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3,113 results for “extremes”
FLAME Deliverable 8 - Extreme Wildfires Dataset (D3.1)
<p>This dataset comprises extreme wildfire growth events that occurred in Greece during the 2002 - 2020 period. The data are provided in GeoPackage (.gpkg) format. The accompanying report documents information concerning the methods used for deriving the dataset. </p>
Datasets for the article "The temperature and density of a solar flare kernel measured from extreme ultraviolet lines of O IV"
<p>This entry contains the following files:</p><p>20120309_030933_kernel_fe8_shift.save<br>20120309_030933_kernel_fe8_shift_fits.txt<br>20110814_055342_qs_offlimb_si10.save<br>20110814_055342_qs_offlimb_si10_fits.txt</p><p>The .save files are IDL save files that can be restored into IDL using the restore command.</p><p>The 20120309 save file contains:</p><p>swspec - An IDL structure containing a 1D spectrum of the flare kernel for the EIS short wavelength (SW) channel. The format is that returned by eis-mask-spectrum.pro.<br>lwspec - As above, but for the long-wavelength (LW) channel.<br>map185 - An IDL map structure containing the Fe VIII 185.21 image that was used to select the flare kernel.<br>mask185 - An IDL structure containing the pixel mask that is used as input to eis-mask-spectrum.pro.</p><p>The Gaussian fits to the spectra (as performed with the routine spec-gauss-eis.pro) are stored in 20120309_030933_kernel_fe8<i>s</i>hift_fits.txt. This file can be read with read_line_fits.pro in Solarsoft.</p><p>The 20110814 dataset is used to obtain an off-limb coronal spectrum for calibration purposes. The save file contains:</p><p>swspec - An IDL structure containing a 1D spectrum of the off-limb region for the EIS SW channel. The format is that returned by eis-mask-spectrum.pro.<br>lwspec - As above, but for the LW channel.<br>map - An IDL map structure containing the Si X 272 image that was used to select off-limb region.<br>mask - An IDL structure containing the pixel mask that is used as input to eis-mask-spectrum.pro.</p><p>The Gaussian fits to the spectra (as performed with the routine spec-gauss-eis.pro) are stored in 20110814_055342_qs_offlimb_si10_fits.txt. This file can be read with read_line_fits.pro in Solarsoft. </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p>
Extreme Variability Quasars Catalog from SDSS DR16Q
<p>We provide all 20,069 available spectral measurements of 14,012 EVQs selected in <a href="https://doi.org/10.3847/1538-4357/ac3828">Ren et al. 2022</a>. Repeated observed spectra of a same EVQ will have the same SDSS NAME with different spectral info (PLATE,MJD, and FIBER). We include the SPECPRIMARY flag to indicate if the spectrum is the best observation of this object. The unmeasurable parameters are set to -9999. The errors are obtained from 100 iterations of Monte Carlo simulation.</p> <p>We extend my heartfelt gratitude to our Data Editor of the AAS Journal, August Muench, for his invaluable assistance in the data verification and MRT format compilation of this dataset. His expertise and meticulous attention to detail were instrumental in enhancing the quality and accessibility of this work.</p> <p>We note that, in most cases, all measurements in a same line complex would be either all null or all valid, however, there exist some exceptions.</p> <p>In our dataset, there are generally two scenarios lead a measurement to the null value (-9999).</p> <ol> <li>The most frequent case is due to the spectral coverage. When a emission line region is not measureable, we will set the whole relavent value to null.</li> <li>Besides, there are some exceptions that we do have a real fitting but still can not give a reasonable measurement for some specific elements. <ol> <li>For any line component with FLUX==0, we reserve the FLUX and EW of this line to 0 but change the rest measurements (FWHM, PEAK, etc.) to -9999.</li> <li>For double peak broad component, we set the line FWHM/FWQM/FW10M and their corresponding center shifts (Z50/Z25/Z10) to -9999</li> </ol> </li> <li>In addition, since the systematic shift is relatively minor feature of a line, spectra with very low S/N ratio can not well constrained the line shape. User could find a bunch of lines with Z50/Z25/Z10 == 0. Nevertheless, in those cases, their error would be extremely high indicating that measurements could be unreliable.</li> </ol>
Data from: Extreme heat reduces host and parasite performance in a butterfly-parasite interaction
<p>Environmental temperature fundamentally shapes insect physiology, fitness, and interactions with parasites. Differential climate warming effects on host versus parasite biology could exacerbate or inhibit parasite transmission, with far-reaching implications for pollination services, biocontrol, and human health. Here, we experimentally test how controlled temperatures influence multiple components of host and parasite fitness in monarch butterflies (<em>Danaus plexippus</em>) and their protozoan parasites <em>Ophryocystis elektroscirrha</em>. Using five constant temperature treatments spanning 18-34°C, we measured monarch development, survival, size, immune function, and parasite infection status and intensity. Monarch size and survival declined sharply at 34°C, as did infection probability, suggesting that hot temperatures decrease both host and parasite performance. The lack of infection at 34°C was not due to greater host immunity or faster larval development but could instead reflect the thermal limits of parasite invasion and within-host replication. In the context of ongoing climate change, our experiment suggests that temperature increases above the upper thermal range will reduce the fitness of both monarchs and their parasites, with lower infection rates potentially mitigating the impact of extreme heat on future monarch abundance and distribution.</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>
Rapid diversification of gray mangroves (Avicennia marina) driven by geographic isolation and extreme environmental conditions in the Arabian Peninsula
<p><span>Biological systems occurring in ecologically heterogeneous and spatially discontinuous habitats provide an ideal opportunity to investigate the relative roles of neutral and selective factors in driving lineage diversification. The gray mangroves (<em>Avicennia marina</em>) of Arabia occur at the northern edge of the species' range and are subject to variable, often extreme, environmental conditions, as well as to historic large fluctuations in habitat availability and connectivity resulting from Quaternary glacial cycles. Here, we analyze fully sequenced genomes sampled from 20 locations across the Red Sea, the Arabian Sea, and the Persian/Arabian Gulf (PAG) to reconstruct the evolutionary history of the species in the region and to identify adaptive mechanisms of lineage diversification. Population structure and phylogenetic analyses revealed marked genetic structure and highly supported clades among and within the seas surrounding the Arabian Peninsula. Demographic modeling showed times of divergence consistent with recent periods of geographic isolation and low marine connectivity during glaciations, revealing the presence of (cryptic) glacial refugia in the Red Sea and the PAG. Significant migration was detected within the Red Sea and the PAG, and across the Strait of Hormuz to the Arabian Sea, suggesting gene flow upon secondary contact among Arabian mangrove populations. Genetic‐environment association analyses revealed high levels of adaptive divergence and detected signs of multi-loci local adaptation driven by temperature extremes and hypersalinity. These results support a process of rapid diversification resulting from the combined effects of historical factors and ecological selection and reveal mangrove peripheral environments as relevant drivers of lineage diversity.</span></p>
Pneumatic elastostatics of multi-functional inflatable lattices: Realization of extreme specific stiffness with active modulation and deployability
<p>Supplementary codes and data: Elastostatics of multi-functional inflatable lattices: Realization of extreme specific stiffness with active modulation and deployability</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>
Climate variability can outweigh the influence of climate mean changes for extreme precipitation under global warming
<p>Dataset used to analyize role of climate variability</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>
Post‐processed data and analysis codes for the research "Significant reduction of potential exposure to extreme marine heatwaves by achieving carbon neutrality"
<p>[Earth's Future] Oh et al. "Significant reduction of potential exposure to extreme marine heatwaves by achieving carbon neutrality"</p> <p>1. Information for Raw datasets<br>- The data of eight global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) can be accessed at https://esgf-node.llnl.gov/search/cmip6/, <br> and can also be accessed in Eyring et al. (2016). <br>- The NOAA OISST high resolution dataset can be obtained in Reynolds et al. (2007) or via https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html. <br>- The five ocean mask dataset can be obtained from https://reccap2-ocean.github.io/regions/. </p> <p>2. Information for Software<br>- The raw data in this study were analyzed using Fortran 90, R version 4.0.3, and Grads version 2.2.1.<br>- The Fortran 90 can be accessed at https://www.intel.com/content/www/us/en/developer/articles/tool/oneapi-standalone-components.html#fortran. <br>- The R version 4.0.3 is available from https://cran.r-project.org/bin/windows/base/old/4.0.3/. <br>- The Grads version 2.2.1 can be downloaded from http://cola.gmu.edu/grads/downloads.php.</p> <p>3. Information for Post-Processed data and Codes used in this work.<br>Please find each folder and the relevant post-processed dataset and codes.</p>
Future Projections of Temperature Extremes and Urban Heat Island in Paris using Deep Learning
<p>Future projections of 2-meter maximum and minimum temperature and land surface temperature in Paris, France, using Deep Learning, under four Shared Socioeconomic Pathways. ERA5 and GCM ensemble data at their original resolution are also included. The DL (Convolutional Neural Network) model architecture and trained weights are also available. The Python script to generate the boxplots of the future projections is also included.</p>
Global LAke Surface water Temperature (GLAST): Compound thermal extremes in lakes
<p>This database contains daily maximum temperature, daily minimum temperature, and daily mean temperature for 92,245 lakes globally from 1981 to 2020. These daily time series were derived from hourly simulation data.</p> <p>The database also includes annual statistics of six types of thermal extreme events calculated from the daily lake temperature time series:</p> <ul> <li>daytime hot extreme events (hot day–mild night)</li> <li>nighttime hot extreme events (mild day–hot night)</li> <li>compound hot extreme events (hot day–hot night)</li> <li>daytime cold extreme events (cold day–mild night)</li> <li>nighttime cold extreme events (mild day–cold night)</li> <li>compound cold extreme events (cold day–cold night)</li> </ul> <p> </p> <p>The annual statistics provided for these events include metrics such as frequency, intensity, duration, and total days. Additionally, annual statistics of extreme air temperature events over the lakes are included.</p> <p>Details about the hourly-scale lake temperature simulation methodology can be found in the paper <em>"Global lakes are warming slower than surface air temperature due to accelerated evaporation"</em> (Tong et al., 2023, Nature Water). Definitions and calculation methods for thermal extreme events in lakes and atmosphere are provided in <em>"Day-night compound thermal extremes in lakes"</em> (Tong et al., 2025).</p> <p>For detailed information about the contents of each data file, please refer to the accompanying <strong>readme.docx</strong> file.</p> <p>For more datasets on global aquatic environments, please visit the official website of the Global Aqua Remote Sensing (GARS) Laboratory, led by Prof. Lian Feng: <a href="https://garslab.com/?cat=1">https://garslab.com/?cat=1</a>.</p>
Data accompanying publication "High-Income Groups Disproportionately Contribute to Climate Extremes Worldwide."
<p>This dataset accompanies the publication "How High-Income Groups Disproportionately Contribute to Climate Extremes Worldwide." </p> <p>In our study, we combine income-based emission inequality data with an emulator-based modeling framework to thoroughly study the link between emissions of individual, wealthy emitter groups and climate extremes worldwide. Specifically, we assess individual contributions to current global temperature levels and systematically attribute changes in regional monthly heat and drought extremes across the globe.</p> <p>We focus on emissions of the top 10/1/0.1 wealthiest individuals globally and in the US, the EU27, India and China. The dataset contains results for 1-in-50/100/10'000 year extremes at grid-cell level and whenever imapcts are aggregated by region we refer to the regionmask AR6 regions. </p> <p>The file contents are the following:</p> <ol> <li>Attributed_GMT.csv: attributed global mean temperature levels by emitter group</li> <li>tas_frequency_hot.nc, spei_frequency_dry.nc, spi_frequency_dry.nc: attributed changes in the frequency of extreme events for extreme heat (tas), potential droughts (spei-3) and meteorological droughts (spi-3) on grid-cell level</li> <li>tas_intensity_hot.nc, spei_intensity_dry.nc, spi_intensity_dry.nc: attributed changes in the intensity of extreme events for extreme heat (tas), potential droughts (spei-3) and meteorological droughts (spi-3) on grid-cell level</li> <li>processed_extremes_frequency.csv: attributed changes in the frequency of extreme events aggregated to ar6 land regions </li> <li>processed_extremes_intensity.csv: attributed changes in the intensity of extreme events aggregated to ar6 land regions</li> </ol>
GTSM-ERA5-E dataset - Data underlying the paper "Global dataset of storm surges and extreme sea levels for 1950-2024 based on the ERA5 climate reanalysis"
<p>Extreme sea levels, generated by storm surges and high tides, have the potential to cause coastal flooding and erosion. Global datasets are instrumental for mapping of extreme sea levels and associated societal risks. Harnessing the backward extension of the ERA5 reanalysis, we present a dataset containing the statistics of water levels based on a global hydrodynamic model (GTSMv3.0) covering the period 1950-2024. This is an extension of a previously published dataset for 1979-2018 <a href="https://www.frontiersin.org/articles/10.3389/fmars.2020.00263/full" target="_blank" rel="noopener">(Muis et al. 2020)</a>. The timeseries (10-min, hourly mean and daily maxima) are available via the Climate Data Store of ECMWF at DOI: 10.24381/cds.a6d42d60. Using this extended ERA5 dataset, we calculate percentiles and estimate extreme water levels for various return periods globally. The percentiles dataset includes the 1, 5, 10, 25, 50, 75, 90, 95 and 99th percentiles. The extreme water levels include return values for 1, 2, 5, 10, 25, 50, 75 and 100 years, and they are estimated using POT-GPD method applied with a threshold of 99th percentile of the timeseries and using a 72-hour window for declustering peak events, and MLE method for fitting the GPD parameters. The parameters (shape, scale and location) are also supplied with this dataset.</p> <p>Validation of the underlying timeseries and the statistical values shows that there is a good agreement between observed and modelled sea levels, with the level of agreement being very similar to that of the previously published dataset. The extended 75-year dataset allows for a more robust estimation of extremes, often resulting in smaller uncertainties than its 40-year precursor. The present dataset can be used in global assessments of flood risk, climate variability and climate changes.</p> <p>Global modelling of water levels and extreme value analysis are associated with a number of uncertainties and limitations, that are particularly important to consider when conducting local assessments. Please refer to the Usage Notes in the corresponding manuscript (Aleksandrova et al. 2025, paper currently under review) for an overview of limitations.</p>
Supplementary material for the paper EXTREME ULTRAVIOLET AND X-RAY DRIVEN PHOTOCHEMISTRY OF GASEOUS EXOPLANETS - Chemical network details
<p>Supplementary material for the paper</p> <p>EXTREME ULTRAVIOLET AND X-RAY DRIVEN PHOTOCHEMISTRY OF GASEOUS EXOPLANETS</p> <p>by Locci et al. 2021, submitted to PSJ (R1 version)</p> <p>This document contains the complete list of the chemical reactions included in the model: bimolecular reactions (neutral-neutral and ion-neutral) in Table 1, termolecular reactions in Table 2, thermodissociative reactions in Table 3, reverse reactions in Table 4, and finally photochemical reactions in Table 5.</p>
Dataset for Extreme Flooding and Nitrogen Dynamics of a Blackwater River
<p>Data used for analysis in the manuscript titled: Extreme Flooding and Nitrogen Dynamics of a Blackwater River.</p> <p> </p>
Multi-ideology ISIS/Jihadist White Supremacist (MIWS) Dataset for Multi-class Extremism Text Classification
<p>**************Information on how to use our Multi-ideology ISIS/Jihadist White Supremacist Dataset(MIWS)for Multi-class Extremism Text Classification ************</p> <p>Folder name: Seed_MIWS<br> Sub Folder 1 : Seed_Dataset<br> Inside this folder there are two .csv files.<br> 1) ISIS/Jihadist_Seed_Dataset<br> 2) White_Supremacist_Seed_Dataset</p> <p>These files have common features as:<br> ***************************** Common Features in Seed ******************************************************<br> *********Source :- Contains Author, Article Name or Hyperlink to Article************************************<br> *********Type_of_Source :- Whether Source is Research Article or Report or Website**************************<br> *********Text :- Contains Extremist Text provided in Source*************************************************<br> *********Ideology :- Ideology of Text mentioned in Source i.e. ISIS/Jihadist or White Supremacist***********<br> *********Label :- Labels for Text provided by Source i.e. Propaganda, Radicalization or Recruitment*********<br> *********Geographical_Location :- Location mentioned in Text. Geographical Location is manually identified**<br> *********Author_Country_Affiliation :- Country of origin of Research Article, Report or Website in Source***</p> <p>Sub Folder 2 : MIWS<br> Inside this folder ther is one .csv file:</p> <p>It contains features as:<br> *****************************Features in MIWS********************************************************************************<br> **********Tweet_ID :- Unique Identification for a Tweet provided by Twitter**************************************************<br> **********Created_Date :- Date and Time at whic Tweet was created or posted**************************************************<br> **********Geo_Enabled :- Boolean value. True if location is made public by User**********************************************<br> **********Geographical_Location :- Manually extracted list of Locations within the tweet. 'Undefined' if no location present*<br> **********Ideology :- Manually provided during Tweet Collection i.e. ISIS/Jihadist or White Supremacist**********************<br> **********Labels :- Annotated by comparing with Seed, i.e. Propaganda, Radicalization and Recruitment***********************</p> <p>MIWS file can be used to collect tweets and train model for extremism detection.</p>
Data from: The extreme rainfall gradient of the Cape Horn Biosphere Reserve and its impact on forest bird richness. Biodiversity and Conservation
<p><strong>Description of dataset</strong></p> <p>This dataset contains information about forest bird species richness and climatic variables in 61 sample sites of the Cape Horn Biosphere Reserve. This dataset was analysed in : Quilodrán CS, Sandvig EM, Aguirre F, Rivero de Aguilar J, Barroso O, Vásquez RA, and R Rozzi. 2022. Effects of the extreme rainfall gradient in the Cape Horn Biosphere Reserve on forest bird richness. <em>Biodiversity and Conservation</em>. </p> <p> </p> <p><strong>Acknowledgments </strong></p> <p>This study was funded by grants for Technological Centers of Excellence with Basal Financing of the National Agency for Research and Development (ANID-Chile), granted to the Cape Horn International Center (CHIC- FB210018) and the Institute of Ecology and Biodiversity (IEB-AFB170008). CSQ acknowledges support from the Swiss National Science Foundation (N°P5R5PB_203169). </p>
Hubble eXtreme Deep Field that has been clipped and processed for ML applications
<p>This is a set of FITS files downloaded from https://archive.stsci.edu/ .</p> <p> </p> <p>The *.npy file has been clipped to the 99.99th percentile and then minmax normalised along each channel.</p>
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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.