessment of the Serbian version of the Measuring the Quality of Prison Life (MQPL) survey, along with details about participants in focus groups and their feedback. The study references the works by Liebling et al. (2012) and Milićević et al. (2023).</p>
Validation of Fracture Caging to Contain Hydraulic Fractures: Timeseries, Videos, and Model Script
<p>The data file include an Excel spreadsheet and two videos for each experimental test.</p> <p>You can start with reading the ReadMeFirst.txt file to understand the whole structure of the dataset.</p> <p>The caging_model.txt file includes python codes to calculate critical flow rates and uncaged fracture radius according to the theory that the authors developed and will be published soon.</p>
Chroma training, test, and validation sets.
<p>Column <strong>pdb</strong> contains the PDB codes for entries used in training Chroma, while column <strong>split</strong> designates whether the corresponding entry was part of the training set (value <strong>train</strong>), the test set (value <strong>test</strong>), or the validation set (value <strong>validation</strong>).</p>
Dataset for Evaluating the Construct Validity of the Charité Alarm Fatigue Questionnaire
<p>These are the datasets that we used for evaluating the construct validity of the Charité Alarm Fatigue Questionnaire (CAFQa) in a forthcoming publication. All items were answered on a 5-point Likert scale and were scored by us as follows: -2/“I do not agree at all”, -1/“I do not agree”, 0/“I agree in part”, 1/“I agree”, 2/"I very much agree".</p> <p>A previous version of this upload included only the data of Study 1. A new version provides the data of Study 2. Please refer to the methods section of the forthcoming publication for more details.</p> Variable names and their corresponding item. Items marked with <table><tbody><tr> <th>Variable Name</th> <th>CAFQa Item</th> </tr> </tbody><tbody> <tr> <td>procedural_instruction</td> <td>In my ward, procedural instruction on how to deal with alarms is regularly updated and shared with all staff.<sup>a</sup></td> </tr> <tr> <td>respond_quickly</td> <td>Responsible personnel respond quickly and appropriately to alarms.<sup>a</sup></td> </tr> <tr> <td>motivation_decrease</td> <td>With too many alarms on my ward, my work performance, and motivation decrease.</td> </tr> <tr> <td>physical_symptoms</td> <td>Too many alarms trigger physical symptoms for me, e.g., nervousness, headaches, and sleep disturbances.</td> </tr> <tr> <td>ward_floor</td> <td>The acoustic and visual monitor alarms used on my ward floor and in my nurse station allow me to assign the patient, the device, and the situation clearly.<sup>a</sup></td> </tr> <tr> <td>reduce_concentration</td> <td>Alarms reduce my concentration and attention.</td> </tr> <tr> <td>alarm_limits</td> <td>Alarm limits are regularly adjusted based on patients' clinical pictures (e.g., blood pressure limits for conditions after bypass surgery).<sup>a</sup></td> </tr> <tr> <td>interrupt_workflow</td> <td>My or neighboring patients' alarms or crisis alarms frequently interrupt my workflow.</td> </tr> <tr> <td>alarms_confuse</td> <td>There are situations when alarms confuse me.</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> Other variables in the data set. <table><tbody><tr> <th>Variable Name</th> <th>Explanation</th> </tr> </tbody><tbody> <tr> <td>self_reported_AF</td> <td>self-estimated alarm fatigue in percent</td> </tr> <tr> <td>estimated_false_alarms</td> <td>perceived rate of false alarms in the participant's ICU</td> </tr> <tr> <td>monthly_time_on_ICU</td> <td>the average number of workdays per month in an intensive care or monitoring area</td> </tr> <tr> <td>ICU_experience</td> <td>number of years/months of ICU experience</td> </tr> <tr> <td>profession</td> <td>physician, nurse, or supporting nurse</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><strong>Members of the Study Group</strong> <strong>in alphabetical order</strong>: <em>Dr. med. Mirza Aghamov</em><sup><em>1</em></sup><em>, Prof. Dr. med. Manfred Blobner<sup>2</sup>, Prof. Dr. med. Ulrich Frey<sup>3</sup></em><em>, Prof. Dr. Christian von Heymann<sup>4</sup></em><em>, Prof. Dr. med. Bettina Jungwirth</em><sup><em>1</em></sup><em>, Dr. med. Dragutin Popovic<sup>4</sup></em><em>, Prof. Dr. med. Michael Sander<sup>5</sup></em><em>. </em></p> <p>1: <em>Department of Anesthesiology and Intensive Care Medicine, University Hospital Ulm, Ulm University, Ulm, Germany</em></p> <p>2: <em>Technical University Munich, School of Medicine, Klinikum Rechts der Isar, Department of Anaesthesiology & Intensive Care Medicine, Munich, Germany</em></p> <p>3: <em>Department for Anesthesiology, Surgical Intensive Care, Pain and Palliative Medicine, Marien Hospital Herne – Universitätsklinikum der Ruhr-Universität Bochum, Herne, Germany</em></p> <p>4: <em>Department for Anaesthesiology, Intensive Care Medicine and Pain Therapy, Vivantes Klinikum im Friedrichshain, Berlin, Germany</em></p> <p>5: <em>Department for Anaesthesiology, Intensive Care Medicine and Pain Therapy, Justus Liebig University, Giessen, Germany</em></p> <p> </p>
Polish validation data of the MASC (multisource assessment of children's social competence) instrument
<p>Dataset of the published open access article: Wiza, A., Koszałka-Silska, A., Jaguszewski, M. <em>et al.</em> Polish adaptation of multisource assessment of children’s social competence. <em>Sci Rep</em> <strong>13</strong>, 12128 (2023). https://doi.org/10.1038/s41598-023-39292-2</p> <p> </p>
validation DISCO step 2 - in-service teachers
<p>data was collected with in-service teachers in the spring of 2018. All schools within one Flemish city were invited to participate. Of the 40 contacted schools, 28 participated in the study, translating to a participation rate of 70%. All teachers within each school were invited to fill out the online survey (N = 600). respondents filled out an online survey on their professional beliefs and self-efficacy with regards to diversity in the classroom</p>
Validation data for macromolecular occupancy estimation
<p>Macromolecular densities used to validate OccuPy v0.1:</p> <ol> <li>Synthetic densities of pdb 1UXI with altered: <ul> <li>Occupancy of chain A</li> <li>Occupancy of NAD cofactors </li> <li>Bfactor of chain A at occupancy 0.5 of chain A</li> </ul> </li> <li>Reconstructions of rotavirus spike protein: <ul> <li>No selection for spike, subsets of various particle numbers </li> <li>Positive selection (enriched) for spike, subsets of various particle numbers</li> </ul> </li> </ol>
DAeVid - Dummy for Aerodynamic Validation
<p>The simplified athlete geometry, denoted DAeVid (Dummy for Aerodynamic Validation), was prepared by merging, sculpting and smoothing existing athlete 3D scans to generate an anonymized 3D model. The legs and parts of the arms were removed to simplify the geometry, and make it more generalized across multiple sports disciplines. The intented purpose is for validation and comparison of,computational fluid dynamics models within sports aerodynamics.</p>
Z/γ + jets event samples at 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>
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.