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410 results for “Data Repositories”
Data supplementing the conference paper "Who you gonna call? Analyzing web requests in Android applications", 14th International Conference on Mining Software Repositories 2017.
<p>This repository contains the data supplementing the paper:</p> <p>M. Rapoport, P. Suter, E. Wittern, O. Lhótak, J. Dolby, "Who you gonna call? Analyzing web requests in Android applications", MSR 2017.</p> <p>A detailed description of the data is included in the archive in README.md.</p>
CALLISTO-SPK: A Stochastic Point Kinetics Code for Performing Low Source Nuclear Power Plant Start-up and Power Ascension Calculations Data Repository
<p>This dataset provides data to accompany the submission named "CALLISTO-SPK: A Stochastic Point Kinetics Code for Performing Low Source Nuclear Power Plant Start-up and Power Ascension Calculations" which has been submitted to Annals of Nuclear Energy. Details of the file included may be found in the readme file.</p>
Data repository for Lin et al. (2022) "Origin of Dawnside Subauroral Polarization Streams during Major Geomagnetic Storms"
This dataset contains the necessary data and plotting tools supporting the paper titled "Origin of Dawnside Subauroral Polarization Streams during Major Geomagnetic Storms", by Lin et al., 2022. The data includes solar wind/IMF data on 20 November 2003, DMSP F16 measurements of electron precipitation energy flux, electron density, cross track ion drift velocity, magnetic perturbation from 13:51 UT to 14:31 UT on 20 November 2003; MAGE model simulation results of EnFlux, Vhorz, and FAC along the same DMSP trajectory; MAGE simulation results of zonal ion drift, FAC, and magnetospheric equatorial plasma pressure at 06:30 UT and 18:30 UT; MAGE/RCM outputs of ring current pressure at 06 MLT and 18 MLT; RCM outputs of effective potential; CHIMP simulation results of test particle ions at 06:30 UT and 18:30 UT.
Data repository for "Supercurrent mediated by helical edge modes in bilayer graphene"
<p>This is the data and scripts for the data presented in the manuscript "Supercurrent mediated by helical edge modes in bilayer graphene". </p>
Data for: Sustainable connectivity in a community repository
<p>Identifiers of many kinds are the key to creating unambiguous and persistent connections between research objects and other items in the global research infrastructure (GRI). Many repositories are implementing mechanisms to collect and integrate these identifiers into their submission and record curation processes. This bodes well for a well-connected future, but many existing resources submitted in the past are missing these identifiers, thus missing the connections required for inclusion in the connected infrastructure. Re-curation of these metadata is required to make these connections.</p> <p>The Dryad Data Repository has existed since 2008 and has successfully re-curated the repository metadata several times, adding identifiers for research organizations, funders, and researchers. Understanding and quantifying these successes depends on measuring repository and identifier connectivity. Metrics are described and applied to the entire repository here.</p> <p>Identifiers for papers (DOIs) connected to datasets in Dryad have long been a critical part of the Dryad metadata creation and curation processes. Since 2019, the % of datasets with connected papers has decreased from 100% to less than 40%. This decrease has significant ramifications for the re-curation efforts described above as connected papers are an important source of metadata. In addition, missing connections to papers make understanding and re-using datasets more difficult.</p> <p>Connections between datasets and papers are many times difficult to make because of time lags between submission and publication, lack of clear mechanisms for citing datasets and other research objects from papers, changing focus of researchers, and other obstacles. The Dryad community of members, i.e. users, research institutions, publishers, and funders have vested interests in identifying these connections and critical roles in the curation and re-curation efforts. Their engagement will be critical in building on the successes Dryad has already achieved and ensuring sustainable connectivity in the future.</p>
Data Repository for Nanoscale magnetism and magnetic phase transitions in atomically thin CrSBr
<p><span>Data repository for: Nanoscale magnetism and magnetic phase transitions in atomically thin CrSBr</span></p> <p><span><span>This data repository contains the raw data as measured on the experimental setup, simulations, analysis scripts and plotting scripts to reproduce the plots shown in the manuscript’s figures.</span></span></p> <p><span><span>Code for plotting: Matlab R2021b<br>The raw data is either stored as MatLab structs (.mat) or accessible through the .json files.</span></span></p> <p><span><span>See ReadMe.txt for more information.</span></span></p>
Data repository for study "Stabilizing international wheat prices through international cooperation after the Russian invasion of Ukraine"
<p>This repository contains both the input and output data associated with the study. The data are crucial for running and understanding the results generated by the <a href="https://gitlab.pik-potsdam.de/twist/twist-global-model/-/tree/ukraine"><em>TWIST</em></a> model and the <a href="https://github.com/mjpuma/FSC-WorldModelers/tree/ukraine"><em>FSC</em></a> models.</p> <h2>Directory Structure and Data Description</h2> <h3>Input Data</h3> <h4>Directory: <code>fsc</code></h4> <p>Contains files necessary to run the <em>FSC</em> model:</p> <ul> <li><code>wheat_export_restriction_*.csv</code>: National export restrictions for various scenarios.</li> <li><code>wheat_total_production_decline_*.csv</code>: National production reductions for different scenarios.</li> </ul> <h4>Directory: <code>twist</code></h4> <p>Includes files required for the <em>TWIST</em> model simulations:</p> <ul> <li><code>psd_wheat_*_world_1961to2031.csv</code>: Historical and projected global wheat data for production, consumption and stocks based on <a href="https://apps.fas.usda.gov/psdonline/app/index.html#/app/downloads">USDA-PSD</a> data</li> <li><code>World_country_codes.csv</code>: World code reference.</li> <li><code>US_BLS_ConsumerPriceIndex_Annual_1960to2019.csv</code>: Annual Consumer Price Index data from the US BLS.</li> <li><code>monthlyNominalGrainPricesWB_WheatUSHRW_1960to2022.csv</code>: Monthly nominal observed wheat prices.</li> <li>Scenario-specific files (<code>world_export_restrictions_*.csv</code>, <code>world_import_strategy_*.csv</code>, <code>world_production_anomaly_*.csv</code>): Global export restrictions, import strategies, and production changes for respective scenarios.</li> </ul> <h3>Output Data</h3> <h4>Directory: <code>fsc</code></h4> <ul> <li><code>raw</code>: Contains the raw output data from the <em>FSC</em> model for all scenarios.</li> <li><code>processed</code>: Includes datasets of national impaired supply relative to baseline supply and baseline domestic reserves, with results summarized per scenario.</li> </ul> <h4>Directory: <code>twist</code></h4> <ul> <li>Contains the raw output data from the <em>TWIST</em> model.</li> </ul> <h2>Contact Information</h2> <p>For inquiries, please contact Dr. <a href="https://orcid.org/0000-0002-8698-1246">Kilian Kuhla</a> at kilian.kuhla@pik-potsdam.de</p>
Data repository for: Frictional weakening leads to unconventional singularities during dynamic rupture propagation
<p>Laboratory data to accompany publication <span>Frictional weakening leads to unconventional singularities during dynamic rupture propagation</span>, submitted to Earth and Planetary Science Letters.</p> <p>For any further queries please contact federica.paglialunga@epfl.ch</p>
Data repository associated with 'A Functional Map of the Human Intrinsically Disordered Proteome'
<p><strong>ES_MAP.zip</strong></p> <ul> <li>a hierarchically clustered map of the human IDR-ome</li> <li>.cdt and .gtr files - outputs of Cluster3.0 software</li> <li>can be visualized using JavaTreeView (see Tutorial_ES.pdf)</li> </ul> <p><strong>TUTORIAL.zip</strong>, information on:</p> <ul> <li>visualization and analysis of the human IDR-ome map</li> <li>search for proteins of interest and exploratory analyses of clusters</li> <li>automatic export and analysis of exported clusters (code available at https://github.com/IPritisanac/ES_PW)</li> </ul> <p><strong>IDROME_SEQUENCES.zip</strong></p> <ul> <li>human proteome fasta file</li> <li>IDRome fasta file</li> <li>SPOT-Disorder v1.0 disorder boundaries <ul> <li>13 044 unique protein sequences with at least one IDR (>=30 amino acids)</li> <li>21 252 total unique human IDRs</li> </ul> </li> </ul> <p><strong>IDR_ALN.zip</strong></p> <ul> <li>alignments of IDR sequences across ENSEMBL orthologs</li> <li>19 459 IDR alignments</li> <li>UniProt ID and IDR boundaries for the human sequence are indicated in the name of the file</li> </ul> <p><strong>FAIDR_TSTATS.zip</strong></p> <ul> <li>hierarchical clustering of FAIDR t-statistics for 148 GO terms<br> <ul> <li>.cdt, .gtr files from Cluster3.0</li> <li>can be visualized using JavaTreeView</li> <li>reveals the most predictive molecular features for the top performing 148 models</li> </ul> </li> </ul> <p><strong>CLUSTERS_EXPLORE.zip</strong></p> <ul> <li>clusters obtained through exploratory analysis of the map provided in ES_MAP.zip</li> <li>93 exported clusters in .cdt file format</li> </ul> <p><strong>CLUSTERS_AUTO.zip</strong></p> <ul> <li>clusters extracted from the hierarchically clustered IDR-ome map at a range of distance thresholds (0.4 - 0.8) in .cdt file format</li> <li>distance refers to the uncentered correlation distance between vectors of Z-scores representing human IDRs</li> <li>clusters extracted at different distance thresholds are split into separate archives</li> <li>AUTO_GO_FEATS.xlsx - summary of GO-term overrepresentation and feature enrichment analyses; each distance threshold is in a separate sheet</li> </ul> <p><strong>FAIDR_HIGH_AUC_PPV_GO.zip</strong></p> <ul> <li>target files with annotations of 148 GO terms for which good quality FAIDR models could be obtained (AUC >= 0.7, PPV >= 0.4)</li> <li>file format: three columns; 1st: IDR ID (includes IDR boundaries); 2nd: protein UniProt ID; 3rd: annotation of the protein to a GO term (1 if known to be associated with the GO term, 0 if not)</li> </ul> <p> </p> <p> </p>
Integrated Model Data Repository
<p>ALLFED integrated food system model supplemental data associated with the paper "Food System Adaptation and Maintaining Trade Greatly Mitigate Global Famine in Abrupt Sunlight Reduction Scenarios"</p>
Data repository for " Built-in Bernal gap in large-angle-twisted monolayer-bilayer graphene"
<p>This is the data presented in the manuscript " Built-in Bernal gap in large-angle-twisted monolayer-bilayer graphene", <em>Commun Phys</em> <strong>7</strong>, 391 (2024). https://doi.org/10.1038/s42005-024-01887-0</p>
The mechanism of amyloid fibril growth from Φ-value analysis - data and analysis repository
<p>Data used for analysis and figure production. Full MD-simulation dataset is available at https://github.com/Aunstrup/_2024_amyloid_PI3KSH3_Phivalues. </p>
Ultra-high scale cytometry-based cellular interaction mapping - Data repository
<p>This is the repository for datasets used in Vonficht, Jopp-Saile, Yousefian, Flore <em>et al. </em>Ultra-high scale cytometry-based cellular interaction mapping, <em>Nature Methods </em>(2025) <a href="https://doi.org/10.1038/s41592-025-02744-w" rel="nofollow">https://doi.org/10.1038/s41592-025-02744-w</a>. Associated analysis code can be found at <a href="https://github.com/agSHaas/ultra-high-scale-cytometry-based-cellular-interaction-mapping">https://github.com/agSHaas/ultra-high-scale-cytometry-based-cellular-interaction-mapping</a>, and the repository for the accompanying R package is hosted at <a href="https://github.com/agSHaas/PICtR">https://github.com/agSHaas/PICtR</a>. </p>
ClimKern Kernel & Data Repository
<h1>ClimKern Kernel and Data Repository</h1> <h2>New in v1.2:</h2> <ul> <li>An error was discovered in the HadGEM2 clear-sky surface albedo kernel. Please use v1.2 or later for that specific kernel.</li> </ul> <h2>What's stored here?</h2> <div>This Zenodo repository contains two types of data to be used by the ClimKern</div> <div> Python package. The subdirectory <code>/kernels/</code> contains 12 radiative kernels generously</div> <div> contributed by various research groups. The other directory <code>/tutorial_data/</code> contains</div> <div> sample Community Earth System Model v1 output for testing purposes.</div> <h2> Where are the kernels from?</h2> <table> <tbody> <tr> <td><strong>Kernel name</strong></td> <td><strong>Source</strong></td> </tr> <tr> <td>BMRC</td> <td><a href="https://doi.org/10.1175/2007JCLI2110.1" target="_blank" rel="noopener">Soden et al. (2008)</a></td> </tr> <tr> <td>CAM3</td> <td><a href="https://doi.org/10.1175/2007JCLI2044.1" target="_blank" rel="noopener">Shell et al. (2008)</a></td> </tr> <tr> <td>CAM5</td> <td><a href="https://doi.org/10.5194/essd-10-317-2018" target="_blank" rel="noopener">Pendergrass et al. (2018)</a></td> </tr> <tr> <td>CERES</td> <td><a href="https://doi.org/10.1175/JCLI-D-18-0045.1" target="_blank" rel="noopener">Thorsen et al. (2018)</a></td> </tr> <tr> <td>CloudSat</td> <td><a href="https://doi.org/10.1029/2018JD029021" target="_blank" rel="noopener">Kramer et al. (2019)</a></td> </tr> <tr> <td>ECHAM5</td> <td><a href="https://doi.org/10.1088/1748-9326/5/2/025211" target="_blank" rel="noopener">Previdi (2010)</a></td> </tr> <tr> <td>ECHAM6</td> <td><a href="https://doi.org/10.1002/jame.20041" target="_blank" rel="noopener">Block & Mauritsen (2013)</a></td> </tr> <tr> <td>ECMWF-RRTM</td> <td><a href="https://doi.org/10.1002/2017JD027221" target="_blank" rel="noopener">Huang et al. (2017)</a></td> </tr> <tr> <td>ERA5</td> <td><a href="https://doi.org/10.5194/essd-15-3001-2023" target="_blank" rel="noopener">Huang & Huang (2023)</a></td> </tr> <tr> <td>GFDL</td> <td><a href="https://doi.org/10.1175/2007JCLI2110.1">Soden et al. (2008)</a></td> </tr> <tr> <td>HadGEM2</td> <td><a href="https://doi.org/10.1029/2018GL079826" target="_blank" rel="noopener">Smith et al. (2018)</a></td> </tr> <tr> <td>HadGEM3-GA7.1</td> <td><a href="https://doi.org/10.5194/essd-12-2157-2020" target="_blank" rel="noopener">Smith et al. (2020)</a></td> </tr> </tbody> </table> <div> </div> <h2>How do I use this data with the ClimKern package?</h2> <p> </p> <div>Visit the <a href="https://github.com/tyfolino/climkern">ClimKern GitHub</a> for installation and use instrucitons.</div> <p> </p> <h2>How do I cite this?</h2> <div>Please cite <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2561/">Janoski et al. (2024)</a> and this Zenodo repository with the DOI corresponding to the version of the data you used. We also encourage you to cite the paper(s) documenting the kernel(s) you use.</div>
Data repository for the paper: Sharp front tracking with geometric interface reconstruction
<h1>Data repository for the paper</h1> <h1><em>Sharp front tracking with geometric interface reconstruction</em></h1> <p> </p> <p>This repository consists of the results data for the paper "Sharp front tracking with geometric interface reconstruction" by Christian Gorges, Fabien Evrard, Robert Chiodi, Berend van Wachem and Fabian Denner. The simulation results stored in this repository have the following data format:</p> <ul> <li> <p>.txt files consisting the raw data used for the plots in the results chapter of the paper</p> </li> <li> <p>.pvtu and .vtu files containing the front mesh data for the rising bubble simulations (Paraview is an exemplary software to view the front mesh data)</p> </li> <li> <p>.py files containing python scripts serving as examples on how to use and plot the raw data of the .txt files</p> </li> </ul> <p>The main folders of this repository are named as the sections in the results chapter of the paper. For instance, the folder translating_droplet contains the data of the "Translating droplet" section. Within the main folders, sub folders contain the raw data for the specific simulations. The naming style of the raw data files and the subfolders for each section is explained in the following.</p> <p><em>stationary_droplet</em>: This main folder contains subfolders for all Laplace numbers simulated. "La_120" corresponds to a Laplace number of 120. The file names of the .txt files within the subfolders consist of the Laplace number, followed by the front tracking method and the d/dx ratio. If roughness smoothing is used it also consists of "WithRoughnessSmoothing". For example "La_120_ClassicFT_ddx_52.txt" consists of the data for a Laplace number of 120, the classic front tracking method and a d/dx ratio of 52. The content in the .txt files is the following: "%e,%e,%e,%e,%e,%e,%e\n" which corresponds to "Physical time, Physical time / \tau_{mu}, Kinetic energy, RMS velocity, Max velocity, Ca_{max}, U_sigma".</p> <p><em>translating_droplet</em>: This main folder contains subfolders for all Laplace numbers simulated. "La_120" corresponds to a Laplace number of 120. The file names of the .txt files within the subfolders consist of the Laplace number, followed by the front tracking method and the d/dx ratio. If roughness smoothing is used it also consists of "WithRoughnessSmoothing". For example "La_120_ClassicFT_ddx_52.txt" consists of the data for a Laplace number of 120, the classic front tracking method and a d/dx ratio of 52. The content in the .txt files is the following: "%e,%e,%e,%e,%e,%e,%e\n" which corresponds to "Physical time, Physical time / \tau_{mu}, Kinetic energy, RMS velocity, Max velocity, Ca_{max}, U_sigma".</p> <p><em>oscillating_droplet</em>: This main folder contains subfolders for all droplet viscosities simulated. "mu_d_05" corresponds to a droplet viscosity of 0.5. The file names of the .txt files within the subfolders consist of the droplet viscosity, followed by the front tracking method and the d/dx ratio. If roughness smoothing is used it also consists of "WithRoughnessSmoothing". For example "mu_d_05_ClassicFT_ddx_52.txt" consists of the data for a droplet viscosity of 0.5, the classic front tracking method and a d/dx ratio of 52. The content in the .txt files is the following: "%f,%f,%e\n" which corresponds to "Physical time, \tau, r".</p> <p><em>rising_bubbles</em>: This main folder contains subfolders for all rising bubble cases simulated. "Case_1_Classic" corresponds to a case 1 simulated with the classic front tracking method. The .txt files within the subfolders consist of the physical time, followed by the non-dimensional time and the Reynolds number. The .zip files contain the .pvtu and .vtu files for the front meshes.</p> <p>The python scripts have been tested with Python 3.11.5.</p> <p>This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant number 420239128, and from the European Unions's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101026017. This work was supported by the US Department of Energy through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001).</p>
A data repository for the study of Alpha-synuclein aggregates trigger anti-viral immune pathways and RNA editing in human astrocytes
<p><span>This repository contains data associated with the study:</span></p> <p><span><strong>"Alpha-synuclein Aggregates Trigger Anti-Viral Immune Pathways and RNA Editing in Human Astrocytes"</strong></span></p> <p><span>Published as a <strong>bioRxiv preprint</strong>: <a href="https://doi.org/10.1101/2024.02.26.582055"><span>DOI: 10.1101/2024.02.26.582055</span></a></span></p>
Additional data repository for the study of Alpha-synuclein aggregates trigger anti-viral immune pathways and RNA editing in human astrocytes
<p>Zip file 1: astrocytes calcium data measured using Fura 2</p> <p>Zip file2: astrocytes ROS measured using DHE (Dihydroethidium)</p> <p>Zip file 3: astrocytes cell death measured using Sytox green</p>
Data repository for study "Understanding agricultural market dynamics in times of crisis: the dynamic agent-based model Agrimate"
<p>Data for the study "Understanding agricultural market dynamics in times of crisis: the dynamic agent-based model Agrimate".</p> <p><strong>hindcasting_analysis</strong></p> <ul> <li>figures of the hindcasting exercise in the main text</li> <li>raw_data <ul> <li> raw model output data for <ul> <li>baseline scenario -- <em>agrimate_baseline=2007-2009_extra_regions=(Egypt=EGY)_regions=AgrimateEU28_start=2000-01-01.nc</em></li> <li>production failure scenario -- <em>agrimate_baseline=2007-2009_extra_regions=(Egypt=EGY)_production_anomalies=FAOsince-2005_regions=AgrimateEU28_start=2000-01-01.nc</em></li> <li>production failure and export restriction scenario -- <em>agrimate_baseline=2007-2009_export_restrictions=2007-2011_extra_regions=(Egypt=EGY)_production_anomalies=FAOsince-2005_regions=AgrimateEU28_start=2000-01-01.nc</em></li> </ul> </li> </ul> </li> </ul> <p><strong>multibreadbasket_analysis</strong></p> <ul> <li>figures of the multibreadbasket analysis in the main text</li> <li>raw_data <ul> <li> raw model output data for <ul> <li>simulations under historical climatic conditions with <number> as an identifier -- <em>agrimate_his-<number>.nc</em></li> <li>simulations under +2°C projection with <number> as an identifier -- <em>agrimate_2p0-<number>.nc</em></li> </ul> </li> </ul> </li> <li>processed_data <ul> <li>processed output data to easier/faster plot</li> </ul> </li> </ul> <p><strong>sensitivity_analysis</strong></p> <ul> <li>raw data and graphics as in <strong>main_output</strong> for different model parameters as given in Table F.1</li> </ul> <p> </p> <p> </p>
Data repository for "Lockdown impact on age-specific contact patterns and behaviours, France, April 2020"
<p>Aggregated contact matrices associated with the publication "Lockdown impact on age-specific contact patterns and behaviours, France, April 2020" . </p> <p> </p>
Data repository - Integrating Degrowth and Efficiency Perspectives to Enable an Emission-neutral Food System
<p>Data repository: Integrating Degrowth and Efficiency Perspectives to Enable an Emission-neutral Food System <br> <br> Benjamin Leon Bodirsky, David Meng-Chuen Chen, Isabelle Weindl, Bjoern Soergel, Felicitas <br> Beier, Edna J. Molina Bacca, Franziska Gaupp, Alexander Popp, Hermann Lotze-Campen. In review.</p> <p>Folder structure: </p> <p>1. Figures: Contains .Rmd notebook for figure production, as well as source data (from model inputs and outputs)</p> <p>2. Magpie_start_script: Contains start script degrowth.R for replication of model runs. See readme.txt for precise instructions.</p> <p>3. Scenario_outputs. Entire output folders of model scenario runs.</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.