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3,113 results for “extremes”
Elevated increase in compound extreme heat-precipitation events over China
<p>This file contains the fractions (in percentage) of the compound extreme precipitation events that are preceded by an extreme heat event in China during 1961-2017. The compound events are identified based on the CN05.1 dataset at 0.5x0.5 resolution. Please contact us with any questions or concerns (email: luo.ming@hotmail.com).</p>
Estimated field scale sediment loss on the North Wyke Farm Platform in typical and extreme wet winters
<p>Based on monitored runoff and turbidity at 15-minute intervals from the North Wyke Fam Platform - a UK National Bioscience Research Infrastructure (NBRI), sediment loss during both typical and more extreme wet winters (December - February, inclusive) over the past decade (2012-2013, 2013-2014, 2015-2016, 2019-2020 and 2023-2024) from 5 grassland field catchments and 5 recently converted arable field catchments was estimated, including uncertainty ranges. Daily rainfall totals for the corresponding winter periods are also included.</p>
Data from: Solar energy resource availability under extreme and historical wildfire smoke conditions
<p>The data in this repository are used to generate the figures in the article "Solar energy resource availability under extreme and historical wildfire smoke conditions" by Corwin et al. (accepted 2024) in <em>Nature Communications</em>. Data are the final processessed and merged datasets sourced from the following publicly available data products:</p> <ul> <li>National Renewable Energy Laboratory’s (NREL) National Solar Radiation Database (NSRDB) (<a href="https://nsrdb.nrel.gov/)">https://nsrdb.nrel.gov/)</a>. <ul> <li>Bulk download in July 2023 via AWS: <a href="https://registry.opendata.aws/nrel-pds-nsrdb/">https://registry.opendata.aws/nrel-pds-nsrdb/</a></li> <li>Variables: modeled irradiance (clear-sky and all-sky direct normal (DNI) and global horizontal (GHI) irradiance, aerosol optical depth, and cloud optical depth</li> </ul> </li> <li>National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke product. <ul> <li>Access: <a href="https://www.ospo.noaa.gov/Products/land/hms.html#maps">https://www.ospo.noaa.gov/Products/land/hms.html#maps</a></li> <li>Variables: smoke plume locations</li> </ul> </li> <li>National Aeronautics and Space Administration's (NASA) Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product (MCD19A2 MODIS/Terra + Aqua land aerosol optical depth daily L2G Global 1km SIN Grid V006). <ul> <li>Access: <a href="https://lpdaac.usgs.gov/products/mcd19a2v006/">https://lpdaac.usgs.gov/products/mcd19a2v006/</a></li> <li>Variables: aerosol optical depth and cloud mask</li> </ul> </li> <li>NASA's Clouds and the Earth’s Radiant Energy System (CERES) cloud data product (SYN1deg-1Hour Edition 4.1) <ul> <li>Access: <a href="https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp">https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp</a></li> <li>Variables: cloud optical depth</li> </ul> </li> </ul> <p>A detailed description of the data processing methods used to produce the final merged data are available in the article by Corwin et al. </p> <p>Associated code scripts are located in the linked code repository.</p>
ARISE-SAI_1.5 : CESM2 Extreme Precipitation and Temperature Indices
<p>Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) is a set of simulations carried out with the Community Earth System Model, version 2 with the Whole Atmosphere Community Climate Model, version 6 (CESM2(WACCM6)) that aims at simulating a plausible deployment of solar climate intervention of stratospheric aerosol injection to enable community assessment of responses of the Earth system.</p> <p>This dataset uses the first set of simulations, called ARISE-SAI-1.5, that utilized the middle-of-the-road SSP2-4.5 emission scenario, and targetted a global mean surface air temperature near 1.5°C above the pre-industrial value. ARISE-SAI-1.5 is described in Richter et al. (2022). Selected data are available at Richter & Visioni (2022a,b).</p> <p>The files contained here contain processed annual daily extremes of surface temperature (TREFHT) and total precipitation (PRECT) from the ARISE-SAI-1.5 simulations and companion SSP245 simulations. Indices are those recommended by the WCRP Expert Team on Climate Change Detection Indices, Zhang et al. 2011). Methods to calculate the indices are also described in Tye et al. (2022).</p> <p><strong>Precipitation Indices</strong></p> <p>PRCPTOT, SDII, RX1D, RX5D, R10mm, R20mm, CDD, CWD, P95TOT, P99TOT</p> <p><strong>Temperature Indices</strong></p> <p>TNN, TNX, FD, TR, TN90, TN10, TN90p, TN10p, TXX, TXN, ID, SU, TX90, TX10, TX10p, TX90p, WSDI</p> <p>Where T?10 is the number of days below an annual 10th percentile threshold and T?90 is the number of days above an annual 90th percentile threshold (i.e. around 30 days per year).</p> <p>T?10p as defined by ETCCDI is the frequency of days below the rolling 5-day average climatological day of year 10th percentile. This threshold is also used for the cold spell duration index (CSDI), or consecutive days that are cool for the season.</p> <p>T?90p as defined by ETCCDI is the frequency of days above the rolling 5-day average climatological day of year 90th percentile. This threshold is also used for the warm spell duration index (WSDI), or consecutive days that are warm for the season.</p>
Data for publication "Statistical characteristics of extreme daily precipitation during 1501 BCE - 1849 CE in the Community Earth System Model".
<p>Here, the data used in Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). "Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model" in <em>Climate of the Past </em>(<a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-6</a>) are provided.</p> <p>Two simulations covering the period 1501 BCE - 2008 CE are performed with CESM 1.2.2: the orbital-only and the full-forcing simulations. The full-forcing transient simulation includes the new long record of volcanic eruptions (<a href="https://doi.org/10.1594/PANGAEA.928646">https://doi.org/10.1594/PANGAEA.928646</a>) that covers the last 3500 years. The output from the simulations is used to examine the long-term variability and characteristics of daily extreme precipitation during 1501BCE-1849 CE.</p> <p>The following files are provided:</p> <ul> <li> <strong>CESM122.transient.PRECT.anom.above99th.1501BCE-1849CE_I and II</strong>: Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the full forcing simulation. The file is split into two parts, with the first file containing the first 50% of extremes (I) and the second file containing the rest 50% (II).</li> <li><strong>CESM122.orbital.PRECT.anom.above99th.1501BCE-1849CE I and II:</strong> Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the orbital-only simulation.</li> <li> <strong>CESM122.transient.variables.mon.1979-2008CE:</strong> monthly precipitation, temperature, and geopotential height at 500 hPa for 1979-2008CE from the full-forcing simulation.</li> <li><strong>CESM122.trans.variable_names.years:</strong> Monthly variables from the full-forcing simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE. The variables are solar insolation (SOLIN), clear-sky net surface shortwave radiation (FSNSC), geopotential height at 500hPa (Z500), and surface temperature (TS).</li> <li><strong>CESM122.orbital.variable_names.years:</strong> Monthly variables from the orbital-only simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE.</li> <li><strong>CESM122.*.log-likelihood-GPDmodel-ExtForcing</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for external forcings.</li> <li> <strong>CESM122.*.log-likelihood-GPDmodel-ModesVar</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for modes of variability.</li> <li><strong>Evolk_EVA_distribution_1501BCE-2015CE</strong>: Distribution of volcanic aerosol for CAM5, produced based on Kim et al. (2021).</li> </ul> <p>If you use this dataset, please cite:</p> <p><em>Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model. Climate of the Past Discussions, 1-38. <a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-61</a></em></p>
Data for Figure 1 of Taubenberger 2017, "The Extremes of Thermonuclear Supernovae"
<p>Data needed to recreate Figure 1 of Taubenberger S., <a href="https://ui.adsabs.harvard.edu/abs/2017hsn..book..317T/abstract">"The Extremes of Thermonuclear Supernovae"</a>, in A. W. Alsabti, P. Murdin, eds., “Handbook of Supernovae”, Springer, ISBN: 978-3-319-20794-0 (2017). A detailed README is included.</p>
Data supporting: "Trends in Europe storm surge extremes match the rate of sea-level rise"
<p><strong>Data supporting the paper:</strong></p> <p><strong>Calafat, F. M., T. Wahl, M. G. Tadesse, & S. Sparrow. Trends in Europe storm surge extremes match the rate of sea-level rise. <em>Nature</em> 603, 841-845.</strong></p> <p>Please cite the paper above when using this data set.</p> <p><em>Data description:</em></p> <ul> <li><strong>Bayesian_solutions_historical_total.nc</strong>: Bayesian estimates (posterior draws) of the GEV parameters, including trends in the GEV location parameter, at both tide gauge sites and prediction locations. This file also contains the observed surge annual maxima from tide gauge records on which these estimates are conditioned.</li> <li><strong>Bayesian_solutions_historical_contributions.nc</strong>: Bayesian estimates (posterior draws) of the contributions from external forcing and internal climate variability to the trends in the GEV location parameter.</li> <li><strong>Surge_annual_max_ensemble.nc</strong>: ensemble of surge annual maxima used to extract the pattern of response to external forcing.</li> </ul>
Supplementary data for "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise"
<p>This dataset comprises topography and bathymetric data at three coastal locations in Australia (Narrabeen), UK (Perranporth) and used for the publication "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise". Please refer to readme files for metadata</p>
Influence of the tropical Indian Ocean tripole on summertime cold extremes over central Siberia
<p>These experiments are used to study atmospheric circulation responses to SST forcing related to Indian Ocean tripole mode, including the precipitation, zonal and meridional winds.</p>
Data for: Probing electron and hole co-localization by resonant four-wave mixing spectroscopy in the extreme-ultraviolet
<p>Data for: Probing electron and hole co-localization by resonant four-wave mixing spectroscopy in the extreme-ultraviolet</p>
Visual-inertial input datasets for SLAM applications containing extreme and human-like motion patterns
<p>Recorded datasets in compressed rosbag format, which contain visual and IMU sensor information that are bearing high resemblance to the movement of a human player with a handheld AR-capable device.</p> <p>For machine learning training and validation tasks, separate dataset are available containing motion patterns in a wide range from steady camera image to extremely challenging movements.</p>
Data/ codes used in the the Natural Hazards and Earth System Sciences (NHESS) publication titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast" by Pranavam Ayyappan Pillai et al. (2022)
<p>The archive contains datasets and codes used in the manuscript titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast", and published in the journal <em>Natural Hazards and Earth System Sciences</em> (<em>NHESS</em>) by Pranavam Ayyappan Pillai et al., 2022.</p> <p>Pranavam Ayyappan Pillai, U., Pinardi, N., Federico, I., Causio, S., Trotta, F., Unguendoli, S., and Valentini, A.: Wind-Wave Characteristics and extremes along the Emilia-Romagna coast, Nat. Hazards Earth Syst. Sci. Discuss. https://doi.org/10.5194/nhess-2022-103, 2022.</p>
Lizards from warm and declining populations are born with extremely short telomeres
<p>These two datasets report the information at the populational ("Population_Biogeography2017-2018.csv") and individual ("Telomere_Zootocavivipara_2015-2017.csv") levels. At populational level, we studied the covariation of multiple biogeographic measures to obtain an integrative index of population extinction risk. At individual level, we examined what factors best explained the variation in lizard telomere length.</p> <p>We also uploaded the R code ("DataAnalysis_Telomerelizards_AndreazDupoue.R") used to analyse these data, in which we detailed all variables.</p>
Data files for figures in "Deep learning extreme precipitation of the past, present, and under 1.5°C and 2.0°C global warming" by Bird et al. 2022
<p>The data files for figures in <em>Deep learning extreme precipitation of the past, present, and under 1.5°C and 2.0°C global warming</em> by Bird, Bodeker and Clem. The data files are provided either as self-describing netCDF files, or .csv files with column descriptors.</p>
Supplemental material for the manuscript "Extreme genome scrambling in marine planktonic Oikopleura dioica cryptic species".
<p><strong>Supplementary material for the manuscript “Extreme genome scrambling in marine planktonic <em>Oikopleura dioica</em> cryptic species”.<br></strong></p> <p><strong><em>BreakpointsData.tar.xz contains:</em></strong></p> <ul> <li>Pairwise genome alignment files for <em>Oikopleura</em>, <em>Ciona</em>, <em>Caenorhabditis</em>, insects and muntjaks in GFF format in `inst/extdata/`.</li> <li>dN / dS computation results in `inst/extdata/dNdS/`.</li> <li>Annotations of gene models and repeat elements in GFF format in `inst/extdata/Annotations/`.</li> <li>OrthoGroups in `inst/extdata/OrthoFinder/`, where N19 represents the _O. dioica_ clade,</li> <li>N3 the tunicates and N20 the _Ciona_ clade.</li> <li>`BreakpointsData_3.11.0.tar.gz`, a R package installing the above files in the R environments where we ran our computations.</li> <li>The files needed to build the `BreakpointsData` package.</li> </ul> <p><em><strong>Oidioi_pairwise_v3.tar.gz contains:</strong></em></p> <ul> <li>The pairwise alignment files between genomes, in MAF format.</li> <li>A copy of the Nextflow pipeline used to generate them.</li> </ul> <p><em><strong>oist-assembler.tar.gz contains:</strong></em></p> <ul> <li>A Singularity image and its definition file for flye version 2.8.3-b1763` Flye-flye.2.8.3-b1763.sif` and `Flye-flye.def`.</li> <li>A copy of the Nextflow pipeline used to assemble the Bar2_p4 genome in `oist-assembler-Bar2_p4`.</li> <li>A copy of the Nextflow pipeline used to assemble the other genome in `oist-assembler-other_genomes`.</li> </ul> <p><em>Please note that these files are provided for reproducibility only and probably can not be used easily for other purposes.</em></p> <p><em><strong>Oidioi_genomes.tar.gz contains:</strong></em></p> <ul> <li>For each genome, one file (`<genome>.fa`) containing the whole genome sequence and one directory (`<genome>`) containing each chromosome, scaffold or contig of the genome as a separate file.</li> <li>For each genome, one R package, its source directory, and the vignette to create it, providing the genome information as a `BSgenome` object.</li> </ul> <p><em><strong>OrthoFinderRun.tar.xz contains:</strong></em></p> <ul> <li>A full copy of the OrthoFinder2 run that we used to compute hierarchical orthogroups.</li> </ul> <p><em><strong>Supplemental_Code.tar.gz contains:</strong></em></p> <ul> <li>A copy of <https://github.com/oist/LuscombeU_OikScrambling>, where the `.git` and `doc` directories were removed to save space.</li> </ul> <p><em><strong>AugustusAnnotation.tar.gz (added July 26th 2024) contains:</strong></em></p> <ul> <li>AUGUSTUS runs to produce the annotations that were input to OrthoFinder2. We provide them for reproducibility, with no guarantee that they are suitable for other purposes. The annotations used in the manuscript are AOM-5-5f.sm.OSKA-CDS, Bar2_p4_Flye.sm, Bsty_SCLE01.1.sm.abi.cionamodel, Fbor_SDII01.1.sm.abi, KUM-M3-7f.sm.OKI-CDS, Mery_SCLF01.1.sm.abi.cionamodel, Oalb_SCLG01.1.sm.abi.cionamodel, OKI2018_I69_annotv2.sm, Olon_SCLD01.1.sm.abi, OSKA2016v1.9.sm and Ovan_SCLH01.1.sm.abi.cionamodel.</li> </ul>
Genome-wide association summary statistics for varicose veins of lower extremities
<p>The dataset contains summary statistics for the discovery and the replication stages of the large-scale genome-wide associations study for varicose veins of lower extremities. The discovery stage was based on genetic association data provided by the Neale Lab (<a href="https://vk.com/away.php?to=http%3A%2F%2Fwww.nealelab.is%2F&cc_key=">http://www.nealelab.is/</a>) for 337,199 UK biobank individuals. Phenotype “varicose veins of lower extremities” was defined based on International Classification of Disease (ICD-10) billing code “I83” present in the electronic patient record. Data were adjusted for two potential confounders – body mass index and deep venous thrombosis. A replication cohort (N=71,256) was generated by means of reverse meta-analysis of two overlapping datasets: genetic association data for 408,455 UK Biobank participants provided by the Gene ATLAS database (<a href="https://vk.com/away.php?to=http%3A%2F%2Fgeneatlas.roslin.ed.ac.uk%2F&cc_key=">http://geneatlas.roslin.ed.ac.uk/</a>), and the above mentioned data provided by the Neale Lab.</p> <p>Please, note, that in Shadrina et al (PLOS Genetics 2019) we only used "discovery" dataset, while in biorxiv preprint (https://doi.org/10.1101/368365) both discovery and replication datasets were used. </p> <p>The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilisation of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. </p> <p><strong>When using downloaded data, please cite corresponding paper and this repository:</strong></p> <ol> <li> <p>Shadrina, A. S., Sharapov, S. Z., Shashkova, T. I. & Tsepilov, Y. A. Varicose veins of lower extremities: Insights from the first large-scale genetic study. <em>PLOS Genet.</em> <strong>15,</strong> e1008110 (2019).</p> </li> <li>Alexandra S. Shadrina, Sodbo Zh. Sharapov, Tatiana I. Shashkova, & Yakov A. Tsepilov. (2018). Genome-wide association summary statistics for varicose veins of lower extremities (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1323484</li> </ol> <p><strong>Funding:</strong></p> <p>The work of ASS was supported by the Russian Science Foundation [Project No 17-75-20223]. <br> The work of YAT was supported by the Russian Ministry of Science and Education under the 5-100 Excellence Programme. <br> The work of SZS was supported by the Institute of Cytology and Genetics [Project No 0324-2018-0017].</p> <p><strong>Column headers - discovery</strong></p> <ol> <li>SNP: SNP rsID</li> <li>b: effect size of effect allele</li> <li>se: standard error of effect size</li> <li>chi2: T^2 value of effect allele</li> <li>Pval: P-value of association (without GC correction)</li> <li>N: sample size</li> <li>Chr: chromosome</li> <li>Pos: position (GRCh37 build)</li> <li>A1: effect allele (coded as "1")</li> <li>A2: reference allele (coded as "0")</li> </ol> <p><strong>Column headers - replication</strong></p> <ol> <li>SNP: SNP rsID</li> <li>A1: effect allele (coded as "1")</li> <li>A2: reference allele (coded as "0")</li> <li>N: Total sample size</li> <li>Z: Z-value of effect allele</li> <li>P: P-value of association (without GC correction)</li> </ol>
Extremely Imbalanced Smell-based Defect Prediction
<p><strong>Abstract: </strong>In continuous integration/continuous delivery, one of the main requirements for high-speed delivery of software is to find bugs efficiently. For this reason, multiple solutions were introduced in the literature. For instance, defect prediction approaches based on bad code smells detected in modules from each version of the software. Nevertheless, these approaches do not consider the problem where there may exist an extremely higher percentage of non-defective modules compared to defective modules. Given that, each version of the software may only have a small number of defects. As a result, in this thesis, we introduce a new model with an autoencoder algorithm that uses design and implementation smells to detect defective modules. Therefore, we trained five autoencoders with distinct architectures. Ad- ditionally, for evaluation, we compared each model against autoencoders with the same architecture, trained with traditional object-oriented metrics and the combination of both. Our analysis did not show promising results, as the use of only smells and the combination of features did not provide an improve- ment compared with the use of metrics. However, we introduce a starting point for smell-based defect prediction in the context of dataset imbalance. Furthermore, we introduce a baseline for future work.</p> <p> </p> <p><strong>Dataset Description:</strong></p> <p>We provide three datasets. The first results from the extraction of traditional object-oriented metrics (metric.csv). The second results from the extraction of design and implementation smells (smell.csv). The third is the combination of all the features (metricsmell.csv). Moreover, these features were extracted from Designite and Bugsdorjar software archives.</p>
Supplementary Material for "Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP"
<p>Supplementary material to accompany pre-print of "Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP" by Jethro Browell and Matteo Fasiolo available on on arXiv. This is version 3. The only changes from version 1 & 2 to forecast evaluation (significance testing and additional plots). Future releases are subject to change following revisions of this article.</p>
Age-dependent extreme event exposure - data accompanying journal publication
<p>This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science</p> <p> </p> <p>Input fields:</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/isimip.zip">isimip.zip</a>: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data.</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/GMT_50pc_manualoutput_4pathways.xlsx">GMT_50pc_manualoutput_4pathways.xlsx</a>: Global mean temperature anomaly trajectories from the IPCC SR15</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/wcde_data.xlsx">wcde_data.xlsx</a>: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer.</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx">WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx</a>: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme</p> <p> </p> <p>Intermediate files *only use if you're interested in reproducing the results*:</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/workspaces.zip">workspaces.zip</a>: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis.</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_isimip.mat">mw_isimip.mat</a>: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation)</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_countries.mat">mw_countries.mat</a>: information on the countries used in the analysis (e.g. border polygon coordinates)</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_exposure.mat">mw_exposure.mat</a>: age-dependent exposure computed from the ISIMIP and population data</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_exposure_pic.mat">mw_exposure_pic.mat</a>: pre-industrial control age-dependent exposure computed from the ISIMIP and population data</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_exposure_pic_coldwaves.mat">mw_exposure_pic_coldwaves.mat</a>: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data</p> <p> </p> <p> </p> <p>Output of the analysis:</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_output.mat">mw_output.mat</a>: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.</p> <p> </p> <p> </p>
Supply Chain Shocks due to extreme weather events
<p>Projected supply chain shocks due to extreme weather events measured in annual percentage change in a country-sector's export activity compared to the baseline period</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.