This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

This SAS code extracts data from EU-SILC User Database (UDB) longitudinal files and edits it such that a file is produced that can be further used for differential mortality analyses. Information from the original D, R, H and P files is merged per person and possibly pooled over several longitudinal data releases. Vital status information is extracted from target variables DB110 and RB110, and time at risk between the first interview and either death or censoring is estimated based on quarterly date information. Apart from path specifications, the SAS code consists of several SAS macros. Two of them require parameter specification from the user. The other ones are just executed. The code was written in Base SAS, Version 9.4. By default, the output file contains several variables which are necessary for differential mortality analyses, such as sex, age, country, year of first interview, and vital status information. In addition, the user may specify the analytical variables by which mortality risk should be compared later, for example educational level or occupational class. These analytical variables may be measured either at the first interview (the baseline) or at the last interview of a respondent. The output file is available in SAS format and by default also in csv format.

File List ACS.zip -- .zip file containing SAS macro and example code, and example Aletris bracteata data sets. acs.sas chekika_ACS_estimation.sas chekika_1.csv chekika_2.csv philippi.3.1.zip

Description "acs.sas" is a SAS macro for computing Horvitz-Thompson and Hansen-Horwitz estimates of population size for adaptive cluster sampling with random initial sampling. This version uses ugly base SAS code and does not require SQL or SAS products other than Base SAS, and should work with versions 8.2 onward (tested with versions 9.0 and 9.1). "chekika_ACS_estimation.sas" is example SAS code calling the acs macro to analyze the Chekika Aletris bracteata example data sets. "chekika_1.csv" is an example data set in ASCII comma-delimited format from adaptive cluster sampling of A. bracteata at Chekika, Everglades National Park, with 1-m2 quadrats. "chekika_2.csv" is an example data set in ASCII comma-delimited format from adaptive cluster sampling of A. bracteata at Chekika, Everglades National Park, with 4-m2 quadrats. "philippi.3.1.zip" metadata file generated by morpho, including both xml and css.

The simulated synthetic aperture sonar (SAS) data presented here was generated using PoSSM [Johnson and Brown 2018]. The data is suitable for bistatic, coherent signal processing and will form acoustic seafloor imagery. Included in this data package is simulated sonar data in Generic Data Format (GDF) files, a description of the GDF file contents, example SAS imagery, and supporting information about the simulated scenes. In total, there are eleven 60 m x 90 m scenes, labeled scene00 through scene10, with scene00 provided with the scatterers in isolation, i.e. no seafloor texture. This is provided for beamformer testing purposes and should result in an image similar to the one labeled "PoSSM-scene00-scene00-starboard-0.tif" in the Related Data Sets tab. The ten other scenes have varying degrees of model variation as described in "Description_of_Simulated_SAS_Data_Package.pdf". A description of the data and the model is found in the associated document called "Description_of_Simulated_SAS_Data_Package.pdf" and a description of the format in which the raw binary data is stored is found in the related document "PSU_GDF_Format_20240612.pdf". The format description also includes MATLAB code that will effectively parse the data to aid in signal processing and image reconstruction. It is left to the researcher to develop a beamforming algorithm suitable for coherent signal and image processing. Each 60 m x 90 m scene is represented by 4 raw (not beamformed) GDF files, labeled sceneXX-STARBOARD-000000 through 000003. It is possible to beamform smaller scenes from any one of these 4 files, i.e. the four files are combined sequentially to form a 60 m x 90 m image. Also included are comma separated value spreadsheets describing the locations of scatterers and objects of interest within each scene. In addition to the binary GDF data, a beamformed GeoTIFF image and a single-look complex (SLC, science file) data of each scene is provided. The SLC data (science) is stored in the Hierarchical Data Format 5 (https://www.hdfgroup.org/), and appended with ".hdf5" to indicate the HDF5 format. The data are stored as 32-bit real and 32-bit complex values. A viewer is available that provides basic graphing, image display, and directory navigation functions (https://www.hdfgroup.org/downloads/hdfview/). The HDF file contains all the information necessary to reconstruct a synthetic aperture sonar image. All major and contemporary programming languages have library support for encoding/decoding the HDF5 format. Supporting documentation that outlines positions of the seafloor scatterers is included in "Scatterer_Locations_Scene00.csv", while the locations of the objects of interest for scene01-scene10 are included in "Object_Locations_All_Scenes.csv". Portable Network Graphic (PNG) images that plot the location of objects of all the objects of interest in each scene in Along-Track and Cross-Track notation are provided.

Sister Study is a prospective cohort of 50,884 U.S. women aged 35 to 74 years old conducted by the NIEHS. Eligible participants are women without a history of breast cancer but with at least one sister diagnosed with breast cancer at enrollment during 2003 - 2009. Datasets used in this research effort include health outcomes, lifestyle factors, socioeconomic factors, medication history, and built and natural environment factors. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Contact NIEHS Sister Study (https://sisterstudy.niehs.nih.gov/English/index1.htm) for data access. Format: Datasets are provided in SAS and/or CSV format.

Behavioral Risk Factor Surveillance System (BRFSS) 2024

Overview

The Behavioral Risk Factor Surveillance System (BRFSS) is the nation's premier system of health-related telephone surveys that collect uniform, state-specific data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services.

The objective of the BRFSS is to gather consistent, state-level data on preventive health practices and risk behaviors associated with chronic diseases, injuries, and preventable infectious diseases among adults (aged 18 and older).

Established in 1984 with 15 states, the BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. The system completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.

2024 Data Notes

The 2024 BRFSS dataset continues to use the raking weighting methodology (introduced in 2011) and includes both landline and cellphone-only respondents, ensuring more accurate representation of the U.S. adult population.

The aggregate dataset combines landline and cell phone data collected in 2024 from 49 states, The District of Columbia, Guam, Puerto Rico, and The U.S. Virgin Islands.

This original dataset contains responses from 457,670 individuals and has 301 features. These features are either questions directly asked of participants, or calculated variables based on individual participant responses.

⚠️ Note: Tennessee was unable to collect enough responses to meet inclusion requirements for 2024 and is not included in this public dataset.

Certain survey questions and responses have been modified or omitted to comply with federal data policies in effect during the 2024 collection period. As a result, some variables may contain missing values or appear inconsistent due to questions that were removed or restructured.

Data Collection

Data are collected from a random sample of adults (one per household) via telephone interviews.

Factors assessed include: - Tobacco use - Health care access and coverage - Alcohol consumption - Physical activity and diet - HIV/AIDS knowledge and prevention - Chronic health conditions
- Preventive health services and screenings

Content

The annual dataset contains 301 variables, covering both core questions and optional modules. Please refer to the official BRFSS 2024 Codebook for detailed variable definitions and coding.

This dataset contains 3 files: 1. brfss_survey_data_2024.csv # Dataset in .csv format (converted from SAS) 2. codebook_2024.HTML # CDC codebook for variable definitions
3. main_data_brfss_2024.XPT # Main dataset

⚙️ Note: The CSV file were converted from the original SAS format using pandas. Minor conversion artifacts may exist.

Complete description about each column of the CSV file can be found in the codebook.

Source & Acknowledgements

Data provided by the U.S. Centers for Disease Control and Prevention (CDC).

Original source and additional years of BRFSS data: CDC BRFSS Annual Data

Citation:

Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2024.

License: Public Domain (U.S. Government Work)

Suggested Citation (for Kaggle users)

If you use this dataset in your analysis or publication, please cite as:

Behavioral Risk Factor Surveillance System (BRFSS) 2024. U.S. Centers for Disease Control and Prevention (CDC). Public Domain.

Prepared for Kaggle public dataset publication. All data are in the public domain as U.S. Government works.

The National Sample Survey of Registered Nurses (NSSRN) Download makes data from the survey readily available to users in a one-stop download. The Survey has been conducted approximately every four years since 1977. For each survey year, HRSA has prepared two Public Use File databases in flat ASCII file format without delimiters. The 2008 data are also offerred in SAS and SPSS formats. Information likely to point to an individual in a sparsely-populated county has been withheld. General Public Use Files are State-based and provide information on nurses without identifying the County and Metropolitan Area in which they live or work. County Public Use Files provide most, but not all, the same information on the nurse from the General Public Use File, and also identifies the County and Metropolitan Areas in which the nurses live or work. NSSRN data are to be used for research purposes only and may not be used in any manner to identify individual respondents.

(SAS7BDAT)

Background: In 1986, the Congress enacted Public Laws 99-500 and 99-591, requiring a biennial report on the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). In response to these requirements, FNS developed a prototype system that allowed for the routine acquisition of information on WIC participants from WIC State Agencies. Since 1992, State Agencies have provided electronic copies of these data to FNS on a biennial basis.FNS and the National WIC Association (formerly National Association of WIC Directors) agreed on a set of data elements for the transfer of information. In addition, FNS established a minimum standard dataset for reporting participation data. For each biennial reporting cycle, each State Agency is required to submit a participant-level dataset containing standardized information on persons enrolled at local agencies for the reference month of April. The 2020 Participant and Program Characteristics (PC2020) is the 17th to be completed using the prototype PC reporting system. In April 2020, there were 89 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and 33 Indian Tribal Organizations (ITOs).Processing methods and equipment used: Specifications on formats (“Guidance for States Providing Participant Data”) were provided to all State agencies in January 2020. This guide specified 20 minimum dataset (MDS) elements and 11 supplemental dataset (SDS) elements to be reported on each WIC participant. Each State Agency was required to submit all 20 MDS items and any SDS items collected by the State agency. Study date(s) and duration The information for each participant was from the participants’ most current WIC certification as of April 2020.Study spatial scale (size of replicates and spatial scale of study area): In April 2020, there were 89 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and 33 Indian Tribal Organizations (ITOs).Level of true replication: UnknownSampling precision (within-replicate sampling or pseudoreplication):State Agency Data Submissions. PC2020 is a participant dataset consisting of 7,036,867 active records. The records, submitted to USDA by the State Agencies, comprise a census of all WIC enrollees, so there is no sampling involved in the collection of this data.PII Analytic Datasets. State agency files were combined to create a national census participant file of approximately 7 million records. The census dataset contains potentially personally identifiable information (PII) and is therefore not made available to the public.National Sample Dataset. The public use SAS analytic dataset made available to the public has been constructed from a nationally representative sample drawn from the census of WIC participants, selected by participant category. The national sample consists of 1 percent of the total number of participants, or 70,368 records. The distribution by category is 5,469 pregnant women, 6,131 breastfeeding women, 4,373 postpartum women, 16,817 infants, and 37,578 children.Level of subsampling (number and repeat or within-replicate sampling): The proportionate (or self-weighting) sample was drawn by WIC participant category: pregnant women, breastfeeding women, postpartum women, infants, and children. In this type of sample design, each WIC participant has the same probability of selection across all strata. Sampling weights are not needed when the data are analyzed. In a proportionate stratified sample, the largest stratum accounts for the highest percentage of the analytic sample.Study design (before–after, control–impacts, time series, before–after-control–impacts): None – Non-experimentalDescription of any data manipulation, modeling, or statistical analysis undertaken: Each entry in the dataset contains all MDS and SDS information submitted by the State agency on the sampled WIC participant. In addition, the file contains constructed variables used for analytic purposes. To protect individual privacy, the public use file does not include State agency, local agency, or case identification numbers.Description of any gaps in the data or other limiting factors: All State agencies provided data on a census of their WIC participants.Resources in this dataset:Resource Title: WIC PC 2020 National Sample File Public Use Codebook.; File Name: PC2020 National Sample File Public Use Codebook.docx; Resource Description: WIC PC 2020 National Sample File Public Use CodebookResource Title: WIC PC 2020 Public Use CSV Data.; File Name: wicpc2020_public_use.csv; Resource Description: WIC PC 2020 Public Use CSV DataResource Title: WIC PC 2020 Data Set SAS, R, SPSS, Stata.; File Name: PC2020 Ag Data Commons.zipResource; Description: WIC PC 2020 Data Set SAS, R, SPSS, Stata One dataset in multiple formats

Approved for Public Release; distribution is unlimited.

Dataset title: Synthetic Aperture Sonar Seabed Environment Dataset (SASSED)

Date: June 2018

Description: This dataset contains 129 complex-valued, single (high frequency) channel, 1001x1001 pixel, synthetic aperture sonar snippets of various seafloor texture types. Each snippet contains one or more seabed environments, e.g., hardpack sand, mud, sea grass, rock, and sand ripple.

For each snippet there is a corresponding hand-segmented and -labeled "mask" image. The labels should not be interpreted as the ground truth for specific seafloor types. The labels were not verified by visual inspection of the actual seafloor environments or by any other method. Instead, interpret the labels as groupings of similar seafloor textures. Example code for preprocessing the data is included.

The data is stored in hdf5 format. The SAS data is stored under the hdf5 dataset 'snippets', and the hand-segmented labels are stored under 'labels'. For information on how to read hdf5 data, please visit one of the following websites: (general) https://support.hdfgroup.org/HDF5/ (python) https://www.h5py.org

Acknowledgements: Thanks go to J. Tory Cobb for curating this dataset. Please credit NSWC Panama City Division in any publication using this data.

Past Usage: Cobb, J. T., & Zare, A. (2014). Boundary detection and superpixel formation in synthetic aperture sonar imagery. Proceedings of the Institute of Acoustics, 36(Pt 1).

Approved for Public Release; distribution is unlimited.

Data set consists of survey data containing PII, water quality sample test results for fecal indicator bacteria and additional supporting information. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Upon request to Tim Wade (wade.tim@epa.gov). Format: Data are stored in SAS datasets with codebooks in MS Word documenting variables. This dataset is associated with the following publication: Napier, M., R. Haugland, C. Poole, A. Dufour, J. Stewart, D. Weber, M. Varma, J. Lavender, and T. Wade. Exposure to human-associated fecal indicators and self-reported illness among swimmers at recreational beaches: A cohort study. ENVIRONMENTAL HEALTH. Academic Press Incorporated, Orlando, FL, USA, 16(1): 103, (2017).

The data are from a descriptive study of the USDA National School Lunch Program (NSLP) and School Breakfast Program (SBP). The data were collected between April and August 2018 using web-based surveys given to a nationally representative sample of School Food Authorities (SFAs) and 53 of 55 State agencies that administer the NSLP and SBP (Puerto Rico and the Virgin Islands were not asked to participate due to hurricanes in the region at the time of data collection). The survey questions collected data about NSLP and SBP operations during school year (SY) 2017–18 and financial data from SY 2016–17. Study date(s) and duration Data collection occurred from April to August 2018. The survey questions collected data about NSLP and SBP operations during SY 2017–18 and financial data from SY 2016–17. Study spatial scale (size of replicates and spatial scale of study area) The study area is the United States and outlying Territories that operate the NSLP and SBP. Sampling precision (within-replicate sampling or pseudoreplication) There was no sampling for the State agency survey. There was sampling for the SFA survey. The target universe was all 14,854 SFAs operating in public school districts in the United States and outlying Territories that were required to submit FNS-742 to FNS in SY 2014–15. The sampling frame of 14,854 SFAs was stratified into 10 strata based on number of students in the SFA and percentage of students certified for free or reduced-price meals. The research team implicitly stratified the 10 strata by sorting SFAs within each stratum by FNS Region and by urbanicity status to ensure the sample selected was balanced on these additional factors. Precision calculations confirmed that a responding sample of 1,750 SFAs allocated among the strata would meet the statistical requirements of the study. Therefore, assuming an 80 percent response rate, a sample of 2,187 SFAs was needed each study year. Additional information about the sampling procedures is presented in the study report: https://www.fns.usda.gov/cn/program-operations-study-school-year-2017-18 Level of subsampling (number and repeat or within-replicate sampling) Study design (before–after, control–impacts, time series, before–after-control–impacts) Descriptive study Description of any data manipulation, modeling, or statistical analysis undertaken Calculating SY 2017-18 nationally representative estimates for the SFA survey required sample weights that account for the sample design, nonresponse, and school year. The weighting procedures are described in detail in the study report: https://www.fns.usda.gov/cn/program-operations-study-school-year-2017-18 The State agency and SFA survey data include variables collected from the survey as well as variables constructed for use in analyses. To preserve the confidentiality of the SFAs all variables that could be used to determine the precise size in terms of number of students, number of schools, and/or number of meals served were “masked” by setting the variable to its size. To avoid the exposure of personal identifiable information, some of the variables in the State agency data file were classified into categories. Open ended text responses and derived variables were dropped from the SA and SFA data files. Description of any gaps in the data or other limiting factors Two of the 55 State agencies that administer the NSLP and SBP were not asked to complete the State agency survey (Puerto Rico and the Virgin Islands were experiencing hurricanes at the time of data collection). The remaining 53 State agencies completed the survey. Of the 2,187 sampled SFAs, seven were given an initial exemption due to hurricane damage, requests for exemptions, and due to lack of contact information. Additionally, four SFAs were found to be either closed or no longer participating in USDA school meals programs. Of the remaining 2,176 SFAs, 1,653 provided valid responses, yielding a response rate of 76.1 percent. Outcome measurement methods and equipment used The surveys asked State agencies and SFAs about the following topics related to operating the NSLP and SBP: eligibility determination and verification, financial management, food and beverage marketing, meal counting, meal pattern requirements, meal prices, revenues and expenditures, school participation, student participation, and Buy American/local food purchasing. Resources in this dataset:

Resource Title: Child Nutrition Program Operations Study, School Year 2017-2018 - Datasets for SA and SFA Surveys File Name: CNOPS 2017-18_PUF_AG_DATA_COMMONS.zip Resource Description: Child Nutrition Program Operations Study, School Year 2017-2018 - Datasets for SA and SFA Surveys in SAS format. Includes Codebooks, Survey Instruments, SAS Datasets, SAS Formats and Data User Guide.

For the TIMSS 2015 fourth grade assessment, the database includes student mathematics and science achievement data as well as the student, parent, teacher, school, and curricular background data for the 47 participating countries and 6 benchmarking entities. For the TIMSS 2015 eighth grade assessment, the database includes student mathematics and science achievement data as well as the student, teacher, school, and curricular background data for the 39 participating countries and 6 benchmarking entities. The TIMSS 2015 International Database also includes data from the TIMSS Numeracy 2015 assessment, with the participation of 7 countries and 1 benchmarking entity. The student, parent, teacher, and school data files are in SAS and SPSS formats. The entire database and its supporting documents are described in the TIMSS 2015 User Guide (Foy, 2017) and its three supplements. The data can be analyzed using the downloadable IEA IDB Analyzer (version 4.0), an application developed by the IEA Data Processing and Research Center to facilitate the analysis of the TIMSS data. A restricted use version of the TIMSS 2015 International Database is available to users who require access to variables removed from the public use version (see Chapter 4 of the User Guide). Users who require access to the restricted use version of the International Database to conduct their analyses should contact the IEA through its Study Data Repository.

Sabotaging milkweed by monarch caterpillars (Danaus plexippus) is a famous textbook example of disarming plant defence. By severing leaf veins, monarchs are thought to prevent the flow of toxic latex to their feeding site. Here, we show that sabotaging by monarch caterpillars is not only an avoidance strategy. While young caterpillars appear to avoid latex, late-instar caterpillars actively ingest exuding latex, presumably to increase sequestration of cardenolides used for defence against predators. Comparisons with caterpillars of the related but non-sequestering common crow butterfly (Euploea core) revealed three lines of evidence supporting our hypothesis. First, monarch caterpillars sabotage inconsistently and therefore the behaviour is not obligatory to feed on milkweed, whereas sabotaging precedes each feeding event in Euploea caterpillars. Second, monarch caterpillars shift their behaviour from latex avoidance in younger to eager drinking in later stages, whereas Euploea caterpil..., , , Readme for the statistical documentation for the publication: Monarchs sabotage milkweed to acquire toxins, not to disarm plant defense Authors: Anja Betz, Robert Bischoff, Georg Petschenka

For the statistical documentation, we provide the following files: This readme gives a brief outline of the different files and data provided in the statistical documentation Subfolders for each experiment containing

• Excel files with just the data, SAS code files for analysis of each dataset with comments SAS dataset files (sas7bdat) a data dictionary.txt that defines all variables of all datasets

Disclaimer: Excel automatically formats numbers. We do not take any responsibility for automatic formatting of the numbers by Excel. This might lead to different results, if the Excel files are used for analysis. The sas7bdat files, or data at the start of the individual sas-analysis files should be resistant to automatic formatting, so we suggest using them for analysis.

The datasets co...

Description of the experiment setting Data collection for the Study of Nutrition and Activity in Childcare Settings (SNACS) started in January 2017 and continued through September 2017. The complex study included web-based surveys, pre-interview surveys, on-site interviews, environmental observations, and telephone interviews of childcare sponsors and providers, as well as interviews of parents of some of the children from the sampled providers. The data were collected from a nationally representative sample of programs, children, and meals. The data cover a range of subjects including the provider’s characteristics, the nutritional quality of meals and snacks served, the dietary intake of children in childcare, the activities of children over the course of the childcare day, and the financial conditions of the childcare operations. Processing methods and equipment used SNACS data were collected via web-based surveys, pre-interview surveys, on-site interviews, environmental observations, and telephone interviews of childcare sponsors and providers, as well as interviews of parents of some of the children from the sampled providers. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. They used many different methods to check the data depending on the data type. The details are described in the study document called “Appendix A: Methods” (https://fns-prod.azureedge.us/sites/default/files/resource-files/SNACS-AppendixA.pdf) available at the study website. Study date(s) and duration Data collection for the Study of Nutrition and Activity in Childcare Settings (SNACS) started in January 2017 and continued through September 2017. The final public data set was produced in 2021. Study spatial scale (size of replicates and spatial scale of study area) The study is nationally representative and the sample design reflects the complexity of the sample needed to answer the research questions. The primary sampling units were 20 states randomly selected with six states selected with certainty due to their size. Secondary sampling units were selected from a random sample of metropolitan areas and clusters of non-metropolitan counties from the 20 States. Further details about the sample design are described in the “Appendix A: Methods” document available at the study website. Level of true replication See the document, “Appendix A: Methods,” available at the study website. Sampling precision (within-replicate sampling or pseudoreplication) See the document, “Appendix A: Methods,” available at the study website. Level of subsampling (number and repeat or within-replicate sampling) See the document, “Appendix A: Methods,” available at the study website. Study design (before–after, control–impacts, time series, before–after-control–impacts) Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken The public use data files contain constructed variables used for analytic purposes. The files do include weights created to produce national estimates for the Study of Nutrition and Activity in Childcare Settings final reports available at the study website. The data files do not include any identifying information about childcare sponsors, providers, or individuals who completed the questionnaires or participated in the study in other ways. Description of any gaps in the data or other limiting factors See the document, “Appendix A: Methods,” available at the study website for a detailed explanation of the study’s limitations. Outcome measurement methods and equipment used The height and weight of sampled children were measured with scales provided by data collectors. See the document, “Appendix A: Methods,” available at the study website for details on other outcomes measured through statistical analysis of the survey responses about outcomes such as food insecurity. Resources in this dataset:

Resource Title: Study of Nutrition and Activity in Childcare Settings (SNACS) - SAS Data Sets, Data Codebooks and Documentation Guides File Name: SNACS-I Public Use Files.zip Description: The zip file contains 19 Data Codebooks, 7 Data Documentation Guides, 19 SAS Datasets and one SAS Formats File.

Abstract (en): This study is part of a time-series collection of national surveys fielded continuously since 1952. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. A Black supplement of 263 respondents, who were asked the same questions that were administered to the national cross-section sample, is included with the national cross-section of 1,571 respondents. In addition to the usual content, the study contains data on opinions about the Supreme Court, political knowledge, and further information concerning racial issues. Voter validation data have been included as an integral part of the election study, providing objective information from registration and voting records or from respondents' past voting behavior. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. United States citizens of voting age living in private households in the continental United States. A representative cross-section sample, consisting of 1,571 respondents, plus a Black supplement sample of 263 respondents. 2015-11-10 The study metadata was updated.1999-12-14 The data for this study are now available in SAS transport and SPSS export formats, in addition to the ASCII data file. Variables in the dataset have been renumbered to the following format: 2-digit (or 2-character) year prefix + 4 digits + [optional] 1-character suffix. Dataset ID and version variables have also been added. In addition, SAS and SPSS data definition statements have been created for this collection, and the data collection instruments are now available as a PDF file. face-to-face interview, telephone interviewThe SAS transport file was created using the SAS CPORT procedure.

The Uniform Appraisal Dataset (UAD) Appraisal-Level Public Use File (PUF) is the nation’s first publicly available appraisal-level dataset of appraisal records, giving the public new access to a selected set of data fields found in appraisal reports. The UAD Appraisal-Level PUF is based on a five percent nationally representative random sample of appraisals for single-family mortgages acquired by the Enterprises. The current release includes appraisals from 2013 through 2021. The UAD Appraisal-Level PUF is a resource for users capable of using statistical software to extract and analyze data. Users can download annual or combined files in CSV, R, SAS and Stata formats. All files are zipped for ease with download.

The objective of the BRFSS is to collect uniform, state-specific data on preventive health practices and risk behaviors that are linked to chronic diseases, injuries, and preventable infectious diseases in the adult population. Factors assessed by the BRFSS include tobacco use, health care coverage, HIV/AIDS knowledge or prevention, physical activity, and fruit and vegetable consumption. Data are collected from a random sample of adults (one per household) through a telephone survey.

The Behavioral Risk Factor Surveillance System (BRFSS) is the nation's premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.

Content

• Each year contains a few hundred columns. Please see one of the annual code books for complete details.
• These CSV files were converted from a SAS data format using pandas; there may be some data artifacts as a result.
• If you like this dataset, you might also like the data for 2001-2010.

Acknowledgements

This dataset was released by the CDC. You can find the original dataset and additional years of data here.

This is the complete dataset for the 500 Cities project 2016 release. This dataset includes 2013, 2014 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2013, 2014), Census Bureau 2010 census population data, and American Community Survey (ACS) 2009-2013, 2010-2014 estimates. More information about the methodology can be found at www.cdc.gov/500cities.

Note: During the process of uploading the 2015 estimates, CDC found a data discrepancy in the published 500 Cities data for the 2014 city-level obesity crude prevalence estimates caused when reformatting the SAS data file to the open data format. . The small area estimation model and code were correct. This data discrepancy only affected the 2014 city-level obesity crude prevalence estimates on the Socrata open data file, the GIS-friendly data file, and the 500 Cities online application. The other obesity estimates (city-level age-adjusted and tract-level) and the Mapbooks were not affected. No other measures were affected. The correct estimates are update in this dataset on October 25, 2017.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Community-Based Survey of Supports for Healthy Eating and Active Living (CBS HEAL) is a CDC survey of a nationally representative sample of U.S. municipalities to better understand existing community-level policies and practices that support healthy eating and active living. The survey collects information about policies such as nutrition standards, incentives for healthy food retail, bike/pedestrian-friendly design, and Complete Streets. About 2,000 municipalities respond to the survey. Participating municipalities receive a report that allows them to compare their policies and practices with other municipalities of similar geography, population size, and urban status. The CBS HEAL survey was first administered in 2014 and was administered again in 2021. Data is provided in multiple formats for download including as a SAS file. A methods report and a SAS program for formatting the data are also provided.