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939 results for “validation study”
Experimental validation data for in silico OA study
<p>The folder contains, ALP assay and PCR data, experimental protocols as well as scripts for plotting and analysis.</p> <p>This is related to the following git repository: https://github.com/Rapha-L/Experimental_validation_for_insilicoOA </p>
Plant regeneration in leaf culture of Centaurium erythraea Rafn. Part 3: de novo transcriptome assembly and validation of housekeeping genes for studies of in vitro morphogenesis
<p>Six centaury transcriptomes (embryogenic calli, globular somatic embryos, cotyledonary somatic embryos, adventitious buds, leaves and roots of <em>in vitro</em> grown plants) were sequenced and <em>de novo</em> assembled using <a href="https://github.com/trinityrnaseq/trinityrnaseq/wiki">Trinity</a> .</p> <p><a href="https://zenodo.org/api/files/a0546879-e382-4cf9-8185-f188d1a0c5f0/CE_Assembly.tar.gz">CE_Assembly.tar.gz</a> - Centaury referent transcriptome comprises of 160.839 Trinity transcripts grouped in 105.726 Trinity genes.</p> <p><a href="https://zenodo.org/api/files/a0546879-e382-4cf9-8185-f188d1a0c5f0/CE_Assembly_fpkm.tar.gz">CE_Assembly_fpkm.tar.gz</a> - fpkm normalized read counts of the assembled transcripts in the six sequenced centaury tissues.</p> <p><a href="https://zenodo.org/api/files/a0546879-e382-4cf9-8185-f188d1a0c5f0/nt.db_CE_assembly.tar.gz">nt.db_CE_assembly.tar.gz</a> - annotation of assembled transcripts by mapping them against NCBI nucleotide (NT) database using BLASTn . The obtained results were filtered with E-value E ≤ 10<sup>-3</sup>.</p> <p><a href="https://zenodo.org/api/files/a0546879-e382-4cf9-8185-f188d1a0c5f0/swissprot.db_CE_assembly.tar.gz">swissprot.db_CE_assembly.tar.gz</a> - annotation of assembled transcripts by mapping them against NCBI nucleotide (<a href="https://zenodo.org/api/files/a0546879-e382-4cf9-8185-f188d1a0c5f0/swissprot.db_CE_assembly.tar.gz">s</a>wissprot) database using BLASTx . The obtained results were filtered with E-value E ≤ 10<sup>-3</sup>.</p> <p><a href="https://zenodo.org/api/files/a0546879-e382-4cf9-8185-f188d1a0c5f0/pfam30.db_CE_assembly.tar.gz">pfam30.db_CE_assembly.tar.gz</a> - annotation of assembled transcripts by mapping them against Pfam30 domain database using hmmer3. The obtained results were filtered with independent E-value E ≤ 10<sup>-3</sup>.</p>
Phenome-wide association studies across large population cohorts support drug target validation
<p>Summary-level data generated by Genomics plc as presented in:<br> Diogo, D. et al. Phenome-wide association studies across large population cohorts support drug target validation. Nat. Commun. 9, 4285 (2018). https://doi.org/10.1038/s41467-018-06540-3</p> <p>If you have any questions or comments regarding these files, please contact Genomics plc at <a href="mailto:research@genomicsplc.com">research@genomicsplc.com</a></p> <p>NOTES<br> -----------------------------<br> These analyses were carried out using the interim UK Biobank imputation data release. Analyses were restricted to a subset of "white-British" unrelated samples with a maximum sample size of 112,337 individuals. </p> <p>Case control phenotypes were defined based on categorical datafields as listed in the accompanying file. <br> Quantitative phenotypes were either rank-normalised before analysis, or beta/se values were standardised after analysis using the variance of the phenotype. The normalisation value is indicated in the accompanying file.<br> <br> All analyses included Age at assessment, sex, genotyping chip, and 10 principal components as covariates. </p> <p>We used plink1.9 linear/logistic regression as appropriate. For chromosome X variants males were treated as having 0 or 2 alternative alleles. </p> <p>The results are not adjusted for genomic control.</p> <p>DATA FILE CONTENT DESCRIPTION<br> -----------------------------<br> CHR - Chromosome<br> SNP - Variant rsID<br> ALT - Alternative allele (effect allele)<br> REF - Reference Allele (non-effect allele)<br> BP - Position in base pairs (b37, 1-based)<br> NMISS - Number of samples with non-missing genotypes<br> BETA - Effect size (log odds ratio or standardised effect size)<br> SE - Standard error<br> P - P-value<br> F_MISS - genotype missing rate<br> P_hwe - Hardy-weinberg p-value<br> MAF - ALT allele frequency</p>
Arctic shoreline displacement and validation data for two pilot study areas
<p>Arctic shoreline displacement data for two pilot study areas are supporting information for the paper <em>Nylén, Calle-Navarro and Gonzales-Inca: Arctic shoreline displacement with open satellite imagery and data fusion – Pilot study 1984–2022</em><em>. </em>The two study areas are:</p> <ul> <li>Tanafjorden: a low-arctic meso-tidal fjord coast in mainland Norway</li> <li>Ny-Ålesund: a high-arctic micro-tidal glaciated coast in north-western Svalbard</li> </ul> <p>The study areas are 2500 km² each.</p> <p>The dataset includes following files:</p> <ul> <li><em>Calculating_coastal_landcover_timeseries_summaries.R</em>: code for summarizing the coastal land cover time-series in the R software.</li> <li><em>Calculating_shoreline_timeseries.R</em>: code for calculating the shoreline by fitting and smoothing a polyline to the land cover raster in the R software.</li> <li><em>X_timeseries.tif</em>: a multiband GeoTIFF raster file, with each band describing coastal land cover during one of the eight time-steps (1984–1988, 1989–1993, 1994–1998, 1999–2003, 2004–2008, 2009–2013, 2014–2018 and 2019–2022).</li> <li><em>X_summary.tif</em>: a multiband GeoTIFF raster file, with bands that summarize the time-series from different viewpoints. These summary variables are: probability of belonging to the land class, long-term trend (between 1984-2003 and 2004-2022), change intensity, first time-step in water class, last time-step in water class, first time-step in land class and last time-step in land class.</li> <li><em>X_shoreline.geojson</em>: a GeoJSON vector file, consisting of polylines for the shoreline during each time-step. The attributes of the polyline layer describe the time-step and the total length of the shoreline.</li> <li><em>NyAlesund_timeseries_REDUCED.tif</em>: a reduced time-series for the Ny-Ålesund study area, including only the time-steps with adequate number of observations (i.e., excluding 1984–1988, 1994–1998 and 2004–2008).</li> <li><em>X_reference_shoreline.geojson</em>: a GeoJSON vector file including the manually digitized (scale 1/5000) reference shoreline corresponding to the time-step 2019–2022.</li> <li><em>X_validationpoints.geojson</em>: a GeoJSON vector file including 2000 random points (within 2 km from the reference shoreline) that have been manually classified into water and land. The classification corresponds to the time-step 2019–2022.</li> </ul>
Dataset for Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients
<p>This dataset contains all the spectra used in the paper "Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients", plus the R code to import the TXT (ASCII) files into a dataset, preprocess data, set-up and cross validate the PCA-LDA model and generate the figures shown in the paper.</p> <p>Data are available in 2 different formats: </p> <p>- 1 compressed archive ("dataset.zip") containing all the 144 TXT files (1 file = 1 spectrum) </p> <p>- 1 single CSV file (“dataset.csv”) with all the 144 spectra in the form of a table. The data are structured as follow, with each row being 1 spectrum, preceded by metadata: "acquisition_date", "substrate_batch", "class", "sample_code".</p> <p>The code for R is available as a single file "Rcode.R".</p> <p> </p>
Mobilise-D Technical Validation Study (TVS) dataset
<h1>Mobilise-D Technical Validation Study (TVS) Dataset</h1> <p>This dataset was recorded as part of the Mobilise-D project, a comprehensive initiative aimed at developing and validating digital solutions for assessing mobility in real-world environments. The Mobilise-D project seeks to address the critical need for accurate, reliable, and scalable tools to monitor and evaluate gait and mobility patterns, particularly in populations with mobility impairments.</p> <p>The dataset comprises recordings from a diverse cohort of participants, including healthy individuals and patients with various mobility-related conditions. Data collection was conducted using state-of-the-art wearable sensors and devices, capturing a wide range of gait parameters and contextual information in both controlled and free-living settings. The primary objective was to ensure the robustness and precision of digital mobility assessment tools under real-world conditions.</p> <p>Key features of the dataset include:</p> <ul> <li> <p>Demographic & Clinical Data: Age, gender, height, weight, and clinical diagnoses.</p> </li> <li> <p>Sensor Data: Raw and processed data from accelerometers, gyroscopes, and other wearable sensors.</p> </li> <li> <p>Reference Gait Parameters: Stride length, stride frequency, gait speed, and variability measures.</p> </li> </ul> <p>The dataset has undergone rigorous validation processes to confirm its accuracy and reliability. It serves as a critical resource for researchers and developers aiming to enhance digital health technologies and improve clinical assessments of mobility. The TVS dataset paves the way for future innovations in digital biomarkers and personalized healthcare solutions.</p> <h2>Brief Overview</h2> <p>This dataset contains data from 108 participants from six cohort groups that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). Data was recorded across five measurement sites. Data availability varies between participants, and some tests might be missing for some participants.</p> <p>The recording was split into a comprehensive in-lab assessment and a 2.5 hour unsupervised free living conditions. For the in-lab measurements reference information from marker-based motion capture systems and the multi-device wearable <a href="https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2023.1143248/full">INDIP system</a> are provided. For the free-living recording, only the INDIP system is available as a reference.</p> <p>Participants wore a McRoberts MM+ IMU at the lower back. Some participants additionally wore a custom wrist-IMU at the non-dominant hand. The IMUs were synchronized with the reference system.</p> <p>The following tests were performed as part of the In-Lab data capture (Name in recording file in parentheses):</p> <ol> <li> <p>Timed-Up-and-Go (Test4)</p> </li> <li> <p>Straight Walk Comfortable (Test5)</p> </li> <li> <p>Straight Walk Slow (Test 6)</p> </li> <li> <p>Straight Walk Fast (Test 7)</p> </li> <li> <p>L-Test (Test8)</p> </li> <li> <p>Surface Test (Test9)</p> </li> <li> <p>Hallway Test (Test10)</p> </li> <li> <p>Simulated daily activities (Test11)</p> </li> </ol> <p>For some tests multiple <em>trials</em> are available. Additional trials were performed in the case of technical or performance issues. Hence, the last trial of each test should always be preferred.<br>Tests not listed above (e.g. Test 1-3) are non-walking tests used for calibration. When performing gait related operations, these tests should be excluded.</p> <p>For the free-living recording <em>Recording4</em> corresponds to the actual recording. Recording 1-3 only contains calibration recordings that are usually not required.</p> <p>Learn more about the data collection protocol:</p> <blockquote> <p>C. Mazzà, L. Alcock, K. Aminian, C. Becker, S. Bertuletti, T. Bonci, P. Brown, et al. "Technical Validation of Real-World Monitoring of Gait: A Multicentric Observational Study." BMJ Open 11, 12 (2021): e050785 (<a href="https://doi.org/10/gt55p7">https://doi.org/10/gt55p7</a>).</p> <p>S. Kirsty, T. Bonci, F. Salis, L. Alcock, E. Buckley, E. Gazit, C. Hansen, et al. "Design and Validation of a Multi-Task, Multi-Context Protocol for Real-World Gait Simulation." Journal of NeuroEngineering and Rehabilitation* 19, 1 (2022):141 (<a href="https://doi.org/10/gt55t6">https://doi.org/10/gt55t6</a>).</p> </blockquote> <h2>Files</h2> <p>/data/: Raw data files sorted by cohort/patientId/measurement_condition.<br>/participant_information.xlsx: Basic demographic and clinical information of all participants and "data quality" overview for all recordings</p> <p>For each recording the following files are provided:</p> <ul> <li> <p>infoForAlgo.mat: Reduced set of relevant demographic information that is required to process the data with the Mobilise-D algorithmic pipeline</p> </li> <li> <p>data.mat: Core data file following the <a href="https://www.nature.com/articles/s41597-023-01930-9">Mobilise-D file format</a>. For each trial the data contains the raw sensor data of the lower-back IMU (SU), the raw data of all reference sensors, and calculated gold standard parameters for all relevant gait parameters based on the reference system. For some participants, data from a wrist worn sensor is included.</p> </li> <li> <p>test_list.json: A json file containing all the available tests and trials including the data.mat file. This information is also available via the data.mat file, but the json file is faster to parse and should help with identifying the correct data files to load.</p> </li> </ul> <h2>Tips and Notes</h2> <h3>Data Quality</h3> <p>Depending on the use case, specific trials should not be used. <code>participant_information.xlsx</code> file contains a sheet named data quality, that indicates for each system used, if the data was recorded properly. "0" indicates that the data is not usable at all, "1" indicates that some issues remain. This usually indicates partial or full data loss in a single test or unreliable reference information. These recordings might be usable for certain types of analysis, but should not be used for proper algorithm validation on the dataset. Only recordings with data quality >=2 for all required systems should be used.</p> <h3>Walking Aid Use</h3> <p>The <code>participant_information.xlsx</code> file contains 3 columns with information about walking aid use. The two columns <code>self_reported_indoors</code> and <code>self_reported_outdoors</code> describe the use of walking aids independent of the study context as reported by the patients of the day of the recording. This information might be different from the actual walking aids used during the assessment. This information can be found <code>use_during_lab_assessment</code> column. This information was recorded by the study conductor. For the free-living tasks patients were allowed to use any assistance they needed. Actual use was not recorded for this assessment.</p> <p>In general, only a small number of participants used walking aids within the study. Therefore, we do not recommend analyzing walking aid users as a different group or including walking aid use as a stratifier.</p> <h3>General Notes</h3> <ul> <li> <p>The participant IDs are "double pseudonymized" and do not correspond to data-ids used within the Mobilise-D project or previously published example data</p> </li> <li> <p>The first digit of the participant IDs identifies the recording center. This information might be helpful to identify systematic domain shifts in the data, as different centers used slightly different measurement setups.</p> </li> <li> <p>In case multiple trials are available for a single test, only use the last one when performing algorithm validation to keep the data between the participants balanced.</p> </li> </ul> <h3>Reference Parameters</h3> <p>Below some notes and general recommendation regarding the reference parameters:</p> <ul> <li> <p>Reference parameters are provided on a MicroWb and ContinousWalkingPeriod level. In most cases, you will likely want to work with the information in "ContinousWalkingPeriod" (if you are using mobgap to load the data, this information is simply called "Wb"). Learn more <a href="https://mobgap.readthedocs.io/en/latest/guides/q_and_a.html#walking-bouts-vs-gait-sequences">here</a></p> </li> <li> <p>For in-lab measurements, the Stereophoto (aka. marker-based Mocap system) should be the preferred reference, as parameters are expected to be more accurate. However, due to limitations of the field-of-view of these systems, some walking trails are not completely covered by the references.</p> </li> <li> <p>Neither reference system includes turning information, as no established reference definition could be identified, that would allow for unbiased comparison of parameters.</p> </li> </ul> <p>Learn more about the methods for extracting reference parameters:</p> <blockquote> <p>T. Bonci, F. Salis, K. Scott, L. Alcock, C. Becker, S. Bertuletti, E. Buckley, et al. “An algorithm for accurate marker-based gait event detection in healthy and pathological populations during complex motor tasks” Frontiers in Bioengineering and Biotechnology, section Biomechanics, 10:868928, 2022 (DOI: 10.3389/fbioe.2022.868928).</p> <p>F. Salis , S. Bertuletti, T. Bonci, M. Caruso, K. Scott, L. Alcock, E. Buckley, et al. “A multi-sensor wearable system for the assessment of diseased gait in real-world conditions”. Frontiers in Bioengineering and Biotechnology, 11, 2023 (<a href="https://doi.org/10.3389/fbioe.2023.1143248">https://doi.org/10.3389/fbioe.2023.1143248</a>).</p> </blockquote> <h2>Usage Recommendation</h2> <p>This dataset is designed to validate algorithms and NOT to derive clinical insights from the patient cohorts.</p> <p>This dataset was used to validate the algorithms of the Mobilise-D computational pipeline for lower trunk IMU data. Details on the publications are reported below.</p> <p>Per-Block Validation:</p> <blockquote> <p>M.E. Micó-Amigo, T. Bonci, A. Paraschiv-Ionescu, M. Ullrich, C. Kirk, A. Soltani, A. Küderle, et al. "Assessing Real-World Gait with Digital Technology? Validation, Insights and Recommendations from the Mobilise-D Consortium." <em>Journal of NeuroEngineering and Rehabilitation</em> 20, no. 1 (June 14, 2023): 78 (<a href="https://doi.org/10/gt55qb">https://doi.org/10/gt55qb</a>).</p> </blockquote> <p>Full Pipeline Validation:</p> <blockquote> <p>K., Cameron, A. Kuederle, M.E. Mico-Amigo, T. Bonci, A. Paraschiv-Ionescu, M. Ullrich, A. Soltani, et al. "Estimating Real-World Walking Speed from a Single Wearable Device: Analytical Pipeline, Results and Lessons Learnt from the Mobilise-D Technical Validation Study." Scientific Reports, 14,1, 1754 2024 (<a href="https://doi.org/10.21203/rs.3.rs-2965670/v1">https://doi.org/10.21203/rs.3.rs-2965670/v1</a>).</p> </blockquote> <p>Implementation of these validation procedures are also available via the open-source library <a href="https://github.com/mobilise-d/mobgap/">mobgap</a>.</p> <p>We recommend the use of this library in all use cases, as it provides high level tools to load and process the dataset. Documentation for this can be found in the following examples:</p> <ol> <li> <p><a href="https://mobgap.readthedocs.io/en/latest/auto_examples/data/_04_tvs_data_no_exc.html">The TVS dataset class</a></p> </li> <li> <p><a href="https://mobgap.readthedocs.io/en/latest/auto_examples/data/_01_loading_example_data.html">Working with data in mobgap</a></p> </li> <li> <p><a href="https://mobgap.readthedocs.io/en/latest/auto_examples/data/_02_working_with_ref_data.html">Working with reference data in mobgap</a></p> </li> </ol> <h2>Suggested Citation</h2> <p>When you are working with the data, we suggest the following citation:</p> <blockquote> <p>Küderle, A. (2024). Mobilise-D Technical Validation Study (TVS) dataset [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.13899385">http://doi.org/10.5281/zenodo.13899385</a></p> </blockquote> <p>Please cite our paper in your publications if our repository helps your research.</p> <blockquote> <p>C. Mazzà, L. Alcock, K. Aminian, C. Becker, S. Bertuletti, T. Bonci et al. "Technical Validation of Real-World Monitoring of Gait: A Multicentric Observational Study". BMJ Open 11, 12 2021): e050785. (<a href="https://doi.org/10/gt55p7">https://doi.org/10/gt55p7</a>).</p> </blockquote> <h2>License and Legal Information</h2> <p>Mobilise-D Technical Validation Study Dataset © 2024 by Mobilise-D Consortium is licensed under CC BY-NC-ND 4.0</p> <h2>Acknowledgments</h2> <p>We extend our gratitude to all participants who contributed to the Mobilise-D project, enabling the comprehensive collection and analysis of mobility data. This work would not have been possible without the dedication and collaboration of the Mobilise-D Consortium members, including researchers, clinicians, and technical staff.</p> <p>We also acknowledge the funding and support provided by the European Union's Horizon 2020 research and innovation program under grant agreement No 820820. Special thanks to our partner institutions and organizations for their invaluable contributions and continued support.</p> <h2>Disclaimer</h2> <p>The Mobilise-D Technical Validation Study Dataset is provided for research purposes only. The Mobilise-D Consortium makes no warranties, express or implied, regarding the accuracy, completeness, or reliability of the dataset. Users of the dataset assume all responsibility for any conclusions drawn from the data.</p> <p>The dataset must be used in accordance with ethical guidelines and applicable laws and regulations. Any publications or presentations based on this dataset should appropriately cite the source. The Mobilise-D Consortium is not liable for any misuse of the dataset or for any direct, indirect, incidental, or consequential damages arising out of the use of the dataset.</p>
Molecular Signatures of Tumour and its Microenvironment for Precise Quantitative Diagnosis of Oral Squamous Cell Carcinoma: An Interna-tional Multi-cohort Diagnostic Validation Study
<p><strong>Supplementary Materials: </strong>The following supporting information can be downloaded at: www.mdpi.com/xxx/s1, <strong>Table ST1</strong> – qMIDS<sup>V2 </sup>Gene panel primer sequences; <strong>Figure S1</strong> – qMIDS<sup>V1</sup> vs qMIDS<sup>V2</sup> 384-well assay format and protocols; <strong>Figure S2.</strong> Individual target gene expression pattern in 1761 samples; <strong>Figure S3.</strong> Various statistical methods used for gene selection analysis on 1761 clinical samples; <strong>Figure S4. </strong>Diagnostic performance comparison between qMIDS<sup>V2</sup> vs qMIDS<sup>V2* </sup>(with 4 less effective genes removed from the panel of 14 target genes of qMIDS<sup>V2</sup>); <strong>Figure S5</strong>. Effect of removing individual genes from the 14-target gene panel qMIDS<sup>V2</sup> (qV2) on diagnostic test performance based on the UK patient cohort data.</p>
The impact of biological bedforms on near-bed and subsurface flow: a laboratory validated numerical study of flow in the vicinity of pits and mounds
<p>The data were used in the paper "The impact of biological bedforms on near-bed and subsurface flow: a laboratory validated numerical study of flow in the vicinity of pits and mounds" which was submitted to "Journal of Geophysical Research-Earth Surface". In this paper, a novel, unified water-sediment three-dimensional model is developed to investigate the impact of simulated biogenic bedforms. The impact of biogenic bedforms on near-bed turbulence and sediment entrainment is discussed in gravelly substrates. Biogenic bedform morphology has an important role in determining the spatial extent of up- and down-welling flow.</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>
Dataset Validation of seven type 2 diabetes mellitus risk scores in a population-based cohort. The CoLaus Study
<p>This dataset is related to "Validation of seven type 2 diabetes mellitus risk scores in a population-based cohort. The CoLaus Study".</p> <p>Vanessa Kraege*, Janko Fabecic*, Pedro Marques Vidal, Gérard Waeber and Marie Méan</p> <p>*Contributed equally; co-first authors</p>
Dataset for "Validation of a Prognostic Staging for Metastatic Uveal Melanoma: A Collaborative Study of the European Ophthalmic Oncology Group"
<p>Raw data corresponding to the paper entitled: "<strong>Validation of a Prognostic Staging for Metastatic Uveal Melanoma: A Collaborative Study of the European Ophthalmic Oncology Group</strong><strong>" </strong>published in <em>Am. J. Ophthalmol.</em> 2016 Aug;168:217-226 by Kivelä <em>et al.</em></p>
Raw data for Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation
<p>Specification of affiliations:</p> <ul> <li>Hassan Ali - Centre of Polymer Systems</li> <li>Muhammad Yasir - Centre of Polymer Systems</li> <li>Hamza Ul Haq - Laboratory of Alternative Fuel and Sustainability, School of Chemical and Materials Engineering,</li> <li>Ali Can Guler - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> <li>Muhammad Nouman Aslam Khan - Laboratory of Alternative Fuel and Sustainability, School of Chemical and Materials Engineering,</li> <li>Michal Machovsky - Centre of Polymer Systems</li> <li>Vladimir Sedlarik - Centre of Polymer Systems</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript. </p>
Linked collectors and determiners for: Taxonomic utility of niche models in validating species concepts: A case study in Anthophora (Heliophila) (Hymenoptera: Apidae).
Natural history specimen data linked to collectors and determiners held within, "Taxonomic utility of niche models in validating species concepts: A case study in Anthophora (Heliophila) (Hymenoptera: Apidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1363c785-b3e0-4322-9283-a6d272748735">https://bionomia.net/dataset/1363c785-b3e0-4322-9283-a6d272748735</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1363c785-b3e0-4322-9283-a6d272748735">https://gbif.org/dataset/1363c785-b3e0-4322-9283-a6d272748735</a>. Formatted as a Frictionless Data package.
Dataset used for the study of "Validation of Aeolus wind profiles using ground-based lidar and radiosonde observations at La Réunion Island and the Observatoire de Haute Provence"
<p>Datasets used to create the figures and statistical study in "Validation of Aeolus wind profiles using ground-based lidar and radiosonde observations at La Réunion Island and the Observatoire de Haute Provence" </p>
Validated names for experimental studies on race and ethnicity
<p>A large and fast-growing number of studies across the social sciences use experiments to better understand the role of race in human interactions, particularly in the American context. Researchers often use names to signal the race of individuals portrayed in these experiments. However, those names might also signal other attributes, such as socioeconomic status (e.g., education and income) and citizenship. If they do, researchers need pre-tested names with data on perceptions of these attributes. Such data would permit researchers to draw correct inferences about the causal effect of race in their experiments. In this paper, we provide the largest dataset of validated name perceptions based on three different surveys conducted in the United States. In total, our data include over 44,170 name evaluations from 4,026 respondents for 600 names. In addition to respondent perceptions of race, income, education, and citizenship from names, our data also include respondent characteristics. Our data will be broadly helpful for researchers conducting experiments on the manifold ways in which race shapes American life.</p> <p>License: CC-By Attribution 4.0 International</p> <p> </p>
Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study'
<p>Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study' consisting of (1) the original and complete dataset ('Data_Brain-IT-Reliability-of-HRV-during-Exergaming_for-publication'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
The Awareness Assessment Model (Case Study Validation)
<p>Dataset of the case study validation of the paper "The Awareness Assessment Model: Measuring Awareness and Collaboration Support Over Participant's Perspective"</p>
Reproducible Validation and Replication Studies in Nanoscale Physics (repro results plots - Ellis et al., 2016)
<p>This archive contains the Jupyter notebooks needed to reproduce the figures of the paper that are related to the Validation results and replication of Ellis et al. 2016. For further information direct to the README.md file.</p>
Reproducible Validation and Replication Studies in Nanoscale Physics (problem datasets for Rockstuhl et al. 2005 replication)
<p>Problem folders including all the input files necessary to reproduce the computations of the results related to Rockstuhl et al. 2005 on the paper: Reproducible Validation and Replication Studies in Nanoscale Physics</p>
Code list used to generate cohorts in ICD-10 CCI US validation study
<p>Code list used to generate cohorts in validating the Charlson comorbidity index (CCI) for ICD-10 in the United States.</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.