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867 results for “repositories”
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>
Repository of fact-checking websites and resources to combat climate mis/disinformation
<p>This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles a list of fact-checking websites and resources dedicated to debunking climate change mis/disinformation. The identification of these resources was achieved by leveraging the expertise of our consortium and, therefore, most of the resources listed are in English, German, Italian and Spanish.</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>
Repository: Turbulent Fluxes and Evaporation/Sublimation Rates on Earth, Mars, Titan, and Exoplanets
<div>Repository: Turbulent Fluxes and Evaporation/Sublimation Rates on Earth, Mars, Titan, and Exoplanets</div> <div>Khuller & Clow (2024)</div> <div> </div> <div>Contents:</div> <div> </div> <div>1. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. turbulent fluxes measured by Fitzpatrick et al. (2017)</div> <div>a) Measured fluxes</div> <div>i) Measured_Abs_LE_Fitzpatrick: Absolute value of measured latent heat fluxes in W/m^2</div> <div>ii) Measured_Abs_SH_Fitzpatrick: Absolute value of measured sensible heat fluxes in W/m^2</div> <div>b) Modeled fluxes</div> <div>i) Modeled_DB_Abs_LE_Fitzpatrick: Absolute value of Dundas & Byrne (2010) modeled latent heat fluxes in W/m^2</div> <div>ii) Modeled_DB_Abs_SH_Fitzpatrick: Absolute value of Dundas & Byrne (2010) modeled sensible heat fluxes in W/m^2</div> <div>iii) Modeled_KC_Abs_LE_Fitzpatrick: Absolute value of Khuller & Clow modeled latent heat fluxes in W/m^2</div> <div>iv) Modeled_KC_Abs_SH_Fitzpatrick: Absolute value of Khuller & Clow modeled sensible heat fluxes in W/m^2</div> <div> </div> <div>2. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. turbulent fluxes measured by Fairall et al. (1996); Fairall et al. (2003)</div> <div>a) Measured fluxes</div> <div>i) Measured_Abs_LE_COARE: Absolute value of measured latent heat fluxes in W/m^2</div> <div>ii) Measured_Abs_SH_COARE: Absolute value of measured sensible heat fluxes in W/m^2</div> <div>b) Modeled luxes</div> <div>i) Modeled_DB_Abs_LE_COARE: Absolute value of Dundas & Byrne (2010) modeled latent heat fluxes in W/m^2</div> <div>ii) Modeled_DB_Abs_SH_COARE: Absolute value of Dundas & Byrne (2010) modeled sensible heat fluxes in W/m^2</div> <div>iii) Modeled_KC_Abs_LE_COARE: Absolute value of Khuller & Clow modeled latent heat fluxes in W/m^2</div> <div>iv) Modeled_KC_Abs_SH_COARE: Absolute value of Khuller & Clow modeled sensible heat fluxes in W/m^2</div> <div> </div> <div>3. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. ice sublimation rates measured by Clow et al. (1988)</div> <div>a) Measured sublimation rates</div> <div>i) Measured_dzdt_Clow: Measured sublimation rates in mm/day</div> <div> </div> <div>b) Modeled sublimation rates</div> <div>i) Modeled_DB_dzdt_Clow: Dundas & Byrne (2010) modeled sublimation rates in mm/day</div> <div>ii) Modeled_KC_dzdt_Clow: Khuller & Clow modeled sublimation rates in mm/day</div> <div> </div> <div>4. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. ice sublimation rate measured by Douglas & Mellon (2019)</div> <div>a) Roughness Lengths used</div> <div>i) z0_DM: Roughness lengths used in cm</div> <div> </div> <div>b) Modeled Sublimation Rates</div> <div>i) Modeled_DB_dzdt_u_001_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.01 m/s in mm/day</div> <div>ii) Modeled_DB_dzdt_u_005_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.05 m/s in mm/day</div> <div>iii) Modeled_DB_dzdt_u_010_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.10 m/s in mm/day</div> <div>iv) Modeled_DB_dzdt_u_015_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.15 m/s in mm/day</div> <div>v) Modeled_KC_dzdt_u_001_DM: Khuller & Clow modeled sublimation rates for u = 0.01 m/s in mm/day</div> <div>vi) Modeled_KC_dzdt_u_005_DM: Khuller & Clow modeled sublimation rates for u = 0.05 m/s in mm/day</div> <div>vii) Modeled_KC_dzdt_u_010_DM: Khuller & Clow modeled sublimation rates for u = 0.10 m/s in mm/day</div> <div>viii) Modeled_KC_dzdt_u_015_DM: Khuller & Clow modeled sublimation rates for u = 0.15 m/s in mm/day</div> <div> </div> <div>5. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. net ice sublimation inferred at Mars Phoenix landing site, by Smith et al. (2009)</div> <div>a) Parameters used</div> <div>i) z0_PHX: Roughness lengths used in cm</div> <div>ii) u_PHX: Wind speeds used in m/s</div> <div>iii) deltatheta1_PHX: Delta-surface temperatures used in K</div> <div> </div> <div>b) Modeled net sublimation</div> <div>i) Modeled_DB_deltatheta1_sensitivity_PHX: Dundas & Byrne (2010) modeled sublimation rates for varied delta-surface temperatures in cm</div> <div>ii) Modeled_DB_u_sensitivity_PHX: Dundas & Byrne (2010) modeled sublimation rates for varied wind speeds in cm</div> <div>iii) Modeled_DB_z0_sensitivity_PHX: Dundas & Byrne (2010) modeled sublimation rates for varied roughness lengths in cm</div> <div> </div> <div>6. Modeled vertical profiles of temperature and wind speed compared to Huygens probe measurements (Fulchignoni et al., 2005)</div> <div>i) Modeled_theta_Huygens_u_0_02: Temperature profile using 10-m wind speed of 0.02 m/s, in K</div> <div>ii) Modeled_theta_Huygens_u_1: Temperature profile using 10-m wind speed of 1 m/s, in K</div> <div>iii) Modeled_Z_Huygens: Altitude profile in m</div> <div> </div> <div>7. Modeled effect of various parameters on Mars ice sublimation rates</div> <div>a) Input data ranges</div> <div>i) Modeled_Mars_Theta2_warm: 3-m air temperatures for warm cases in K</div> <div>ii) Modeled_Mars_Theta2_cold: 3-m air temperatures for cold cases in K</div> <div>iii) Modeled_Mars_z0_range: Roughness lengths in cm</div> <div>iv) Modeled_Mars_P_range: Surface air pressure values in mbar</div> <div>v) Modeled_Mars_beta_range: Dimensionless gustiness parameters</div> <div> </div> <div>b) Modeled sublimation rates from Dundas & Byrne (2010) model</div> <div>i) Modeled_DB_dzdt_Mars_warm_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_DB_dzdt_Mars_warm_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_DB_dzdt_Mars_cold_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_DB_dzdt_Mars_cold_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>v) Modeled_DB_dzdt_Mars_warm_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vi) Modeled_DB_dzdt_Mars_cold_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vii) Modeled_DB_dzdt_Mars_warm_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>viii) Modeled_DB_dzdt_Mars_cold_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>xi) Modeled_DB_dzdt_Mars_warm_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div>x) Modeled_DB_dzdt_Mars_cold_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div> </div> <div>c) Modeled sublimation rates from Khuller & Clow model</div> <div>i) Modeled_KC_dzdt_Mars_warm_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_KC_dzdt_Mars_warm_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_KC_dzdt_Mars_cold_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_KC_dzdt_Mars_cold_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>v) Modeled_KC_dzdt_Mars_warm_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vi) Modeled_KC_dzdt_Mars_cold_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vii) Modeled_KC_dzdt_Mars_warm_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>viii) Modeled_KC_dzdt_Mars_cold_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>xi) Modeled_KC_dzdt_Mars_warm_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div>x) Modeled_KC_dzdt_Mars_cold_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div> </div> <div>d) Modeled latent heat fluxes from Dundas & Byrne (2010) model</div> <div>i) Modeled_DB_LE_Mars_warm_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_DB_LE_Mars_warm_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_DB_LE_Mars_cold_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_DB_LE_Mars_cold_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>v) Modeled_DB_LE_Mars_warm_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vi) Modeled_DB_LE_Mars_cold_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vii) Modeled_DB_LE_Mars_warm_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>viii) Modeled_DB_LE_Mars_cold_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>xi) Modeled_DB_LE_Mars_warm_beta_var: Latent heat flux in W/m^2 using range of beta values</div> <div>x) Modeled_DB_LE_Mars_cold_beta_var: Latent heat flux in W/m^2 using range of beta values</div> <div> </div> <div>e) Modeled latent heat fluxes from Khuller & Clow model</div> <div>i) Modeled_KC_LE_Mars_warm_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_KC_LE_Mars_warm_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_KC_LE_Mars_cold_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_KC_LE_Mars_cold_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>v) Modeled_KC_LE_Mars_warm_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vi) Modeled_KC_LE_Mars_cold_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vii) Modeled_KC_LE_Mars_warm_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>viii) Modeled_KC_LE_Mars_cold_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>xi) Modeled_KC_LE_Mars_warm_beta_var: Latent heat flux in W/m^2 using range of beta values</div> <div>x) Modeled_KC_LE_Mars_cold_beta_var: Latent heat flux in W/m^2 using range of beta values</div>
The Awareness Assessment Model repository
<p>Dataset of the paper "The Awareness Assessment Model: Measuring Awareness and Collaboration Support Over Participant's Perspective". This dataset contains the supplementary materials about:</p> <p>+ the systematic mapping study;</p> <p>+ the taxonomy elaboration;</p> <p>+ the awareness assessment process;</p> <p>+expert panel validation;</p> <p>+ the case study validation;</p> <p>+ R scripts and observations.csv</p> <p>For more information, please get in touch with us (marcio.mantau@gmail.com).</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>
Online repository for `sceptrehub` package
<p>This repository contains the example data used within the `sceptrehub` package. The `sceptrehub` package is a companion package to the R package `sceptre`, providing examples for the vignettes and man pages.</p>
Repository of "How Stress Biaxiality Controls Crack Morphology and Apparent Fracture Energy of Dikes and Sills"
<p>This repository contains the dataset of the future publication "How Stress Biaxiality Controls Crack Morphology and Apparent Fracture Energy of Dikes and Sills". It contains:</p> <ul> <li>1 file recapitulating sample dimensions and informations on the tests.</li> <li>12 “raw” mechanical results of experiments on Carrara marble (6 WST — 3 dry/3 saturated tests, 6 MRT — 3 dry/3 saturated tests) with minimal filtering.</li> <li>12 “computed” mechanical results of experiments on Carrara marble (6 WST — 3 dry/3 saturated tests, 6 MRT — 3 dry/3 saturated tests) with the use of the compliance method.</li> <li>2 compressed and stitched back-scattered SEM scans imaging the crack tip in xz direction after crack arrest on WST and MRT.</li> <li>5 stitched back-scattered SEM scans imaging the crack in yz direction on WST at 20, 40, 60 mm MRT at 10, 25 mm of crack propagation.</li> </ul> <p>For more details and information about the dataset, do not hesitate to contact me at antoine.guggisberg@epfl.ch</p> <p> </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>
SCAR DistAnt Ecological Model Output Repository
<p>This repository provides access to a collection of ecological model outputs (species distribution and similar models) from Antarctica and the Southern Ocean. It is a project of the SCAR <a href="https://scar.org/science/egabi/home/" rel="nofollow">Expert Group on Biodiversity Informatics</a> in conjunction with <a href="https://www.belspo.be/belspo/impuls/project_en.stm#ADVANCE" rel="nofollow">ADVANCE</a> (Royal Belgian Institute of Natural Sciences) and the Integrated Digital East Antarctica program at the <a href="https://www.antarctica.gov.au/science/" rel="nofollow">Australian Antarctic Division</a>.</p> <p><strong>Please note:</strong> the inclusion of a layer in this collection is not an endorsement of its quality or suitability for your intended purpose. Users should consult the associated publication for details on the source data and modelling processes. We encourage users to contact the original publication authors to discuss their intended use of these layers.</p> <p>Model outputs are provided as cloud-optimized geotiffs (COGs), with generally one file per species. The COG will have multiple bands if the original model predictions include uncertainty estimates or multiple model output variables. Each output has been kept on its original coordinate reference system (map projection) and spatial resolution.</p> <p>There is a <a href="https://github.com/SCAR/distant/blob/master/metadata.csv">table of minimal metadata</a> that describes the layers in the collection. This metadata is intended to be sufficient for users to find potential layers of interest, and make an initial evaluation of their high-level characteristics such as taxonomic details, spatial coverage and resolution, and model output types. Our metadata is NOT intended to provide a comprehensive description of each layer: users are referred to the original publication for that level of detail (see the layer’s <code>reference</code> entry).</p> <div> <h3>Citing</h3> The layers in this collection have been re-released under their original license where applicable, or a CC-BY licence otherwise. Please cite when using, and also cite the original data sources used. For example:</div> <div> <p>Kovacs J (2020) A model of my favourite Southern Ocean species. <em>Journal of Southern Ocean Stuff</em> <strong>123</strong>:1–10. Data obtained from the SCAR DistAnt Ecological Model Output Repository, doi:10.5281/zenodo.10910076</p> </div> <p>Individual data sources might have varying licence conditions: consult the <code>licence</code> field for details.</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>
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.