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939 results for “Validation studies”
The Multidimensional Scale of Perceived Social Support (MSPSS): Validation Study of the Slovak version for adolescents
<p>Dataset for an article called The Multidimensional Scale of Perceived Social Support (MSPSS): Validation Study of the Slovak version for adolescents. </p>
Dataset: Multi-Stakeholder Validation of Entrustable Professional Activities for a Family Medicine Care of the Elderly Residency Program: A Focus Group Study
<p>ZOOM Meeting transcripts from 5 stakeholder group meetings used in the study. </p>
RGB data [front and side views] of the wearable system validation study
<p>This is RGB data of the wearable system validation study. Two video cameras were used. One had the front view. Another was setted at the left side. The recordings were synchronized with Qualisys and Xsens systems.</p> <p>Data structure:</p> <ul> <li>Subjxx: Subject folder <ul> <li>xxxx_front_anonymozied.avi : the front view video of certain movement.</li> <li>xxxx_side_anonymozied.avi : the side view video of certain movement.</li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Validation of LEXO® End-Effector Robot-assisted Training in Patients with Gait Deficits after Central Nervous System Diseases. A Descriptive Cross-sectional Study.
Open the record for dataset details and reuse information.
Outputs from fitted models across the cross-validation scenarios for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'
<p>Three Zenodo repositories are linked to the preprint <em>Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area </em>(Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>):</p> <ul> <li>Data, scripts and, code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> <p>This repository contains the outputs from fitted models across the cross-validation scenarios.</p> <p>In the folder <em>Ouputs_cross_validation</em>, each species is represented by a .RData file, numbered from 1 to 77 (excluding 7, which corresponds to <em>Bombycilla garrulus</em>; see the preprint for details). This dataset is specifically used to generate Figure 1, which shows the AUC of various cross-validation scenarios. To reproduce this figure in R, place all the files in the <em>Results/Fitted_models</em> folder and run the <em>Models_Outputs.R</em> script located in the <em>Results</em> folder of Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052.</a></p> <p>Note: These data have been separated due to memory requirements (23.14GB).</p>
Transactional e-health literacy and the use of e-health services in Polish society: validation of the instrument and a cross-sectional study
<p>Three data sets used in the paper titled "Transactional e-health literacy and the use of e-health services in Polish society: validation of the instrument and a cross-sectional study" for exploratory and confirmatory factor analysis in the validity assessment of the Polish version of Transactional e-Health Literacy Instrument and for the analysis of the impact of Transactional e-Health Literacy components on the use of e-health services. </p> <p>This research was funded in whole by the National Centre of Science, Poland within the project titled "Determinanty akceptacji i gotowości wykorzystania aplikacji e-zdrowia i technologii Zdrowia 4.0 w polskim społeczeństwie" (Grant No 2021/43/B/NZ7/02140). </p> <p>For the purpose of Open Access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.</p>
Data and code used for 'Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children'
<p>This repository contains all data necessary to reproduce the results for the manuscript titled 'Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children'.</p>
Dataset for Validity and reliability study of Personal Resource Questionnaire-2000 Indonesia version (PRQ2000-INA) to measure perceived social support among people with dementia in Indonesia
<p>This is a dataset for "Validity and reliability study of Personal Resource Questionnaire-2000 Indonesia version (PRQ2000-INA) to measure perceived social support among people with dementia in Indonesia"</p>
Nerve ultrasound for diagnosing chronic inflammatory neuropathy: a multicenter validation study
Objective <p>To validate the diagnostic accuracy of a previously described short sonographic protocol to identify chronic inflammatory neuropathy (CIN), including chronic inflammatory demyelinating polyneuropathy (CIDP), Lewis Sumner syndrome (LSS) and multifocal motor neuropathy (MMN) and to determine the added value of nerve ultrasound to detect treatment-responsive patients compared to nerve conduction studies (NCS) in a prospective multicenter study.</p> Methods <p>We included 100 consecutive patients clinically suspected of CIN in three centers. The study protocol consisted of neurological examination, laboratory tests, NCS and nerve ultrasound. We validated a short sonographic protocol (median nerve at forearm, upper arm, and C5 nerve root) and determined its diagnostic accuracy using the EFNS/PNS criteria of CIDP/MMN (reference standard). In addition, to determine the added value of nerve ultrasound in detecting treatment-responsive patients, we used previously published diagnostic criteria based on clinical, NCS, sonographic findings and treatment response (alternative reference standard).</p> Results <p>Sensitivity and specificity of the sonographic protocol for CIN according to the reference standard were 87.4% and 67.3%, respectively. Sensitivity and specificity of this protocol according to the alternative reference standard were 84.6% and 72.8%, respectively, and of NCS 76.1% and 93.4%. With addition of nerve ultrasound 44 diagnoses of CIN were established compared to 33 diagnoses with NCS alone.</p> Conclusions <p>A short sonographic protocol shows high diagnostic accuracy for detecting CIN. Nerve ultrasound is able to detect up to 25% more patients who respond to treatment.</p> Classification of evidence <p>This multicenter study provides Class IV evidence that nerve ultrasound improves diagnosing of CIN.</p>
FIGURE1. Map showing the sampling localities and distribution of all valid Korean species identified and described so far. The black line between Stations 6a and 6b represents a man-made road with no gap for water entry from either side. Stations 1 to 15 refer to the study sites in Vakati et al. (2019), station 16 refers to the study site in Kim et al. (2017). in -On- two- new- species- of- Nannopus- Brady,- 1880- (Copepoda:- Harpacticoida Nannopodidae)-from-intertidal-mudflats-of-the-Korean-west-coast-(Yellow-Sea)
FIGURE1. Map showing the sampling localities and distribution of all valid Korean species identified and described so far. The black line between Stations 6a and 6b represents a man-made road with no gap for water entry from either side. Stations 1 to 15 refer to the study sites in Vakati et al. (2019), station 16 refers to the study site in Kim et al. (2017).
Replication Kit: "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"
<p><strong>Replication Kit for the Paper "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"</strong><br> This additional material shall provide other researchers with the ability to replicate our results. Furthermore, we want to facilitate further insights that might be generated based on our data sets.</p> <p><strong>Structure</strong><br> The structure of the replication kit is as follows:</p> <ul> <li><strong>additional_visualizations</strong>: contains additional visualizations (Venn-Diagrams) for each projects for each of the data sets that we used</li> <li><strong>data_analysis</strong>: contains python scripts that we used to analyze our raw data</li> <li><strong>data_collection_tools</strong>: contains all source code used for the data collection, including the used versions of the <a href="https://github.com/comfort-framework">COMFORT framework</a>, the <a href="https://github.com/ftrautsch/BugFixClassifier">BugFixClassifier</a>, and the used tools of the <a href="https://github.com/smartshark">SmartSHARK environment</a>;</li> <li><strong>mongodb_no_authors</strong>: Archived dump of our MongoDB that we created by executing our data collection tools. The "comfort" database can be restored via the mongorestore command.</li> </ul> <p><br> <strong>Additional Visualizations</strong><br> We provide two additional visualizations for each project:<br> 1) <project_name>\_disj\_ieee\_venn (visualizations for the DISJ data set)<br> 2) <project_name>\_all\_ieee\_venn (visualizations for the ALL data set)</p> <p>For each of these data sets there exist one visualization for each project that shows four Venn-Diagrams for each of the different defect types. These Venn-Diagrams show the number of defects that were detected by either unit, or integration tests (or both).</p> <p>Furthermore, we added boxplots for each of the data sets (i.e., ALL and DISJ) showing the scores of unit and integration tests for each defect type.</p> <p><br> <strong>Analysis scripts</strong><br> Requirements:<br> - python3.5<br> - tabulate<br> - scipy<br> - seaborn<br> - mongoengine<br> - pycoshark<br> - pandas<br> - matplotlib</p> <p>Both python files contain all code for the statistical analysis we performed.</p> <p><strong>Data Collection Tools</strong><br> We provide all data collection tools that we have implemented and used throughout our paper:</p> <ul> <li><strong>BugFixClassifier</strong>: Used to classify our defects.</li> <li><strong>comfort-core</strong>: Core of the comfort framework. Used to classify our tests into unit and integration tests and calculate different metrics for these tests.</li> <li><strong>comfort-jacoco-listner</strong>: Used to intercept the coverage collection process as we were executing the tests of our case study projects.</li> <li><strong>jSHARK</strong>: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Java).<strong> </strong></li> <li><strong>pycoSHARK</strong>: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Python).</li> <li><strong>tools-changedistiller</strong>: Version of ChangeDistiller that we used within our comfort-core framework.</li> <li><strong>vcsSHARK</strong>: Used to collect data from the VCSs of the projects.</li> </ul> <p> </p> <p> </p>
Case studies from doubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models
<p>Case studies from "doubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models"</p>
Case studies from: Sequence assignment validation in protein crystal structure models with checkMySequence
<p>Case studies from "Sequence assignment validation in protein crystal structure models with checkMySequence"</p>
Resulting Pseudonymized Classification Data for "Automatic Core-Developer Identification on GitHub: A Validation Study"
<p>Resulting pseudonymized classification data of the study "Automatic Core-Developer Identification on GitHub: A Validation Study". The corresponding input data, from which the output data have been derived, can be found here: https://zenodo.org/record/7775078</p>
Pseudonymized Raw Data for "Automatic Core-Developer Identification on GitHub: A Validation Study"
<p>Pseudonymized raw data (i.e., commit data and issue data for 25 GitHub projects) that has been used as input for the study published as "Automatic Core-Developer Identification on GitHub: A Validation Study".</p> <p>The pseudonymized raw data has been extracted via the tools <a href="https://github.com/se-sic/codeface/">Codeface</a>, <a href="https://github.com/se-sic/GitHubWrapper/">GitHubWrapper</a>, <a href="https://github.com/mehdigolzadeh/BoDeGHa">BoDeGHa</a>, and <a href="https://github.com/se-sic/codeface-extraction/">codeface-extraction</a> (and additional manual corrections after sanity checks).</p>
thus and genetic % 1 than less indicates Green . ) kb 15 . ca ( Ixodes of ) individuals 40 ( species bold 34 in of are genomes study present mitochondrial the in entire sequenced the Species among. differences reference for genetic, species ) % ( same Pairwise the. 3 from FIGURE sequences in A new subgenus, Australixodes n. subgen. (Acari: Ixodidae), for the kiwi tick, Ixodes anatis Chilton, 1904, and validation of the subgenus Coxixodes Schulze, 1941 with a phylogeny of 16 of the 22 subgenera of Ixodes Latreille, 1795 from entire mitochondrial genome sequences
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Comparison of Tele Dentistry with Clinical Diagnosis (on-site) by Employing ICDAS in the Population of Rawalpindi & Islamabad: A Validation Study
<p>The study was cross sectional validation study was done from Jan 2022 to Jan 2023 in private<br> clinics of Rawalpindi and Islamabad in which tele dental diagnosis was compared with onsite<br> clinical diagnosis by using the instrument of ICDAS. A dentist primarily examined the patient<br> caries than that same patient was diagnosed by another dentist through photographs, which<br> were taken by patients’ attendant on verbal guidelines by a dentist. later, this whole data was<br> assessed by a third dentist.</p>
Bermuda Atlantic Time-Series Study (BATS) Pigment Data Validation
<p>This dataset is published on Zenodo by the Simons CMAP curators for long-term care. All credits go to the data producers at the Bermuda Atlantic Time-series Study (BATS): https://bats.bios.asu.edu/bats-data/ </p><p>The BATS (Bermuda Atlantic Time-series Study) discrete HPLC pigment validation dataset is time-series spanning from 1996 to 2022. The dataset contains the 21 separate in-situ pigment measurements along with sampling depth and the BATS Cruise ID.</p><p>This description has been reproduced using https://www.dropbox.com/s/6ajl545hyua8ot8/bval_pigments.txt?dl=0</p>
Assessing Functional Capacity in Directly and Remotely Monitored Home-based Settings: A Protocol for a Multinational Validation Study in Individuals With Chronic Respiratory Diseases
ClinicalTrials.gov study NCT06447831. IPD Sharing: UNDECIDED. Countries: 2. Publications: 1.
Cohort Study of Prospective Validation of Predictive Factors and Biological Imaging of Response to Bevacizumab and Paclitaxel in Patients With Metastatic Breast Cancer
ClinicalTrials.gov study NCT01745757. IPD Sharing: YES. Countries: 1. Publications: 3.
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.