Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,899
datasets available to search
ShareScore release 0.9.0
Dataset results
1,899 results for “Running”
Is switchgrass good for carbon savings? Long-term results in marginal land Running title: Switchgrass SOC storage capacity in marginal land Walter Zegada-Lizarazu 1, Federica Zanetti 1, Nicola Di Virgilio 2, Andrea Monti 1 1Department of Agricultural and Food Sciences, University of Bologna, Viale G. Fanin 44 – 40127, Bologna, Italy; 2CNR, Institute for the BioEcononomy, National Research Council of Italy, Via P Gobetti 101, I-40129 Bologna, Italy
<p>Growing switchgrass in marginal land can be a valuable option to mitigate the risk of agricultural land abandonment whilst storing a significant amount of soil C. It was found a significant increase in SOC in a 13-years period. A significant positive correlation was observed between the C derived from switchgrass and SOC gain (the estimated switchgrass derived C was 12.7 Mg C ha<sup>-1</sup>). The increased SOC along the field, however, was patchy, with the highest increments registered in the center of the field, while the lowest ones in the top and bottom of the hill.</p>
Per-Run Algorithm Selection with Warm-starting using Trajectory-based Features - Data
<p>This repository contains the code and data for reproducing the results in the paper 'Per-Run Algorithm Selection with<br> Warm-starting using Trajectory-based Features'</p> <p>The file 'figure_generation.ipnb' contains the code used to create the heatmaps shown in the paper.</p> <p>The file 'Raw_data' contains the raw performance data, for both running A1 and recording all relevant features and for running the switching algorithms for each of the 6 algorithms in the portfolio while recording only performance. The processed version of the A2 data into relative performance is available in 'perf_relative'.</p> <p>The file 'data-collection' contains the code used to run the switching algorithms with the used warmstarting procedures.</p> <p>The file 'compute_features.ipnyb' contains the code used to extract the timeseries features, and those features themselves are included in 'TS_Features'.</p> <p>The file 'AlgorithmSelector' contains the scripts and data used to create the algorithm selector.</p> <p>The files 'selector_5_as_f' and 'selector_ng_f' contain the results of running the final models on the test-instances and nevergrad functions respectively.</p>
Estimating Running Ground Reaction Forces from Plantar Pressure during Graded Running Dataset
<p>Ground reaction force predictions, models, and ground truth measurements.</p>
Amyloid-beta 16-22 peptide dimer simulation (150mM NaCl) with the CHARMM36m force field and Gromacs (Run 2)
<p>Amyloid-beta 16-22 peptide dimer simulation with the CHARMM36m force field. This directory contains the second of the three independent trajectories and the simulation length is 1 microseconds. The system contains two amyloid-beta 16-22 monomers, 30 Na+, 30 Cl-, and 10564 TIP3P water.</p>
Amyloid-beta 16-22 peptide dimer simulation (150mM NaCl) with the CHARMM36m force field and Gromacs (Run 1)
<p>Amyloid-beta 16-22 peptide dimer simulation with the CHARMM36m force field. This directory contains first of the three independent trajectories and the simulation length is 1 microseconds. The system contains two amyloid-beta 16-22 monomers, 30 Na+, 30 Cl-, and 10564 TIP3P water.</p>
Dataset for paper submission: "Revisiting the Long-Run Relationship Between Inward/Outward FDI and Income Inequality: New Evidence from the OECD"
<p>The relatively small panel cointegration literature on the dynamics between FDI and income inequality predominantly finds that FDI will reduce income inequality in the long-run in developed countries. However, we point out an important technical oversight in the literature. Not accounting for cross-section dependence in panel data methodologies may yield unreliable results. Expanding on the work of @herzer/nunnenkamp:13, who pioneered the use of panel cointegration in the European context, we obtain different results when we account for cross-section dependence and employ economic procedures robust to it. Using a panel containing 16 OECD countries (1979-2017), 2 income inequality measures, and 4 FDI measures, we begin by showing strong evidence for the existence of cross-section dependence. Then, using second-generation econometric procedures, we do not find any evidence for a cointegrating relationship between inward FDI and income inequality. We do find evidence that outward FDI is cointegrated with income inequality; however, contrary to the main results of the literature, we find that it widens the income gap in the long-run. Additionally, our results support the view that fiscal policy is an important tool to reduce income inequality.</p>
MPAS-Ocean Barotropic Tidal Simulation Run Directories
<p>This file contains the initial files needed to run the barotropic tidal simulations in MPAS-Ocean.</p> <p>Download the resolution you want (icos7-10 or vr45to5). Inside the resolution directory is "inline", "scalar", and "shared". "Inline" is the run directory for inline SAL. "Scalar" is the run directory for scalar SAL. "Shared" contains data needed to run either case.</p> <p> The steps are:</p> <ol> <li>download</li> <li>unzip: tar xvfz filename.tar.gz</li> <li>navigate to run directory (resolution/inline OR resolution/scalar)</li> <li>add a link to shared files (e.g., ln -s ../shared/* )</li> <li>add a link to your compiled executable of MPAS-Ocean</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 Github page: https://github.com/E3SM-Project/E3SM for Quick Start Guide and more.</p>
MAgPIE model runs outputs: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>Each folder contains the fulldata.gdx and the configuration files for each MAgPIE run based on the nine crop impact models and 5 gcms used in the paper.</p>
CWL run of RNA-seq Analysis Workflow (CWLProv 0.5.0 Research Object)
<p>This workflow adapts the approach and parameter settings of <a href="https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL">Trans-Omics for precision Medicine (TOPMed)</a>. The <a href="https://w3id.org/cwl/view/git/018d344b12e9e1b888e21e0819096f9b337d371d/topmed-workflows/TOPMed_RNAseq_pipeline/rnaseq_pipeline_fastq.cwl">RNA-seq pipeline</a> originated from the Broad Institute. There are in total five steps in the workflow starting from:</p> <ol> <li>Read alignment using <strong>STAR</strong> which produces aligned BAM files including the Genome BAM and Transcriptome BAM.</li> <li>The Genome BAM file is processed using <strong>Picard MarkDuplicates.</strong> producing an updated BAM file containing information on duplicate reads (such reads can indicate biased interpretation).</li> <li><strong>SAMtools index</strong> is then employed to generate an index for the BAM file, in preparation for the next step.</li> <li>The indexed BAM file is processed further with <strong>RNA-SeQC</strong> which takes the BAM file, human genome reference sequence and Gene Transfer Format (GTF) file as inputs to generate transcriptome-level expression quantifications and standard quality control metrics.</li> <li>In parallel with transcript quantification, isoform expression levels are quantified by <strong>RSEM</strong>. This step depends only on the output of the STAR tool, and additional RSEM reference sequences.</li> </ol> <p>For testing and analysis, the workflow author provided example data created by down-sampling the read files of a TOPMed public access data. <em>Chromosome 12</em> was extracted from the <em>Homo Sapien Assembly 38</em> reference sequence and provided by the workflow authors. The required GTF and RSEM reference data files are also provided. The workflow is well-documented with a detailed set of instructions of the steps performed to down-sample the data are also provided for transparency. The availability of example input data, use of containerization for underlying software and detailed documentation are important factors in choosing this specific CWL workflow for CWLProv evaluation.</p> <p>This dataset folder is a <strong>CWLProv Research Object</strong> that captures the Common Workflow Language execution provenance, see <a href="https://w3id.org/cwl/prov/0.5.0">https://w3id.org/cwl/prov/0.5.0</a> or use <a href="https://pypi.org/project/cwl">https://pypi.org/project/cwl</a></p> <p><strong>Steps to reproduce</strong></p> <p>To build the research object again, use Python 3 on macOS. Built with:</p> <ul> <li>Processor 2.8GHz Intel Core i7</li> <li>Memory: 16GB</li> <li>OS: macOS High Sierra, Version 10.13.3</li> <li>Storage: 250GB</li> </ul> <ol> <li> <p>Install <strong>cwltool</strong></p> <pre><code class="language-bash">pip3 install cwltool==1.0.20180912090223</code></pre> </li> <li> <p>Install <strong>git lfs</strong><br> The data download with the git repository requires the installation of Git lfs:<br> <a href="https://www.atlassian.com/git/tutorials/git-lfs#installing-git-lfs">https://www.atlassian.com/git/tutorials/git-lfs#installing-git-lfs</a></p> </li> <li> <p>Get the data and make the analysis environment ready:</p> <pre><code class="language-bash">git clone https://github.com/FarahZKhan/cwl_workflows.git cd cwl_workflows/ git checkout CWLProvTesting ./topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/download_examples.sh</code></pre> </li> <li> <p>Run the following commands to create the CWLProv Research Object:</p> <pre><code class="language-bash">cwltool --provenance rnaseqwf_0.6.0_linux --tmp-outdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp --tmpdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp topmed-workflows/TOPMed_RNAseq_pipeline/rnaseq_pipeline_fastq.cwl topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/Dockstore.json zip -r rnaseqwf_0.5.0_mac.zip rnaseqwf_0.5.0_mac sha256sum rnaseqwf_0.5.0_mac.zip > rnaseqwf_0.5.0_mac_mac.zip.sha256</code></pre> </li> </ol> <p>The <a href="https://github.com/FarahZKhan/cwl_workflows">https://github.com/FarahZKhan/cwl_workflows</a> repository is a frozen snapshot from <a href="https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL">https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL</a> commit <a href="https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL/tree/027e8af41b906173aafdb791351fb29efc044120">027e8af41b906173aafdb791351fb29efc044120</a></p>
Mediterannean Sea-Temperature and Salinity Annual Climatology for 57 running decades from 1950-1959 to 2006-2015
<p>Mediterranean Sea Annual Climatology for 57 running decades from 1950-1959 to 2006-2015 using the SeaDataNet V2 aggregated dataset and the DIVA software tool 4.6.11. The Climatology covers the geographical region W6.25-E36.5, N30-N40 and is computed on 30 standards depths from 5-4000m. The observations are from the period 1950-2015.</p>
Mediterannean Sea-Temperature and Salinity Seasonal Climatology for 57 running decades from 1950-1959 to 2006-2015
<p>Mediterranean Sea Seasonal Climatology for 57 running decades from 1950-1959 to 2006-2015 using the SeaDataNet V2 aggregated dataset and the DIVA software tool 4.6.11. The Climatology covers the geographical region W6.25-E36.5, N30-N40 and is computed on 31 standards depths from 0-4000m. The observations are from the period 1950-2015.</p>
A Hydrodynamic-Based Physical Unified Modeling for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behavior
<p>Simulation results for manuscript "A Hydrodynamic-Based Physical Unified Modelling Framework for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behaviour", submitted to Water Resources Research.</p>
A Hydrodynamic-Based Physical Unified Modeling for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behavior
<p>Simulation results for manuscript "A Hydrodynamic-Based Physical Unified Modelling Framework for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behaviour", submitted to Water Resources Research.</p>
Example dataset to run end-to-end pipeline (Xenium benchmarking)
<p>This repository contains an. This example dataset is a reduced form of the one shared by Kukanja, Langseth et al. 2024. (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.cell.2024.02.030" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.cell.2024.02.030</a>) and represents a human section of spinal chord (inactive lession of Multiple schlerosis) profiled with Xenium. Please refer to the original publication for further information.</p> <p>This dataset is expected to be used as an input for the end-to-end pipeline built in the context of Marco Salas et al. 2024 (Xenium benchkmarking). </p>
Extra data to run the demo for marmo3Dpose
<p>Extra data to run the demo code for marmo3Dpose, <a href="https://github.com/PrimatoModelling/marmo3Dpose">https://github.com/PrimatoModelling/marmo3Dpose</a></p> <p> </p>
Testing script and data needed to run it for NeurIPS 2024 submission 18634
<p>Dependencies:<br>pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 (get specific command for your environment from Pytorch website. If you can't use CUDA, you must edit the code to use the CPU.)<br>pip install timm<br>pip install opencv-python<br>pip install pillow<br>pip install joblib<br>pip install scipy<br>pip install scikit-learn</p> <p>Please make the following edits to run the script. Also, either move the models folder (https://zenodo.org/records/11245477) or change the directory.<br>Also, add a folder to the directory with the testing script called "Results"</p> <p>The testing script also includes the models for directionality, another statistic we attempted to predict. We did not report the values for directionality in our paper, so feel free to comment lines 2175-2253 and lines 2296-2308 to speed up the computation for the statistics presented in the paper. Due to time constraints, this version also doesn't include the class-modifying arguments for Brownian and straight-trajectory motion. These will be included in the testing script on the official release of DeepTrakStat on Github. <br>line 485: sim_directory='ground_truth_trajectories/'<br>line 486: tmate_directory='tmate_trajectories/'</p> <p>lines 509-525:<br>directories = [<br>'simulated_imagery/1000part_16xspeed_heterogeneous/','simulated_imagery/1000part_32xspeed_heterogeneous/',<br>'simulated_imagery/1000part_4xspeed_heterogeneous/',<br>'simulated_imagery/2000part_16xspeed_heterogeneous/','simulated_imagery/2000part_32xspeed_heterogeneous/', ## Group 1<br>'simulated_imagery/500part_16xspeed_heterogeneous/','simulated_imagery/500part_32xspeed_heterogeneous/',<br>'simulated_imagery/500part_4xspeed_heterogeneous/',<br>'simulated_imagery/sim111_brown/', 'simulated_imagery/sim112_brown/',<br>'simulated_imagery/sim113_brown/','simulated_imagery/sim114_brown/', # Group 2<br>'simulated_imagery/sim115_brown/','simulated_imagery/sim119_brown/','simulated_imagery/sim120_brown/',<br>'simulated_imagery/sim2201/', 'simulated_imagery/sim2210/', 'simulated_imagery/sim2215/',<br>'simulated_imagery/sim2220/','simulated_imagery/sim2230/','simulated_imagery/sim2235/','simulated_imagery/sim2240/', # Group 3<br>'simulated_imagery/sim2241/','simulated_imagery/sim2242/','simulated_imagery/sim2243/','simulated_imagery/sim2244/', #Group 4<br>'simulated_imagery/sim2251/','simulated_imagery/sim2252/','simulated_imagery/sim2253/',<br>'simulated_imagery/sim2254/','simulated_imagery/sim2255/','simulated_imagery/sim2256/',<br>'simulated_imagery/sim2257/','simulated_imagery/sim2258/','simulated_imagery/sim2259/','simulated_imagery/sim2260/', # Group 5<br>'simulated_imagery/test1/','simulated_imagery/test2/','simulated_imagery/test5/','simulated_imagery/test8/' #Group 6<br>]</p> <p>line 606:<br> sorted_frames = sorted(files, key=lambda x: int(x[4:-4]))</p>
Supplementary materials for the manuscript "Run-away transition to turbulent strong-field dynamo"
<p>Supplementary materials for the manuscript "Run-away transition to turbulent strong-field dynamo" including simulation data and Jupyter notebooks to read them</p>
My Benchmark Run
Description of the benchmark run.
My Benchmark Run
Description of the benchmark run.
Replication package for: "The Short- and Long-Run Impacts of Free Education on Schooling: Direct Effects and Intra-household Spillovers"
<p>The replication package contains the following folders: (1) The "do" folder, which contains all ".do" and ".R" files used to reproduce all figures and tables in the paper and the appendix; (2) The "outputs" folder, which stores all figures and tables; and (3) The "add-ons" folder, which includes all necessary Stata or R add-on packages. Additionally, the replication package contains a README PDF file to illustrate the result replication procedure.</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.