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Dataset results
5 results for “False negatives”
Correcting for population stratification reduces false positive and false negative results in joint analyses of host and pathogen genomes [G2G-Simulator: Simulated dataset]
<p>Data associated with the paper 'Correcting for population stratification reduces false positive and false negative results in joint analyses of host and pathogen genomes'.</p> <p>It contains the raw simulated data from the 'G2G-Simulator' program. </p> <p>Those data need to be loaded in a R environment .</p> <p>You can reproduce plots present in the paper by parsing the R object using the script 'parse_paper_data.R' present in the G2G-Simulator GitHub repository (https://github.com/onaret/G2G-Simulator/paper/parse_paper_dataset.R). </p>
Data from: Occupancy models for data with false positive and false negative errors and heterogeneity across sites and surveys
False positive detections, such as species misidentifications, occur in ecological data, although many models do not account for them. Consequently, these models are expected to generate biased inference. The main challenge in an analysis of data with false positives is to distinguish false positive and false negative processes while modeling realistic levels of heterogeneity in occupancy and detection probabilities without restrictive assumptions about parameter spaces. Building on previous attempts to account for false positive and false negative detections in occupancy models, we present hierarchical Bayesian models that utilize a subset of data with either confirmed detections of a species' presence (CP model) or both confirmed presences and confirmed absences (CACP model). We demonstrate that our models overcome the challenges associated with false positive data by evaluating model performance in Monte Carlo simulations of a variety of scenarios. Our models also have the ability to improve inference by incorporating previous knowledge through informative priors. We describe an example application of the CP model to quantify the relationship between songbird occupancy and residential development, plus we provide instructions for ecologists to use the CACP and CP models in their own research. Monte Carlo simulation results indicated that, when data contained false positive detections, the CACP and CP models generated more accurate and precise posterior probability distributions than a model that assumed data did not have false positive errors. For the scenarios we expect to be most generally applicable, those with heterogeneity in occupancy and detection, the CACP and CP models generated essentially unbiased posterior occupancy probabilities. The CACP model with vague priors generated unbiased posterior distributions for covariate coefficients. The CP model generated unbiased posterior distributions for covariate coefficients with vague or informative priors, depending on the function relating covariates to occupancy probabilities. We conclude that the CACP and CP models generate accurate inference in situations with false positive data for which previous models were not suitable.
Factors Associated With PSA False Negative and False Positive Results and the Impact on Patient's Health.
ClinicalTrials.gov study NCT03978299. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Data from: Occupancy models for data with false positive and false negative errors and heterogeneity across sites and surveys
Open the record for dataset details and reuse information.
Influencing Factors and Prediction Model of False-negative Central Lymph Nodes in Thyroid Cancer Patients
ClinicalTrials.gov study NCT06423105. IPD Sharing: NO. Countries: 0. 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.