The DHS Data Inventory is a list of datasets used in and by the department. The dataset details include a verbose set of metadata identifying the source, quality, governance, publication, size and characteristics of the data, with pointers to the data access URL(s) and access rights for use within the department. The Data Inventory was created in response to legislative requirements of the Evidence Act (and Open Government Data Act). Metadata about datasets that are marked as 'public' are sent to Data.gov on a weekly basis.

This dataset displays demographics for the families and individuals residing in the Department of Homeless Services (DHS) shelter system.

Advancing Research on Nutrition and Agriculture (AReNA) is a 6-year, multi-country project in South Asia and sub-Saharan Africa funded by the Bill and Melinda Gates Foundation, being implemented from 2015 through 2020. The objective of AReNA is to close important knowledge gaps on the links between nutrition and agriculture, with a particular focus on conducting policy-relevant research at scale and crowding in more research on this issue by creating data sets and analytical tools that can benefit the broader research community. Much of the research on agriculture and nutrition is hindered by a lack of data, and many of the datasets that do contain both agriculture and nutrition information are often small in size and geographic scope. AReNA team constructed a large multi-level, multi-country dataset combining nutrition and nutrition-relevant information at the individual and household level from the Demographic and Health Surveys (DHS) with a wide variety of geo-referenced data on agricultural production, agroecology, climate, demography, and infrastructure (GIS data). This dataset includes 60 countries, 184 DHS, and 122,473 clusters. Over one thousand geospatial variables are linked with DHS. The entire dataset is organized into 13 individual files: DHS_distance, DHS_livestock, DHS_main, DHS_malaria, DHS NDVI, DHS_nightlight, DHS_pasture and climate (mean), DHS_rainfall, DHS_soil, DHS_SPAM, DHS_suit, DHS_temperature, and DHS_traveltime.

This collection consists of geospatial data layers and summary data at the country and country sub-division levels that are part of USAID's Demographic Health Survey Spatial Data Repository. This collection includes geographically-linked health and demographic data from the DHS Program and the U.S. Census Bureau for mapping in a geographic information system (GIS). The data includes indicators related to: fertility, family planning, maternal and child health, gender, HIV/AIDS, literacy, malaria, nutrition, and sanitation. Each set of files is associated with a specific health survey for a given year for over 90 different countries that were part of the following surveys:Demographic Health Survey (DHS)Malaria Indicator Survey (MIS)Service Provisions Assessment (SPA)Other qualitative surveys (OTH)Individual files are named with identifiers that indicate: country, survey year, survey, and in some cases the name of a variable or indicator. A list of the two-letter country codes is included in a CSV file.Datasets are subdivided into the following folders:Survey boundaries: polygon shapefiles of administrative subdivision boundaries for countries used in specific surveys. Indicator data: polygon shapefiles and geodatabases of countries and subdivisions with 25 of the most common health indicators collected in the DHS. Estimates generated from survey data.Modeled surfaces: geospatial raster files that represent gridded population and health indicators generated from survey data, for several countries.Geospatial covariates: CSV files that link survey cluster locations to ancillary data (known as covariates) that contain data on topics including population, climate, and environmental factors.Population estimates: spreadsheets and polygon shapefiles for countries and subdivisions with 5-year age/sex group population estimates and projections for 2000-2020 from the US Census Bureau, for designated countries in the PEPFAR program.Workshop materials: a tutorial with sample data for learning how to map health data using DHS SDR datasets with QGIS. Documentation that is specific to each dataset is included in the subfolders, and a methodological summary for all of the datasets is included in the root folder as an HTML file. File-level metadata is available for most files. Countries for which data included in the repository include: Afghanistan, Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Congo, Congo (Democratic Republic of the), Cote d'Ivoire, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini (Swaziland), Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, India, Indonesia, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mexico, Moldova, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Russia, Rwanda, Samoa, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sri Lanka, Sudan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, Uzbekistan, Viet Nam, Yemen, Zambia, Zimbabwe

The Demographic and Health Surveys (DHS) Program overseen by the US Agency for International AID (USAID) uses nationally representative surveys, biomarker testing, and geographic location to collect data on monitoring and impact evaluation indicators for individual countries and for cross-country comparisons.

Standardized DHS surveys include the Demographic and Health Survey, Service Provision Assessment, HIV/AIDS Indicator Survey, Malaria Indicator Survey, and Key Indicators Survey. The DHS Program also collects biomarkers and geographic data. Data availability varies by year and country. A table that lists all currently available data can be found here.

ST - DHS Public Access Database: Consistent with the 2013 OSTP Memorandum and the 2022 update, “Increasing Access to the Results of Federally Funded Scientific Research,” directed all agencies with greater than $100 million in R&D expenditures each year to prepare a plan for improving the public’s access to the results of federally funded research, specifically peer-reviewed scholarly publications and digital data. In response to the memorandum, DHS developed a DHS Public Access Plan, and intends to make available to the public digitally formatted scientific data that support the conclusions in peer-reviewed scholarly publications that are the results of DHS R&D funding. This data repository site with a customized DHS Storefront allows DHS to post releasable scientific digital data from peer-reviewed publications resulting from DHS-funded research. The data repository is configured to allow DHS users (and publishers acting on behalf of these users) to deposit data sets into the repository, making them available to the general public.

This collection consists of summary data for countries, country subdivisions, and demographic categories that were generated from USAID's Demographic Health Survey (DHS). The indicators are population-level estimates that were generated from sample surveys that were conducted in over 90 low and middle income countries at various points over several decades. Almost 2,000 indicators capture information related to: fertility, family planning, maternal and child health, gender, HIV/AIDS, literacy, malaria, nutrition, and sanitation.Each set of files is associated with a specific health survey for a given year and country. The surveys include:AIDS Indicator Survey (AIS)Demographic Health Survey (DHS)Malaria Indicator Survey (MIS)Service Provisions Assessment (SPA)Other qualitative surveys (OTH)The top-level folder includes a list of country codes and field names used in all of the data files, and a file list with a record count. The data was extracted from the DHS program's API, and is structured as follows:Datasets are organized in folders that are named with a two-letter country code, survey name, and year.Each survey has three sets of files: a National file with country-level records, a Subnational file with country subdivision-level records, and a Background file with categorical records (such as urban / rural, education level, income level, etc.)Each set of files comes in two formats: a long-format JSON file with key-value pairs of labels and values, and a wide-format CSV file where labels have been pivoted to column headings.A _CODEBOOKS folder contains CSV, JSON, and HTML files that describe the variables and all coded values, and PDF documentation for the overall DHS statistics program.Reports associated with a specific survey that were available via the API are stored in the survey folder with the data.A PUBS_NODATA folder includes reports that were available via the API, but that did not have machine-readable data associated with them.Countries for which data was included in the indicators API include:Afghanistan, Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Congo, Congo (Democratic Republic of the), Cote d'Ivoire, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini (Swaziland), Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, India, Indonesia, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mexico, Moldova, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Rwanda, Samoa, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sri Lanka, Sudan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, Uzbekistan, Viet Nam, Yemen, Zambia, Zimbabwe

Contains data from the DHS data portal. There is also a dataset containing Mozambique - Subnational Demographic and Health Data on HDX.

The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.

Abstract

The Demographic and Health Survey (DHS) Program is a USAID funded program that supports governments with their demographic and health data surveys and makes the data available on their website. Demographic and health data for 46 African countries https://www.dhsprogram.com/Countries/Country-List.cfm is available from the DHS site. Researchers can register their projects with the Program on the site to obtain access to this data https://www.dhsprogram.com/data/new-user-registration.cfm Reports for each survey round are also available from the site https://www.dhsprogram.com/publications/index.cfm

Analysis unit

Households and individuals

Kind of data

Survey data

The DHS Data Inventory Program is working to create a single data inventory of all data within DHS, including the DHS Components, the DHS Functional Data Domains, and DHS HQ. rnrnThe Data Inventory Program is designed to exceed DHS obligations under the Foundations for Evidence-Based Policymaking Act (the Evidence Act), the OPEN Government Data Act, the DHS Data Framework Act of 2018, and DHS Delegation Number 04004 rev 00 of May 18, 2021 from Secretary Mayorkas to the Chief Data Officer. The goal is to create a data inventory that will be useful for all of DHS to help answer questions about DHS data in a timely manner and help DHS leadership plan new activities.

Annual Reporting data of Work Health and Safety, and Return to Work performance. Dataset includes: Data from 2013-14 financial year through to 2023-24 financial year. Includes data from predecessor agency Department for Communities and Social Inclusion (DCSI).

Datasets dating from 1986 to the present are available for 93 countries in which data were collect through Household questionnaires, Women's questionnaires, Men's questionnaires, Biomarker's questionnaires, and Fieldworker's questionnaires. The following data types are produced from the collected data : Household Recode, Household Member Recode, Individual Women's Recode, Births Recode, Children's Recode, Men's Recode, Couple's Recode, Geographic Data, Geospatial Covariates. To view surveys and available datasets go to https://dhsprogram.com/data/available-datasets.cfm. Access to datasets for DHS surveys and their supporting documents may be granted to individuals who register at https://dhsprogram.com/data/new-user-registration.cfm and create a new research project request.

This package contains two files designed to help read individual level DHS data into Stata. The first file addresses the problem that versions of Stata before Version 7/SE will read in only up to 2047 variables and most of the individual files have more variables than that. The file will read in the .do, .dct and .dat file and output new .do and .dct files with only a subset of the variables specified by the user. The second file deals with earlier DHS surveys in which .do and .dct file do not exist and only .sps and .sas files are provided. The file will read in the .sas and .sps files and output a .dct and .do file. If necessary the first file can then be run again to select a subset of variables.

Cancer-specific questions by country and survey years.

The intended use of the dataset is forecasting of the heat demand in the existing infrastructure, namely the Faculty of Mechanical Engineering in Nis District Heating System (FMEDH). FMEDH has 12 consumers (and respective heating substations - published data is from the individual substation) of different demands, namely secondary education, higher-education and research organizations, student dormitory, restaurant and a residential block and total of nearly 90.000m2 heating area (new consumers are expected to be connected to FMEDH in 2021, increasing the total heating area to 120.000m2). Natural gas is used as primary fuel. FMEDH is equipped with a SCADA system with continuous data acquisition facility (in plant and substations, primary and secondary supply and return lines water temperature, fluid flow and pressure).

The dataset includes following relevant features: Outside air temperature, acquired by the sensor in the substation (C, tsp), Water temperature in the secondary supply line (C, tns), Water temperature in the secondary return line (C, tps), Water temperature in the primary supply line (C, tns), Water temperature in the primary return line (C, tps) and Heat energy transmitted (Kw, qizm).

Spatial Data Repository and STATcompilerUSAID-funded The Demographic and Health Surveys (DHS) Program: Produced by ICF International This spatial Demographic and Health Surveys Program Data Package is made available from the Spatial Data Repository and STATcompiler under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/CITATIONData users should cite the Spatial Data Respository as the source of all derived reports, publications, presentations, or other products. To use the recommended citation, simply replace the date of download.Spatial Data Repository, The Demographic and Health Surveys Program. ICF International. Available from spatialdata.dhsprogram.com [Accessed Day Month Year].

Abstract

The principal objective of the Ethiopia Demographic and Health Survey (DHS) is to provide current and reliable data on fertility and family planning behavior, child mortality, children’s nutritional status, the utilization of maternal and child health services, and knowledge of HIV/AIDS. This information is essential for informed policy decisions, planning, monitoring, and evaluation of programs on health in general and reproductive health in particular at both the national and regional levels. A long-term objective of the survey is to strengthen the technical capacity of the Central Statistical Authority to plan, conduct, process, and analyze data from complex national population and health surveys. Moreover, the 2000 Ethiopia DHS is the first survey of its kind in the country to provide national and regional estimates on population and health that are comparable to data collected in similar surveys in other developing countries. As part of the worldwide DHS project, the Ethiopia DHS data add to the vast and growing international database on demographic and health variables. The Ethiopia DHS collected demographic and health information from a nationally representative sample of women and men in the reproductive age groups 15-49 and 15-59, respectively.

The Ethiopia DHS was carried out under the aegis of the Ministry of Health and was implemented by the Central Statistical Authority. ORC Macro provided technical assistance through its MEASURE DHS+ project. The survey was principally funded by the Essential Services for Health in Ethiopia (ESHE) project through a bilateral agreement between the United States Agency for International Development (USAID) and the Federal Democratic Republic of Ethiopia. Funding was also provided by the United Nations Population Fund (UNFPA).

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The Ethiopia DHS used the sampling frame provided by the list of census enumeration areas (EAs) with population and household information from the 1994 Population and Housing Census. A proportional sample allocation was discarded because this procedure yielded a distribution in which 80 percent of the sample came from three regions, 16 percent from four regions and 4 percent from five regions. To avoid such an uneven sample allocation among regions, it was decided that the sample should be allocated by region in proportion to the square root of the region's population size. Additional adjustments were made to ensure that the sample size for each region included at least 700 households, in order to yield estimates with reasonable statistical precision.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

The Ethiopia DHS used three questionnaires: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire, which were based on model survey instruments developed for the international MEASURE DHS+ project. The questionnaires were specifically geared toward obtaining the kind of information needed by health and family planning program managers and policymakers. The model questionnaires were then adapted to local conditions and a number of additional questions specific to on-going health and family planning programs in Ethiopia were added. These questionnaires were developed in the English language and translated into the five principal languages in use in the country: Amarigna, Oromigna, Tigrigna, Somaligna, and Afarigna. They were then independently translated back to English and appropriate changes were made in the translation of questions in which the back-translated version did not compare well with the original English version. A pretest of all three questionnaires was conducted in the five local languages in November 1999.

All usual members in a selected household and visitors who stayed there the previous night were enumerated using the Household Questionnaire. Specifically, the Household Questionnaire obtained information on the relationship to the head of the household, residence, sex, age, marital status, parental survivorship, and education of each usual resident or visitor. This information was used to identify women and men who were eligible for the individual interview. Women age 15-49 in all selected households and all men age 15-59 in every fifth selected household, whether usual residents or visitors, were deemed eligible, and were interviewed. The Household Questionnaire also obtained information on some basic socioeconomic indicators such as the number of rooms, the flooring material, the source of water, the type of toilet facilities, and the ownership of a variety of durable items. Information was also obtained on the use of impregnated bednets, and the salt used in each household was tested for its iodine content. All eligible women and all children born since Meskerem 1987 in the Ethiopian Calendar, which roughly corresponds to September 1994 in the Gregorian Calendar, were weighed and measured.

The Women’s Questionnaire collected information on female respondent’s background characteristics, reproductive history, contraceptive knowledge and use, antenatal, delivery and postnatal care, infant feeding practices, child immunization and health, marriage, fertility preferences, and attitudes about family planning, husband’s background characteristics and women’s work, knowledge of HIV/AIDS and other sexually transmitted infections (STIs).

The Men’s Questionnaire collected information on the male respondent’s background characteristics, reproduction, contraceptive knowledge and use, marriage, fertility preferences and attitudes about family planning, and knowledge of HIV/AIDS and STIs.

Response rate

A total of 14,642 households were selected for the Ethiopia DHS, of which 14,167 were found to be occupied. Household interviews were completed for 99 percent of the occupied households. A total of 15,716 eligible women from these households and 2,771 eligible men from every fifth household were identified for the individual interviews. The response rate for eligible women is slightly higher than for eligible men (98 percent compared with 94 percent, respectively). Interviews were successfully completed for 15,367 women and 2,607 men.

There is no difference by urban-rural residence in the overall response rate for eligible women; however, rural men are slightly more likely than urban men to have completed an interview (94 percent and 92 percent, respectively). The overall response rate among women by region is relatively high and ranges from 93 percent in the Affar Region to 99 percent in the Oromiya Region. The response rate among men ranges from 83 percent in the Affar Region to 98 percent in the Tigray and Benishangul-Gumuz regions.

Note: See summarized response rates by place of residence in Table A.1.1 and Table A.1.2 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the Ethiopia DHS to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the Ethiopia DHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the Ethiopia DHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the Ethiopia DHS is the ISSA Sampling Error Module (SAMPERR). This module used the Taylor linearisation method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: See detailed estimate of sampling error calculation in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age

Annual Report data on instances of fraud in DHS Dataset includes: Data from 2012-13 financial year through to 2024-25 financial year Source: DHS Incident Management Unit Includes data from predecessor agency Department for Communities and Social Inclusion (DCSI).

Comprehensive dataset containing 32 verified DHS locations in United States with complete contact information, ratings, reviews, and location data.

195157 Global import shipment records of Dhs with prices, volume & current Buyer’s suppliers relationships based on actual Global import trade database.