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641 results for “data validation”
Canfield Joint Validation Closed Loop Data
<p>Validation trajectories for closed-loop tests of a hardware prototype Canfield joint. Each dataset has a video file (.wmv) showing the test, and a corresponding .csv file with XY coordinates. These datasets will be cited in a forthcoming paper on the validation of the Canfield joint. </p> <p> </p> <p>NOTE: The "quadrant trajectory track 24mm at 0.5mm-s" files are mislabeled. The rate for those is 1 mm/s, not .5 mm/s. </p>
Data from: Sorting specimen-rich invertebrate samples with cost-effective NGS barcodes: validating a reverse workflow for specimen processing
Biologists frequently sort specimen-rich samples to species. This process is daunting when based on morphology, and disadvantageous if performed using molecular methods that destroy vouchers (e.g., metabarcoding). An alternative is barcoding every specimen in a bulk sample and then presorting the specimens using DNA barcodes, thus mitigating downstream morphological work on presorted units. Such a "reverse workflow" is too expensive using Sanger sequencing, but we here demonstrate that is feasible with an NGS barcoding pipeline that allows for cost-effective high throughput generation of short specimen-specific barcodes (313 bp of COI; lab cost <$0.50 per specimen) through Next Generation Sequencing of tagged amplicons. We applied our approach to a large sample of tropical ants, obtaining barcodes for 3290 of 4032 specimens (82%). NGS barcodes and their corresponding specimens were then sorted into molecular operational taxonomic units (mOTUs) based on objective clustering and Automated Barcode Gap Discovery (ABGD). High diversity of 88-90 mOTUs (4% clustering) was found and morphologically validated based on preserved vouchers. The mOTUs were overwhelmingly in agreement with morphospecies (match ratio 0.95 at 4% clustering). Because of lack of coverage in existing barcode databases, only 18 could be accurately identified to named species, but our study yielded new barcodes for 48 species, including 28 that are potentially new to science. With its low cost and technical simplicity, the NGS barcoding pipeline can be implemented by a large range of laboratories. It accelerates invertebrate species discovery, facilitates downstream taxonomic work, helps with building comprehensive barcode databases, and yields precise abundance information.
Data Set_Exploring the Molecular Mechanisms of Endothelial Dysfunction Affecting Myocardial Infarction by Integrating Multiple Datasets with In Vivo Experimental Validation
Open the record for dataset details and reuse information.
Validation of the Bond et. al. (2010) SDSS-derived kinematic models for the Milky Way's disk and halo stars with Gaia Data Release 3 proper motion and radial velocity data
<p>We validate the Bond et. al. (2010) kinematic models for the Milky Way's disk and halo stars with Gaia Data Release 3 data. Bond et al. constructed models for stellar velocity distributions using stellar radial velocities measured by the Sloan Digital Sky Survey (SDSS) and stellar proper motions derived from SDSS and the Palomar Observatory Sky Survey astrometric measurements. These models describe velocity distributions as functions of position in the Galaxy, with separate models for disk and halo stars that were labeled using SDSS photometric and spectroscopic metallicity measurements. We find that the Bond et al. model predictions are in good agreement with recent measurements of stellar radial velocities and proper motions by the Gaia survey. In particular, the model accurately predicts the skewed non-Gaussian distribution of rotational velocity for disk stars and its vertical gradient, as well as the dispersions for all three velocity components. Additionally, the spatial invariance of velocity ellipsoid for halo stars when expressed in spherical coordinates is also confirmed by Gaia data at galacto-centric radial distances of up to 15 kpc.</p>
Manual cross-validation data for the article: "Comparison of bibliographic data sources: Implications for the robustness of university rankings"
<p>These are sets of data collected from the manual cross-validation of DOIs (and related research outputs) that are sampled from Web of Science (WoS), Scopus and Microsoft Academic (MSA). For each of the 15 universities, we initially collect all DOIs indexed by each of the three bibliographic sources. Subsequently, we randomly sample 40, 30 and 30 DOIs from sets of DOIs that are exclusively indexed by WoS, Scopus and MSA, respectively, for each university. A manual cross-validation process is then followed to validate certain characteristics across the data sources. This cross-validation process was carried out by a data wrangler, on a part-time basis over a few months, for which online data was accessed from 18 December 2018 to 20 May 2019.</p>
Validation data for tttsa, automated tilt-series alignment in PyTorch
<p>This dataset contains a tilt-series ordered by tilt angle saved in mrc format (.st), and the corresponding tilt angles for the collection (.rawtlt). The approximate tilt-axis angle was -88.7 in the serial-em mdoc file.</p> <p>The dataset also contains a bash script to run AreTomo (v1.3.4) for the same tilt-series and an output alignment from AreTomo (.aln).</p>
Fig. 1 in Tridacna noae (Röding, 1798) - a valid giant clam species separated from T. maxima (Röding, 1798) by morphological and genetic data
Fig. 1. Map of Taiwan showing sampling sites for giant clams.
Supplementary Website, Data, and Scripts for the Paper "Automatic Core-Developer Identification on GitHub: A Validation Study"
<p>Supplementary website containing result plots, pseudonymized input data, resulting pseudonymized classification data, analysis scripts, and Dockerfile used to produce the results of the paper "Automatic Core-Developer Identification on GitHub: A Validation Study".</p> <p>The pseudonymized input data is also availble separately here: https://zenodo.org/record/7775078</p> <p>The resulting pseudonymized classification data is also available separately here: https://zenodo.org/record/7775385</p>
Validation of StrainIQ software performance using metagenomic data from ATCC staggered mix genomes
<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. Here, the gut_mix.zip file contains the metagenomic sequence data from the ATCC 20 strain staggered genomic mix (MSA-1003). 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 varying sequencing datasets.</p> <p>Please find the new link for this data at https://zenodo.org/record/8132164</p>
Data from the Psychometric validation of the SOLARIS Perception Scale
<p><span><span>Our </span><span>main </span><span>aim </span><span>was </span><span>to </span><span>test </span><span>the </span><span>psychometric </span><span>properties </span><span>of </span><span>the </span><span>Perceived </span><span>Deepfake </span><span>Trustworthiness </span><br><span>Questionnaire (PDTQ) across three languages, namely English, Italian, and Slovenian. The PDTQ was </span><br><span>comprehensive of 31 items</span><span>, which pertain to four domains, namely the content of the video, the person in the </span><br><span>video, the source of the video, and other video characteristic. <span>With its validation procedure, we were interested </span><br><span>in the factorial structure of the questionnaire, the measu</span><span>rement equivalence of the three language versions, and </span><br><span>the internal reliability of the questionnaire or dimensions that may emerge.</span></span></span></p> <p><span><span> The dataset contains responses from the validation study. No identifiable information is given. <br></span><br></span></p>
Validation Study of an Assistive Pulse Data Collection Device
ClinicalTrials.gov study NCT06974227. IPD Sharing: NO. Countries: 1. Publications: 0.
Validation of Software for Assessment of Atrial Fibrillation From Data Acquired by a Wearable Smartwatch
ClinicalTrials.gov study NCT04176926. IPD Sharing: NO. Countries: 2. Publications: 0.
A Volunteer Study to Collect Imaging Data for the Development and Validation of ScanNav Anatomy PNB
ClinicalTrials.gov study NCT04830891. IPD Sharing: NO. Countries: 1. Publications: 0.
Development and Validation of a Preoperative Frailty Index Using Health Administrative Data
ClinicalTrials.gov study NCT03354546. IPD Sharing: NO. Countries: 0. Publications: 0.
Dynamic FDG PET/CT: Optimization and Validation of Data Acquisition
ClinicalTrials.gov study NCT04660279. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Validation of Software for Assessment of Sleep Apnea From Data Acquired by a Wearable Smartwatch
ClinicalTrials.gov study NCT03775291. IPD Sharing: NO. Countries: 2. Publications: 0.
Validation of Early Prognostic Data for Recovery Outcome After Stroke for Future, Higher Yield Trials
ClinicalTrials.gov study NCT05338697. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Influence of Data Collection Mode on Self-Report Validity of Asthma Therapy Adherence
ClinicalTrials.gov study NCT00233233. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data Collection for Validation of an NIBP Algorithm in Pregnant Women
ClinicalTrials.gov study NCT05789849. IPD Sharing: NO. Countries: 1. Publications: 0.
Data Collection for the Validation of an Artificial Intelligence Software to Support Musculoskeletal Ultrasound Examination
ClinicalTrials.gov study NCT07336407. IPD Sharing: NO. Countries: 1. Publications: 0.
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.