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570 results for “Exposure data”
Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study (Supplementary Data)
<p>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).</p> <p>The description of the files are noted below:</p> <p><strong>1. Readme File for SAPALDIA Noise and Air Pollution EWAS Single Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_SingleExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_SingleExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p> </p> <p><strong>General footnote for all files:</strong>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. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: 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/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. 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.</p> <p>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.</p> <p> </p> <p><strong>2. Readme File for SAPALDIA Noise and Air Pollution EWAS Multi Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_MultiExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_MultiExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p><strong>General table footnotes: </strong>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. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: 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/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. 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, NO<sub>2</sub> and PM<sub>2.5</sub>) 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.</p>
Data: An experimental sound exposure study at sea: No spatial deterrence of free-ranging pelagic fish
<p>Data abstract:</p> <p>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).</p> <p> </p> <p>Paper reference:</p> <p>Jeroen Hubert<span>, </span>Jozefien M. Demuynck<span>, </span>M. Rafa Remmelzwaal<span>, </span>Carlota Muñiz<span>, </span>Elisabeth Debusschere<span>, </span>Benoit Berges<span>, </span>Hans Slabbekoorn; An experimental sound exposure study at sea: No spatial deterrence of free-ranging pelagic fish. <em>J. Acoust. Soc. Am.</em> 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></p> <p> </p> <p>Paper abstract:</p> <p>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.</p> <p> </p> <p> </p>
Climate Solutions Explorer - hazard, impacts and exposure data
<p><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"><strong>www.climate-solutions-explorer.eu</strong></a></p> <p>Using the latest data, state-of-the-art models were used to assess the future trends of indicators of development- and climate-induced challenges.</p> <p>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.</p> <p>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.<br><br></p> <p><strong>The dataset includes: </strong></p> <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<br><br>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.<br><br></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 <br><br>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. <br><br></li> <li>Tabular data (.csv) aggregating the indicators to country (or region) level, for both hazards and exposure, population and land-area weighted<br><br>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. <br><br></li> <li>Tabular data (.csv) for avoided impacts by mitigating to 1.5 °C (land and population exposure)<br><br>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> <p> </p> <p>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></p> <p> </p> <p><strong>Release notes (v1.1)</strong></p> <p>Changes in this version:</p> <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> <p> </p> <p><strong>Release notes (v1.0)</strong></p> <p>Changes in this version:</p> <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<br><br></li> </ul> <p><strong>Release Notes (v0.4)</strong></p> <p>Changes in this version:</p> <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>
Risk assessment on Glycoalkaloids in feed and food: Occurrence data in food and feed submitted to EFSA and dietary exposure assessment for humans
<p><strong>UPDATE to version 2 of this upload:</strong></p> <p>Also the raw (no data cleaning applied to it) occurrence dataset as extracted from EFSA DWH is provided <em>in csv format</em>. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields).</p> <p><strong>Description - Version 1</strong></p> <p><strong>Annex: Tables on GAs on occurrence data in food and feed, and dietary exposure assessment for humans</strong></p> <p>Table A.1. Dietary surveys used for the estimation of acute dietary exposure to GA</p> <p>Table A.2. Number of results and samples per food category submitted to EFSA through the continuous call for data</p> <p>Table A.3. Analytical results excluded from the final dataset used to estimate dietary exposure and the criteria applied for exclusion</p> <p>Table A.4. Occurrence of alpha-chaconine and alpha-solanine (UB mg/kg) in the samples included in the final dataset (left censored results highlighted in yellow)</p> <p>Table A.5. European Starch Association data on feed and potatoes for starch</p> <p>Table A.6. Details acute assessment across surveys (consumption days only)</p> <p>Table A.7. Comparison of exposure summary results obtained using the uniform vs the normal distribution for reduction factors</p>
Exposure to pesticides data for residents and bystanders, and for environmental risk assessment
<p>In 2014, EFSA has commissioned a study to review and evaluate all published data related to the exposure to pesticides for residents and bystanders and for environmental risk assessment. The aim was to conduct a literature review and to produce a database containing all published data (predominately peer-reviewed publications supplemented by grey-literature) for the last 25-years, which will support the non-dietary exposure assessment to pesticides for bystanders and residents, as well as daily air concentration (vapours and aerosols) of pesticides, drift values from spray, seed and granular applications, and dislodgeable foliar residues.</p> <p>The data has been collated via a systematic and extensive literature review defined and managed according to a pre-defined 'review protocol'. The data was also exported in a format that meets the requirements of the EFSA Data Collection Framework (DCF).</p> <p>Based on quality and relevance criteria, articles and related studies have been selected. For dislodgeable foliar residues the assessment includes 27 articles (containing 49 discrete studies); for air concentrations, 26 articles (containing 84 discrete studies); for resident and bystander exposure, 5 articles (containing 8 discrete studies); and for drift values 55 articles (containing 275 discrete studies). </p> <p>For dislodgeable foliar residues the data retained covered 17 crops (including grass, glasshouse crops, lucerne, and citrus) and 29 pesticides; for air concentrations the data retained covered 21 crops (including fruit, glasshouse crops, ornamentals, grass, vegetables and cereals) and 39 pesticides. For drift values, the data covers a range of crops and landscapes from cereals, grass and turf, orchards, vineyards and regenerated forestry. The vast majority of the data retrieved applies to field studies for liquid spray drift, measured either as ground deposits or collected at various heights and were conducted using fluorescent tracers rather than pesticides. No data was found for microbials (biopesticides). For resident and bystander exposure, many articles were rejected due to the applied inclusion/exclusion criteria.</p>
Fetal exposure to the Ukraine famine of 1932-1933 and adult Type 2 Diabetes Mellitus (Public data and analytical code)
<p><strong>Abstract</strong></p> <p>The short-term impact of famines on death and disease is well documented but it is difficult to estimate their potential long-term impact. We used the setting of the man-made Ukrainian Holodomor famine of 1932-1933 to examine the relationship between prenatal famine and adult Type 2 diabetes mellitus (T2DM). This ecological study included 128,225 T2DM cases diagnosed between 2000-2008 among 10,186,016 male and female Ukrainians born between 1930 and 1938. Individuals who were born in the first half-year of 1934, and hence exposed in early gestation to the mid-1933 peak famine period, had a larger than two-fold likelihood of T2DM (OR 2.21; 95% CI 2.00-2.45) compared to unexposed controls. There was a dose-response relationship between severity of famine exposure and adult T2DM risk comparing individuals born in regions with severe, very severe, and extreme famine to births in the no-famine region.</p> <p> </p> <p><strong>Description of the data and analytical code</strong></p> <p>In exploratory analyses we first examined whether the odds for T2DM were elevated for any month of birth in the period January 1930 to December 1938 in any of the four regions of varying famine intensity. This was achieved by comparing, within each region, the T2DM odds for births in any month and year of birth relative to the T2DM odds for births in the same month combining all other years of birth. The analysis served to identify potential relations of famine with specific months and years of birth, controlling for month of birth effects. We observed increased T2DM odds ratios for births between January and June 1934 in famine-exposed oblasts, with smaller increases for births in 1935 and 1936 in these months. Our findings suggested that in multivariate modelling statistical control for month of birth effects could be accomplished by adjusting for the January-June period. Our findings are presented in the data file '01 Odds Ratio for T2DM Over Time' and show the odds ratios (ORs) for Type 2 Diabetes Mellitus (T2DM) comparing the region-specific T2DM odds for each birth year and month relative to births in the same months but combining all other years of birth. The R syntax file '01 Odds of T2DM Over Time Figure' provides the code necessary to reproduce the figure.</p> <p> </p> <p>For confirmatory analyses we employed a Difference-in-Differences approach to quantify associations between prenatal exposure to famine and T2DM, taking into account year of birth, half-year of birth (Jan-Jun vs Jul-Dec), region, and their interactions. This analysis was conducted initially for each gender separately and then for both genders combined, adjusting for We carried out sensitivity analyses to assess potential changes in T2DM odds arising from the use of pre-famine births vs post-famine births as controls. Our findings are presented in the data file '02 Ukraine Famine 1932-33 Main Data'. Information on the number of T2DM cases by gender, region of residence, and year and month of birth 1930-1938 in Ukraine was collected by the national Ukraine Diabetes Register (Komisarenko Institute of Endocrinology and Metabolism, Kyiv) between 2000-2008. The number of births in the same subgroups, representing the populations at risk for T2DM, was estimated by demographic population reconstruction methods as reported in the publication. We classified the birth counts by year of birth, the semi-annual birth period (January-June vs. July-December), region of birth, and gender. The SPSS syntax file titled '02 Ukraine Famine 1932-33 Main Analysis' provides the code to replicate our main findings as presented in the publication.</p> <p> </p> <p>In a separate analysis we visualized by a meta-regression approach the relation between famine intensity at the oblast level in 1933 and the odds for adult T2DM. The data required for the replication of our findings are included in the file '03 Odds Ratio for T2DM and Famine Intensity at Oblast Level'. The R syntax file titled '03 Ukraine Famine 1932-33 Meta-regression' provides details on conducting the meta-regression using the R package ‘metafor’.</p> <p> </p> <p><strong>Funding</strong></p> <p>Ukraine State complex program Diabetes Mellitus, project number 0106U000844 (M.K.). Holodomor Research and Education Consortium in Canada (L.H.L., O.W.). NIDI-NIAS Fellowship of the Royal Netherlands Academy of Sciences (L.H.L.). National Institute of Aging R01 AG028593 (L.H.L.). National Institute of Aging R01 AG06687 (L.H.L.).</p> <p> </p> <p><strong>Sharing/Access information</strong></p> <p>Data sharing and use are unrestricted with acknowledgement of the original publication and listing of the funding sources as per the above. Researchers are encouraged to contact the Principal Investigators (PIs) for consultations on data structure and use as needed (L.H. Lumey, <a href="mailto:lumey@columbia.edu">lumey@columbia.edu</a>; Oleh Wolowyna, <a href="mailto:olehw@aol.com">olehw@aol.com</a>).</p>
Performance of an Electrothermal MEMS Cantilever Resonator with Fano-Resonance Annoyance under Cigarette Smoke Exposure (Data)
<p>Origin projects, figures and LabVIEW software used for the article "Performance of an Electrothermal MEMS Cantilever Resonator with Fano-Resonance Annoyance under Cigarette Smoke Exposure", published in <em>Sensors </em>on 14 Jun 2021.</p>
European Exposure Model Data Repository
<p>A repository of the exposure data used to develop the ESRM20 exposure models.</p> <p>More information available here: <a href="https://eu-risk.eucentre.it/exposure/">https://eu-risk.eucentre.it/exposure/</a></p>
Data for: Prior exposure of a fungal parasite to cyanobacterial extracts does not impair infection of its Daphnia host
<p>This dataset supports the findings of the study 'Prior exposure of a fungal parasite to cyanobacterial extracts does not impair infection of its <em>Daphnia</em> host', published in Hydrobiologia (https://doi.org/10.1007/s10750-022-04889-7)</p>
HANZE v2.0 exposure model input data
<p>This dataset provides all input data needed to run HANZE v2.0 model. The two ZIP files need to be downloaded and unpacked in the same directory, which has to be defined in "get_file.py" (variable "main_path" at the beginning of the file). For detailed description of the files, see the documentation provided with the code.</p>
Validation data for a Microwave Exposure Prototype for In Vitro Growth Inhibition of Plasmodium falciparum
<p>The dataset presented in this article revolves around the validation of an innovative irradiation device designed for <em>in vitro</em> malaria tests. The motivation stems from the escalating need for new malaria treatments due to the high mortality rates, the absence of an effective vaccine, and rising antimalarial drug resistance. The theoretical background for generating this data is based on prior studies indicating that microwave exposure can non-thermally kill malaria parasites by interacting with hemozoin crystals within infected erythrocytes, a process in which healthy cells remain unaffected, probably due to the absence of these crystals. This dataset aims to provide a thorough validation of the device's biological and electrical efficacy, by providing models to simulate various parameters such as the magnetic and electric field strength, and experimentally validate the biological effects on parasite viability under controlled microwave exposure. This contribution is pivotal for advancing research in electromagnetic therapy as a potential treatment for malaria, providing a foundational dataset for future experimental and theoretical work.</p>
Multi-omics data for pro-inflammatory and anti-inflammatory exposure to THP-1 macrophages
<p>This data characterizes gene expression levels in THP-1 macrophages. The data was generated using RNA sequencing and analyzed with DeSeq2 (version 1.24.0). The analysis included raw count data and normalized count matrices obtained from DESeq2's dds_deseq objects.<br>This data describes the methylation levels of individual CpG sites in THP-1 macrophages. The data was obtained using the Infinium MethylationEPIC v2.0 Kit (Illumina) and analyzed with the minfi package (version 1.46). Specifically, the data underwent quantile normalization using the preprocessQuantile function within minfi. Only CpG sites with a detection p-value less than 0.05 were included to obtain MatrixProcessedGEO.txt file. The beta values (bValues.xlsx) were obtained using the function “getBeta” from the same package, considering each time point individually.<br>The macrophages were exposed to phorbol 12-myristate 13-acetate (PMA) for 48 hours, followed by treatment with either a combination of LPS (10 pg/ml) and interferon-gamma (IFNγ) (20 ng/ml) or a combination of interleukins 13 (IL-13) (20 ng/ml) and 4 (IL-4) (20 ng/ml) for 24, 48, and 72 hours.</p>
Minimal data set for: Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend
<p>This minimal data set presents the values behind the means and standard deviation for the publication entitled: "Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend"</p>
Data archive for Pepper, Bateson and Nettle, 'Telomeres as integrative markers of exposure to stress and adversity: A systematic review and meta-analysis'
<p>Data archive for the paper 'Telomeres as integrative markers of exposure to stress and adversity: A systematic review and meta-analysis' by Gillian Pepper, Melissa Bateson and Daniel Nettle. This version was uploaded in July 2018 after peer-review in the journal Royal Society Open Science. Compared to earlier version, it incorporates some minor error correction to the dataset, and reflects the revised analyses we performed after peer review. </p> <p>Our protocol and recording guide, which were preregistered on the Open Science Framework in 2016, are also included here, as is our PRISMA diagram.</p> <p>The data file 'unprocessed data' contains the data as extracted from the literature, with associations shown both as provided in the original papers, and converted to correlation coefficients. The algorithms for converting all the different associations to correlation coefficients are described in the flowchart and implemented in the R script 'effect conversion algorithms.r'.</p> <p>The data file 'processed data.csv' is the dataset analysed in the paper. Compared to 'unprocessed data.csv', it excludes: associations from studies of non-human animals; duplicate associations; a small number of associations from studies of medical treatments; and associations considered subparts or subscales of other associations. These exclusions are outlined in Methods section of the paper. In addition, in the processed data file, all correlations are aligned in direction so as to make them comparable (variable 'ValencedEffect'); and all associations are assigned to broad and fine categories.The script 'unprocessed to processed.r' makes the processed data file from the unprocessed one, or you can simply work from the processed one directly. </p> <p>The R script 'telomere metanalysis script RSOS REVISED.r' reproduces the analyses found in the paper.</p> <p>This version of the archive (July 17 2018) contains one small correction in the data files compared to all earlier versions. </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>
NHS England COVID-19 Exposure Notification App and Test Availability Data
<p>This dataset was scraped from the API serving the NHS COVID-19 App for England and Wales, and the NHS COVID-19 test availability service.</p> <p>It contains the following files:</p> <p><strong>exposure_keys.csv</strong><br> Metadata associated with the published exposure keys for the Bluetooth Google/Apple Exposure Notification (GAEN) system. The actual broadcast keys were not collected, only the metadata attached to them. The columns in the table match those in the <a href="https://developers.google.com/android/exposure-notifications/exposure-key-file-format">exposure key export format</a>, with the exception of the "export_date" field which is the "end_timestamp" of the key export in which that key was first seen. This file is believed to be complete between 2020-09-13 and 2023-04-29 when the NHS COVID-19 app was retired.</p> <p><strong>exposure_configuration.csv</strong><br> This table contains the NHS COVID-19 App's exposure configuration JSON file fetched from the API, with a new record inserted whenever this changed. History of this file is also available in the <a href="https://github.com/ukhsa-collaboration/covid19-app-system-public">app's git repository</a>, and entries in this file from before 2021-07-11 were imported from there. The timestamps for entries dated since that point will match the time that the configuration was published to the API, which may not be the case for the git repository as this was normally updated after a delay.</p> <p><strong>risky_venues.csv</strong><br> "Risky venue" notification data for the COVID-19 App. This was an NHS App-specific feature, not part of the GAEN specification, which allowed users to "check in" to a venue and receive a notification if they were present at the same time as someone who subsequently tested positive for COVID-19. This file is believed to be complete between 2020-09-24 and 2022-02-22 (when it appears the "risky venue" feature was retired), with a known data collection gap between 2021-08-03 and 2021-08-06.</p> <p><strong>walk_in_pcr_availability.csv</strong><br> Walk-in PCR test availability for the entire UK, used by the NHS PCR test booking service. This contains JSON objects provided by the API, which are broken down by region. A new row was inserted whenever this JSON object changed. This file is believed to be complete between 2021-12-27 and 2022-03-30 (after which it appears the online test booking service was retired).</p> <p><strong>home_test_availability.csv</strong><br> Home test (PCR or Lateral Flow Device) availability, for the public and for "key workers" who had priority ordering PCR tests. A new row was inserted whenever the availability changed. This file is believed to be complete between 2021-12-27 and 2022-08-22 when the data ended.</p> <p> </p> <p>The final version of the source code used to fetch this data is <a href="https://doi.org/10.5281/zenodo.7883754">available here</a>.</p>
Processed metabolomic data from the EXPOsOMICS Personal Exposure Monitoring study
<p>Metabolomic data from the 'Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study' paper <a href="https://doi.org/10.1021/acs.est.3c03233">DOI: 10.1021/acs.est.3c03233</a> . </p> <p>The data was originally collected and generated by the multicenter EXPOsOMICS Personal Exposure Monitoring study. Details on data collection and processing are described in the aforementioned paper. The statistical analysis from that paper is available at <a href="https://github.com/moosterwegel/variability-metabolites-paper">https://github.com/moosterwegel/variability-metabolites-paper</a> and may contain useful information/code to work with this data.</p> <p>`processed_covariate_data.csv`:<br> ```<br> Rows: 298<br> Columns: 7<br> $ subjectid: hashed identifier subject<br> $ sample_code: indicates if it's the first (A) or second (B) blood sample<br> $ centre: indicates in which centre the data was collected<br> $ age_cat: indicates age category at the time of a PEM session<br> $ sq_sex: indicates the sex of the participant (male, female) as filled in during the screening questionaire<br> $ traf: indicates the exposure to traffic (PM2.5 and UFP) as measured during the PEM sessions. <br> $ bmi_cat: indicates BMI category at the time of a PEM session<br> ```</p> <p>`processed_lcms_data data.csv` contains the processed LCMS data:<br> ```<br> Rows: 298<br> Columns: 4297<br> $ subjectid: hashed identifier subject<br> $ sample_code: indicates if it's the first (A) or second (B) blood sample<br> $ centre: indicates in which centre the data was collected<br> $ compounds: measured features (compounds) are prefixed by the letter X. The name contains information on the measured monoisotopicmass_retentiontime.<br> Non-detects (below limit of detection (LOD) are coded as 1 for the compounds.<br> ....<br> ```<br> In the datasets each row indicates a measurement on a day (`sample_code`) and person (`subjectid`). The datasets can be joined on these variables.</p> <p>The other data files (`annotations.xslx`, `ancestors_annotations.xlsx`, `annotations_plus_kegg_pathways.csv`) contain the annotations, ancestors of the annotations (to assign a class to a compound based on ChEBI ontology, see our paper for details), annotations plus KEGG pathways respectively. </p>
Data from: Do the health benefits of boiling drinking water outweigh the negative impacts of increased indoor air pollution exposure?
<p><strong>Background: </strong>Billions of the world's poorest households are faced with the lack of access to both safe drinking water and clean cooking. One solution to microbiologically contaminated water is boiling, often promoted without acknowledging the additional risks incurred from indoor air degradation from using solid fuels.</p> <p><strong>Objectives: </strong>This modeling study explores the tradeoff of increased air pollution from boiling drinking water under multiple contamination and fuel use scenarios typical of low-income settings.</p> <p><strong>Methods: </strong>We calculated the total change in disability-adjusted life years (DALYs) from indoor air pollution (IAP) and diarrhea from fecal contamination of drinking water for scenarios of different source water quality, boiling effectiveness, and stove type. We used Uganda and Vietnam, two countries with a high prevalence of water boiling and solid fuel use, as case studies. </p> <p><strong>Results: </strong>Boiling drinking water reduced the diarrhea disease burden by a mean of 1110 DALYs and 368 DALYs per 10,000 people for adults and children <5 years in Uganda, respectively, for high-risk water quality and the most efficient (lab-level) boiling scenario, with smaller reductions for less contaminated water and ineffective boiling. Similar results were found in Vietnam, apart from fewer avoided DALYs in children due to different demographics. In both countries, for households with high baseline IAP from existing solid fuel use, adding water boiling to cooking on a given stove was associated with a limited increase in IAP DALYs due to the log-linear dose-response curves. Boiling, even at low effectiveness, was associated with <em>net </em>DALY reductions for medium- and high-risk water, even if using unclean stoves/fuels. Replacing traditional stoves with improved stoves coupled with effective boiling practices significantly reduced total DALYs. </p> <p><strong>Discussion: </strong>Boiling water generally resulted in a net decrease in DALYs. Future efforts should empirically measure health outcomes from IAP vs. diarrhea associated with boiling drinking water using field studies with different boiling methods and stove types.</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>
Data from: Is exposure to chytrid fungus and ranavirus higher in ponds invaded by American bullfrogs?
<p>Data associated with manuscript: https://doi.org/10.1007/s10530-025-03648-8<br><br>The spread of emerging infectious diseases (EIDs) and non-native invasive species are interconnected processes driving biodiversity loss. A key example involves Batrachochytrium dendrobatidis (Bd) and ranavirus (Rv), pathogens contributing to global amphibian declines. Their occurrence has been linked to invasive American bullfrogs (Lithobates catesbeianus) serving as asymptomatic vectors. To determine the relationship between Bd and Rv exposure and bullfrogs, we investigated whether pathogen occurrence and environmental loads are correlated with the presence and density of bullfrogs and whether the seasonal variation in Bd exposure is related to bullfrog phenology. We sampled 157 ponds in Belgium, including four with known bullfrog and Bd presence that were monitored monthly for two years, using quantitative environmental DNA (eDNA) barcoding. We validated a duplexed assay that simultaneously targets Bd and Rv, and we quantified eDNA concentrations of bullfrogs, Bd, and Rv serving as proxies for density and pathogen loads. Bd was detected more frequently when bullfrogs were present and both the frequency of detection and environmental loads of Bd increased in ponds with high bullfrog densities. In contrast, Rv detection likelihood and load were not significantly related to bullfrog presence or density. Seasonal fluctuations in Bd loads varied between ponds with and without breeding activity. In the former, Bd was consistently detected, likely due to overwintering tadpoles. In the latter, Bd loads varied throughout the year, with peak loads coinciding with juvenile immigration. Collectively, our findings indicate that amphibian exposure to Bd, but not Rv, is higher in areas and periods where bullfrogs are present and occur at high densities</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.