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2,152 results for “outputs”
Dataset of transactions of 10 Ethereum addresses controlled by a private key, each has at least 2000 output transactions, which include a transfer of cryptocurrency, and all transactions are performed within no longer than three months period.
<p>Starting from the block Nr. 8578851, we have evaluated every block to find enough unique addresses that satisfy the following requirements. The address that appeared in a block must be controlled by private key (i.e., CA), have at least 2000, and not more than 6000 outgoing transactions (i.e., digitally signed), which however include a transfer of any amount of cryptocurrency (i.e., Ether) and must be performed within a period no longer than three months. The whole dataset includes a total of 30505 transactions.</p> <p>Each transaction has data about:</p> <ul> <li>transaction hash</li> <li>block number</li> <li>timestamp (Unix),</li> <li>date and time (m/dd/yyyy)</li> <li>from (Ethereum address)</li> <li>to (Ethereum address)</li> <li>output value (ETH)</li> <li>transaction fee (ETH)</li> <li>transaction fee (USD)</li> <li>historical price (USD)</li> </ul>
Aurora Subglacial Basin GlaDs inputs, outputs and geophysical data
<p>The GlaDS_ASB_outputs.txt file includes the following:</p> <p>Glacier Drainage System (GlaDS) model inputs: node easting (m), node northing (m), bed elevation (m), ice thickness (m), basal velocity (m/year) and basal water production (m/year). GlaDS model results for water pressure as a fraction of overburden (Pw/Pi) and water depth (m):base line model, high conductivity, low conductivity, static water and static velocity model runs. </p> <p>Specularity content data for Aurora Subglacial Basin as an xyz file called: filtered.spec.asb.xyz with easting (m), northing (m) and specularity content. </p> <p>The ICECAP basal interface specularity content profiles can also be found at the U.S Antarctic Program (USAP) Data Center: <a href="https://doi.org/10.15784/601371">https://doi.org/10.15784/601371</a></p>
FireMIP model output
<p>Model output required for FireMIP benchmarking used in the submission of "Quantitative assessment of fire and vegetation properties in historical simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project" to GMD</p>
Model output used in the manuscript "Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (hereafter L84-S61; <a href="https://doi.org/10.3402/tellusa.v53i5.12229" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the manuscript "<em>Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective</em>", published by the Bulletin of the American Meteorological Society (<a href="https://doi.org/10.1175/BAMS-D-24-0064.1" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2025</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a> and <a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al. (2024)</a>.</p> <p>All files uploaded were generated from simulations run by the lead author.</p> <p>For specific information about each file uploaded, please refer to the README file. The details of each experiment are also presented in the supplementary materials of the manuscript. If you have any questions, please feel free to contact me.</p>
The WRF model output for selected foehn events at the northern foreland of the Moravian-Silesian Beskids, Czech Republic
<p>This dataset includes three output files from the Weather Research and Forecasting (WRF) model in the NetCDF format. The files are valid for three selected foehn events which affected the northern foreland of the Moravian-Silesian Beskids, Czech Republic and allows a deeper analysis of mechanism and impacts of these events.</p> <p>The dataset contains the model output for the following events:</p> <p>1) 14 January 2008, 10:00 UTC</p> <p>2) 31 October 2010, 06:00 UTC</p> <p>3) 30 October 2021, 06:00 UTC</p>
Gold dataset for checking transliteration output from ChatGPT 4.0 and local tool
<p>This dataset includes 385 names and short phrases in Ancient and Modern Greek. This dataset has been used as gold dataset to evaluate the transliteration output from ChatGPT 4.0 and a local transliteration tool developed by the 'International Hellenic University, Department of Information and Electronic Engineering' and 'Open Knowledge Greece'. On the 13th-14th August 2024, both ChatGPT and local tool were tested against two well-known standards, i.e, the ALA-LC Romanization for Greek and the ISO 843 standard. </p> <p>The Gold dataset includes the names and short phrases in Ancient and Modern Greek, the expected output, the ChatGPT output, and the local tool output. For evaluating the results the EXACT excel formula was used. The results of the evaluation are also included in the dataset. </p> <p>The Gold dataset complements a manuscript submitted to the Metadata and Semantics Research (MTSR'24) conference. <br> </p>
MapReader_railspace_and_building_outputs_2024
<p><strong>This record reproduces the<a href="https://zenodo.org/records/7147906"> railspace and building predictions</a> relating to </strong><strong>MapReader in GeoHumanities workshop (SIGSPATIAL 2022). </strong></p> <p><strong>The model used here is fine-tuned on the <a href="https://zenodo.org/records/11241371">railspace and building annotations</a> and is available at <a href="https://huggingface.co/Livingwithmachines/mr_resnest101e_finetuned_OS_6inch_2nd_ed_railspace_and_building">https://huggingface.co/Livingwithmachines/mr_resnest101e_finetuned_OS_6inch_2nd_ed_railspace_and_building</a>.</strong></p> <p>See <code>README.md</code> for detailed decription of this record.</p>
Can green hydrogen drive economic transformation in Saudi Arabia? - An input-output analysis of different Power-to-X configurations. Supplementary Data
<p>Supplementary material for peer review</p> <ul> <li>Modelling Data (input & results)</li> <li>Literature Review</li> </ul>
CORDEX-FPS-Aerosol Protocol 1B output
<p>CORDEX-FPS-Aerosol has set up a dedicated protocol of simulations (called protocol 1B) to evaluate the impact of aerosols on regional climate using non interactive aerosol climatologies (still widely used by the climate community).</p> <p>This protocol relies on three simulations for each regional climate model: a historical run (1971-2000) and two future RCP8.5 simulations (2021-2050), a first one with evolving aerosols (RCMevol or evoaer), and a second one with the same aerosols as in the historical period (RCMcst or cstaer). The historical run and one of the two scenario runs are already published on ESGF (official CORDEX simulations). The present data on Zenodo provides the output for the new scenario simulation designed in the FPS-Aerosols, that is to say either RCMevol if the official CORDEX simulation has constant aerosols of RCMcst if the CORDEX simulation has already evolving aerosols.</p> <p>9 GCM-RCM pairs are available : REMO2015/EC-EARTH, ALADIN63/CNRM-CM5, ALADIN/HadGEM2-ES, ALADIN/NorESM1-M, ALADIN/MPI-ESM-LR, WRF/CCSM4, COSMO/MPI-ESM-M, RegCM/EC-EARTH and RACMO/EC-EARTH.</p> <p>Note that the two other simulations used in FPS-Aerosols are available on the Earth System Grid Federation (ESGF), except for RegCM and WRF for which all simulations have been included in this Zenodo repository.</p>
Model output from "Potential impacts of marine fuel regulations on an Arctic stratocumulus case and its radiative response"
<p>Model output from "Potential impacts of marine fuel regulations on an Arctic stratocumulus case and its radiative response", in review at Atmospheric Chemistry and Physics, 2024, same authors.<br>The simulations were done using MIMICA version 4 (Savre at el., 2014) and the data includes all model output presented in the publication.</p>
MITgcm simulations of sea level response to freshwater injected at the surface and at depth in southern high latitudes: Model output and analysis code
<p>Model output (netcdf) and python code (included in both py and ipynb formats) to create the figures in Eisenman et al. (2024).</p> <div> <p>See https://eisenman-group.github.io for further details.</p> </div>
Output pathway data for "Income and inequality pathways consistent with eradicating poverty"
<p>This is a dataset presenting GDP and Gini pathways developed for the publication of our article "Income and inequality pathways consistent with eradicating poverty" (forthcoming at ERL) under the SHAPE project (Sustainable development pathways achieving Human well-being while safeguarding the climate And Planet Earth).</p> <p>More info about the SHAPE project is <a href="https://shape-project.org/">here</a>.</p>
D6.3 - Outputs of the assessment of the biodiversity loss induced by the soy commodities market between Brazil and the European Union
<p><span>This dataset compiles the results of an assessment of biodiversity loss caused by the soy commodities trade between Brazil and the European Union from 2004 to 2020. The file “SM” refers to supplementary material. The file “POA1” contains biodiversity loss characterisation factors for land occupation and land transformation, estimated based on CLEVER D2.4. The file “POA2” includes indicators of biodiversity loss due to soy cultivation in Brazil at the municipality level. Finally, the file “POA3” presents the biodiversity footprint of the Brazilian soy commodities traded in the European Union.</span></p>
Example input and output for RSYD-BASIC pipeline
<p><strong>Input data:</strong></p> <ul> <li>20230914-data-overview.xlsx: mapping of placeholder sample numbers to original SRA accession numbers</li> <li>1199234567-*.fastq.gz: renamed Illumina MiSeq reads; reads for 1199234567-5 have been obtained by downsampling SRR10955980</li> <li>madsdata: LIS report and bacteria list used for LIS-specific results</li> </ul> <p><strong>Expected results:</strong></p> <ul> <li>Results of the RSYD-BASIC pipeline, version 1.15.1, with the test dataset</li> </ul>
Dataset: A review of methods to trace material flows into final products in dynamic material flow analysis - from industry shipments in physical units to monetary input-output tables (p
<p>Dynamic material flow analysis (dMFA) is widely used to model stock-flow dynamics. To appropriately represent material lifetimes, recycling potentials, and service provision, dMFA requires data about the allocation of economy-wide material consumption to different end-use products or sectors, that is, the different product stocks, in which material consumption accumulates. Previous estimates of this allocation only cover few years, countries, and product groups. Recently, several new methods for estimating end-use product allocation in dMFA were proposed, which so far lack systematic comparison. We review and systematize five methods for tracing material consumption into end-use products in inflow-driven dMFA and discuss their strengths and limitations. Widely used data on industry shipments in physical units have low spatio-temporal coverage, which limits their applicability across countries and years. Monetary input–output tables (MIOTs) are widely available and their economy-wide coverage makes them a valuable source to approximate material end-uses. We find four distinct MIOT-based methods: consumption-based, waste input–output MFA (WIO-MFA), Ghosh absorbing Markov chain, and partial Ghosh. We show that when applied to a given MIOT, the methods’ underlying input–output models yield the same results, with the exception of the partial Ghosh method, which involves simplifications. For practical applications, the MIOT system boundary must be aligned to those of dMFA, which involves the removal of service flows, sector (dis)aggregation, and re-defining specific intermediate outputs as final demand. Theoretically, WIO-MFA, applied to a modified MIOT, produces the most accurate results as it excludes massless and waste transactions. In part 2 of this work, we compare methods empirically and suggest improvements for aligning MIOT-dMFA system boundaries.</p>
Output Data for "Effective adaptation options to alleviate nuisance flooding in coastal megacities – learning from Ho Chi Minh City, Vietnam"
<p>This data repository pertains to the research article "Effective adaptation options to alleviate nuisance flooding in coastal megacities - learning from Ho Chi Minh City, Vietnam" by Leon Scheiber, Nivedita Sairam, Mazen Hoballah Jalloul, Kasra Rafiezadeh Shahi, Christian Jordan, Jan Visscher, Tara Evaz Zadeh, Laurens J.N. Oostwegel, Danijel Schorlemmer, Ngo Thanh Son, Hong Nguyen Quan, Torsten Schlurmann, Matthias Garschagen, Heidi Kreibich, published in Earth’s Future, 2024.</p> <p>The provided output data comprise:<br>- Risk Components (incl. inundation depths, exposed households and building values as well as relative losses)<br>- Expected Annual Damage (incl. 25/50/75th percentiles)<br>- Annually Affected Households </p> <p> </p>
HOTSSea Model v1 monthly outputs
<p>Monthly outputs from the Hindcast of the Salish Sea (HOTSea) v1.02 (temperature bias corrected) 1980 - 2018. Monthly statistics (mean, min, max, standard deviation) are processed from daily mean model outputs of temperature and salinity. The monthly data are provided for the surface, bottom layer, and for several depth strata (0to30m, 30to150m, 150mtoBottom). In the cases where depth strata are provided, the daily means are first processed to monthly. Then, the depth integrated mean is produced. For example, the 'min' temperature for 0 to 30 m is produced by taking the minimum mean daily temperature across the month at each modelled depth. Then, the depth integrated mean of these mins is produced. Rationale is provided on the 'pacea' R data repository. The monthly data are then stored in NC files. See the parent publication for model details. </p> <p>outputs v4 - added min, max, standard deviation, changed name conventions, fixes to indexing over depths, fix to fill_values and missing vals to be compatible with R.</p>
West Antarctic Peninsula (WAP) MITgcm-REcoMv2 outputs for 1991 (T, S, DIC, TA, DIN, Chl, air-sea CO2 flux)
<p>This dataset contains the netCDF files for the year 1991 of dissolved inorganic nitrogen (DIN), dissolved inorganic carbo (DIC), total alkalinity (TA), diatom chlorophyll concentration (DiaPhyto), small phytoplankton chlorophyll (SmPhyto), air-sea CO<sub>2</sub> fluxes (CO2flx), ocean temperature (Temp) and salinity (Salt) for the MITgcm-REcoMv2 ocean circulation and biogeochemistry model implemented for the West Antarctic Peninsula. Latitude and longitude for all variables are contained in the grid.glob.nc file.</p> <p>Files names start with the variable name, followed by the month and year. They are saved either as monthly means, as 10 day means, or 5 day means.</p> <p>The units for each variable are as follows:</p> <p>DIN: mmol/m<sup>3</sup></p> <p>DIC: mmol/m<sup>3</sup></p> <p>TA: mmol/m<sup>3</sup></p> <p>DiaPhyto: mg/m<sup>3</sup></p> <p>SmPhyto: mg/m<sup>3</sup></p> <p>CO2flx: mmol/s</p> <p>Temp: ˚C</p> <p>Results from this simulation are published in:</p> <p><strong><span>SCHULTZ, C., </span></strong><span>DONEY, S.C., ZHANG, W.G., REGAN, H.C., HOLLAND, P., MEREDITH, M., STAMMERJOHN, S.</span><strong><span> </span></strong><span>Modeling of the Influence of Sea Ice Cycle and Langmuir Circulation on the Upper Ocean Mixed Layer Depth and Freshwater distribution at the West Antarctic Peninsula. Journal of Geophysics Research Oceans, 125, 8, 2020. doi:10.1029/2020JC016109</span></p> <p><strong><span>SCHULTZ, C., </span></strong><span>DONEY, S.C., HAUCK, J., KAVANAUGH, M.T., SCHOFIELD, O.</span><strong><span> </span></strong><span>Modeling Phytoplankton Blooms and Inorganic Carbon Responses to Sea-Ice Variability in the West Antarctic Peninsula. Journal of Geophysical Research Biogeosciences, 126, 4, 2021. doi: 10.1029/2020JG006227</span></p> <p> </p>
Main output data used in "Exploring the Greenland Ice Sheet's response to future atmospheric warming-threshold scenarios over 200 years" (Delhasse et al., 2025)
<p>Outputs used in:</p> <p>Delhasse, A., Kittel, C. and Beckmann, J.: Exploring the Greenland Ice Sheet's response to future atmospheric warming-threshold scenarios over 200 years, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-709, 2025.</p> <p>Each MAR-PISM coupling experiment (1991-2200) is related to the Greenland warming over a 10-year period compared to our reference period (1961-1990) at which climate is stabilized until 2200. The last experiment is the Reverse one, where the climate is year by year reversed after 2100 to go back to 2000-climate as forcing in 2200, the last year of the simulation. Please refer to Delhasse et al. (2024) for the coupling description.</p> <div> <table> <tbody> <tr> <th> <p>Experiment </p> </th> <th> <p>Exact Greenland warming at 600hPa (°C)</p> </th> <th> <p>10-years period</p> </th> </tr> </tbody> <tbody> <tr> <td> <p>CTRL</p> </td> <td> <p>+0.00</p> </td> <td> <p>1961-1990</p> </td> </tr> <tr> <td> <p>+1</p> </td> <td> <p>+1.04</p> </td> <td> <p>1995-2004</p> </td> </tr> <tr> <td> <p>+1.5</p> </td> <td> <p>+1.51</p> </td> <td> <p>2010-2019</p> </td> </tr> <tr> <td> <p>+2</p> </td> <td> <p>+2.04</p> </td> <td> <p>2021-2030</p> </td> </tr> <tr> <td> <p>+3</p> </td> <td> <p>+2.98</p> </td> <td> <p>2040-2049</p> </td> </tr> <tr> <td> <p>+4</p> </td> <td> <p>+4.04</p> </td> <td> <p>2058-2067</p> </td> </tr> <tr> <td> <p>+5</p> </td> <td> <p>+5.00</p> </td> <td> <p>2074-2083</p> </td> </tr> <tr> <td> <p>+6</p> </td> <td> <p>+5.96</p> </td> <td> <p>2083-2092</p> </td> </tr> <tr> <td> <p>+7</p> </td> <td> <p>+6.85</p> </td> <td> <p>2091-2100</p> </td> </tr> </tbody> </table> </div> <p><strong>Table 1. Greenland warmings at 600hPa since 1961-1990 used to define our experiments and the corresponding 10-years periods over which warmings are determined. </strong></p> <p>For each experiment, 3 types of output are available (where <em>EXP</em> corresponds to the name of the experiment as referenced in Table 1) : </p> <ul> <li> <p>EXP-PISM-thk-msk-1991-2200.nc: contain yearly ice thickness (THK) and ice mask (MASK) as simulated by PISM (PISM grid, 4.5 km);</p> </li> <li> <p>EXP-SMB-ME-RU-MAPI-CESM2-1991-2200.nc: contain yearly SMB (surface mass balance), ME (melt), and RU (runoff) on the MAR grid (25 km);</p> </li> <li> <p>EXP-ts-MB-D-SMB-1991-2200.nc: contain time series of the total MB (mass balance), D (discharge), and SMB (surface mass balance) integrated over the all ice sheet mask from PISM.</p> </li> </ul> <p>The MAR code used in this dataset is tagged as v3.11.3 on https://gitlab.com/Mar-Group/MARv3/-/tree/v3.11.3 (last access: 24 October 2024) (MARTeam, 2024). The PISM code used is tagged as PISMv1.2.2 on <a href="https://github.com/pism/pism/releases/tag/v1.2.2">https://github.com/pism/pism/releases/tag/v1.2.2</a> (last access: 24 October 2024).</p> <p>If you need other variables from MAR or PISM, send us an email (alison.delhasse@uliege.be) and we will be glad to help you. We will also be happy to share the scripts we have developed to analyze the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR-PISM outputs.<br><br><strong><em>Data usage notice:</em></strong></p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgments should be similar to the one below that contains information related to MAR and PISM. To document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact me to add their works to the list of MAR-related publications. </p> <p>"We thank A. Delhasse, C. Kittel, and J. Beckmann, as well as the MAR and PISM teams which make available the model outputs. We also thank agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR-PISM simulations. "</p> <p>You should also refer to and cite the following paper in its latest version:</p> <p>Delhasse, A., Kittel, C. and Beckmann, J.: Exploring the Greenland Ice Sheet’s response to future warming-threshold scenarios over 200 years, [JOURNAL UNDER REVIEW], 2024.</p> <p><strong><em>References</em></strong></p> <p>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</p> <p>MARTeam: MARv3.11, GitLab [data set], <a href="https://gitlab.com/Mar-Group/MARv3">https://gitlab.com/Mar-Group/MARv3#</a> (last access: 24 October 2024), 2024.</p> <p> </p>
Output from Linear Inverse Models (LIMs) emulating the observed spatiotemporal statistics of Australian precipitation and global sea surface temperatures
<p><strong>Data repository for <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em></strong></p> <p>This repository contains LIM data underpinning the paper <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em> [doi: 10.1016/j.wace.2024.100734 <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.wace.2024.100734" target="_blank" rel="noopener">available online in <em>Weather and Climate Extremes</em> 17 October 2024</a>]. All other datasets used in the paper are freely available online (see Data Availability statement in the paper for details). </p> <p>The repository contains 12 netcdf files, which together comprise the Linear Inverse Model (LIM) outputs described in the paper. <strong>In all cases, please see the paper for important details on the data and how they were produced.</strong> </p> <p><em>Global LIMs</em></p> <ul> <li>`LIM5000_COBE-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the Australian Gridded Climate Dataset v2 (AGCD) and global SST data from 'Centennial in situ Observation-Based Estimates of the Variability of SST and Marine Meteorological Variables version 2' (COBE)</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and global SST data from US National Oceanic and Atmospheric Administration 'Extended Reconstruction SST version 5’ (ERSST)</li> </ul> </li> <li>`LIM5000_COBE-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from ERSST</li> </ul> </li> </ul> <p><em>Tropical Pacific Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from ERSST</li> </ul> </li> </ul> <p><em>Indian Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from ERSST</li> </ul> </li> </ul> <p><strong>How to cite this</strong> <strong>repository</strong></p> <p>If using this data, please cite the original publication, available from <a href="https://www.sciencedirect.com/science/article/pii/S2212094724000951" target="_blank" rel="noopener">https://www.sciencedirect.com/science/article/pii/S2212094724000951.</a> </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.