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9,674 results for “covid-19”
Monthly aerosol emissions and GHG concentration projections from 2020-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>This repository holds the netcdf files for emissions and concentrations projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020. The details of these activity estimates are available from <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/tree/endof2020</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> to have a different timeframe. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
Four-year blip emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>This repository holds the netcdf files for aerosol emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database ( <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020 before returning to baseline. The details of these activity estimates runs in parallel to those described in <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>, except instead of a 2-year blip, we have done a 4-year blip. Note that it is one year after the blip has finished before things return to baseline.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> for aerosols emissions. We present only a single scenario (called 4-year blip, featuring a one year recovery after the end of the 4 years) compared to the baseline.</p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
Dataset: The Role of News Consumption on Influencers' Facebook Pages in Threat Perception and Political Conservatism During Times of COVID-19: A Comparative Study between the USA, Spain, and Egypt
<p>Este archivo ofrece los datos en bruto de una encuesta examina el impacto del consumo de noticias en las páginas de Facebook de los influencers en la motivación del conservadurismo político durante amenazas como el terrorismo o las pandemias. Muestra: N=1309, jóvenes de entre 18 y 35 años en Estados Unidos, España y Egipto. Trabajo de campo realizado entre el 10 de agosto de 2021 y el 5 de septiembre de 2021.</p> <p><span>Dataset correspondiente al proyecto El rol de la ciudadanía en la comunicación política digital CI-COMPOL (PID2020-119492GB-I00) financiado por MCIN/AEI/10.13039/501100011033/. IP: Andreu Casero-Ripollés, Departamento de Ciencias de la Comunicación, Universitat Jaume I de Castellón</span></p>
National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19- DECEMBER 2020)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the sixth dataset of the project, corresponding to December 2020, collected nine months after the lockdown began in Mexico. Data collection was performed from November 27 to December 11, 2020.</p>
National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19 - APRIL 2022)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the eleventh dataset of the project, corresponding to April 2022, collected 24 months after the lockdown began in Mexico. Data collection was performed from March 17 to May 2, 2022.</p>
National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19- MAY 2021)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the ninth dataset of the project, corresponding to May 2021, collected thirteen months after the lockdown began in Mexico. Data collection was performed from May 21 to Jun 17, 2021.</p>
National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19- OCTOBER 2021)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the tenth dataset of the project, corresponding to October 2021, collected 19 months after the lockdown began in Mexico. Data collection was performed from October 20 to November 13, 2021.</p>
National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19 - MARCH 2021)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 provides information on the well-being of Mexican households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a project consisting of a series of cross-sectional telephone surveys collected in key moments of the COVID-19 pandemic. In addition to the four main domains and a set of COVID19-related questions, the survey includes new key indicators every month to capture the impact of the pandemic on issues like education, social programs, and crime. This is the sixth dataset of the project, corresponding to March 2021, collected one year after the lockdown began in Mexico. Data collection was performed from February 26 to March 27, 2021.</p>
Appendix - Potential COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches
<p>The methods and results of the publication "COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches" are described in more detail in this appendix. The R-syntax for the calculation is provided, as well as a pseudo data set with which the syntax can also be tested.</p>
Physical Health of Adults during Covid-19
<h1>Background</h1> <p>This dataset is one of the studies of the <a href="https://www.corona-health.net/en/">Corona Health project</a>. It addresses how the physical health and habits of adults during the global pandemic changed over time. It consists of two questionnaires, a baseline questionnaire and a bi-weekly follow up questionnaire to track the behaviour in the last 14 days. The whole datasets contains more than <strong>1800 users</strong> (98 % of them German) and a total of <strong>7000 questionnaires</strong>. For some users, it also has GPS and app usage data.</p> <h1>Files</h1> <ul> <li>rki_heart_baseline.csv -> The baseline questionnaire, containing demographic data as well</li> <li>rki_heart_followup.csv -> The follow-up questionnaire</li> <li>answersheets.csv -> The unprocessed answersheets from both, baseline and follow-up questionnaires in a raw format.</li> <li>codebook.xlsx -> The Codebook that describes the three files in detail</li> </ul> <h1>More information</h1> <ul> <li> <p><strong>Key Facts</strong></p> <ul> <li>No. of questionnaires: 1805 Baseline + 5895 Follow-up</li> <li>n Tracking Consent GPS (ratio): 1366 (75%)</li> <li>n Tracking Consent App Usage (ratio): 101 (5.6%)</li> </ul> </li> <li> <p><strong>Sociodemographics</strong></p> <ul> <li>Age, mean (SD): 41.7 (15.1)</li> <li>Gender Ratio: <ul> <li>Male: 36%</li> <li>Female: 64%</li> <li>Diverse: 0%</li> </ul> </li> <li>Body Mass Index, mean (SD): 26.7 (6.23)</li> <li>Users located in Germany (ratio): 98.7%</li> </ul> </li> <li> <p><strong>Lifestyle habits at baseline</strong></p> <ul> <li>Daily Smokers (ratio): 292 (16.2%)</li> <li>Daily fruit consumers before lockdown (ratio): 533 (29.5%)</li> <li>Daily fruit consumers after lockdown (ratio): 538 (29.8%)</li> <li>Daily vegetable consumers before lockdown (ratio): 562 (31.1%)</li> <li>Daily vegetable consumers after lockdown (ratio): 559 (31.0%)</li> </ul> </li> <li> <p><strong>Cardiovascular Health at baseline</strong></p> <ul> <li>History of hypertension (ratio): 454 (25.2%)</li> <li>History of diabetes mellitus (ratio): 113 (6.3%)</li> <li>History of hyperlipidemia (ratio): 461 (25.5%)</li> </ul> </li> </ul> <p>For a more detailed description of the dataset, please go on <a href="https://github.com/joa24jm/CH-Heart" target="_blank" rel="noopener">GitHub/joa24jm/ch-heart.</a> There, you can also find a link to our publication.</p>
Sub-National COVID-19 Incidence and Determinants Dataset
<p>The Sub-National COVID-19 Incidence and Determinants Dataset contains rich sub-national data on COVID-19 cases and deaths combined with data on factors associated with the spread and severity of COVID-19 outbreaks in 2020. The data covers 503 sub-national areas (NUTS-2 level and equivalents) of 46 countries in five continents (Europe, Asia, North America, South America and Oceania). Indicators were mostly gathered weekly, with the exception of some variables that are monthly and yearly. The dataset was compiled to study the determinants of COVID-19 outbreaks with a focus on the effects of international airline travel. However, the data are useful to investigate also other questions on the sub-national diffusion of COVID-19. The information used to build this dataset was drawn from a variety of sources in order to cover four major areas of interest: health outcomes of the pandemic (COVID-19 cases and deaths), international air travel (number of incoming air passengers, centrality of local airports in the global airline network and air travel limitation policies), population mixing and government policy responses, and pre-pandemic area characteristics (socioeconomic, demographic, public health and co-morbidity). A complete list of sources can be found in the “Data sources” Pdf document attached.</p> <p>Please cite as: Recchi, E., A. Ferrara, A. Rodríguez Sánchez, E. Deutschmann, L. Gabrielli, S. Iacus, L. Bastiani, S. Spyratos & M. Vespe. 2022. The Impact of Air Travel on the Precocity and Severity of Covid-19 Deaths in Sub-National Areas across 45 Countries. Scientific Reports 12: 16522. https://doi.org/10.1038/s41598-022-20263-y</p> <p> </p>
COVID-19_WORLD_2020
<p>This dataset contains quantitative data on the evolution of the COVID-19 pandemic of early 2020. The data has been obtained from the web https://www.worldometers.info/coronavirus with web scraping techniques (published on https://github.com/dmar10862/uoc_tipologia_y_ciclo_de_vida_de_los_datos_practica_1.git).</p> <p>Each row in the dataset is identified by the country and date. The columns are as follows:</p> <ul> <li>total_cases: Total number of confirmed cases.</li> <li>new_cases: Number of new cases confirmed compared to the previous day.</li> <li>total_deaths: Total number of confirmed deaths.</li> <li>new_deaths: Number of new deaths confirmed compared to the previous day.</li> <li>total_recovered: Number of confirmed cases recovered.</li> <li>active_cases: Number of confirmed active cases.</li> <li>servious_critical: Number of confirmed serious or critical cases.</li> <li>total_cases_1M_pop: Total number of confirmed cases per million inhabitants.</li> <li>total_deaths_1M_pop: Total number of confirmed deaths per million inhabitants.</li> <li>total_tests: Number of tests performed.</li> <li>tests_1M_pop: Number of tests performed per million inhabitants. </li> </ul>
Monthly CO2 emissions projections from 2015-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>Monthly CO2 emissions projections 2015-2025, modified by country-specific impacts of COVID-19 lockdown in 2020-2023, with 4 different projections for the period 2024-2025. </p> <p>This repository holds the netcdf files for CO2 emissions from ground-level and aviation sources from the MESSAGE_GLOBIOM scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020. Sector activity level in 2020 is based on data up until June, and a fixed estimate is used thereafter. This is the monthly equivalent of <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a> for this time period.</p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details.</p>
Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (PNAS, 2020), "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates"
<p>This upload contains all replication material for "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates" (PNAS, 2020). Please note that previous versions of this upload provided data and code for the pre-print version of the article, which changed somewhat through the peer review process. </p> <p><strong>Authors:</strong> Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor.</p> <p><strong>Code is located within CCHMP_covid_climate_code_release.zip</strong>, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative).</p> <p>Please find the code needed to replicate the main findings of the paper described below:</p> <ul> <li>Plots of data: R and Stata scripts to make figures 1B, 2A/B/C, S1, S2, and S3, can be found within “code/analysis/data_plots/”.</li> <li>Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, 3C, S5, S6, S7, S8, S10, and S14, as well as Table S1, can be found within “code/analysis/regressions/”. R scripts for data analysis and plotting for figures 3A/B and S9 are also within "code/analysis/regressions/".</li> <li>Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 4, S4 and S11 can be found within “code/analysis/seasonal_sim/”.</li> <li>SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S12 and S13 can be found within “code/analysis/SEIR/”.</li> </ul> <p><strong>Data are located within CCHMP_covid_climate_data_release.zip.</strong></p>
COVID-19 Vaccine Tweets in Turkish
<p>This dataset contains the Turkish tweets that are collected with the keyword vaccine, sinovac and biontech in Turkish by using Twitter Academic API. The new versions will be more up-to-date and will be classified monthly folders.</p> <p>You can visit the github repository of the project:</p> <p><a href="https://github.com/burakozturan/tria-covid19">https://github.com/burakozturan/Turkish-Vaccine-Tweets</a></p> <p> </p> <p> </p> <p> </p> <p> </p>
Data for "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada".
<p>Epidemiological and mobility data analysed in the paper "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada".</p>
A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19
<p><strong>Overview</strong></p> <p>This dataset is the repository for the following paper submitted to <em>Data in Brief</em>:</p> <p>Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. <em>Data in Brief</em> (submitted: December 2023).</p> <p>The <em>Data in Brief</em> article contains the supplement information and is the related data paper to:</p> <p>Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. <em>Journal of Arid Environments</em> (revision submitted: December 2023).</p> <p><strong>Description/abstract</strong></p> <p>The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant. </p> <p><strong>Folder structure</strong></p> <p>The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files: </p> <p>“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.</p> <p>“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.</p> <p>“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).</p> <p>“yield_productivity” contains .csv files of yield information for all countries listed above.</p> <p>“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).</p> <p>“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.</p> <p>“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”. </p> <p><strong>Code structure</strong></p> <p>1_MODIS_NDVI_hdf_file_extraction.R </p> <p><br>This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.</p> <p><br>2_MERGE_MODIS_tiles.R</p> <p><br>In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory__MODIS/merged").</p> <p><br>3_CROP_MODIS_merged_tiles.R</p> <p><br>Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS. <br>The repository provides the already clipped and merged NDVI datasets.</p> <p><br>4_TREND_analysis_NDVI.R</p> <p><br>Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.<br>To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique. </p> <p><br>5_BUILT_UP_change_raster.R</p> <p><br>Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022. </p> <p><br>6_POPULATION_numbers_plot.R</p> <p><br>For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration. </p> <p><br>7_YIELD_plot.R</p> <p><br>In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R. </p> <p><br>8_GLDAS_read_extract_trend</p> <p><br>The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection). <br>Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.<br>From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).<br>From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables. </p> <p><br>(9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis.</p> <p>___</p> <p>Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael Kempf</p> <p>___</p> <p><strong>Acknowledgements</strong></p> <p><span><span><span><span>I would like to thank three </span></span></span></span><span><span><span><span><span>anonymous</span></span></span></span></span><span><span><span><span> reviewers for their constructive comments and suggestions that sharpened the paper in the Journal of Arid Environments. I am particularly grateful to the Swiss National Science Foundation (SNSF/SNF) to fund my research project </span></span></span></span><span><span><span><span><em><span>EXOCHAINS - Exploring Holocene Climate Change and Human Innovations across Eurasia</span></em></span></span></span></span><span><span><span><span> at the University of Basel under grant number </span></span></span></span><span><span><span><span>TMPFP2_217358.</span></span></span></span></p> <p> </p> <p><span><span><span><span>__</span></span></span></span></p> <p><br>All data underlying the results of this article are publicly available on the internet:</p> <p>GLDAS Noah Land Surface Model L4 data: NASA's Earth Science Data Systems (ESDS) Program, https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 09th December 2023); </p> <p><br>Country borders: https://www.geoboundaries.org (last accessed 7th of March 2023) and Natural Earth https://www.naturalearthdata.com/ (last accessed 5th of December 2023);</p> <p><br>FAOstats (Food and Agriculture Organisation of the United Nations: https://www.fao.org/faostat/en/#data/QCL (last accessed 7th of March 2023);</p> <p><br>Global Human Settlement Layer datasets (GHSL): https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 7th of March 2023);</p> <p><br>Population development: <br>FAO, https://www.fao.org/countryprofiles/index/en/?iso3=JOR (last accessed 4th of March 2023); <br>the Worldbank, https://www.worldbank.org/en/home (last accessed: 04th of March 2023); <br>Worlddata.info, https://www.worlddata.info/asia/palestine/populationgrowth.php (last accessed 4th of March 2023);</p> <p><br>Water demand and population numbers (Tab. 1): https://www.fao.org/faostat/en/#data/OA; https://databank.worldbank.org/reports.aspx?source=world-development-indicators# (last accessed 13th of December 2023);</p> <p><br>MODIS: Earthdata server of the United States Geological Survey (USGS), MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006, https://lpdaac.usgs.gov/products/mod13q1v061/ (last accessed 7th of March 2023).</p> <p><br>Competing interests statement:<br>The author declares no conflict of interest.<br>The author has no relevant financial or non-financial interests to disclose.<br>Data availability: All data underlying the analyses are freely available on the internet and where applicable, sources are cited in the text.<br>Ethical approval: This article does not contain any studies with human participants performed by any of the authors.<br>Informed consent: This article does not contain any studies with human participants performed by any of the authors.</p>
A comparative dataset on public perceptions of multiple risks during the COVID-19 pandemic in Italy and Sweden
<p>These datasets are the result of two nation-wide surveys conducted in Italy and Sweden in August 2020 and in november 2020. The surveys (which are identical in the two rounds) explore the respondents' risk perception, preparedness, knowledge, and experience regarding a set of hazards, namely: epidemics, floods, droughts, earthquakes, wildfires, terror attacks, domestic violence, economic crises, and climate change. </p> <p>The data files include the questionnaire survey (the Italian and Swedish versions as well as the English translation) and the two datasets of all the answers to the two surveys. Each column in the dataset refers to an item in the survey (e.g. a question or a sub-question), and each row represents a single respondent. </p> <p>For additional information on the August 2020 dataset, see <a href="https://www.nature.com/articles/s41597-020-00778-7">Mondino et al. (2020)</a>.</p>
DATA SET: Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units
<p>This repository contains the data sets of the article:</p> <p>Mesquida, J., Caballer, A., Cortese, L. <em>et al.</em> Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units. <em>Crit Care</em> <strong>25, </strong>381 (2021). https://doi.org/10.1186/s13054-021-03803-2</p>
Dataset - paper: Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France
<p>Dataset corresponding to a paper that has been published in Appetite (Philippe K, Chabanet C, Issanchou S, Monnery-Patris S. <em>Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France: (How) did they change? </em>Appetite. 2021 Jun 1;161:105132. doi: <strong>10.1016/j.appet.2021.105132</strong>. Epub 2021 Jan 23. PMID: 33493611; PMCID: PMC7825985).</p> <p>The objective of the study was to evaluate possible changes in eating behaviors in children aged 3–12 years, in parental eating and cooking behaviors, in parental feeding practices, and also in parental motivations when shopping for food during the lockdown, compared to the period before the lockdown.</p> <p>Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-COVID.docx".</p>
ScienceDex guides
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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.