Well Sub Download ((EXCLUSIVE))
Click on the "browse gene sets" links in the table below to view the gene sets in a collection. Or download the gene sets in a collection by clicking on the links below the "Download Files" headings. For a description of the GMT file format see the Data Formats in the Documentation section. The gene sets can be downloaded as NCBI (Entrez) Gene Identifiers or HUGO (HGNC) Gene Symbols. There are also JSON bundles containing the HUGO (HGNC) Gene Symbols along with some useful metadata. An XML file containing all the Human MSigDB gene sets is available as well.
Well sub download
Using the spatial boundaries of the harmonized regions, we complemented the aggregated DHS data with additional external information about the climatic and socio-economic conditions in the regions. As climatic factors, we obtained monthly information on temperature and precipitation from the Climatic Research Unit of the University of East Anglia8 which we used to identify anomalies and extreme events in the years prior to the DHS data collection. As external socio-economic indicators, we included information on the regional Gross Domestic Product (GDP) per capita and Human Development Index (HDI), which are based on Kummu, Taka, and Guillaume (2018)9. In addition to the dataset, we have created the companion R package livwelldata that makes it easy to use the dataset in R and subset it by selecting indicators of interest. The package and instruction can be found on the git repository of the R package: -potsdam.de/belmin/livwelldata.
The Demographic and Health Survey (DHS)5 represents the primary source of data. Since its creation in 1986, the DHS Program has collected hundreds of surveys in over 90 low- and middle-income countries, focusing on population issues and health. The DHS Program typically adopts a two-stage cluster sampling design that ensures representativeness of the data at the national and sub-national level16. Most DHS variables are the same across countries and time, enabling the comparison over space and time. LivWell includes 52 countries out of the 90 countries covered by DHS. Countries had to be excluded either because they had only one DHS wave or because the harmonization of their regions over time was not possible. In addition, two surveys, India 2015 and Turkey 1998, had to be excluded because it was not feasible to load the files on our system. It is possible, however, to calculate the indicators in LivWell also for single DHS survey using the tutorial adding_country_livwell.Rmd on the git repository of this article.
Once the assignment of harmonized indices was done, we collected the harmonized boundaries using the function download_boundaries() from the R package rdhs27 and whenever necessary, merged the region boundaries using the function st_union() from the R package sf28 (Figs. 3 and 4). Supplementary Fig. 3 and the file methodology_harmonization.Rmd on the data record4 and the git repository provide a complete description of the mapping of the raw and harmonized regions and the steps involved in the harmonization.
Harmonized DHS regions in Bangladesh. The regions of Dhaka and Mymensingh as well as Rajashahi and Rangpur were combined to a larger spatial entity to account for boundary changes and split-ups of the regions over time.
We used the information on the spatial boundaries of the regions to crop the gridded data and calculate average GDP per capita and HDI values for each of the DHS regions. The boundary information was saved as a multi-polygon geopackage and is available for download together with the other materials on the data record4 and on the git repository of the paper. The R package raster39 was used for the data transformation. First, the raster data over time is clipped to the spatial boundary data to obtain a multilayered rasterbrick object containing information on the DHS region each raster grid falls into (either fully or partly). Based on this, an aggregate regional value was derived using the extract() function, which returns the mean value of all raster cells whose center lie inside the boundaries of a region (the weight option can be used to also consider partly covered cells). The resulting dataset contains information on a yearly basis from 1990 to 2015. By joining these data to the aggregated DHS data, we obtain information on regional GDP per capita and HDI values for each of the DHS waves until 2015.
The SPEI database provides information about drought conditions globally at a 0.5 spatial resolution. It combines information on monthly precipitation and potential evapotranspiration and uses a standardized intensity scale with higher values indicating more humid, and lower values indicating drier conditions. The SPEI can be calculated for different time periods from 1 to 48 months, reflecting the time scale over which the water balance in a region is measured. Here, we provide indicators based on SPEI03 data, as previous research has suggested that a 3-month SPEI is well-suited to monitor drought impacts on vegetation40. Users can flexibly adjust the temporal scale by replacing the SPEI03 data with other datasets.
where Ximy represents the temperature, precipitation or SPEI03 in a region i in month m and year y, and Xim and SDim the average value and standard deviation of X for region i in month m calculated over all years. The derived monthly anomaly measures are standardized and can be interpreted as monthly (positive or negative) deviations from the long-run mean in terms of standard deviations of the local distribution. Based on these monthly values, we calculate the mean temperature, precipitation and SPEI03 anomaly as well as the mean absolute (_abs) anomaly 12, 36, and 60 months prior to the DHS survey. While the first of the two measures averages over both the positive and negative anomalies, the second one takes only the absolute deviation from the mean into account, reflecting the intensity of the fluctuations over time.
The data and codes to reproduce the dataset are hosted on Zenodo, which is a general-purpose open data repository developed under the European OpenAIRE program and operated by CERN. The data are hosted at the permanent DOI There are three csv files and one gpkg (GeoPackage) file in the repository. The first file livwell.csv contains the LivWell dataset itself with all 265 indicators for 52 countries, 447 sub-national regions over 30 years. The livwell.csv file also contains the standard errors for each indicator based on DHS data. The second file, livwell_lin_interpolated.csv contains the Livwell dataset including the linearly interpolated data. The third file indicators.csv includes the metadata and description of indicators. The fourth file harmonized_boundaries.gpkg contains the harmonized boundaries of the sub-national regions used in our dataset, together with the indices and names of regions. The dataset LivWell may be shared and adapted under the conditions of the CC-BY 4.0 License:
In addition, the records contain a folder code_livwell with all the codes and intermediary data to reproduce the dataset or to add new indicators or new surveys. We also included two tutorials: name adding_indicator_livwell.Rmd, to add a new indicator, and adding_country_livwell.Rmd, to add a new country or survey. However, this is a static version of the code. We encourage users to use the git repository to have the latest version of the code: -potsdam.de/belmin/livwelldata-paper/.
We also created a R package named livwelldata allowing to easily use the dataset in R. The package contains four functions. The function livwell_data() allows to load the whole dataset or a subset based on a selection of countries, years and/or indicators. It contains a parameter interpolated allowing to load the data including interpolation. The function livwell_indicators() allows to extract the list of all available indicators. The function livwell_countries() allows to extract the list of countries in the dataset. The function livwell_harmonized_regions() allows to extract the list of harmonized sub-national regions. The R package livwelldata as well as the instruction to use it can be found on this git repository: -potsdam.de/belmin/livwelldata.
The processing steps to obtain the dataset were carried out in R and Rmarkdown and are reproducible (except for one step of the harmonization of DHS regions and variables that had to be done manually). All the code is available on the git repository of this article: -potsdam.de/belmin/livwelldata-paper. The source code for the companion R package livwelldata is available on the git repository of the package: -potsdam.de/belmin/livwelldata. The following R packages were central to the development of LivWell: tidyverse41, knitr42, rdhs27, DHS.rates32 and survey31.
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