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 event”
Species diversity and plant dominance influence grassland stability in response to extreme climatic events and anthropogenic drivers across three LTER sites: Cedar Creek, Konza Prairie, and Kellogg Biological Station, 1982-2023.
The data in this package is associated with the analysis for a manuscript titled "Multiple community properties drive ecosystem resistance and resilience to extreme climate events across mesic grasslands". The files include compiled data on plant biomass production, species abundance, experimental treatments, extreme climate event values, and calculated diversity and stability measures from grassland plots in experiments at CDR, KBS, and KNZ LTER sites.
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>
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>
Semi-automatic and manual shallow landslide inventories of two extreme rainfall events.
<p>This dataset contains the polygons of automatic ( PL) and manually (ML) based shallow landslides related to two extreme rainfall events. In KML format, the dataset can be visualized on GIS software or Google Earth.</p><p>With more details, it is possible to find:</p><ul><li>AOI_2016: The study area of the extreme rainfall of November 2016, Tanerello and Arroscia Valleys NW Italy.</li><li>The 2016_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in November 2016</li><li>The 2016_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in November 2016</li><li>AOI_2019_large: The study area of the extreme rainfall of October 2019 NW Italy.</li><li>AOI_2019: The testing area of the extreme rainfall of October 2019, Gavi Area NW Italy.</li><li>The 2019_PL_all: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (whole Study area)</li><li>The 2019_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (Gavi test area)</li><li>The 2019_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in October 2019</li></ul><p>GEE_Script: A list of codes used in Google Earth Engine to produce NDVI time series or averaged NDVI on some sample studied areas are reported in the attached PDF. The code may be pasted and copied to the Google Earth Engine console. </p><p>The codes (if an account on Google Earth Engine is active) may be reached directly from the following URLs: </p><p><strong>Script 1. </strong>NDVI time series of some sampled areas to select the best pair of images for the PL creation (Tanarello and Arroscia Valley and GAVI AOIs; Fig. 16 of the paper). Link to GEE: <a href="https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true">https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true</a></p><p><strong>Script 2.</strong> sampled NDVI time series from different intersection cases for the Tanarello and Arroscia Valley study area (2016 Event). Link to GEE: <a href="https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true">https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true</a></p><p><strong>Script 3. </strong>Sampled NDVI time series from different land-use cases for the Gavi study area (2019 Event). Link to GEE: <a href="https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true">https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true</a></p><p><strong>Script 4.</strong> Multi-temporal-averaged NDVIvar Link to GEE Script: <a href="https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true">https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true</a> for the whole Gavi study area (2019 flood) and <a href="https://code.earthengine.google.com/89e1c0a1361860cd407b7e6ab8bb95de?noload=true">https://code.earthengine.google.com/a3390b262cef1b5f42837c88d8791b5b?noload=true</a> for the entire Arroscia-Tanarello study area</p><p>The full description of the methodology can be found in the paper of Notti et al., 2023</p><p>Notti, D., Cignetti, M., Godone, D., and Giordan, D.: Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine, Nat. Hazards Earth Syst. Sci., 23, 2625–2648, <a href="https://doi.org/10.5194/nhess-23-2625-2023">https://doi.org/10.5194/nhess-23-2625-2023</a>, 2023</p>
Extreme Weather Event database over Aotearoa New Zealand
<p><strong>The Aotearoa New Zealand (ANZ) Extreme Weather Events (EWE) database </strong>(EWE_database_V1.0.0.xlsx)<strong> is a comprehensive record of extreme weather events in ANZ. The events listed in this database have been carefully assessed and categorized based on their meteorological significance, considering their rarity and whether they broke records or triggered official weather warnings. Some of the metrics used to classify each event rely on subjective judgment and expert opinions. The database captures meteorologically significant events, including those that have caused substantial damage to properties or led to casualties, and, in some cases, includes supplementary information about their socioeconomic impacts. The information in the EWE database is primarily sourced from the Meteorological Service of New Zealand Ltd (MetService) and the National Institute of Water and Atmospheric Research (NIWA). Additional impact data have been added from various media sources, with insured loss data for some events sourced from the Insurance Council of New Zealand (ICNZ).</strong></p> <p>Note - For more information about the database and the other additional files, please look into the Metadata (Metadata_EWE_V.1.0.0.docx) and the supplementary document (Supplementary document on EWE_V.1.0.0.docx).</p>
Global dry and hot extreme events detection
<p>Workflow for the global detection of dry and hot extreme weather events. ERA5 is the ECMWF Reanalysis of the climate. PET is potential reference evapotranspiration. PEI is the daily difference between precipitation and evapotranspiration averaged over the preceding days (here 30, 90 and 180). Data cubes are stored in zarr format. Statistics are saved in a csv table.</p>
Dataset: Employing the Generalized Pareto Distribution to Analyze Extreme Rainfall Events on Consecutive Rainy Days in Thailand's Chi Watershed: Implications for Flood Management
<p>This data set is used to employing the generalized Pareto distribution to analyze extreme rainfall events on consecutive rainy days in Thailand's Chi watershed. A case of implications for flood management. Observational raw data from Thailand were provided by the Climate Information Services (CIS) at https://www.tmd.go.th/cis/main.php.</p>
Data and code for "Extreme and compound ocean events are key drivers of projected low pelagic fish biomass"
<p>This repository provides the data and code for the paper "Extreme and compound ocean events are key drivers of projected low pelagic fish biomass". Almost all data required to produce the figures in this study are provided. However, not all raw data are provided, because of too large file sizes. For more information, please contact natacha.legrix@unibe.ch</p> <p>In Version 2, an error has been corrected in the computation of the grid cell area, which significantly affected values in Fig. A1.</p>
Extreme wildfire events analysis for dry pyrocloud hypothesis
<p>Dataset for EWE to test the dry pyrocloud hypothesis. The files are Excel files from 182 extreme wildfires (EWE_globla.xlsx). From those fires, we extract extreme fire spread events to use in the research when we can reconstruct the vertical profile from the ERA5 ECMWF reanalysis data (fires_verticalprofile.xlsx) and the accurate rate of spread from observations (fire_spread_events.xlsx).</p> <p>The dataset contains two videos ilustratingthe concept of dry pyrocloud</p> <p>The dataset contains a variable explanatory document ('Table of variables on the dataset. docx')</p> <p>The dataset contains a README file to guide the use of code contained in the Demo ZIP</p> <p>The demo ZIP contains the phyton codes to obtain the fire-spread-events variables and a DEMO fire to test the codes. The fire is the Santa Coloma wildfire from 24 and 25 of July 2021 in Catalonia, SPAIN.</p> <p> </p>
Supplementary Material: Climate-sensitive disease outbreaks in the aftermath of extreme climatic events: a scoping review
<p><strong>Supplemental experimental procedures</strong></p> <p><em>General Information</em></p> <p>Here we provide the data extraction of the studies retrieved for the scoping review "Climate-sensitive disease outbreaks in the aftermath of extreme climatic events" following PRISMA-ScR guidelines. Data were extracted for the following variables: title, first author, year of publication, country/region studied, extreme climate event, extreme climate event name (tropical cyclones are often named e.g. Typhoon Haiyan), Index used to measure climate anomaly, extreme climate event definition, text description of extreme climate event, disease, outbreak definition, time period of the study, data source, baseline/reference period, study design, statistics, outcome, outcome quantification, outbreak risk, qualitative description of extreme climate event and outbreak risk, time lag. Outcome was defined as either disease cases or incidence. </p>
Extreme hydrometeorological events, a challenge for gravimetric and seismology networks
<p>“Data supporting the paper published by Earth's Future: "Extreme hydrometeorological events, a challenge for gravimetry and seismological networks" by Van Camp, de Viron, Dassargues, Delobbe, Chanard, and Gobron, doi: 10.1029/2022EF002737, 2022.</p> <p>The data contains:</p> <p>1) A NETCDF mvc_data_EF.nc file, and the Python mvc_data_EF.py code for reading it. The nc file contains the time series from and around the Membach station, Belgium, as recorded during the July 2021 flood of the Vesdre River. Time in second since 2021-01-01 00:00:</p> <p>-Gravity [nm/s²]: from the superconducting gravimeter (after correcting for tidal and atmospheric pressure effects),</p> <p>To convert it into mm water: use factor -1/0.39 [mm/nm/s²]</p> <p>-Seismology [counts]: from the broadband Guralp CMG-3ESP seismometer.</p> <p>Calibration factor: 838.86 [count/µm/s]</p> <p>-Rain [mm water]: data inferred from the weather radar</p> <p>-Flow [m³/s]: water entering the Eupen reservoir</p> <p>2) An ASCII file SG_C021.TSF contains the complete recording of the superconducting gravimeter GWR#C021, since 1995-10-01. The data are corrected for polar motion, tidal, and atmospheric pressure effects (local admittance of -3.3 nm/s²/hPa), and instrumental drift. This drift is determined by using 323 absolute gravity measurements performed at the Membach station by the FG5#202 gravimeter since January 1996.</p> <p>3) PDF Document "<em>Supplement 1 Other seismic spectra during high flow events</em>" providing moving window spectra of seismic recordings during other significant floods in the Vesdre Valley</p> <p>4) PDF document "<em>Supplement 2_GRACE_GLDAS_GNSS</em>" providing the observations from GNSS and GRACE, and the predictions of the GLDAS hydrological model during the July 2021 event.</p>
DATASET - Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals
<p>Data for experiments presented in the paper "Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals" </p>
Mehrabi et al. 2022. Research priorities for global food security under extreme events. Supplementary data and code.
<p>Data and script for reproducing the final results shown in Mehrabi et al., Research priorities for global food security under extreme events, One Earth (2022), https://doi.org/10.1016/j.oneear.2022.06.008.</p> <p>Simply download and read the Mehrabi2022_EEGFS.pdf or the Mehrabi2022_EEGFS.Rmd file from which it was created.</p>
Extreme weather events threaten biodiversity and functions of river ecosystems: dataset for conducting the meta-analysis
<p>This repository contains the code and dataset to replicate the meta-analysis conducted by Sabater et al. entitled "Extreme weather events threaten biodiversity and functions of river ecosystems: evidence from a meta-analysis"</p> <p>Metadata:</p> <p>- metaanalysis_GlobalEvidenceRivers_Rscript.R - R Script to conduct the meta-analysis</p> <p>- structural_resp.csv - table with data to perform the species richness, density, and biomass meta-analysis. It includes the mean, SD (or SE), and sample number of the studies included in the meta-analysis, as well as information on the paper authors, year of publication, and type of study (experimental or observational). It also includes co-variates and the author who subtracts the information from the paper.</p> <p>- functional_resp.csv - table with data to perform the primary productivity, respiration, and decomposition meta-analysis. It includes the mean, SD (or SE), and sample number of the studies included in the meta-analysis, as well as information on the paper authors, year of publication, and type of study (experimental or observational). It also includes co-variates and the author who subtracts the information from the paper.</p> <p>-refMap.csv - Geographical information of the papers included in the meta-analysis.</p>
Comparing climatic suitability and niche distances to explain populations responses to extreme climatic events
<p><span>Habitat suitability calculated from Species Distribution Models (SDMs) has been used to assess population performance, but empirical studies have provided weak or inconclusive support to this approach. Novel approaches measuring population distances to niche centroid and margin in environmental space have been recently proposed to explain population performance, particularly when populations experience exceptional environmental conditions that may place them outside of the species niche. Here, we use data of co-occurring species' decay, gathered after an extreme drought event occurring in the SE of the Iberian Peninsula which highly affected rich semiarid shrubland communities, to compare the relationship between population decay (mortality and remaining green canopy) and (1) distances between populations' location and species niche margin and centroid in the environmental space, and (2) climatic suitability estimated from frequently used SDMs (here MaxEnt) considering both the extreme climatic episode and the average reference climatic period before this. We found that both SDMs-derived suitability and distances to species niche properly predict populations performance when considering the reference climatic period; but climatic suitability failed to predict performance considering the extreme climate period. In addition, while distance to niche margins accurately predict both mortality and remaining green canopy responses, centroid distances failed to explain mortality, suggesting that indexes containing information about the position to niche margin (inside or outside) are better to predict binary responses. We conclude that the location of populations in the environmental space is consistent with performance responses to extreme drought. Niche distances appear to be a more efficient approach than the use of climate suitability indices derived from more frequently used SDMs to explain population performance when dealing with environmental conditions that are located outside the species environmental niche. The use of this alternative metrics may be particularly useful when designing</span><span> conservation measures to mitigate impacts of shifting environmental conditions.</span></p>
Data from: Can extreme climatic events induce shifts in adaptive potential? A conceptual framework and empirical test with Anolis lizards
<p>Multivariate adaptation to climatic shifts may be limited by trait integration that causes genetic variation to be low in the direction of selection. However, strong episodes of selection induced by extreme climatic pressures may facilitate future population-wide responses if selection reduces trait integration and increases adaptive potential (i.e., evolvability). We explain this counter-intuitive framework for extreme climatic events in which directional selection leads to increased evolvability and exemplify its use in a case study. We tested this hypothesis in two populations of the lizard <em>Anolis scriptus</em> that experienced hurricane-induced selection on limb traits. We surveyed populations immediately before and after the hurricane as well as the offspring of post-hurricane survivors, allowing us to estimate both selection and response to selection on key functional traits: forelimb length, hindlimb length, and toepad area. Direct selection was parallel in both islands and strong in several limb traits. Even though overall limb integration did not change after the hurricane, both populations showed a non-significant tendency toward increased evolvability after the hurricane despite the direction of selection not being aligned with the axis of most variance (i.e., body size). The population with comparably lower between-limb integration showed a less constrained response to selection. Hurricane-induced selection, not aligned with the pattern of high trait correlations, likely conflicts with selection occurring during normal ecological conditions that favor functional coordination between limb traits, and would likely need to be very strong and more persistent to elicit a greater change in trait integration and evolvability. Future tests of this hypothesis should use G-matrices in a variety of wild organisms experiencing selection due to extreme climatic events. </p>
The Meltwater Pulse1A Triggered an Extreme Cooling Event: Evidence From Southern China. Meltwater Pulse Cooling Event (MCE). Winter temperature data during the last deglacial of Huguangyan Maar lake, Surface water temperature and seasonal diatom assemblage data of Huguangyan and Yunlong Lake.
<p>Here we present results of The lake averaged monthly mean surface water temperature over the period from September 2013 to August 2015 from Yunlong Tianchi Lake(YL)(25°52.2′N, 99°16.8′E, altitude: 2551 m a.s.l), southwestern China. The dataset include sediment trap main diatom percentages over the period from September 2013 to August 2015 from YL. Lake water temperature profiles at different depths (1, 3, 6, 9, 11, 13, 16 m) from November 2008 to May 2009 in Huguang Maar Lake (HML)(21°9′N, 110°17′E), Southern China. AMS radiocarbon dates of plant remains and bulk sediment samples for Huguangyan Maar Lake over the last ~17 cal ka BP. The main diatom assemblage percentages (%) from 17 to 10 cal ka BP at Huguangyan Maar Lake. Diatom-based reconstruction of winter temperature (WT) from 17 to 10 cal ka BP at Huguangyan Maar Lake.</p>
Figure 2 in Climate variability of extreme air temperature events in the Eastern Black Sea
Figure 2. Changes in the mean monthly air temperature anomalies at the surface (relative to seasonal variability) smoothed by annual (orange) and eight-year (violet) gliding averaging in the eastern part of the Black Sea (42° - 45°N, 37° - 42°E). Their linear trend is shown by black line and the accumulated sum of anomalies after removing the linear trend – by green line. Average values of anomalies for warm and cold half-year are marked by red and blue dots respectively.
Figure 1 in Climate variability of extreme air temperature events in the Eastern Black Sea
Figure 1. Changes in mean monthly air temperature at the surface (red) and their linear trend (blue) in the eastern part of the Black Sea (42° - 45°N, 37° - 42°E).
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.