Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
151
datasets available to search
ShareScore release 0.9.0
Dataset results
151 results for “extreme events”
Particle tracking simulations of marine macro-litter in Rostock during extreme events
<p>The simulations show the passive near-surface transport of macro-litter during extreme events (HanseSail in Rostock, Northern Germany). The simulations are based on a high resolution 3D flow model of the Warnow estuary.</p>
Supporting datasets for the article Using rare event algorithms to understand the statistics and dynamics of extreme heatwave seasons in South Asia
<h3>Supporting datasets for the article<em><strong> Using rare event algorithms to understand the statistics and dynamics of extreme heatwave seasons in South Asia, </strong></em>submitted to <em>Environmental Research: Climate</em></h3> <p> </p> <p>The dataset contains all intermediate data used for the article. The raw outputs of the model may be available upon reasonable request to clement.lpr@gmail.com</p> <p>The climate model Plasim can be downloaded, together with its documentation, from the webpage of the « Theoretische Meteorologie » group at the University of Hamburg: <a href="https://www.mi.uni-hamburg.de/en/arbeitsgruppen/theoretische-meteorologie/modelle/plasim.html">https://www.mi.uni-hamburg.de/en/arbeitsgruppen/theoretische-meteorologie/modelle/plasim.html</a></p> <p>The three jupyter notebooks <a href="../api/records/10888194/draft/files/Figures_article_archive.ipynb/content" target="_blank" rel="noopener noreferrer">Figures_article_archive.ipynb</a>, <a href="../api/records/10888194/draft/files/Zg500_maps_article_archive.ipynb/content" target="_blank" rel="noopener noreferrer">Zg500_maps_article_archive.ipynb</a>, <a href="../api/records/10888194/draft/files/Correlation_maps_3days_ERA5_Plasim_archive.ipynb/content" target="_blank" rel="noopener noreferrer">Correlation_maps_3days_ERA5_Plasim_archive.ipynb</a> contain the analysis and code used to produce the figures in the article.</p> <p><strong><a href="../api/records/10888194/draft/files/pyscripts.tar/content" target="_blank" rel="noopener noreferrer">pyscripts.tar</a> </strong>contains 4 python utilities, <em>data_proceeding_module.py, plot_routines.py, subseasonal_stats_utilities.py, utilities_REA_analysis.py </em>that are imported by the notebooks.</p>
Extreme Weather Event Real-time Attribution Machine (EWERAM) forecasts for Cyclone Gabrielle
<p>These files contain the hourly precipitation, wind, humidity, and pressure data as well as the land-sea mask, orography, and regional council data supporting the investigation into the human role in Cyclone Gabrielle performed by the EWERAM (Extreme Weather Event Real-time Attribution Machine) consortium. The EWERAM experiment design is outline by Tradowsky and co-authors (2023, 10.1088/2752-5295/acf4b4).</p> <p>Directory and data format follows the conventions of the Climate of the 20th Century Plus Detection and Attribution (C20C+ D&A) Project. Details are provided at https://portal.nersc.gov/c20c/experiment.html and in Stone and co-authors (2019, 10.1016/j.wace.2019.100206).</p>
Dataset for "Extreme Energetic Particle Events by Superflare Associated CMEs from Solar-like Stars"
<p>Here we present the simulation results (time-intensity profiles and event integrated fluence spectra) for the paper "Extreme Energetic Particle Events by Superflare Associated CMEs from Solar-like Stars". Detailed descriptions of the simulation settings can be found in the manuscript. </p>
Temporal dynamics of range-expander and congeneric native plant responses during and after extreme drought events
<p>Current climate change causes range shifts of many species to higher latitudes and altitudes, and enhances their exposure to extreme weather events. It has been shown that range shifting plant species may perform differently in the new soil than related natives, however, little is known about how extreme weather events influence range-shifting plants compared to related natives. Here, we used outdoor mesocosms to study how range-shifting plant species respond to extreme drought in live soil from a habitat in the new range with and without live soil from a habitat in the original range. During summer drought, shoot biomass of the range-expanders was reduced. In spite of this, in the mixed community range-expanders produced more shoot biomass than congeneric natives. In mesocosms with a history of range-expanders in the previous year, native plants produced less biomass. Plant legacy or soil origin effects did not change the response of natives or range-expanders to summer drought. During rewetting, range-expanders had less biomass than congeneric natives, but had higher drought resilience (survival) in soils from the new range where in the previous year native plant species had grown. The biomass patterns of the mixed plant communities were dominated by Centaurea species, however, not all plant species within the groups of natives and of range expanders showed the general pattern. Drought reduced litter decomposition, microbial biomass and abundances of bacterivorous, fungivorous and carnivorous nematodes. Their abundances recovered during rewetting. There was less microbial biomass, fungal biomass and there were fewer fungivorous nematodes in soils from the original range (Hungary) where range-expanders had grown in the previous year. We conclude that in mixed plant communities of range-expanders and congeneric natives, range-expanders performed better, both under ambient and drought conditions, than congeneric natives. However, when considering the responses of individual species, we observed variations among couples of congenerics, so that under the present-mixed community-conditions there was no uniformity in responses to drought of range expanders versus congeneric natives. Range-expanding plant species reduced soil fungal biomass and numbers of soil fungivorous nematodes, suggesting that effects of range-expanding plant species can trickle up in the soil food web.</p>
Extreme event of water flow in Tucurui River
<p>Video made by residents near the Tucurui River.</p>
Additional Supporting Information to 'Quantifying the Contribution of Ocean Mesoscale Eddies to Low Oxygen Extreme Events.'
<p>Additional Supporting Information to 'Quantifying the Contribution of Ocean Mesoscale Eddies to Low Oxygen Extreme Events.'. Submitted to Geophysical Research Letters for publication. 2022.</p> <p>NetCDF files and Python NumPy arrays of data used to create all figures in the manuscript main text and supporting information.</p>
Extreme power shortage events of wind-solar supply systems for individual countries
<p><span>Raw data of extreme power shortage events in wind-solar supply system in the paper entitled “Climate change impacts on the power shortage events of wind-solar supply systems worldwide during 1980–2022” on Nature Communications.</span></p>
Annual probability of extreme heat and drought events, derived from Lange et al 2020
<p>The time series of extreme events given by Lange et al has been processed into an annual probability of occurrence by researchers at the University of Oxford, using the pipeline available online at <a href="https://github.com/nismod/isimip-exposure">https://github.com/nismod/isimip-exposure</a> - this is a draft dataset, used for visualisation in <a href="https://global.infrastructureresilience.org/">https://global.infrastructureresilience.org/</a> but not otherwise reviewed or published.<br><br>If you use this, please cite: Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI 10.1029/2020EF001616<br><br>This is shared under a CC0 1.0 Universal Public Domain Dedication (CC0 1.0) When using ISIMIP data for your research, please appropriately credit the data providers, e.g. either by citing the DOI for the dataset, or by appropriate acknowledgment.<br><br>Annual probability of drought (soil moisture below a baseline threshold) or extreme heat (temperature and humidity-based indicators over a threshold) events on a 0.5° grid. 8 hydrological models forced by 4 GCMs under baseline, RCP 2.6 & 6.0 emission scenarios. Current and future maps in 2030, 2050 and 2080.<br><br>The ISIMIP2b climate input data and impact model output data analyzed in this study are available in the ISIMIP data repository at ESGF, see <a href="https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=input">https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=input</a> and <a href="https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=output">https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=output</a>, respectively. More information about the GHM, GGCM, and GVM output data is provided by Gosling et al. (2020), Arneth et al. (2020), and Reyer et al. (2019), respectively.<br><br>Event definitions are given in Lange et al, table 1. Land area is exposed to drought if monthly soil moisture falls below the 2.5th percentile of the preindustrial baseline distribution for at least seven consecutive months. Land area is exposed to extreme heat if both a relative indicator based on temperature (Russo et al 2015, 2017) and an absolute indicator based on temperature and relative humidity (Masterton & Richardson, 1979) exceed their respective threshold value.</p> <p>Population exposure is calculated as annual expected population directly exposed to the occurrence of extreme heat or drought events, assuming any population directly within the footprint of an event is exposed, but not otherwise taking any other risk-mitigating or -propagating factors into account.</p> <p>Population is held constant at 2020 levels, using the JRC GHSL GHS-POP R2023A release, which can be cited as Schiavina M., Freire S., Carioli A., MacManus K. (2023) GHS-POP R2023A - GHS population grid multitemporal (1975-2030).European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe"> http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:<a href="https://doi.org/10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE">10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE</a> and for the concept and methodology, Freire S., MacManus K., Pesaresi M., Doxsey-Whitfield E., Mills J. (2016) Development of new open and free multi-temporal global population grids at 250 m resolution. Geospatial Data in a Changing World; Association of Geographic Information Laboratories in Europe (AGILE), AGILE 2016</p> <h3>Updates</h3> <p>2024-06-11: New draft with corrected aggregate population, created using https://github.com/nismod/isimip-exposure/tree/fcfbeff6778f697a5a470ac20dd912fd426a7f98</p>
GITM Simulation Results for Extreme Events
<p>GITM Simulation Results for Extreme Events. This data files are prepared for the paper titled 'Possible Influence of Extreme Magnetic Storms on the Thermosphere in the High Latitudes' by Y. Deng et al (2017).</p> <p>In the filenames, 'Run1' represents a standard GITM run, 'Run2' represents a GITM run with both strong solar and geomagnetic forcing (see manuscript for details), and 'Run3' represents a GITM run with strong geomagnetic forcing.</p>
Model calibration and streamflow simulations for the extreme drought event of 2018 on the Rhine River Basin using WRF-Hydro 5.2.0
<p>This repository contains the data and software used for the study "Model calibration and streamflow simulations for the extreme drought event of 2018 on the Rhine River Basin using WRF-Hydro 5.2.0." The data and model were used to simulate an extreme low-water event in the River Rhine Basin.</p> <p>This archive contains the following:</p> <p><strong>wrf_hydro_nwm_public-5.2.0.tar.gz</strong>: Model code of the hydrological model WRF-Hydro. (Source: https://ral.ucar.edu/projects/wrf_hydro/model-code)</p> <p><strong>ERA5_Dataset_2016_2018.zip</strong>: ERA5 Reanalysis data and modified for the domain of the project, it includes all the mandatory variables necessary to run the model (Source:https://doi.org/10.24381/cds.bd0915c6)</p> <p><strong>GRDC_HydrologicalData_Rhine_Basin.zip</strong>: Daily stremaflow from gauges in the Rhine River from the Global Runoff Dataset Center (GRDC) (Source: https://portal.grdc.bafg.de/applications/public.html?publicuser=PublicUser#dataDownload/Stations)</p> <p><strong>RegriddedFormatData.sh: </strong>Bash file to create the the input data for the regridding scripts using the ERA5 data. (Source: Campoverde, A.)</p> <p><strong>ESMFregrid_NLDAS.tar.gz</strong>: Earth System Modeling Framework (ESMF) Regridding Scripts (Source: https://ral.ucar.edu/projects/wrf_hydro/pre-processing-tools#regridding2)</p> <p><strong>wrf_hydro_arcgis_preprocessor-5.2.0.tar.gz</strong>: ArcGIS tools for preparing WRF-Hydro Routing Grids. (Source: https://ral.ucar.edu/projects/wrf_hydro/pre-processing-tools#preprocessing1)</p> <p><strong>eu_dem_3s.zip</strong>: Digital Elevation Model for Europe from HydroSHEDS - 3" (~90m) (Source: https://www.hydrosheds.org/hydrosheds-core-downloads)</p> <p><strong>S2GLC_Europe_2017_v1.2_grey.zip</strong>: Land Cover data used to create spatially distributed hydrological parameters from WRF-Hydro (RETDEPRTFAC, REFKDT, SLOPE) (Source: https://s2glc.cbk.waw.pl/extension)</p> <p><strong>WRF_Hydro_Setup_withLakeScheme.zip</strong>: Necesary files for WRF-Hydro model when considering the Lake Scheme. (Source: Campoverde, A.)</p> <p><strong>WRF_Hydro_Setup_withoutLakeScheme.zip</strong>: Necessary files for the WRF-Hydro model when <strong>not</strong> considering the Lake Scheme. (Source: Campoverde, A.)</p> <p><strong>ERA5_Land_Soil_Moisture_Dataset_2016_2018.zip</strong>: ERA5 Land data for the domain of the project includes the values of soil moisture for the period 2016-2018. (Source:https://doi.org/10.24381/cds.e2161bac)</p> <p>With this repository, we aim to provide to the comminity the necessary tools to replicate the experiments that led to the calibration of WRF-Hydro and the results of our study. </p> <p> </p>
Interior Crisis Route to Extreme Events in a Memristor-Based 3D Jerk System
<p>These are experimental time series for an electronic model of extreme event generation. They are provided to support the replication of the results reported in the associated publication, as well as any further public-domain academic research in the field of neural dynamics, nonlinear electronic circuits, and related aspects, in compliance with the specified license terms and all applicable legal clauses.</p> <p>The following reference must be cited when using these data:<br>Vivekanandan G, Kengne LK, Chandrasekhar D, Fozin TF, Minati L. Interior Crisis Route to Extreme Events in a Memristor-Based 3D Jerk System. International Journal of Bifurcation and Chaos, epub ahead of print 2024, DOI https://doi.org/10.1142/S021812742450161X </p> <p>L.M. gratefully acknowledges the support of the “Hundred Talents” program of the University of Electronic Science and Technology of China, the “Outstanding Young Talents Program (Overseas)” program of the National Natural Science Foundation of China, and the talent programs of the Sichuan province and Chengdu municipality. G.V. and D.C. were supported by the Center for Nonlinear Systems, Chennai Institute of Technology (CIT), India vide funding number CIT/CNS/2021/RD/022.</p>
Resulting videos from the article: A Novel Technique for the Extraction of Dynamic Events in Extreme Ultraviolet Solar Images
<p>Videos called 'MFM PCP DMD ...' correspond to Figure 5 in the mentioned article. They show comparison of MFM, PCP, and DMD algorithms. Upper images in each year correspond to the separated background matrix, the lower images correspond to matrix of dynamic component. The videos cover 1.7 hours of observations starting at 06:00:00 UTC on 2011 June 7, 16:50:00 UTC on 2012 April 16 and 18:30:11 UTC on 2014 October 2.<br><br>The other videos correspond to Figure 7 in the article. They compare the results from PCP algorithm applied to the 30.4 and 17.1 nm AIA bandpasses. The videos starting at 16:50:00 UTC on 2012 April 16 and 18:30:11 UTC on 2014 October 2, respectively, and cover 1.7 hours of observations.</p>
Ecological resilience and resistance to extreme weather events - review data
<p>Extreme weather events (EWEs) are expected to increase in stochasticity, frequency, and intensity due to climate change. Documented effects of EWEs, such as droughts, hurricanes, and temperature extremes, range from shifting community stable states to species extirpations. To date, little attention has been paid to how populations resist and/or recover from EWEs through compensatory (behavioural, demographic or physiological) mechanisms; limiting the capacity to predict species responses to future changes in EWEs. Here, we systematically reviewed the global variation in species' demographic responses, resistance to, and recovery from EWEs across weather types, species, and biogeographic regions. Through a literature review and meta-analysis, we <span>tested the prediction that population abundance and probability of persistence will decrease in populations after an EWE and how compensation affects that probability. Across 524 species population responses to EWEs reviewed (27 articles), we noted large variation in responses, such that, on average, the effect of EWEs on population demographics was not negative as predicted. The majority of species populations (80.4%) demonstrated compensatory mechanisms during events to reduce their deleterious effects. However, for populations that were negatively impacted, the demographic consequences were severe. Nearly 20% of the populations monitored experienced declines of over 50% after an EWE</span>, and 6.8% of populations were extirpated. Population declines were reflected in a <span>reduction in survival. Further, resilience was not common, as 80.0% of populations that declined did not recover to before EWE levels while monitored. </span>However, average monitoring time was only two years with over a quarter of studies tracking recovery for less than the study species generation time. We conclude that EWEs have positive and negative impacts on species demography, and this varies by taxa. Species population recovery over short time intervals is rare, but long-term studies are required to accurately assess species resilience to current and future events.</p>
Extreme precipitation event over Henan in July 2021
<p>HYSPLIT-generated trajectories associated with extreme precipitation evnets over Henan in July 2021 and August 1975.</p>
The observed and simulated datasets of "21·7" Henan extremely heavy rainfall event
<p>The uploaded files are the observed and simulated datasets of “21·7” Henan extremely heavy rainfall event, including OTT disdrometers, Radar, national and regional gauges, and WRFOUT files. </p>
Date of extreme precipitating events over Burkina Faso
<p>The present ASCII file provides the dates of the daily extreme precipitating events (EPEs) for each of the 15 1°x1° pixels covering Burkina Faso as detected by Sanogo et al. (2022). The original raingauge dataset was provided by the Burkinabe National Meteorological Agency (Agence Nationale de la Météorologie). It consists in 142 raingauge stations covering most of Burkina Faso and providing daily rainfall accumulation over the period 1995–2016. Raingauge data were aggregated in 1°x1° pixels, and only pixels with at least five stations were considered. EPEs are detected for each of these pixels as the days when rainfall exceeds the 99th all-day percentile computed over the 1995-2016 period (Note Sanogo et al. 2022 used the period 2001-2013 for consistency with other datasets). See Sanogo et al. 2022 for details.</p> <p>The present dates serve as a basis for the composite analysis in Peyrillé et al. (2023).</p> <p>The ASCII file is organised as follows: one line per event, with latitude (degrees north), longitude (degrees east), year, month and day separated by spaces. Latitude and longitude refer to the center of each 1°x1° pixel.</p> <p>Peyrillé, P., R. Roehrig, and S. Sanogo, 2023: Tropical Waves are key drivers of Extreme Precipitation Events in the Central Sahel. Submitted to Geophysical Research Letters.</p> <p>Sanogo, S., P. Peyrilé, R. Roehrig, F. Guichard, F., and O. Ouedraogo, 2022: Extreme precipitating events in satellite and rain gauge products over the Sahel. Journal of Climate, 35(6), 1915– 1938. <a href="https://doi.org/10.1175/JCLI-D-21-0390.1">https://doi.org/10.1175/JCLI-D-21-0390.1</a></p>
Effects of winter wheat irrigation on local climate and extreme events over the North China by using the high resolution non-hydrostatic regional climate model
<p>The control and irrigation simulation dataset from RegCM4.7.</p>
Particle tracking simulations of marine macro-litter in Rostock during extreme events
<p>The simulations show the passive near-bottom transport of macro-litter during extreme events (HanseSail in Rostock, Northern Germany). The simulations are based on a high resolution 3D flow model of the Warnow estuary.</p>
Data from: Can physiological engineering/programming increase multi-generational thermal tolerance to extreme temperature events?
Open the record for dataset details and reuse information.
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.