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1,899 results for “Running”
Running performance of professional soccer players
<p>These data were used to carry out an original research</p>
EMEP rv5.3 trend runs with CAMS-REG emissions
<h2>EMEP MSC-W results for ecosystem deposition<br><br></h2> <p>Provided by : david.simpson@met.no</p> <p>Date: : June 2024</p> <p>Model version: rv5.3 (git v5.3-7-g227eeda4)</p> <p> - see Simpson et al (2012,2024, and refs therein).</p> <p>Reslution : 0.1x0.1 degree resolution, CAMS domain, 20 layers.</p> <p>Aim: delivered to EU AVENGERS project</p> <h3>Runs performed:<br><br></h3> <p> tot: Default run with all emissions</p> <p> noKL: Run with no GNFR K+L emissions</p> <p> (2018 only:)</p> <p> noKLf: as noKL but also without fertilizer-induced soil NO emssions (just to allow an estimate of this impact).</p> <p> </p> <p>See README_Jun2024.txt for further details of emissions, caveats, etc.</p>
Observing scenarios simulations for HLVK-Configuration for O4 Runs, using 20 million injections. This simulation led to 17,009 BNS useful for training Parameter Estimations of EM counterparts of GW. (July 2024 edition).
<p>We have conducted a simulation of the HLVK-configuration deployed during the ongoing O4 run. This project supports the training of kilonova regression with machine learning processes, requiring thousands of BNS to pass the threshold cutoff. Here we have 17,009 BNS passing the SNR threshold, along with 3,148 NSBH and 121,718 BBH, from 20 million CBCs injected. The upper-lower limit between NS and BH is 3 sun masses.</p> <p>Due to the large file sizes, we have split them into three parts and uploaded them to Zenodo with the following DOIs:<br><br></p> <ol> <li><strong>The first files is located :</strong> <code>runs_part_aa</code> and <code>runs_part_ab</code> : <a title="https://zenodo.org/doi/10.5281/zenodo.12693652" href="../doi/10.5281/zenodo.12693652">https://zenodo.org/doi/10.5281/zenodo.12693652</a></li> <li><strong>The second files is located : </strong><code>runs_part_ac</code> and <code>runs_part_ad</code> : <a href="../doi/10.5281/zenodo.12694779">https://zenodo.org/doi/10.5281/zenodo.12694779</a></li> <li><strong>The third files is located : </strong><code>runs_part_ae</code> and <code>runs_part_af</code> : <a href="../doi/10.5281/zenodo.12696695">https://zenodo.org/doi/10.5281/zenodo.12696695</a></li> </ol> <blockquote> <p>Download them or use this Python script from GitHub to download all of them by running the script: </p> <p> <a href="https://github.com/weizmannk/ObservingScenariosInsights/blob/main/chunk-xml/zenodo-process/download_split_chunk_data.py">hchunk-files-downloader</a>.</p> </blockquote> <p>After downloading them (6 files ), you will need to combine them in a single file using the following process:</p> <blockquote> <p>1.Combine the parts:<br><code>cat runs_part_* > runs.zip</code></p> </blockquote> <blockquote> <p>2.Verify the combined file:<br><code>ls -lh runs.zip</code><br><code>file runs.zip</code></p> </blockquote> <blockquote> <p>3.Unzip the combined file:<br><code>unzip runs.zip</code></p> </blockquote> <p> </p> <p>In the <code>runs</code> folder, we have three subfolders:</p> <ul> <li><code>O4</code>: This contains the <code>.fits</code> files for skymap localization and all GW parameters of the CBCs that passed the threshold cutoff of 8.</li> <li><code>statistics_results</code>: This contains the summary results of the statistical predictions of GW detections.</li> <li><code>subpopulations</code>: This folder is the split of BNS, NSBH, and BBH events. It is useful for those who need quick parameters of BNS and NSBH for their lightcurve simulations or EM counterpart statistical estimation.</li> </ul> <p> </p> <p>For more information, visit: <a href="https://github.com/lpsinger/observing-scenarios-simulations" target="_new" rel="noreferrer">Observing Scenarios Simulations</a></p> <p>Contact: <a rel="noreferrer">weizmann.kiendrebeogo@oca.eu</a> or <a rel="noreferrer">kiend.weizman7@gmail.com</a></p>
Observing Runs for Binary Neutron Star and Neutron Star-Black Hole mergers in the HLVK-Configuration during O4, using 20 million CBC injections (July 2024 edition)
<p>We have conducted a simulation of the HLVK configuration deployed during the ongoing O4 run. This project supports the training of kilonova regression with machine learning processes, requiring thousands of BNS to pass the threshold cutoff. Here we have 17,009 BNS passing the SNR threshold, along with 3,148 NSBH and 121,718 BBH, from 20 million CBCs injected. The upper-lower limit between NS and BH is 3 sun masses.</p> <p>However, due to size constraints, this repository contains only the simulation data and sky maps of BNS and NSBH detections that have passed the cutoff threshold. For the complete dataset, including BBH, please refer to <a href="../doi/10.5281/zenodo.12693652">https://zenodo.org/doi/10.5281/zenodo.12693652</a> .</p>
Data release of the Swift-LVK subthreshold search during the third LIGO-Virgo-KAGRA observing run
<div> <div>Here we describe the structure of the data release.</div> <br> <div>1. The folder fits contains all the upper limit maps in the for of fits files. Run the code plot_maps_paper.py to obtain the figures 3 and 4 of the paper</div> <br> <div>2. The folder pdf contains all the upperlimit maps, with the GW sky localizations, in pdf format</div> <br> <div>3. The file lum.csv contains the data to reproduce Fig. 6. If the 'cred' colums is empty, then the event is only detected in low latencyl. If 'no' then the event has p_astro<0.5, otherwise it has p_astro>0.5.</div> <br> <div>4. The file joint_far.cvs contains the data to reproduce the Figure 7. Columns are self explanatory</div> <br> <div>5. The file data_BBH.txt, data_BBH_highpastro.txt, data_BBH_allreal.txt, data_BBH_allreal_earth.txt contain the likelihood to produce Figs 8-9-10-11. The plots are produced runnning read.py and read_allreal.py</div> <br> <div>6. All the tables are provided in cvs format</div> </div>
Run-time Performance Measurements to "Instanced Rendering of Parameterized 3D Glyphs with Adaptive Level-of-Detail using three.js"
Open the record for dataset details and reuse information.
Dataset for SeRUN® running profiles
<p>This repository contains the data for the study entitled: "SeRUN® study: Development of running profiles using a mixed methods analysis".</p> <p>The purpose of this study was to determine profiles of urban runners based on socio-demographic, health, motivational, training characteristics and running-related beliefs and behaviours, through a mixed, exploratory, sequential study (quantitative-qualitative).</p> <p>Attached is the database used (.xlsx) in the quantitative stage and the 15 semi-structured interviews (.pdf) to the participants in the qualitative stage. </p>
MPAS-Ocean V6 Run Directories
<p>Each file includes everything needed to run a simulation with MPAS-Ocean V6.0. The steps are:</p> <ol> <li>download</li> <li>unzip: tar xvfz filename.tar.gz</li> <li>add a link to your compiled executable of MPAS-Ocean V6</li> <li>run, e.g. mpirun -n $N ocean_model</li> </ol> <p>If the simulation was successful, you will see:</p> <pre><code>tail -n 1 log.ocean.0000.out Logging complete. Closing file at ...</code></pre> <p>See <a href="http://mpas-dev.github.io">http://mpas-dev.github.io</a> for User's Guide, github release page, description of each test case, and more.</p>
Intermediate data objects from running the machine learning code for Shen et al, Nature, 2018
<p>These are the RData objects of the processed cfMeDIP data that were used to run the machine learning analyses in "Sensitive tumour detection and classification using plasma cell-free DNA methylomes", Nature, 2018. This archive also includes the models we generated, and training and validation data matrices that people can use to fit new models and evaluate performance. These objects are to be used with the scripts and markdown at doi: 10.5281/zenodo.1242697 . The ReadMe contains descriptions. </p>
Unholey shoes: experimental considerations when estimating ankle joint complex power during walking and running
<p>See ReadMe.txt</p>
Test data for running snakePipes : WGBS 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 the WGBS workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for the mouse (<strong>GRCm38</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>
Test data for running snakePipes : RNA-seq 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 the RNA-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for mouse (<strong>GRCm38</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>
Test data for running snakePipes : HiC 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 the HiC workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for the mouse (mm9) 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 command in <strong>README</strong>.</li> </ul>
Test data for running snakePipes : ATAC-seq 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 the ATAC-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for fruit fly (<strong>dm6</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>
Test data for running snakePipes : ChIP-seq 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 the ChIP-seq 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>
Replication package for: What Drives Demand for State-Run Lotteries? Evidence and Welfare Implications
<p>Replication package:</p> <p>Lockwood, Benjamin B., Hunt Allcott, Dmitry Taubinsky, and Afras Y. Sial. 2024. "What Drives Demand for State-Run Lotteries? Evidence and Welfare Implications." Review of Economic Studies. </p> <p>This package contains all replication scripts and non-confidential data sets necessary to reproduce results in the paper. </p>
FIG. 5 in The Walker Scale: Details of a Method for Assessing Beach-Spawning Runs of California Grunion Leuresthes tenuis (Atheriniformes: Atherinopsidae)
FIG. 5. Clutch densities measured following spawning runs at several southern California beaches show a high positive correlation with Walker Scale reports, r ¼ 0.71, df ¼ 33, P, 0.0001 by Spearman Rank correlation.
FIG. 3 in The Walker Scale: Details of a Method for Assessing Beach-Spawning Runs of California Grunion Leuresthes tenuis (Atheriniformes: Atherinopsidae)
FIG. 3. Two examples of PVC pipe square meter transects deployed during a California Grunion run at Cabrillo Beach. Photos by Michael Schaadt.
FIG. 2 in The Walker Scale: Details of a Method for Assessing Beach-Spawning Runs of California Grunion Leuresthes tenuis (Atheriniformes: Atherinopsidae)
FIG. 2. (A) Collecting eggs after a run. Clutches are indicated with blue flags. These were found within 10 min following a run scored at W-5. Not all clutches present are marked. (B) Grunion eggs. Photos by Karen Martin.
FIG. 7 in The Walker Scale: Details of a Method for Assessing Beach-Spawning Runs of California Grunion Leuresthes tenuis (Atheriniformes: Atherinopsidae)
FIG. 7. Report using Walker Scale in smaller segments, in the context of a coastal construction project. Map of Santa Ana River, California from Merkel & Associates, 2017 (used with permission of Keith Merkel). This would be reported as a W-4 for the Grunion Greeters for 4/12, a W-2 on 4/13, and a W-1 on 4/14. The detailed locations given for the run assist in project management along the shoreline to avoid nest sites during construction.
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.