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1,899 results for “Running”
ROMS-PISCES coupled implementation for the Marquesas (Run Biosope - Y4)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Biosope) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Sed5 - Y4)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Sed5) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Sed2.5 - Y4)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Sed2.5) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Sed5 - Y5)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Sed5) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Ref - Y5)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Ref - no sediment) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Ref - Y4)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Ref - no sediment) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Biosope - Y5)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Biosope) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Sed2.5 - Y5)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Sed2.5) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: implications for science, sport and wearable tech
<p>An interactive user interface and the raw data from the manuscript titled: "Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: implications for science, sport and wearable tech". </p>
Data: The effect of external lateral stabilization on the use of foot placement to control mediolateral stability in walking and running
<p>Data and code for Mahaki et al. (2019): The effect of external lateral stabilization on the use of foot placement to control mediolateral stability in walking and running (<a href="https://doi.org/10.7717/peerj.7939">https://doi.org/10.7717/peerj.7939</a>).</p> <p>The Gas, Force, and Optotrak data.rar folder contains three subfolders as follows:</p> <p><strong>Gasdata2</strong> - contains all oxygen data, plus a scripts to run the data.</p> <p> - Subjectxx - folder with oxygen data</p> <p> - MainMohammadRunning_EC - script to analyze the oxygen data.</p> <p> - EC_walkRun_Stat - script to statistically test the hypotheses in terms of energetic cost. The results have been presented in supplementary material (<strong>Fig. S5.</strong>)</p> <p><strong>KinData</strong> - contains all kinematic and kinetic data (one folder per subject)</p> <p> - Marker.xlsx - info on which markers were placed where</p> <p> - Pointers.xlsx - info on which pointer trials contain which virtual markers</p> <p> - Trials.xlsx - info on which files contain which conditions</p> <p> - Subjectxx - folder with all the data.</p> <p> - The *.ndf files contain the kinematics (can be read with readndf.m, available in a subfolder of the VU 3d model (see below)</p> <p> - The *.afp files contain the raw voltage data from the force plates. These can be converted to forces and moments using the function FBW_dualbelt.m, available in a subfolder of the VU 3d model (see below).</p> <p> - The _HS.mat files contain calculated events (see script A below), and are normal matlab files.</p> <p><strong>Software</strong> - contains all code used to get from the kinematic data to outcome variables, script were ran as:</p> <p> - A_Main_Heelstrikes - script which calculates heelstrikes, and prompts the user to check these. Results are saved in the same folder (and under same name, but with extension HS.mat) as original data.</p> <p> - B_Main_Calculate_step - script which was used to calculate main outcomes for each subject and condition. results of this script are stored in foot_placement_thorax.mat and used in subsequent scripts.</p> <p> - C_Combine_Test_Legs - script which was used to statistically investigate the effect of leg and speed on R<sup>2</sup> time series (lines 67-69). The results have been presented in Supplementary Material file (<strong>Fig. S1.</strong>).</p> <p> - D_main_plot_WalkRun_normalOnly - script which was used to check our second hypothesis which was the comparison between normal walking and running in terms of foot placement strategy. The results have been presented in <strong>Fig 4. & 5.</strong>.</p> <p> - E_main_plot_WalkRun - script which was used to check our third and fourth hypotheses which we investigated the effect of external lateral stabilization on R<sup>2</sup> values in walking and running (hypothesis 3) and differences of external lateral stabilization effect between walking and running (i.e. mode of locomotion X condition interaction, hypothesis 4) during swing phase. The results have been presented in <strong>Fig 5. and Fig 7.</strong>.</p> <p> - SW_SWvariability_WalkRun - script which was used to check our hypotheses in terms of step width and step width variability. The results have been presented in <strong>Fig 6</strong>.</p> <p> - F_Supplementary_main_plot_run - script which was used to compare R<sup>2</sup> values between running trials (speed effect) and the results have been presented in supplementary material (<strong>Fig .S2.</strong>).</p>
driverMAPS run results for 20 tumor types from TCGA
<p>driverMAPS run results for 20 tumor types from TCGA, fixed a formatting error in this version. </p>
DAVE - Measuring run on Mittellandkanal in Hannover, Germany - Canal
<p>The dataset was collected during a measurement run on the Mittellandkanal in Hanover, Germany.</p> <p>The data is provided in the ros2bag mcap storage format.</p> <p>The bag files contain data from the following sensors </p> <ul> <li>Lidar</li> <li>Camera</li> <li>GPS</li> <li>Wind</li> </ul> <p>The bag files can be played using the free foxglove studio software. A layout json file is also provided. </p> <p>These bag files show parts of the canal with a focus on bridges.</p> <p> </p> <p>This publication is a result of the research of the Center of Excellence CoSA and funded by the Federal Ministry for Digital and Transport of the Federal Republic of Germany (Id 19F2225C, DAVE).</p> <p> </p> <p>Project website: https://www.th-luebeck.de/cosa/projekt/dave/</p>
Silva, Harder et al. Detectability of runs of homozygosity is influenced by analysis parameters and population-specific demographic history Data
<p>Dataset used in the "Detectability of runs of homozygosity is influenced by analysis parameters and population-specific demographic history" paper by Silva, Harder et al.</p> <p>This dataset includes Fasta files for Simulated dataset and VCF files for both Empirical and Simulated datasets.</p>
DAVE - Measuring run on Mittellandkanal in Hannover, Germany - Lock entrance
<p>The dataset was collected during a measurement run on the Mittellandkanal in Hanover, Germany.</p> <p>The data is provided in the ros2bag mcap storage format.</p> <p>The bag files contain data from the following sensors </p> <ul> <li>Lidar</li> <li>Camera</li> <li>GPS</li> <li>Wind</li> </ul> <p>The bag file can be played using the free foxglove studio software. A layout json file is also provided. </p> <p>This bag file shows the lock Anderten in the canal.</p> <p> </p> <p>This publication is a result of the research of the Center of Excellence CoSA and funded by the Federal Ministry for Digital and Transport of the Federal Republic of Germany (Id 19F2225C, DAVE).</p> <p> </p> <p>Project website: https://www.th-luebeck.de/cosa/projekt/dave/</p>
Evaluating rapid water excitation techniques for 5D whole-heart fat-suppressed free-running cardiac MRI at 1.5T scanner
<p>Volunteer dataset of the paper: <em>"Evaluating Rapid Water Excitation Techniques for 5D Whole-Heart Fat-Suppressed Free-Running Cardiac MRI at 1.5T Scanner"</em></p> <p>The folder "InVivo" contains raw data from five different volunteers, with each volunteer having undergone 5 different acquisitions: bSSFP, FISS, BORR, LIBRE, and LIBOR. </p>
Supplemental High Resolution IPWM Run for "Ion heating in the polar cap under northwards IMF Bz": SDOP_April2016
<p>Full high resolution output from the Ionosphere/Polar Wind Model (IPWM) for results published in "Ion heating in the polar cap under northwards IMF Bz", by L. J. Lamarche, R. H. Varney, and A. S. Reimer. This is a simulation of the April 2016 event, with IPWM driven by SuperDARN convection maps and Ovation Prime precipitation (SDOP_April2016). Scripts to visualize these model output and produce the figures in the article are available in a separate repository <a href="http://doi.org/10.5281/zenodo.4453390">10.5281/zenodo.4453390</a>.</p> <p>If you are configuring the Resen bucket available in <a href="http://doi.org/10.5281/zenodo.4453390">10.5281/zenodo.4453390</a>, mount this repository to /home/jovyan/mount/SDOP_April2016_output.</p>
Supplemental High Resolution IPWM Run for "Ion heating in the polar cap under northwards IMF Bz": SDOP_May2014
<p>Full high resolution output from the Ionosphere/Polar Wind Model (IPWM) for results published in "Ion heating in the polar cap under northwards IMF Bz", by L. J. Lamarche, R. H. Varney, and A. S. Reimer. This is a simulation of the May 2014 event, with IPWM driven by SuperDARN convection maps and Ovation Prime precipitation (SDOP_May2014). Scripts to visualize these model output and produce the figures in the article are available in a separate repository <a href="http://doi.org/10.5281/zenodo.4453390">10.5281/zenodo.4453390</a>.</p> <p>If you are configuring the Resen bucket available in <a href="http://doi.org/10.5281/zenodo.4453390">10.5281/zenodo.4453390</a>, mount this repository to /home/jovyan/mount/SDOP_May2014_output.</p>
Hypothetical ensemble dispersion model runs with statistical verification
<p>This dataset contains output from the dispersion model NAME (Numerical Atmospheric-dispersion Modelling Environment) generated by modelling the hypothetical eruption of two volcanoes (Hekla and Oraefajokull both in Iceland) and a radiological release from 12 different locations across Europe. The hypothetical eruption of Hekla is a 12km eruption lasting 24 hours and the hypothetical eruption of Oraefajokull is a 25km eruption lasting 24 hours. Each of the radiological releases is a 1PBq Cs-137 release over 6 hours. The scenarios were modelling using three different sets of met data from the Met Office unified model; data from the global deterministic forecast, data from the global ensemble forecast and data constructed to form a global analysis. </p> <p>Output from the runs is stored in gzipped tar files labelled <name>_scenario_<year><month>.tar.gz where <name> is hekla, orae (short for oraefajokull) or radiological. Each of these tarballs contains the output from all the runs with a release start date in the year and month given in the file name. The data is stored in NetCDF format with each NetCDF file containing the run output from a single run with one type of met data. For example the NetCDF file: 20181109T1200Z_engl_members.nc contains all the output from the run started at 12 UTC on 9 November 2018 using the ensemble global forecast met data. </p> <p>For the radiological scenario output is the total integrated air concentration and the total deposition after 48 hours. For the volcanic eruption scenarios output is the hourly air concentration of volcanic ash on 22 vertical levels, hourly ash column load and hourly ash deposits.</p> <p>Two additional files are included. These contain the Brier skill score computed by comparing the ensemble and deterministic output to the analysis output and the maximum distance at which threshold concentrations are exceeded. Full details of the computation can be found in a paper submitted to Atmospheric Chemistry and Physics.</p> <p>@Crown Copyright, Met Office</p>
Replication package for "How does Migrating to Kotlin Impact the Run-time Efficiency of Android Apps?"
<p><strong>Replication Package</strong></p> <p>This section briefly describes the contents of the replication package, which makes it possible to replicate this study.</p> <p><strong>Dataset construction:</strong> The recreation of the GitLab AndroidTimeMachine instance is done using the snapshot related code found in the Kotlin program. This program uses a running instance of the AndroidTimeMachine’s Neo4j database and uses its data to make API calls to a running GitLab instance which imports the GitHub projects found. This step can be skipped, and a GitLab instance can be instantiated directly using our created Docker volumes. The is also capable of performing the Kotlin filtering steps. For this, it uses the results of the previous steps as input and clones each repository for performing the filtering procedure.</p> <p><strong>SLOC counting:</strong> SLOC counting related scripts are found in and implement our methods by cloning the project and running CLOC. It also plots the line graphs that we manually categorized into evolution trends. The raw SLOC data is found in , and all of the plotted evolution trends are found in.</p> <p><strong>Kotlin migration mining:</strong> The implementation for mining is located in , and implements our methods by cloning a repository from the running GitLab instance and executing described methods. All commits we found are located in.</p> <p><strong>Run-time efficiency:</strong> As mentioned in the thesis, we used AndroidRunner. A fork of AndroidRunner we used for conducting our experiments is located in . In the folder , each configuration combined with raw results is found per test subject. The final data we used after removing corrupt frame times is located in the folder, which also contains . This python script implements our statistical analysis methods.</p>
Figure 3 in Run to the nest: A parody on the Iron Maiden song by Corotoca spp. (Coleoptera, Staphylinidae)
Figure 3. Simple regressions using each of the independent variables of the multiple linear regressions for time until extrusion and return to the nest by Corotoca melantho and C. fontesi. Left: return time (y-var) vs time until stopping (x-var) (R² = 0. 6152; F = 35. 17, P <0. 001); Right: return time vs distance traveled (x-var) (R² = 0. 10; F = 6. 461, P = 0. 13).
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.