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1,956 results for “test data”
Data from: Origins of global mountain plant biodiversity: testing the “mountain-geobiodiversity hypothesis”
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Data from: Experimental test of plant defense evolution in four species using long-term rabbit exclosures
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Data from: Is biodiversity energy-limited or unbounded? A test in fossil and modern bivalves
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Data from: Testing models of sex ratio evolution in a gynodioecious plant: female frequency covaries with the cost of male fertility restoration
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Data from: Phylogenomic incongruence, hypothesis testing, and taxonomic sampling: the monophyly of characiform fishes
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Data from: Space use and social association in a gregarious ungulate: testing the conspecific attraction and resource dispersion hypotheses
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Data from: Do an ecosystem engineer and environmental gradient act independently or in concert to shape juvenile plant communities? Tests with the leaf-cutter ant Atta laevigata in a Neotropical savanna
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Data from: A 45-second self-test for cardiorespiratory fitness: heart rate-based estimation in healthy individuals
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Data from: Metabolism drives demography in an experimental field test
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Data from: The tonic immobility test: do wild and captive Golden Mantella frogs (Mantella aurantiaca) have the same response?
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Is Niagara Falls a barrier to gene flow in riverine fishes? A test using genome-wide SNP data from seven native species
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Data from: Olfactory testing in Parkinson’s disease & REM behavior disorder: a machine learning approach
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Data for: The biomechanics of tooth strength: testing the utility of simple models for predicting fracture in geometrically complex teeth
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Data from: Sexually dimorphic dorsal coloration in a jumping spider: testing a potential case of sex-specific mimicry
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Data from: Species richness, endemism, and abundance patterns: tests of two fractal models in a serpentine grassland
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Seismic data collected at the Tinguatón volcano (Lanzarote, Canary Islands) during the European Space Agency (ESA) testing campaign PANGAEA-X 2018
<p>This dataset contains the seismic data collected between 19 and 21 November 2018 at the Tinguatón volcanic region (Los Volcanes Natural Park, Geoparc of Lanzarote, Canary Islands, Fig. 1), within the A1TRAP experiment which formed part of the Analog-1 geology and science support activity (Rossi et al., 2019). Analog 1 was part of a larger European Space Agency (ESA) testing campaign PANGAEA-X 2018 (Bessone et al., 2018), aimed at integrating astronaut training-data collection, documentation, analogue field geology procedures with remote sensing and in situ geophysical methods. </p> <p>Single-station, free-field ambient seismic noise data were collected along two orthogonal profiles: Traverse A, crossing the Tinguatón volcano, and Traverse B passing alongside it (Fig. 1c). Traverse A is ESE-WNW oriented and aligned to the regional fault (as well as along the fissure vent inside the volcano crater), and consists of 12 stations (P1-P12), approximately 50 m apart, with a total profile length of 620 m. Traverse B is NNW-SSE oriented and orthogonal to the regional fault strike, and consists of 9 stations (P13-P20), approximately 50 m apart, with a total profile length of 390 m.</p> <p>Data were collected using a Tromino® model ENGY digital tromograph (Micromed, 2011). This is an ultralight all-in-one device, using a compact 3-directional, 24-bit digital seismometer developed by MoHo s.r.l. (1 dm<sup>3</sup> volume and 1 kg weight), including both sensors and the data acquisition system, and works at frequencies down to 0.3 Hz. This seismograph is equipped with three orthogonal electrodynamic sensors (velocimeters), powered by two 1.5 V AA batteries. It includes an internal Global Positioning System (GPS) antenna and does not have any external cables.</p> <p>For all the measurements, the seismometer’s axis referred to as N-S was aligned to N15W direction, i.e., the strike of the western edge of the Tinguatón volcano, the area’s main topographic feature. Good ground coupling on scoria deposits or highly weathered basalt was obtained by using three, 6 cm-long metal spikes screwed into the base of the unit. The seismometer was levelled. Each seismic noise acquisition involved a 16-minute trace length with a 1024 Hz sampling rate, in accordance with the recommendations from SESAME Project (Bard et al., 2004).</p> <p>Four MASW (Multichannel Analysis of Surface Waves) active seismic surveys (A3_5, A7, A8_10, A18_19) were undertaken along the two profiles (Fig. 1c) to acquire the shear wave velocity of the shallow layer which was later to be used to constrain the H/V inversion. These surveys were carried out using the same equipment, along with a wireless trigger by MoHo s.r.l., and a heavy metal plate struck with a 5 kg hammer for the generation of compressional waves. A redundancy test, which involved ground energization by an ESA astronaut (Matthias Maurer) jumping up and down, was also performed (Fig. 1c). This test tried to mimic deployment and testing during possible future planetary missions. However, this test did not provide satisfactory results in term of signal clarity. The seismometer was kept fixed on the ground while shot points were moved at increasing distances involving a 5 m minimum offset and 1 m spacing for the first 11 shots and 5 m spacing for subsequent shots for total profile lengths ranging between 50 m and 100 m (Fig. 1c). Each MASW acquisition involved a 3 s trace window with a 512 Hz sampling rate.</p> <p>The data are presented in ASCII format files. The recordings of each channel were saved all together in the same file. Information about each file was printed on the header of the same file.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors are grateful to ESA and all PANGAEA-X 2018 staff, particularly Loredana Bessone and Matthias Maurer for their participation in data collection during some of the experiments and to the MilesBeyond Team, particularly Francesco Maria Sauro for his logistical support. We also thank MoHo s.r.l., particularly Jeremy Magnon, for providing instrumental support.</p> <p> </p> <p><strong>References</strong></p> <p>Bessone, L., et al., 2018, Testing technologies and operational concepts for field geology exploration of the</p> <p> Moon and beyond: the ESA PANGAEA-X campaign, Geophysical Research Abstract, #EGU2018-4013.</p> <p>Micromed, 2011. Dati tecnici Tromino e download pacchetto software Grilla. Available online from the</p> <p> website <a href="http://www.tromino.it/">http://www.tromino.it</a>.</p> <p>Bard, P., Duval, A., Koehler, A., Rao, S., 2004, Guidelines for the Implementation of the H/V Spectral Ratio</p> <p> Technique on Ambient Vibrations Measurements, Processing and Interpretation. SESAME H/V User Guidelines., pp. 1–62. Available online: <a href="http://sesame.geopsy.org/SES_Reports.htm">http://sesame.geopsy.org/SES_Reports.htm</a>.</p> <p>Rossi, A.P., et al., 2019, Morphometry and trafficability of planetary analogue terrains based on very high</p> <p> resolution remote sensing imagery, Geophysical Research Abstract, #EGU2019-17614.</p>
Gullspång Pull-out Test Data Set
<p>This data set contains the outcome of a series of pull-out tests (and 3D scan analysis) in the investigation of the bond behavior of naturally-corroded, plain reinforcement sourced from a decommissioned structure. The contents of this data set includes 1) photos and test measurements for all tested rebars; 2) an SQL database containing all pullout (unprocessed) data; and 3) an SQL database containing processed data. Data is provided in SQL format to permit querying of entries across the otherwise large, and parametrically diverse, data set. A "Read Me" file is also provided for additional descriptions of the content within.</p>
Test data for running snakePipes : DNA-mapping workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run DNA-mapping workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for human (<strong>hg38</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
Data and Code for Publication "Testing the Utility of Dental Morphological Trait Combinations for Inferring Human Neutral Genetic Variation"
<p>Data and code for publication: H. Rathmann, H. Reyes-Centeno, Testing the utility of dental morphological trait combinations for inferring human neutral genetic variation. <em>Proc. Natl. Acad. Sci. U.S.A.</em> 117, 10769-10777 (2020). DOI: 10.1073/pnas.1914330117</p> <p>The repository contains:</p> <ul> <li>“R-code.txt”: R code for an exhaustive search algorithm testing the utility of dental morphological traits and trait combinations for inferring human neutral genetic variation.</li> <li>“dental trait frequencies.csv”: Data set with 27 dental morphological trait frequencies for 20 modern human populations worldwide used for analysis. Data from G. R. Scott, C. G. Turner, G. C. Townsend, M. Martinón-Torres, <em>The Anthropology of Modern Human Teeth</em> (Cambridge University Press, 2018). DOI: 10.1017/ 9781316795859</li> <li>“microsatellite loci mean sizes.csv”: Data set with 645 microsatellite mean allele sizes for 20 modern human populations worldwide used for analysis. Data from T. J. Pemberton, M. DeGiorgio, N. A. Rosenberg, Population structure in a comprehensive genomic data set on human microsatellite variation. <em>G3: Genes Genom. Genet.</em> 3, 891–907 (2013). DOI: 10.1534/g3.113.005728</li> <li>“utility estimates for 134217727 trait combinations.txt”: A large table with utility estimates for 27 dental morphological traits and all 134,217,700 possible trait combinations.</li> </ul> <p>Abbreviations for the 20 population names (rows) in “dental trait frequencies.csv” and “microsatellite loci mean sizes.csv” as follows:</p> <ul> <li>AUS = Australia</li> <li>CAS = Central Asia</li> <li>EAF = Eastern Africa</li> <li>EAS = East Asia</li> <li>EEU = Eastern Europe</li> <li>IND = India</li> <li>MAM = Mesoamerica</li> <li>MEL = Melanesia</li> <li>MIC = Micronesia</li> <li>NAF = North Africa</li> <li>NAM = North America</li> <li>NESI = Northeast Siberia</li> <li>NGU = New Guinea</li> <li>NWAM = Na-Dene</li> <li>POL = Polynesia</li> <li>SAM = South America</li> <li>SAN = San</li> <li>SEAS = Southeast Asia</li> <li>WEU = Western Europe</li> <li>WSAF = Sub-Saharan Africa</li> </ul> <p>Abbreviations for the 27 dental morphological trait names (columns) in “dental trait frequencies.csv” as follows:</p> <ul> <li>T1 = Winging (UI1)</li> <li>T2 = Shoveling (UI1)</li> <li>T3 = Double-Shoveling (UI1)</li> <li>T4 = Interruption Grooves (UI2)</li> <li>T5 = Tuberculum Dentale (UI2)</li> <li>T6 = Mesial Ridge (UC)</li> <li>T7 = Distal Accessory Ridge (UC)</li> <li>T8 = Hypocone (UM2)</li> <li>T9 = Carabelli Trait (UM1)</li> <li>T10 = Cusp 5 (UM1)</li> <li>T11 = Enamel Extensions (UM1)</li> <li>T12 = Peg-Reduced-Missing (UM3)</li> <li>T13 = Lingual Cusp Number (LP2)</li> <li>T14 = Groove Pattern (LM2)</li> <li>T15 = Cusp 6 (LM1)</li> <li>T16 = Cusp Number (LM2)</li> <li>T17 = Deflecting Wrinkle (LM1)</li> <li>T18 = Distal Trigonid Crest (LM1)</li> <li>T19 = Protostylid (LM1)</li> <li>T20 = Cusp 7 (LM1)</li> <li>T21 = Odontomes (UP-LP)</li> <li>T22 = Root Number (UP1)</li> <li>T23 = Root Number (UM2)</li> <li>T24 = Root Number (LC)</li> <li>T25 = Tomes’ Root (LP1)</li> <li>T26 = Root Number (LM1)</li> <li>T27 = Root Number (LM2)</li> </ul> <p>Abbreviations for the 645 microsatellite allele locus names (columns) in “microsatellite loci mean sizes.csv” as in T. J. Pemberton, M. DeGiorgio, N. A. Rosenberg, Population structure in a comprehensive genomic data set on human microsatellite variation. <em>G3: Genes Genom. Genet.</em> 3, 891–907 (2013). DOI: 10.1534/g3.113.005728</p>
Data: The effects of general fatigue induced by incremental exercise test and active recovery modes on energy cost, gait variability and stability in male soccer players
<p>Data, code, and movies for Mahaki et al. (2020); The effects of general fatigue induced by incremental exercise test and active recovery modes on energy cost, gait variability and stability in male soccer players. Published in The Journal of Biomechanics (<a href="https://doi.org/10.1016/j.jbiomech.2020.109823">https://doi.org/10.1016/j.jbiomech.2020.109823</a>).</p> <p><strong>Gas data</strong> - contains all Oxygen data plus a script to run the data.</p> <p> - Subjectxx - folder with oxygen data.</p> <p> - GasdataMohammad [type of file: MATLAB code (.m)] - script to analyze the oxygen data. The outcomes have been saved as RestECnet, PWSECnet, and IVTECnet [type of files: MATLAB data (.mat)].</p> <p> - EC_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of energetic cost, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery) and to show the outcome in boxplot together with individual data points (The results have been presented in <strong>Fig 2.</strong>)</p> <p> </p> <p><strong>Kinematic Data</strong> - contains all Kinematic data plus scripts to run the data:</p> <p> - Subjectxx - folder with kinematics data.</p> <p> - Rest_VAR [type of file: MATLAB code (.m)] - script to analyze the kinematics data in the Rest recovery session. The outcomes have been saved as RestVAR [type of file: MATLAB data (.mat)]. The corresponding plots, per subject & per trial, have been saved in the Plots folder.</p> <p> - PWS_VAR [type of file: MATLAB code (.m)] - script to analyze the kinematics data in the PWS recovery session. The outcomes have been saved as PWSVAR [type of file: MATLAB data (.mat)]. The corresponding plots, per subject & per trial, have been saved in the Plots folder.</p> <p> - IVT_VAR [type of file: MATLAB code (.m)] - script to analyze the kinematics data in the IVT recovery session. The outcomes have been saved as IVTVAR [type of file: MATLAB data (.mat)]. The corresponding plots, per subject & per trial, have been saved in the Plots folder.</p> <p> - VAR_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait variability (VAR sagittal, frontal, and horizontal) and to show the outcome in boxplot together with individual data points (The results have been presented in <strong>Fig 3.</strong>).</p> <p> - VAR_Sagittal_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait variability in sagittal plane, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery), and to show the outcome in boxplot together with individual data points.</p> <p> - VAR_Frontal_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait variability in frontal plane, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery), and to show the outcome in boxplot together with individual data points.</p> <p> - VAR_Horizontal_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait variability in horizontal plane, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery), and to show the outcome in boxplot together with individual data points.</p> <p> - normalizetimebase [type of file: MATLAB code (.m)] – function to normalize VAR from 0 to 100% of the gait cycle.</p> <p> - VAR_Sagittal [type of file: JASP/Jamovie files (.jasp/.omv)] - file to statistically test gait variability in sagittal plane. Imported data have been saved as VAR_Sagittal [type of file: Excell file (.csv)].</p> <p> - VAR_Frontal [type of file: JASP/Jamovie files (.jasp/.omv)] - file to statistically test gait variability in frontal plane. Imported data have been saved as VAR_Frontal [type of file: Excell files (.csv)].</p> <p> - VAR_Horizontal [type of file: JASP/Jamovie files (.jasp/.omv)] - file to statistically test gait variability in horizontal plane. Imported data have been saved as VAR_Horizontal [type of file: Excell file (.csv)].</p> <p> + Trunk Stability - folder includes all outcomes and codes in terms of gait stability:</p> <p> -Thoracic [type of file: Excell file (.xlsx)] - general file includes all outcomes in terms of gait stability.</p> <p> - LDS_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait stability (λ sagittal, λ frontal, and λ horizontal) and to show the outcome in boxplot together with individual data points (The results have been presented in <strong>Fig 5.</strong>).</p> <p> - LDS_Sagittal_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses inn terms of gait stability in sagittal plane, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery), and to show the outcome in boxplot together with individual data points.</p> <p> - LDS_Frontal_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait stability in frontal plane, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery), and to show the outcome in boxplot together with individual data points.</p> <p> - LDS_Horizontal_Stat_boxplot [type of file: MATLAB code (.m)] - script to statistically test the hypotheses in terms of gait stability in horizontal plane, to calculate the effect sizes of main effects of Time, Recovery and Interaction effect (Time vs Recovery), and to show the outcome in boxplot together with individual data points.</p> <p> - Rest_LDS_Sagittal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the Rest recovery session and in the sagittal plane.</p> <p> - Rest_LDS_Frontal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the Rest recovery session and in the frontal plane.</p> <p> - Rest_LDS_Horizontal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the Rest recovery session and in the horizontal plane.</p> <p> - PWS_LDS_Sagittal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the PWS recovery session and in the sagittal plane.</p> <p> - PWS_LDS_Frontal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the PWS recovery session and in the frontal plane.</p> <p> - PWS_LDS_Horizontal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the PWS recovery session and in the horizontal plane.</p> <p> - IVT_LDS_Sagittal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the IVT recovery session and in the sagittal plane.</p> <p> - IVT_LDS_Frontal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the IVT recovery session and in the frontal plane.</p> <p> - IVT_LDS_Horizontal [type of file: Excell file (.xlsx)] - file includes LDS outcomes in the IVT recovery session and in the horizontal plane.</p> <p> - LDS_sagittal [type of files: JASP/Jamovie files (.jasp/.omv)] - file to statistically test gait stability in sagittal plane. Imported data have been saved as LDS_sagittal [type of file: Excell file (.csv)].</p> <p> - LDS_frontal [type of files: JASP/Jamovie files (.jasp/.omv)] - file to statistically test gait stability in frontal plane. Imported data have been saved as LDS_frontal [type of file: Excell file (.csv)].</p> <p> - LDS_horizontal [type of files: JASP/Jamovie file (.jasp/.omv)] - file to statistically test gait stability in horizontal plane. Imported data have been saved as LDS_horizontal [type of file: Excell file (.csv)].</p> <p> </p> <p><strong>Movies</strong> –contains some movies [type of files: .mp4] to show the Noraxon calibration, IMUs placement on body segments, fatigue protocol, walking at PWS, and resting metabolic cost measurement.</p> <p> </p> <p><strong>The characteristics of participants</strong> [type of file: Excell file (.xlsx)] – file includes 4 sheets:</p> <p> - Sheet 1 includes height (cm), weight (kg), age (year), BMI, and body fat percentage of each participant.</p> <p> - Sheets 2-4 include the physiological parameters of each participant during IVT, PWS, and Rest recovery modes. The physiological parameters such as Vo2, Vo2max, HR, and RER of each participant have been reported during rest (i.e. sitting position) and incremental exercise test.</p> <p> </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.