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1,956 results for “test data”
Reference panel and test data for hapCon
<p>This dataset contains the reference panel needed to run hapCon and some test data. After unzipping, the folder ./reference contains the reference panel and its associated metadata, and the folder ./data contains two BAM files as test data, which is used in our <a href="https://github.com/hyl317/hapROH/blob/master/Notebooks/Vignettes/hapCon_vignette.ipynb">jupyter notebook tutorial</a>. Please visit our <a href="https://haproh.readthedocs.io/en/latest/hapCon.html">readthedocs</a> site for detailed documentation. </p>
Test data for Imputation Workflow
<p>Test data for Imputation Workflow</p>
Raw data testing colour and non-target semiochemical lures on Psylloidea and Pentatomoidea in Perth, Western Australia
<p>This raw data tests the following: 1. Asian Citrus Psyllid and Brown Marmorated Stinkbug lures, 2. sentinal plants (citrus/tomato) and 3. colour (yellow/yellow-green) on the tomato potato psyllid, other triozids, other psyllids and stinkbugs in Perth Western Australia in October 2020</p>
Mapping test data with various range sensors
<p>This is a test data for range-IMU SLAM systems recorded with various range sensors:</p> <p>- Ouster OS0-32 & OS0-64</p> <p>- Livox Avia</p> <p>- Intel Realsense L515 & D455</p> <p>- Microsoft Azure Kinect</p> <p>- Stereolabs ZED2i</p> <p> </p> <p>The groundtruth trajectories are estimated by aligning point cloud scans with a 3D environment map created with a survey-grade LiDAR (FARO Focus).</p> <p> </p> <p> </p>
Raw Data of SwissADME Test Result
<p>This data contains raw data which is the result of analysis from SwissADME webtool.</p>
Artificial signal data for signal alignment testing.
<p>This is a set of signals-pairs, univariate and multivariate, that can be used to test alignment algorithms.</p>
Data from: Testing hypotheses of marsupial brain size variation using phylogenetic multiple imputations and a Bayesian comparative framework
<p>Considerable controversy exists about which hypotheses and variables best explain mammalian brain size variation. We use a new, high-coverage dataset of marsupial brain and body sizes, and the first phylogenetically imputed full datasets of 16 predictor variables, to model the prevalent hypotheses explaining brain size evolution using phylogenetically corrected Bayesian generalised linear mixed-effects modelling. Despite this comprehensive analysis, litter size emerges as the only significant predictor. Marsupials differ from the more frequently studied placentals in displaying much lower diversity of reproductive traits, which are known to interact extensively with many behavioural and ecological predictors of brain size. Our results therefore suggest that studies of relative brain size evolution in placental mammals may require targeted co-analysis or adjustment of reproductive parameters like litter size, weaning age, or gestation length. This supports suggestions that significant associations between behavioural or ecological variables with relative brain size may be due to a confounding influence of the extensive reproductive diversity of placental mammals.</p>
Supplemental Data for Generalized Testing for Finite State Specifications
<p>Additional supplemental data for <em>Generalized Testing for Finite State Specifications</em>.</p> <p>Files in this dataset:<br> - `edge-pair-analysis.tgz` contains all data from analysis done in Section III-B (On the bugginess of mined specs).<br> - `preliminary-study-mined-fsms.tgz` contains all mined FSMs used for analysis in Section III-B (On the bugginess of mined specs).<br> - `temari-study-data.tgz` contains all Temari experiment data for RQ1 (How generalizable is Temari?).</p>
Frictionless Data Test Dataset Multiple File Types
<p>This is the test dataset</p>
Frictionless Data Test Dataset Multiple File Types Without Descriptor
<p>This is the test dataset</p>
A2TEA.Workflow test data
<p>Test data for <a href="https://github.com/tgstoecker/A2TEA.Workflow">https://github.com/tgstoecker/A2TEA.Workflow</a></p>
Frictionless Data Test Dataset - Draft
<p>This is a test dataset</p>
Frictionless Data Test Dataset - Version
<p>This is test dataset</p>
Data for article "Why do red/purple young leaves suffer less insect herbivory: tests of the warning signal hypothesis and the undermining of insect camouflage hypothesis"
<p>This data is associated with the manuscript titled “Why do red/purple young leaves suffer less insect herbivory: tests of the warning signal hypothesis and the undermining of insect camouflage hypothesis”.</p> <p>https://doi.org/10.1007/s11829-022-09924-x</p>
Processed data for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This is the data used to reproduce the results from "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Scatter plots for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This file contains the test-score-vs-metric plots generated by the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Generalization metrics for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This file contains all the generalization metrics that can be used to reproduce the results of "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Rank correlation results for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This file contains the rank correlation results from the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
A decade of cumulative radiocesium testing data for foodstuffs throughout Japan after the 2011 Fukushima Daiichi Nuclear Power Plant accident
<p>This site shares a decade of cumulative radiocesium testing data for foodstuffs throughout Japan after the 2011 Fukushima Daiichi Nuclear Power Plant accident.</p> <p>The unexpected accident at the Fukushima Daiichi Nuclear Power Station in Japan, which occurred on March 11th, 2011, after the Great East Japan Earthquake and tsunami struck the north-eastern coast of Japan, released radionuclides into the environment. Today, because of the amounts of radionuclides released and their relatively long half-life, the levels of radiocesium contaminating foodstuffs remain a significant food safety concern. Foodstuffs in Japan have been sampled and monitored for <sup>134,137</sup>Cs since the accident. More than 2.5 million samples of foodstuffs have been examined with the results reported monthly during each Japanese fiscal year (FY, from April 1<sup>st</sup> to March 31<sup>st</sup>) from 2012 to 2021. A total of 5,695 samples of foodstuffs within the “general foodstuffs” category collected during this whole period and 13 foodstuffs within the “drinking water including soft drinks containing tea as a raw material” category sampled in FY 2012 were found to exceed the Japanese maximum permitted level (JML) set at 100 and 10 Bq/kg, respectively. No samples from the “milk and infant foodstuffs” category exceeded the JML (50 Bq/kg). The annual proportions of foodstuffs exceeding the JML in the “general foodstuffs” category varied between 0.37% and 2.57%, and were highest in FY 2012. The <sup>134,137</sup>Cs concentration for more than 99% of the foodstuffs monitored and reported has been low and not exceeding the JML in recent years, except for those foodstuffs that are difficult to cultivate, feed or manage, such as wild mushrooms, plants, animals and fish. The monitoring data for foodstuffs show the current status of food safety risks from <sup>134,137</sup>Cs contamination, particularly for cultured and aquaculture foodstuffs on the market in Japan.The unexpected accident at the Fukushima Daiichi Nuclear Power Station in Japan, which occurred on March 11th, 2011, after the Great East Japan Earthquake and tsunami struck the north-eastern coast of Japan, released radionuclides into the environment. Today, because of the amounts of radionuclides released and their relatively long half-life, the levels of radiocesium contaminating foodstuffs remain a significant food safety concern. Foodstuffs in Japan have been sampled and monitored for <sup>134,137</sup>Cs since the accident. More than 2.5 million samples of foodstuffs have been examined with the results reported monthly during each Japanese fiscal year (FY, from April 1<sup>st</sup> to March 31<sup>st</sup>) from 2012 to 2021. A total of 5,695 samples of foodstuffs within the “general foodstuffs” category collected during this whole period and 13 foodstuffs within the “drinking water including soft drinks containing tea as a raw material” category sampled in FY 2012 were found to exceed the Japanese maximum permitted level (JML) set at 100 and 10 Bq/kg, respectively. No samples from the “milk and infant foodstuffs” category exceeded the JML (50 Bq/kg). The annual proportions of foodstuffs exceeding the JML in the “general foodstuffs” category varied between 0.37% and 2.57%, and were highest in FY 2012. The <sup>134,137</sup>Cs concentration for more than 99% of the foodstuffs monitored and reported has been low and not exceeding the JML in recent years, except for those foodstuffs that are difficult to cultivate, feed or manage, such as wild mushrooms, plants, animals and fish. The monitoring data for foodstuffs show the current status of food safety risks from <sup>134,137</sup>Cs contamination, particularly for cultured and aquaculture foodstuffs on the market in Japan.</p>
Frictionless Data Test Dataset Without Descriptor
<p>This is a test dataset</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.