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641 results for “data validation”
Auxiliary and validation data for Project SaGEA
<p>Auxiliary and validation data for Project SaGEA (Error Assessment for Satellite Gravity) at <a href="https://github.com/NCSGgroup/SaGEA" target="_blank" rel="noopener">https://github.com/NCSGgroup/SaGEA</a>.</p>
Data from: Measuring couple relationship quality in a rural African population: validation of a couple functionality assessment tool in Malawi
Available data suggest that individual and family well-being are linked to the quality of women's and men's couple relationships, but few tools exist to assess couple relationship functioning in low and middle-income countries. In response to this gap, Catholic Relief Services has developed a Couple Functionality Assessment Tool (CFAT) to capture valid and reliable data on various domains of relationship quality. This tool is designed to be used by interventions which aim to improve couple and family well-being as a means of measuring the effectiveness of these interventions, particularly related to couple relationship quality. We carried out a validation study of the CFAT among 401 married and cohabiting adults (203 women and 198 men) in rural Chikhwawa District, Malawi. Using psychometric scales, the CFAT addressed six domains of couple relationship quality (intimacy, partner support, sexual satisfaction, gender roles, decision-making, and communication and conflict management), and included questions on intimate partner violence. We used exploratory factor analysis to assess scale performance of each domain and produce a shortened Relationship Quality Index (RQI) composed of items from five relationship quality domains. This article reports the performance of the RQI. Internal reliability and validity of the RQI were found to be good. Regression analyses examined the relationship of the RQI to outcomes important to health and development: intra-household cooperation, positive health behaviors, intimate partner violence, and gender-equitable norms. We found many significant correlations between RQI scores and these couple- and family-level development issues. There is a need to further validate the tool with use in other populations as well as to continue to explore whether the observed linkages between couple functionality and development outcomes are causal relationships.
Data from: Single-nucleotide polymorphism discovery and validation in high-density SNP array for genetic analysis in European white oaks
An Illumina Infinium SNP genotyping array was constructed for European white oaks. Six individuals of Quercus petraea and Q. robur were considered for SNP discovery using both previously obtained Sanger sequences across 676 gene regions (1371 in vitro SNPs) and Roche 454 technology sequences from 5112 contigs (6542 putative in silico SNPs). The 7913 SNPs were genotyped across the six parental individuals, full-sib progenies (one within each species and two interspecific crosses between Q. petraea and Q. robur) and three natural populations from south-western France that included two additional interfertile white oak species (Q. pubescens and Q. pyrenaica). The genotyping success rate in mapping populations was 80.4% overall and 72.4% for polymorphic SNPs. In natural populations, these figures were lower (54.8% and 51.9%, respectively). Illumina genotype clusters with compression (shift of clusters on the normalized x-axis) were detected in ~25% of the successfully genotyped SNPs and may be due to the presence of paralogues. Compressed clusters were significantly more frequent for SNPs showing a priori incorrect Illumina genotypes, suggesting that they should be considered with caution or discarded. Altogether, these results show a high experimental error rate for the Infinium array (between 15% and 20% of SNPs potentially unreliable and 10% when excluding all compressed clusters), and recommendations are proposed when applying this type of high-throughput technique. Finally, results on diversity levels and shared polymorphisms across targeted white oaks and more distant species of the Quercus genus are discussed, and perspectives for future comparative studies are proposed.
Data from: Genetic structuring among colonies of a pantropical seabird: Implication for subspecies validation and conservation
Appendix S1 <table> <tbody> <tr> <td>Table S1</td> <td>Details of field researchers and licences under which they took blood samples from white-tailed tropicbirds from populations in the years of study</td> </tr> <tr> <td>Table S2</td> <td>Morphometrics of 616 individual white-tailed tropicbirds from 11 populations. Population codes are as described in Table 1.</td> </tr> <tr> <td>Table S3</td> <td>Raw microsatellite genotypes for 382 individual White-tailed tropicbird from 13 populations. Population codes are as described in Table 1</td> </tr> <tr> <td>Table S4</td> <td>Details of mtDNA sequences </td> </tr> <tr> <td>Table S5</td> <td>Tests of bottleneck (P-values for one-tailed Wilcoxon's signed rank test for heterozygosity excess) based on 10 microsatellites in 13 populations of Phaethon lepturus</td> </tr> <tr> <td>Table S6</td> <td>Pairwise FST estimates based on nuclear microsatellite variation (above diagonal), and ΦST estimates based on mtDNA sequence (below diagonal) for 11 populations with sample sizes >5 ('Pop's) of Phaethon lepturus (see Table 1 for population codes)</td> </tr> </tbody> </table>
CRM Variance Transport validation in E3SM-MMF - analysis code and condensed data
<p>This tarball contains a collection of code and data used to performa a detailed analysis of the CRM variance transport feature added to E3SM-MMF. The data subset is condensed from the model output and observational datasets, so the ability to reproduce results directly is limited, but this archive mainly serves to document the methods used for our analysis. </p>
Data supplement for: Validating the Nernst--Planck transport model under reaction-driven flow conditions using RetroPy v1.0
<p>This is the repository for the publication's supplementary data and plotting scripts: Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0.</p> <p>The dependency of the scripts can be installed using conda and pip:</p> <pre><code>conda create -n plot numpy matplotlib==3.6.1 h5py python=3.9 conda activate plot pip install palettable</code></pre> <p>To reproduce the figures, execute the files using python:</p> <pre><code>python figure03.py</code></pre> <p> </p>
Data for DESI Publication DESI-2022-0160: DESI Survey Validation Spectra Reveal an Increasing Fraction of Recently Quenched Galaxies at z~1
<p>This folder contains all the data on figures for DESI Publication DESI-2022-0160, which is about to be sent to final reading.</p>
Shock Induced Dilation - Validation data
<p>Input files needed for running shock simulations in LIGGGHTS as well as the results of a single granular shock run. Full description is provided in Frizzell and Hartzell, 2023 - Simulation of Shock Induced Inertial Dilation at the Surface of an Unconfined Granular Assembly</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>
Validation of StarinIQ software performance using metagenomic data from the ATCC gut microbiome genomic mix
<p>StrainIQ (Strain Identification and Quantification) is a novel tool that implements a new <em>n</em>-gram based algorithm for predicting and quantifying strain-level taxa from whole genome metagenomic sequencing data. We tested our method using simulated (GI tract reference genomes) and mock metagenomic datasets (from ATCC microbial genomic mix) and compared its performance with existing methods. The gut_even.zip file contains the metagenomic sequence data from the ATCC Gut Microbiome Genomic Mix (MSA-1006). From this original sequencing sequence (nearly 120X), we created additional datasets with 90X, 60X, 30X, 5X, 3X, and 1X coverage and tested StrainIQ performance on those sequencing datasets with varying coverage.</p> <p>Please find the new link for this data at https://zenodo.org/record/8132164</p>
Sequencing data from validation experiment for base editing of SCN2A
<p>It is challenging to apply traditional mutational scanning to voltage-gated sodium channels (NaVs) and functionally annotate the large number of coding variants in these genes. Using a cytosine base editor and a pooled viability assay, we screened a library of 368 guide RNAs (gRNA) tiling NaV1.2 to identify more than 100 gRNAs that changed NaV1.2 function. We sequenced base edits made by a subset of these gRNAs to confirm specific variants that drove changes in channel function. Electrophysiological characterization of these channel variants validated the screen results and provided functional mechanisms of channel perturbation. The majority of the changes caused by these gRNAs were classified as loss-of-function along with two missense mutations that led to gain-of-function in NaV1.2 channels. This two-tiered strategy to functionally characterize ion channel protein variants at scale identifies the largest set of loss-of-function mutations in a single NaV1.2 study to date.</p>
Input Data for: On the External Validity of Average-Case Analyses of Graph Algorithms
<p><strong>Data</strong></p> <p>All networks from <a href="https://networkrepository.com"><code>networkrepository.com</code></a> [1] with at most 1M edges (fall 2020). Weights and edge directions have been removed. For graphs with isomorphic largest connected component, only one copy has been kept.</p> <p>This is the raw data necessary to reproduce our experiments in <em>On the External Validity of Average-Case Analyses of Graph Algorithms</em>.</p> <p>[1] Ryan A. Rossi and Nesreen K. Ahmed, <em>The Network Data Repository with Interactive Graph Analytics and Visualization</em> (AAAI 2015)</p>
Data set for the validation of the Bench scale photocatalytic reactor used in ProjectO D2.3
<p>This data set contain the time profiles of treated water parameters (TOC and pollutant concentration) for the validation of the bench scale PHOTO.CAT reactor</p>
The Data and Codes for Training, Testing, and Prognostic Validation of A ResNet Ensemble for Moist Physics (ResCu-en)
<p>Note: the monthly averaged NCAM, SPCAM, and CAM5 results are uploaded as *_h0.tar.gz!</p> <p>The data and codes for Training, Testing, and Prognostic Validation of A ResNet Ensemble for Moist Physics (ResCu-en) are stored in this repositary.</p> <p>This project is built on python3.7 and tensorflow-gpu2.3.0, and the scripts for analysis and plots are on jupyter-notebook.</p> <p>Please make sure to install all python packages used in an environment.</p> <p>Please read the ReadME-2.txt.</p> <p>For the entire training and testing datasets in both the baseline and +4K SST climates. Please download them from Dryad (<a href="https://doi.org/10.6075/J0CZ35PP">https://doi.org/10.6075/J0CZ35PP</a> and https://doi.org/10.6075/J03J3BGF), Onedrive (https://1drv.ms/u/s!ArKTPPs6U_9DjxPJeSReKlbsLzyh?e=PDlWYJ), and Dropbox (https://www.dropbox.com/s/yc4fx35laqwt0fu/SPCAM_ML_4K.tar.gz?dl=0 and https://www.dropbox.com/s/4pxahzwt9v55u2m/SPCAM_ML_RAD.tar.gz?dl=0).</p>
Raw data: Validation of a method for surveillance of nanoparticles in mussels using single particle inductively coupled plasma mass spectrometry
<p>The compressed archive contains SP-ICP-MS data to reproduce results for the paper with the working title "Validation of a method for surveillance of nanoparticles in mussels using single particle inductively coupled plasma mass spectrometry"".</p> <p>The remaining data to produce all other results, visualizations and statistics for the paper is included in the supplementary or GitHub. Associated code is deposited in a GitHub repository, github.com/arebruvold/mussel_validation .</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>
Validation of the Quality of Sleep Data for Xiaomi Domestic Wristbands
ClinicalTrials.gov study NCT04568408. IPD Sharing: UNDECIDED. Countries: 1. Publications: 25.
A Study to Collect Imaging Data for the Validation of the Intelligent Ultrasound's ScanNav Anatomy Peripheral Nerve Block (PNB) - US v1.0
ClinicalTrials.gov study NCT04906018. IPD Sharing: NO. Countries: 1. Publications: 1.
Gamifying Patient's Personal Data Validation and Completion in a Personal Health Record
ClinicalTrials.gov study NCT02970461. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
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.