Find research datasets worth reusing

openCC (other)Jun 2025View details →

Non-Targeted Screening of Organic Compounds in Environmental and Biological Matrices Related to Children's Environmental Exposure in South Florida, 2022-2024

This dataset provides a comprehensive list of chemicals relevant to children’s exposure from both dietary and non-dietary sources, across five environmental and biological matrices: drinking water (n = 206), food (n = 203), urine (n = 183), soil (n = 178), and household dust (n = 164). Samples were collected between May 2022 and June 2024 in Miami-Dade and Broward counties, Florida. A non-targeted screening approach using high-resolution mass spectrometry (HRMS) coupled with liquid chromatography was employed for analysis, with matrix-specific preparation methods: online solid-phase extraction (SPE) for water and urine, QuEChERS for food, and accelerated solvent extraction (ASE) for soil and dust. Analyses were conducted in full-scan mode under both positive and negative electrospray ionization to maximize compound detection coverage. Compound identification was performed using Compound Discoverer software, incorporating spectral and structural databases such as mzCloud, ChemSpider, ClassyFire, and MassList. Annotations were based on exact mass, mass error threshold (<5ppm), predicted molecular formula, retention time alignment, isotopic pattern fit, MS/MS spectral similarity, and match confidence levels derived from integrated spectral libraries and database scoring algorithms. Quality assurance was maintained through the use of quality control (QC) samples across all matrices and analytical batches. The integration of non-targeted analysis, matrix-optimized extraction, and rigorous QA/QC practices makes this dataset a valuable resource for environmental exposomics, chemical risk assessment, and evidence-based public health policy development.

openCC (other)Oct 2025View details →

Microcystin-LR (one of the toxic congeners of microcystin) exposure causes crayfish to forget previous fight outcomes as well as previous opponents, MI, 2025

Harmful algal blooms are increasing across the globe as a function of nutrient run off and climate change. While the study of the mechanisms that underlie the blooms themselves are abundant, the impact of the toxins produced by blooms needs more research. Crayfish are keystone species in aquatic habitats and rely on chemoreception for many of their ecosystem functions. Crayfish were placed in a fight arena for measure both the outcome and dynamics aspects of agonistic encounters. Then the loser of the fights were exposed to either controls or microcystin for four days. Afterwards, the crayfish fought either another unfamiliar opponent or the previous opponent. The data contained in this package has the concentrations of microcystin exposure, morphological data on the crayfish, and then behavioral data from the two different fights. The behavioral data has two categories: time spent at different levels of intensity and time it took the crayfish pair to reach a certain intensity.

openCC (other)Nov 2025View details →

Increased inflammation and oxidative stress caused by accumulated metal particle exposure among metro station staff, Tianjin, China, 2023

Metro is a significant part of world transport, delivering over 58 billion passengers annually. The dilution effect of particulate matter (PM) from natural ventilation was limited in underground metro stations. What's worse, train operation processes generated PM rich in heavy metals. Though PM pollution in metro stations was reported widely, there is limited evidence of the adverse health effect of metro station PM. This dataset collected urinary samples from 74 metro station staff from three different metro stations in Tianjin, China, for inflammation and oxidative stress biomarker tests to better understand the potential health effects induced by metal particulates in metro stations. Also, an indoor air quality survey was conducted simultaneously in the metro stations.

openCC0Feb 2024View details →

EXPOS Model for Estimating Topographic Exposure to Wind

EXPOS is a simple model of topographic exposure to wind that utilizes digital elevation data to predict which points on a landscape are exposed to or protected from a given wind direction. The model was developed to approximate the effects of topography on hurricane wind flow at a landscape scale (~ 10 km). The model requires an accurate digital elevation map and a specified wind direction. Each point on the elevation map is then classified as protected or exposed, depending on whether or not it falls within the wind shadow cast by points upwind. The wind shadow is estimated by assuming that the wind bends downward no more than a fixed inflection angle from the horizontal as it passes over a height of land. The effects of topographic features beyond the margins of the input elevation map are of course not predicted by the model. For a given landscape and wind direction, predicted protected areas decrease in size as the inflection angle increases. Application of the model in studies of two recent hurricanes suggests that the probability of wind damage in protected areas also decreases as the inflection angle increases. An inflection angle of about 5 to 10 degrees was found to give good results. For more information on the EXPOS model, please see the published paper (Boose, E. R., D. R. Foster, and M. Fluet. 1994. Hurricane impacts to tropical and temperate forest landscapes. Ecological Monographs 64: 369-400). Additional information is contained in the documentation that accompanies the program. For an updated version of the EXPOS model in R and Python, please see HF447.

zenodo52/100

Residential exposure to natural hazards in Europe, 2000–2020

This dataset provides average national-level current gross replacement costs of the stock of residential assets (buildings and household contents) per m2 of useful floor space. The dataset includes annual time series (2000–2020) for 33 European countries, in nominal and real prices. It is intended for application in microscale disaster models by enabling approximation of the monetary value of individual buildings exposed to hazards, especially floods, for which damage functions often distinguish between vulnerability of the building structure and contents inside.

opencc-by-4.0May 2022View details →

edi52/100

Cardiorespiratory metrics of cobia during and following a Ucrit trial upon a 3 wk exposure to either ambient or elevated pCO2

This study examined cardiac and swimming performance during a maximum sustained swimming trial (Ucrit) of cobia (Rachycentron canadum) following a three-week exposure to either elevated (~1,600 µAtm) or ambient (~500 µAtm) pCO₂. This dataset encompasses cardiac variables (stroke volume, heart rate, and cardiac output) during swimming and recovery from a Ucrit trial, hematological variables (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration) following the swimming test and hour long recovery, oxygen consumption variables (MO₂ min, MO₂ max, aerobic scope, and factorial aerobic scope) during and following the swimming test.

openCC (other)Mar 2025View details →

opencc-by-4.0May 2020View details →

Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study (Supplementary Data)

The zip file contains supplementary data for the publication - Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study, accepted for publication in Environmental Health Perspectives (DOI: 10.1289/EHP6174). The description of the files are noted below: 1. Readme File for SAPALDIA Noise and Air Pollution EWAS Single Exposure.zip This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO2 and PM2.5), and genome-wide DNA methylation, derived from multi-exposure models. SAPALDIA_EWAS_SingleExposure_AircraftLden.txt contains the results for aircraft noise SAPALDIA_EWAS_SingleExposure_RailwayLden.txt contains the results for railway noise SAPALDIA_EWAS_SingleExposure_RoadtrafficLden.txt contains the results for road traffic noise SAPALDIA_EWAS_SingleExposure_NO2.txt contains the results for nitrogen dioxide SAPALDIA_EWAS_SingleExposure_PM25.txt contains the results for fine particulate matter   General footnote for all files:SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO2: nitrogen dioxide. PM2.5: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m3 increase in NO2 or PM2.5. All estimates were from single exposure epigenome-wide linear mixed models, with random intercept at the level of participant. Each model was adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator (for Lden models) and leukocyte composition. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites. Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.   2. Readme File for SAPALDIA Noise and Air Pollution EWAS Multi Exposure.zip This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO2 and PM2.5), and genome-wide DNA methylation, derived from multi-exposure models. SAPALDIA_EWAS_MultiExposure_AircraftLden.txt contains the results for aircraft noise SAPALDIA_EWAS_MultiExposure_RailwayLden.txt contains the results for railway noise SAPALDIA_EWAS_MultiExposure_RoadtrafficLden.txt contains the results for road traffic noise SAPALDIA_EWAS_MultiExposure_NO2.txt contains the results for nitrogen dioxide SAPALDIA_EWAS_MultiExposure_PM25.txt contains the results for fine particulate matter General table footnotes: SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO2: nitrogen dioxide. PM2.5: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m3 increase in NO2 or PM2.5. All estimates were from multi-exposure epigenome-wide linear mixed models, with random intercept at the level of participant, and were adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator and leukocyte composition. Multi-exposure models included all five exposures (Aircraft, railway, road traffic Lden and respective truncation indicators, NO2 and PM2.5) at the same time. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites. Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.

opencc-by-4.0Mar 2023View details →

Temperature-related mortality exposure-response functions for 854 cities in Europe

This repository contains data to reconstruct the exposure-response functions (ERF) of temperature-related mortality by five 5 age groups in 854 cities in Europe.These ERFs have been derived in the study by Masselot et al. 2023, Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe, The Lancet Planetary Health (<a href="https://protect-eu.mimecast.com/s/zqg2Cg204i4ZMYKf3NUKN?domain=doi.org">https://doi.org/10.1016/S2542-5196(23)00023-2</a>). An associated semi-replicable GitHub repository is available at <a href="https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM">https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM</a> to reproduce part of the analysis and the full results, as well as to provide technical details on the derivation of these ERFs.Note: This updated version contains revised data after the correction of an error in the code related to the computation of the age-specific baseline mortality rates. Details about the error can be found in the GitHub repository linked above. This correction only affects the figures of excess mortality (found in the `results.zip` archive) while the ERFs are negligibly affected. The originally published results can be found in V1.0.0 of this repository.Extraction of the ERFsThe ERFs are provided as coefficients of B-spline functions that can be used to reconstruct the ERFs, along with variance-covariance matrices and quantiles from location-specific temperature distributions. The parametrisation associated with these coefficients is a quadratic B-spline (degree 2), with knots located at the 10th, 75th and 90th percentiles of the temperature distribution. In R, the associated basis can be constructed using the dlnm package, with a temperature series x, as follows:<blockquote>library(dlnm) basis <- onebasis(x, fun = "bs", degree = 2, knots = quantile(x, c(.1, .75, .9)))</blockquote>The main files associated with ERFs are the following:coefs.csv: The B-spline coefficients for each age group and city.vcov.csv: The variance-covariance matrix of the coefficients in each city and age group. It is provided here as the lower triangular part of the matrix with names indicating the position of each value (v[row][column]). In R, assuming x is a row of this file, the matrix can be reconstructed using xpndMat(x) after loading the mixmeta package.coef_simu.csv: 1000 simulations from the distribution of each city and age-specific coefficients. Useful to derive empirical confidence intervals for derived measures such as excess deaths or attributable fractions.tmean_distribution.csv: The city-specific temperature percentiles representing the distribution of the data derived from the ERA5-Land dataset.Health impact assessment resultsresults.zip: A summary of the results from the health impact assessment reported in the analysis. The dataset includes several impact measures provided in files representing different geographical levels, including city, country and regional level. Different files are also provided for age-group specific or all age results.Additional dataWe provide additional data that are useful to reproduce or extend the analysis. Please note that due to restrictive data-sharing agreements for the mortality series, only a part of the code is reproducible. See the <a href="https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM">associated GitHub repository</a> for more details.metadata.csv: City-specific metadata used to create the ERFs and perform the health impact assessment.additional_data.zip: contains further data used to replicate the second stage of the analysis and the final health impact assessment. It includes the full city-level daily temperature series (era5series.csv), the detail of extracted metadata for available years (metacityyear.csv), a description of the city-level characteristics (metadesc.csv), and the first-stage ERF coefficients for all available city and age-groups (stage1res.csv). Additionally, the file meta-model.RData contains R object defining the second-stage model that can be used to predict new ERFs.

opencc-by-4.0Dec 2023View details →

Data: An experimental sound exposure study at sea: No spatial deterrence of free-ranging pelagic fish

Data abstract: All data and scripts to replicate all plots and statistical results of the paper mentioned below. The data are sound recordings and processed echosounder data (raw echosounder data is available on request but > 100 GB in size and require specialized software).   Paper reference: Jeroen Hubert, Jozefien M. Demuynck, M. Rafa Remmelzwaal, Carlota Muñiz, Elisabeth Debusschere, Benoit Berges, Hans Slabbekoorn; An experimental sound exposure study at sea: No spatial deterrence of free-ranging pelagic fish. J. Acoust. Soc. Am. 1 February 2024; 155 (2): 1151–1161. <a href="https://doi.org/10.1121/10.0024720" target="_blank" rel="noopener">https://doi.org/10.1121/10.0024720</a>   Paper abstract: Acoustic deterrent devices are used to guide aquatic animals from danger or toward migration paths. At sea, moderate sounds can potentially be used to deter fish to prevent injury or death due to acoustic overexposure. In sound exposure studies, acoustic features can be compared to improve deterrence efficacy. In this study, we played 200–1600 Hz pulse trains from a drifting vessel and investigated changes in pelagic fish abundance and behavior by utilizing echosounders and hydrophones mounted to a transect of bottom-moored frames. We monitored fish presence and tracked individual fish. This revealed no changes in fish abundance or behavior, including swimming speed and direction of individuals, in response to the sound exposure. We did find significant changes in swimming depth of individually tracked fish, but this could not be linked to the sound exposures. Overall, the results clearly show that pelagic fish did not flee from the current sound exposures, and we found no clear changes in behavior due to the sound exposure. We cannot rule out that different sounds at higher levels elicit a deterrence response; however, it may be that pelagic fish are just more likely to respond to sound with (short-lasting) changes in school formation.

opencc-by-4.0Oct 2023View details →

Statistical analysis and dataset for: Acute exposure to caffeine improves foraging in an invasive ant

Linked to the journal article published in iScience (https://doi.org/10.1016/j.isci.2024.109935). Abstract Argentine ants, Linepithema humile, are a particularly concerning invasive species. Control efforts often fall short likely due to a lack of sustained bait consumption. Using neuroactives, such as caffeine, to improve ant learning and navigation could increase recruitment and consumption of toxic baits. Here, we exposed L. humile to a range of caffeine concentrations and a complex ecologically relevant task: an open landscape foraging experiment. Without caffeine, we found no effect of consecutive foraging visits on the time the ants take to reach a reward, suggesting a failure to learn the reward’s location. However, under low to intermediate caffeine concentrations ants were 38% faster with each consecutive visit, implying that caffeine boosts learning. Interestingly, such improvements were lost at high doses. In contrast, caffeine had no impact on the ants’ homing behavior. Adding moderate levels of caffeine to baits could improve ant’s ability to learn its location, improving bait efficacy.   <ul> <li>sample_videos.zip: A subset of the videos used for data extraction. The complete collection of videos is not publicly accessible primarily due to their considerable size (105.35GB). Requests for access to the entire video set are encouraged.</li> <li>Preregistration.pdf: The preregistration created for data collection and analysis with justifications for deviations from it.</li> <li>OpLan_D1_metadata.csv: Manually collected metadata pertaining to experimental conditions, subjects, and treatments.</li> <li>OpLan_D2_DLC_coordinates.zip: Cartesian coordinates obtained from DeepLabCut for each of the videos analysed.</li> <li>OpLan_C1_reproject_coordinates.py: Python code used to standardise the ants' coordinates by ensuring the same corner of the A4 platform was used as the origin of the cartesian referential of all videos. The known dimensions of the A4 were further used to convert coordinates from pixels to millimetres.</li> <li>OpLan_C2_remove_impossibilities.py: Python code used to account for DeepLabCut tracking errors, with any ant movement exceeding two millimetres per frame being considered implausible and subsequently removed.</li> <li>OpLan_C3_find_changepoints.py: Python code used to automatically derive the times at which an ant reached and left the reward from the tracking data.</li> <li>OpLan_C4_inward_outward_data.py: Python code used to calculate relevant measures for the foodward (inward) and nestward (outward) journey such as journey duration, mean instantaneous speed and path tortuosity.</li> <li>OpLan_C5_Figure_2.R: R code used to produce the raw elements of Figure 2.</li> <li>OpLan_C6_Figure_4.R: R code used to produce the raw elements of Figure 4.</li> <li>OpLan_C7_Statistical_Analysis.html: Complete statistical analysis and code for the manuscript.</li> </ul>

Inter-Chemical Correlation results for the study: HHEARx2018-2120 (The impact of tobacco smoke exposure and environmental exposures on the pulmonary microbiome and outcomes of critically ill children)

Title: The impact of tobacco smoke exposure and environmental exposures on the pulmonary microbiome and outcomes of critically ill children Species: Homo sapiens Number of samples: 1090 Number of named analytes: 12 Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=42

Inter-Chemical Correlation results for the study: HHEARx2017-1977 (Microbial and metabolic variations mediate the influence of childhood and adolescent EDC and trace element exposure on breast density.)

Title: Microbial and metabolic variations mediate the influence of childhood and adolescent EDC and trace element exposure on breast density. Species: Homo sapiens Number of samples: 1116 Number of named analytes: 41 Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=46

Inter-Chemical Correlation results for the study: HHEARx2017-1740 (Mitochondrial DNA biomarkers of prenatal metal mixture exposure: intergenerational inheritance and infant growth)

Title: Mitochondrial DNA biomarkers of prenatal metal mixture exposure: intergenerational inheritance and infant growth Species: Homo sapiens Number of samples: 1423 Number of named analytes: 20 Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=19

Inter-Chemical Correlation results for the study: HHEARx2017-1598 (Evaluation of Environmental Exposures in TEDDY)

Title: Evaluation of Environmental Exposures in TEDDY Species: Homo sapiens Number of samples: 1025 Number of named analytes: 45 Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=37

Inter-Chemical Correlation results for the study: HHEARx2016-1534 (A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function)

Title: A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function Species: Homo sapiens Number of samples: 5789 Number of named analytes: 17 Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=14

Inter-Chemical Correlation results for the study: HHEARx2016-1461 (ECHO ReCHARGE Study - Environmental Exposures)

Title: ECHO ReCHARGE Study - Environmental Exposures Species: Homo sapiens Number of samples: 1231 Number of named analytes: 75 Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=17

opencc-zeroJun 2024View details →

Inter-Chemical Correlation results for the study: HHEARx2016-1432 (Micronutrient deficiencies, environmental exposures and severe malaria: Risk factors for adverse neurodevelopmental outcomes in Ugandan children)

Title: Micronutrient deficiencies, environmental exposures and severe malaria: Risk factors for adverse neurodevelopmental outcomes in Ugandan children Species: Homo sapiens Number of samples: 1256 Number of named analytes: 51 Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=5

opencc-by-4.0Nov 2023View details →

Climate Solutions Explorer - hazard, impacts and exposure data

<a name="_GoBack"></a>The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. <a href="https://www.climate-solutions-explorer.eu">www.climate-solutions-explorer.eu</a> Using the latest data, state-of-the-art models were used to assess the future trends of indicators of development- and climate-induced challenges. Updated gridded global climate and impact model data are based on CMIP6 and CMIP5 projections, using a subset of models from the ISIMIP project that have been consistently downscaled and bias-corrected.  The data includes various indicators (~42) relating to extremes of precipitation and temperature (e.g. from Expert Team on Climate Change Detection and Indices), hydrological variables including runoff and discharge, heat stress (from wet bulb temperature) events (multiple statistics and durations), and cooling degree days, as well as further indicators relating to air pollution (PM2.5 from the GAINs model), and crop yields and natural habitat land-use change (biodiversity pressure) from the GLOBIOM model. Indicators were calculated at a spatial resolution of 0.5° (approximately 50km at the equator), and subsequently spatially aggregated to the country level – from which population and land area exposure to the impacts were calculated. This has enabled the country-by-country comparison of national climate impacts and avoided exposure. Impacts were calculated at global mean temperature intervals, i.e. 1.2, 1.5, 2, 2.5, 3, and 3.5 °C, compared to a pre-industrial climate. The dataset includes:  <ul> <li>Global gridded projections (in netCDF format) of all the climate impact indicators at 0.5° spatial resolution, at global warming levels of 1.2, 1.5, 2, 2.5, 3, and 3.5 °C For each GWL, maps for the absolute indicator values, the relative difference, and the scores are provided. The naming format is: cse_[short_indicator_name]_[ssp]_[gwl]_[metric].nc4. Please note that the Greenland ice sheet and the desert areas have been masked out for the hydrology indicators for these datasets. </li> <li>Intermediate output data, including gridded maps of absolute values, relative differences, and scores for all ensemble members, as well as gridded maps of the multi-model ensemble statistics for the global warming levels and the reference period For the ensemble member data, the naming format is [gcm]_[ssp/rcp]_[gwl]_[short_indicator_name]_global_[start_year]_[end_year].nc4 or [ghm]_[gcm]_[ssp/rcp]_[gwl]_[soc]_[short_indicator_name]_global_[start_year]_[end_year]_[metric].nc4 for the hydrology indicators. </li> <li>Tabular data (.csv) aggregating the indicators to country (or region) level, for both hazards and exposure, population and land-area weighted The .zip archives ‘table_output_climate_exposure_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_climate_exposure_land_air_pollution.zip’ contains the table data for theland and air pollution indicators.  </li> <li>Tabular data (.csv) for avoided impacts by mitigating to 1.5 °C (land and population exposure) The .zip archives ‘table_output_avoided_impacts_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_avoided_impacts_land_air_pollution.zip’ contains the table data for the land and air pollution indicators.</li> </ul>   Further details are available on the Data Story page – <a href="http://www.climate-solutions-explorer.eu/story/data">www.climate-solutions-explorer.eu/story/data</a>. A detailed description of the methodology and the calculation of the ISIMIP-derived indicators has been published in <a title="Global warming levels indicators of climate change and hotspots of exposure" href="https://doi.org/10.1088/2752-5295/ad8300" target="_blank" rel="noopener">Werning, M. et al. (2024).</a>   Release notes (v1.1) Changes in this version: <ul> <li>Only table output data for the land and air pollution indicators have been changed, all other indicator data remain unchanged from v1.0</li> <li>Updated land and air pollution indicators to use scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023</li> <li>Fixed issue with the region mask for the EU</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> </ul>   Release notes (v1.0) Changes in this version: <ul> <li>Fixed calculation of the indicator “Drought intensity” (both for the version using discharge and run-off)</li> <li>Masked out the Greenland ice sheet and the desert areas for the global gridded projections for the hydrology indicators in the final output files</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> <li>Used scaled population data to match the latest SSP population projections from the Wittgenstein Center from <a>2023</a></li> <li>Added the indicator ‘Heatwave days’</li> <li>Added intermediate outputs for all ensemble members for energy, hydrology, precipitation, and temperature indicators </li> </ul> Release Notes (v0.4) Changes in this version: <ul> <li>Removed ssp and metric from variable name in netCDF files</li> <li>Removed obsolete coordinates in netCDF files for 'Drought intensity'</li> <li>Added intermediate outputs for energy, hydrology, precipitation, and temperature indicators</li> </ul> <div> </div>

opencc-by-4.0Dec 2021View details →

Building Exposure map

Building Exposure map is a layer in support to Area Of Interest (AOI) definition for a Tsunami event. It provides an estimation of the number of buildings exposed to the tsunami event.