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558 results for “Training Data”
Silva taxonomic training data formatted for DADA2 (Silva version 132)
<p>These DADA2-formatted training fasta files were derived from the Silva Project's version 132 release. They are available under the Silva dual-licensing model for academia and commercial users: https://www.arb-silva.de/silva-license-information/</p> <p>These fastas were generated by the following commands (using the dada2 R package version 1.7.6):</p> <blockquote> <p>path <- "~/Desktop/Silva/Silva.nr_v132"<br> dada2:::makeTaxonomyFasta_Silva(file.path(path, "silva.nr_v132.align"), file.path(path, "silva.nr_v132.tax"), "~/tax/silva_nr_v132_train_set.fa.gz")</p> <p>dada2:::makeSpeciesFasta_Silva("~/Desktop/Silva/SILVA_132_SSURef_tax_silva.fasta.gz", "~/tax/silva_species_assignment_v132.fa.gz")</p> </blockquote>
MT Training Data
<p>MT Training Data</p>
Figure 4 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Figure 4 Initial expertise (color of the bar) vs final confidence (y-axis) after the GRU workshop for participants responding to final survey. Example for how to interpret this graphic: the blue color bar at the top indicates that before the workshop roughly 50% of respondents said their knowledge of GEOLocate was "neither high nor low" but after the workshop these same respondents selected "much higher" for their knowledge of GEOLocate.
Figure 3 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Figure 3 An illustrative example of the two methods of uncertainty capture when georeferencing specimens. Method A, or polygon, creates a shape around the river (in blue). Method B, or point-radius, creates a circle of uncertainty around the origin. The illustration is based on output from GeoLocate software (Rios 2018) for both polygon and point-radius.
Figure 2 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Figure 2 This specimen record is an example from the University of California Collection Network Symbiota Portal. The large image is an edit of the record to include a medium size version of the image for easier viewing in this article. The portal software is open source and it is freely available for reuse through the Symbiota GitHub repository. The image is an example of a specimen record that includes an image of the specimen with label data. The image is contributed by the UCSB Invertebrate Zoology Collection at the Cheadle Center for Biodiversity and Ecological Restoration. The usage rights for the image is Creative Commons 0 (public domain).
Figure 1 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Figure 1 Map created using SimpleMappr (Shorthouse 2010) that illustrates geolocated specimens for Genus=Cicindela in California as found on iDigBio.
Volcano Plot (training data)
<p>Files for a Galaxy volcano plot tutorial</p>
Heatmap2 (training data)
<p>Files for a Galaxy heatmap2 tutorial.</p>
RNA-seq genes to pathways (training data)
<p>Files for a Galaxy tutorial RNA-seq genes to pathways.</p>
Data for paper "Using synthetic semiochemicals to train canines to detect bark beetle-infested trees" in Ann For Sci
<p><strong>ESM_0</strong> Photo. Entrainment of semiochemicals with Porapak <sup>® </sup>Q plug from cylinders used in stimuli delivery in dog training platform. (DOCX)</p> <p><strong>ESM_1</strong> Fig. Educational scent platform. (PDF)<br> <strong>ESM_2 </strong>Fig. Training platform stimuli layout and decline in response to no<br> target scent. (PDF)<br> <strong>ESM_3 </strong>Table. Evaluation of the dog detection performance as number of<br> indications with decreasing amounts of scent molecules over time. (PDF)</p> <p><strong>ESM_4_V1</strong> Video. Educational scent platform in operation. (AVI)<br> <strong>ESM_4_V2</strong> Video.<em> </em>Placement of cotton scent pad and the location of the scent by dog on a pine (a non-host tree of the beetle). (AVI)<br> <strong>ESM_4_V3</strong> Video. The search, GPS tracking, and location of natural attacks.<em> </em>(AVI)<br> <strong>ESM_4_V4</strong> Video. The search, location of two adjacent natural attacks, and rewarding. (AVI)</p> <p>The dog detection allows timely removal by sanitation logging of first beetle-attacked trees before offspring emergence, preventing local beetle increases. Detection dogs rapidly learned responding to synthetic bark beetle pheromone components, with known chemical titres, allowing search training during winter in laboratory and field. Dogs trained on synthetics detected naturally attacked trees in summer at a distance of >100 m.</p>
Extended data integration of teacher training in Gamification
<p>Reports of included studies</p>
Data Tabels (One-dimensional N-layer thermal modelling as a basis for effective machine learning training data generation for nondestructive testing of composite parts.)
Open the record for dataset details and reuse information.
Train and Test data
Open the record for dataset details and reuse information.
Training data (bead stacks) for the red channel of the Zeiss microscope
Open the record for dataset details and reuse information.
The relevant data and trained model of LGS-PPIS
Open the record for dataset details and reuse information.
Ancient Tamil Word Segmentation Training Data
Open the record for dataset details and reuse information.
Data from: Generalized polyspike train: an EEG biomarker of drug-resistant idiopathic generalized epilepsy
Objective: To identify clinical and EEG biomarkers of drug resistance in adults with idiopathic generalized epilepsy. Methods: We conducted a case-control study consisting of a discovery cohort and a replication cohort independently assessed at two different centres. In each centre patients with idiopathic generalized epilepsy phenotype and generalized spike-wave discharges on EEG were classified as drug-resistant or drug-responsive. EEG changes were classified into pre-defined patterns and compared between the two groups in the discovery cohort. Factors associated with drug resistance in multivariable analysis were tested in the replication cohort. Results: The discovery cohort included 85 patients (29% drug-resistant and 71% drug-responsive). Their median age at assessment was 32 years and 50.6% were female. Multivariable analysis showed that having higher number of seizure types (3 vs. 1: odds ratio [OR]= 31.1, 95% confidence interval [CI] 4.5-214, p<0.001; 3 vs. 2: OR=14.6, 95% CI: 2.3-93.1, p=0.004) and generalized polyspike train (burst of generalized rhythmic spikes lasting less than 1 second) during sleep were associated with drug resistance (OR=10.8, 95% CI: 2.4-49.4, p=0.002). When these factors were tested in the replication cohort of 80 patients (27.5% drug-resistant and 72.5% drug-responsive; 71.3% female; median age 27.5 years), the proportion of patients with generalized polyspike train during sleep was also higher in the drug-resistant group (OR=4.0, 95% CI: 1.35-11.8, p=0.012). Conclusion: Generalized polyspike train during sleep may be an EEG biomarker for drug resistance in adults with idiopathic generalized epilepsy.
Covid-IF Training Data
<p>Training data for https://onlinelibrary.wiley.com/doi/full/10.1002/bies.202000257</p>
TCEQ Ozone Forecasting Training Data
<p>training data that were used in the models that were constructed for 6 Texas urban sites. these are the CSV files that can be used directory with our modeling software. they are merged data sources and include parameters from MOS NAM-12 forecasts, NCEP 12z forecasts, and TAMIS ozone measurements</p>
Training Data From : "Squishing skyrmions: symmetry guided dynamic transformation of polar topologies under compression"
<ul> <li>Zip file contains folder of training data in XSF Format </li> <li>.nn files are neural network binary parameter files</li> <li>.nn_asc files neural network parameter text files </li> </ul> <p>.nn files were generated and can be read by the AENET software (http://ann.atomistic.net/) </p> <p> </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.