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Dataset results
67 results for “High accuracy”
Data from: Corrigendum to: Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars
<p>Corrigendum to "Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars". In a previous paper, we presented some convolutional neural network (CNN) models to classify images of tooth scores made by lions and jaguars through deep learning computer vision. In that work, we reached an accuracy of 82% of the testing set correctly classified. However, such an accuracy is biased, since the original sample was highly unbalanced. Therefor, now we present the results which correct the problems of the previously published models by producing more balanced classifications and also by achieving higher accuracy.</p>
Data from: Evaluation of Argos telemetry accuracy in the High-Arctic and implications for the estimation of home-range size
Animal tracking through Argos satellite telemetry has enormous potential to test hypotheses in animal behavior, evolutionary ecology, or conservation biology. Yet the applicability of this technique cannot be fully assessed because no clear picture exists as to the conditions influencing the accuracy of Argos locations. Latitude, type of environment, and transmitter movement are among the main candidate factors affecting accuracy. A posteriori data filtering can remove "bad" locations, but again testing is still needed to refine filters. First, we evaluate experimentally the accuracy of Argos locations in a polar terrestrial environment (Nunavut, Canada), with both static and mobile transmitters transported by humans and coupled to GPS transmitters. We report static errors among the lowest published. However, the 68th error percentiles of mobile transmitters were 1.7 to 3.8 times greater than those of static transmitters. Second, we test how different filtering methods influence the quality of Argos location datasets. Accuracy of location datasets was best improved when filtering in locations of the best classes (LC3 and 2), while the Douglas Argos filter and a homemade speed filter yielded similar performance while retaining more locations. All filters effectively reduced the 68th error percentiles. Finally, we assess how location error impacted, at six spatial scales, two common estimators of home-range size (a proxy of animal space use behavior synthetizing movements), the minimum convex polygon and the fixed kernel estimator. Location error led to a sometimes dramatic overestimation of home-range size, especially at very local scales. We conclude that Argos telemetry is appropriate to study medium-size terrestrial animals in polar environments, but recommend that location errors are always measured and evaluated against research hypotheses, and that data are always filtered before analysis. How movement speed of transmitters affects location error needs additional research.
Data from: Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding
Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.
Supplementary dataset for journal article "High-accuracy current measurement with low-cost shunts by means of dynamic error correction"
<p>Supplementary material for the journal article "High-accuracy current measurement with low-cost shunts by means of dynamic error correction".</p> <p>Measurements include</p> <ul> <li>Temperature dependence of a single 10mOhm shunt resistor in TO-247 package.</li> <li>Calibration sequence measurements for automated 600A test equipment for studies on automotive Li-Ion cells.</li> <li>Obtained dynamic and steady-state calibration parameters</li> <li>Parameter distribution for 100 test systems.</li> <li>Parameter drift for a single tester after one year of operation</li> </ul> <p>All data has been combined into a single binary Matlab-File.</p>
Supporting data for "KAPow: High-accuracy, Low-overhead Online Per-module Power Estimation for FPGA Designs"
<p>Supporting data for "KAPow: High-accuracy, Low-overhead Online Per-module Power Estimation for FPGA Designs"</p>
Raw datasets and media accompanying the manuscript: Fused Raman spectroscopy analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics
<p>Raw datasets and media accompanying the manuscript: <strong>Fused Raman spectroscopy analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics</strong></p>
Supplement 1 of High fidelity anthropomorphic 3D printed models – accuracy, precision and quality control
<p>Raw data for "High fidelity anthropomorphic 3D printed models – accuracy, precision and quality control" presented at EHB2022e</p>
High thermal quality rookeries facilitate high thermoregulatory accuracy in pregnant female rattlesnakes
<p>Data for study of thermal ecology in <em>Crotalus viridis</em> in Colorado, U.S.A.</p>
Error-corrected HG002 experimental high-accuracy UL reads
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More Flexible Number Formats for High-Accuracy Simulations - Randomized Inputs
<p>The random input matrices and vectors used in the thesis "More Flexible Number Formats for High-Accuracy Simulations".</p>
Explainable Deep Learning for Automatic Rock Classification: High Accuracy Does Not Mean Great Model Performance <Dataset>
<p>This is the dataset of manuscript entitled "Explainable Deep Learning for Automatic Rock Classification: High Accuracy Does Not Mean Great Model Performance". The manuscript is currently under review. Full access of this dataset will be released once the manuscript is accepted.</p>
Accuracy of Sentinel Lymph Node Biopsy in Nodal Staging of High Risk Endometrial Cancer
ClinicalTrials.gov study NCT01886066. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparing Diagnostic Accuracy of High-end Intestinal Ultrasound Versus Mid-end Ultrasound With Tandem Ileo-colonoscopy in Inflammatory Bowel Disease : a Paired, Validating Confirmatory Study
ClinicalTrials.gov study NCT06938295. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Corrigendum to: Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars
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Data from: Museum genomics: low-cost and high-accuracy genetic data from historical specimens
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Data from: Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars
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Data from: Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding
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Data from: Evaluation of Argos telemetry accuracy in the High-Arctic and implications for the estimation of home-range size
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Data from: Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models
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Unexpected high accuracy of landscape genetics inference with convolutional neural networks
<p>During the last decade convolutional neural networks (CNNs) have revolutionized the application of machine learning methods to classification tasks and object recognition. These procedures can summarize with great effectiveness image data in key features that allow to classify and predict with exceptional precision. Here we show for the first time how CNNs provide highly accurate predictions of small-scale genetic differentiation and diversity in a subterranean rodent from central Argentina. Using microsatellite genotypes and high resolution satellite imagery we trained a simple CNN which was able to predict local Fst and allele diversity accounting for more than 99% of their variation. When trained with changed landscape settings the CNN still highly accounted for ~60% of variation emerging as a promising tool for population and conservation genetics.</p>
ScienceDex guides
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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.