Data Dictionary template for Tempe Open Data.

The Delta Neighborhood Physical Activity Study was an observational study designed to assess characteristics of neighborhood built environments associated with physical activity. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns and neighborhoods in which Delta Healthy Sprouts participants resided. The 12 towns were located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys between August 2016 and September 2017 using the Rural Active Living Assessment (RALA) tools and the Community Park Audit Tool (CPAT). Scale scores for the RALA Programs and Policies Assessment and the Town-Wide Assessment were computed using the scoring algorithms provided for these tools via SAS software programming. The Street Segment Assessment and CPAT do not have associated scoring algorithms and therefore no scores are provided for them. Because the towns were not randomly selected and the sample size is small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one contains data collected with the RALA Programs and Policies Assessment (PPA) tool. Dataset two contains data collected with the RALA Town-Wide Assessment (TWA) tool. Dataset three contains data collected with the RALA Street Segment Assessment (SSA) tool. Dataset four contains data collected with the Community Park Audit Tool (CPAT). [Note : title changed 9/4/2020 to reflect study name] Resources in this dataset:Resource Title: Dataset One RALA PPA Data Dictionary. File Name: RALA PPA Data Dictionary.csvResource Description: Data dictionary for dataset one collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA Data Dictionary. File Name: RALA TWA Data Dictionary.csvResource Description: Data dictionary for dataset two collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA Data Dictionary. File Name: RALA SSA Data Dictionary.csvResource Description: Data dictionary for dataset three collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT Data Dictionary. File Name: CPAT Data Dictionary.csvResource Description: Data dictionary for dataset four collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One RALA PPA. File Name: RALA PPA Data.csvResource Description: Data collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA. File Name: RALA TWA Data.csvResource Description: Data collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA. File Name: RALA SSA Data.csvResource Description: Data collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT. File Name: CPAT Data.csvResource Description: Data collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary. File Name: DataDictionary_RALA_PPA_SSA_TWA_CPAT.csvResource Description: This is a combined data dictionary from each of the 4 dataset files in this set.

The Delta Produce Sources Study was an observational study designed to measure and compare food environments of farmers markets (n=3) and grocery stores (n=12) in 5 rural towns located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys from June 2019 to March 2020 using a modified version of the Nutrition Environment Measures Survey (NEMS) Farmers Market Audit tool. The tool was modified to collect information pertaining to source of fresh produce and also for use with both farmers markets and grocery stores. Availability, source, quality, and price information were collected and compared between farmers markets and grocery stores for 13 fresh fruits and 32 fresh vegetables via SAS software programming. Because the towns were not randomly selected and the sample sizes are relatively small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Resources in this dataset:Resource Title: Delta Produce Sources Study dataset . File Name: DPS Data Public.csvResource Description: The dataset contains variables corresponding to availability, source (country, state and town if country is the United States), quality, and price (by weight or volume) of 13 fresh fruits and 32 fresh vegetables sold in farmers markets and grocery stores located in 5 Lower Mississippi Delta towns.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Delta Produce Sources Study data dictionary. File Name: DPS Data Dictionary Public.csvResource Description: This file is the data dictionary corresponding to the Delta Produce Sources Study dataset.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

Supplementary_Data_Dictionary_Sheet_v1.0.xls

The data dictionary Excel sheet is the main supporting document for the paper.

DD_-_Neonatal_Data.csv

The patient dataset is provided as a format for capturing data with respect to data dictionary.

These are the most recent Data Dictionary (pop-ups) and Panarctic Species List (PASL) zip files for all the vegetation plot data entered into Turboveg for the Alaska AVA. These files are necessary to correctly use the Turboveg data with regards to coded data. The Data Dictionary file will be updated when new datasets are entered into Turboveg which result in additions to coded data such as references, author code, habitat type, surficial geology, etc. Updates to the PASL will occur less frequently. Check the dates in the file names to be certain that you are using the most current files. Our data model is a set of tables that comprise our relational database. The Excel spreadsheet included in the resources below provides information about each field in our database, such as data type, description, if it is a required field, whether the information within the field is selected from a pop-up list, and whether the field is a standard within Turboveg or is specific to the AVA. Using Turboveg: 1) Download the installation file available through the link at Alaska Arctic Geoecological Atlas portal from the official Turboveg webpage (general installation file for worldwide users, however, some adjustments will be needed when using data from AAVA after installation of this program). 2) Open the Turboveg program and restore the most recent Data Dictionary and PASL zipped files into the Turboveg program by using the function 'Database-Backup/Restore-Restore.' All the previous versions of data dictionary files and PASL that are already in program will be overwritten. 3) Use the Alaska-AVA following the manual for Turboveg for Windows which is available at http://www.synbiosys.alterra.nl/turboveg/tvwin.pdf

Data dictionary and brochure for REAP (Resilient Economic Agricultural Practices). https://data.nal.usda.gov/node/5594

Data Entry Template 2017 includes

Excel templates for Experiment description worksheets, Site characterization worksheets, Management worksheets, Measurement worksheets where experimental unit data are reported, and Information that may be useful to the user, including drop down lists of treatment specific information and ranges of expected values. General and introductory instructions, as well as a Data Validation check are also included.A data dictionary typically provides a detailed description for each element or variable in a dataset or data model. Data dictionaries are used to document important and useful information such as a descriptive name, the data type, allowed values, units, and text description.Dataset citation: (dataset) USDA Agricultural Research Service. (2017). REAP (Resilient Economic Agricultural Practices). Agricultural Research Service. https://doi.org/10.15482/USDA.ADC/1372394.

The Delta Food Outlets Study was an observational study designed to assess the nutritional environments of 5 towns located in the Lower Mississippi Delta region of Mississippi. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns in which Delta Healthy Sprouts participants resided and that contained at least one convenience (corner) store, grocery store, or gas station. Data were collected via electronic surveys between March 2016 and September 2018 using the Nutrition Environment Measures Survey (NEMS) tools. Survey scores for the NEMS Corner Store, NEMS Grocery Store, and NEMS Restaurant were computed using modified scoring algorithms provided for these tools via SAS software programming. Because the towns were not randomly selected and the sample sizes are relatively small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one (NEMS-C) contains data collected with the NEMS Corner (convenience) Store tool. Dataset two (NEMS-G) contains data collected with the NEMS Grocery Store tool. Dataset three (NEMS-R) contains data collected with the NEMS Restaurant tool. Resources in this dataset:Resource Title: Delta Food Outlets Data Dictionary. File Name: DFO_DataDictionary_Public.csvResource Description: This file contains the data dictionary for all 3 datasets that are part of the Delta Food Outlets Study.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One NEMS-C. File Name: NEMS-C Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for convenience stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two NEMS-G. File Name: NEMS-G Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for grocery stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three NEMS-R. File Name: NEMS-R Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for restaurants.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

The National Health and Nutrition Examination Survey (NHANES) provides data on the health and environmental exposure of the non-institutionalized US population. Such data have considerable potential to understand how the environment and behaviors impact human health. These data are also currently leveraged to answer public health questions such as prevalence of disease. However, these data need to first be processed before new insights can be derived through large-scale analyses. NHANES data are stored across hundreds of files with multiple inconsistencies. Correcting such inconsistencies takes systematic cross examination and considerable efforts but is required for accurately and reproducibly characterizing the associations between the exposome and diseases (e.g., cancer mortality outcomes). Thus, we developed a set of curated and unified datasets and accompanied code by merging 614 separate files and harmonizing unrestricted data across NHANES III (1988-1994) and Continuous (1999-2018), totaling 134,310 participants and 4,740 variables. The variables convey 1) demographic information, 2) dietary consumption, 3) physical examination results, 4) occupation, 5) questionnaire items (e.g., physical activity, general health status, medical conditions), 6) medications, 7) mortality status linked from the National Death Index, 8) survey weights, 9) environmental exposure biomarker measurements, and 10) chemical comments that indicate which measurements are below or above the lower limit of detection. We also provide a data dictionary listing the variables and their descriptions to help researchers browse the data. We also provide R markdown files to show example codes on calculating summary statistics and running regression models to help accelerate high-throughput analysis of the exposome and secular trends on cancer mortality. csv Data Record: The curated NHANES datasets and the data dictionaries includes 13 .csv files and 1 excel file. The curated NHANES datasets involves 10 .csv formatted files, one for each module and labeled as the following: 1) mortality, 2) dietary, 3) demographics, 4) response, 5) medications, 6) questionnaire, 7) chemicals, 8) occupation, 9) weights, and 10) comments. The eleventh file is a dictionary that lists the variable name, description, module, category, units, CAS Number, comment use, chemical family, chemical family shortened, number of measurements, and cycles available for all 4,740 variables in NHANES ("dictionary_nhanes.csv"). The 12th csv file contains the harmonized categories for the categorical variables ("dictionary_harmonized_categories.csv"). The 13th file contains the dictionary for descriptors on the drugs codes (“dictionary_drug_codes.csv”). The 14th file is an excel file that contains the cleaning documentation, which records all the inconsistencies for all affected variables to help curate each of the NHANES datasets (“nhanes_inconsistencies_documentation.xlsx”). R Data Record: For researchers who want to conduct their analysis in the R programming language, the curated NHANES datasets and the data dictionaries can be downloaded as a .zip file which include an .RData file and an .R file. We provided an .RData file that contains all the aforementioned datasets as R data objects (“w - nhanes_1988_2018.RData”). Also in this .RData file, we make available all R scripts on customized functions that were written to curate the data. We also provide an .R file that shows how we used the customized functions (i.e. our pipeline) to curate the data (“m - nhanes_1988_2018.R”).

The Pesticide Data Program (PDP) is a national pesticide residue database program. Through cooperation with State agriculture departments and other Federal agencies, PDP manages the collection, analysis, data entry, and reporting of pesticide residues on agricultural commodities in the U.S. food supply, with an emphasis on those commodities highly consumed by infants and children. This dataset provides information on where each tested sample was collected, where the product originated from, what type of product it was, and what residues were found on the product, for calendar years 1992 through 2020. The data can measure residues of individual compounds and classes of compounds, as well as provide information about the geographic distribution of the origin of samples, from growers, packers and distributors. The dataset also includes information on where the samples were taken, what laboratory was used to test them, and all testing procedures (by sample, so can be linked to the compound that is identified). The dataset also contains a reference variable for each compound that denotes the limit of detection for a pesticide/commodity pair (LOD variable). The metadata also includes EPA tolerance levels or action levels for each pesticide/commodity pair. The dataset will be updated on a continual basis, with a new resource data file added annually after the PDP calendar-year survey data is released. Resources in this dataset:Resource Title: CSV Data Dictionary for PDP. File Name: PDP_DataDictionary.csvResource Description: Machine-readable Comma Separated Values (CSV) format data dictionary for PDP Database Zip files. Defines variables for the sample identity and analytical results data tables/files. The ## characters in the Table and Text Data File name refer to the 2-digit year for the PDP survey, like 97 for 1997 or 01 for 2001. For details on table linking, see PDF. Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Data dictionary for Pesticide Data Program. File Name: PDP DataDictionary.pdfResource Description: Data dictionary for PDP Database Zip files.Resource Software Recommended: Adobe Acrobat,url: https://www.adobe.com Resource Title: 2019 PDP Database Zip File. File Name: 2019PDPDatabase.zipResource Title: 2018 PDP Database Zip File. File Name: 2018PDPDatabase.zipResource Title: 2017 PDP Database Zip File. File Name: 2017PDPDatabase.zipResource Title: 2016 PDP Database Zip File. File Name: 2016PDPDatabase.zipResource Title: 2015 PDP Database Zip File. File Name: 2015PDPDatabase.zipResource Title: 2014 PDP Database Zip File. File Name: 2014PDPDatabase.zipResource Title: 2013 PDP Database Zip File. File Name: 2013PDPDatabase.zipResource Title: 2012 PDP Database Zip File. File Name: 2012PDPDatabase.zipResource Title: 2011 PDP Database Zip File. File Name: 2011PDPDatabase.zipResource Title: 2010 PDP Database Zip File. File Name: 2010PDPDatabase.zipResource Title: 2009 PDP Database Zip File. File Name: 2009PDPDatabase.zipResource Title: 2008 PDP Database Zip File. File Name: 2008PDPDatabase.zipResource Title: 2007 PDP Database Zip File. File Name: 2007PDPDatabase.zipResource Title: 2005 PDP Database Zip File. File Name: 2005PDPDatabase.zipResource Title: 2004 PDP Database Zip File. File Name: 2004PDPDatabase.zipResource Title: 2003 PDP Database Zip File. File Name: 2003PDPDatabase.zipResource Title: 2002 PDP Database Zip File. File Name: 2002PDPDatabase.zipResource Title: 2001 PDP Database Zip File. File Name: 2001PDPDatabase.zipResource Title: 2000 PDP Database Zip File. File Name: 2000PDPDatabase.zipResource Title: 1999 PDP Database Zip File. File Name: 1999PDPDatabase.zipResource Title: 1998 PDP Database Zip File. File Name: 1998PDPDatabase.zipResource Title: 1997 PDP Database Zip File. File Name: 1997PDPDatabase.zipResource Title: 1996 PDP Database Zip File. File Name: 1996PDPDatabase.zipResource Title: 1995 PDP Database Zip File. File Name: 1995PDPDatabase.zipResource Title: 1994 PDP Database Zip File. File Name: 1994PDPDatabase.zipResource Title: 1993 PDP Database Zip File. File Name: 1993PDPDatabase.zipResource Title: 1992 PDP Database Zip File. File Name: 1992PDPDatabase.zipResource Title: 2006 PDP Database Zip File. File Name: 2006PDPDatabase.zipResource Title: 2020 PDP Database Zip File. File Name: 2020PDPDatabase.zipResource Description: Data and supporting files for PDP 2020 surveyResource Software Recommended: Microsoft Access,url: https://products.office.com/en-us/access

Resource Description: The dataset contains variables corresponding to availability, source (country, state and town if country is the United States), quality, and price (by weight or volume) of 13 fresh fruits and 32 fresh vegetables sold in farmers markets and grocery stores located in 5 Lower Mississippi Delta towns.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Delta Produce Sources Study data dictionary. File Name: DPS Data Dictionary Public.csvResource Description: This file is the data dictionary corresponding to the Delta Produce Sources Study dataset.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

An Excel file including: raw data, data dictionary, and all final data sets. A PDF containing detailed equations for calculating the daily dose using the creatinine correction and UFR, figures comparing the HI between the creatinine correction and UFR, figures and tables comparing temporal trends in the MCR between the HI and potency-weighted approach, tables describing frequency of participants by cycle, temporal limit of detection by metabolite, tolerable daily intakes by phthalate, Group designation of the MCR, complete regression equations used in the regression analysis, comparison of Group counts by creatinine correction and UFR, and relative potency factors by phthalate. This dataset is associated with the following publication: Reyes, J., and P. Price. Temporal Trends in Exposures to Six Phthalates from Biomonitoring Data: Implications for Cumulative Risk. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 52(21): 12475-12483, (2018).

The Flash report provides monthly performance data on key ACS child welfare, child care, and juvenile justice functions such as children using vouchers for child care, child protective caseloads, and the number of admissions to detention. Each row of data in the Excel file posted to Open Data is a distinct measure in the Flash Report.The columns are the month of the data. Data are updated semiannually in September and April using data from queries of administrative data systems and data provided directly from program areas. A graphic version of the Flash Report is posted monthly to the ACS internet webpage https://www1.nyc.gov/site/acs/about/flashindicators.page

For the User Guide, please follow this link.

For the Data Dictionary, please follow this link.

Resource Description: Description of variables in Condensed_HV data fileResource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: Consolidated data. File Name: consolidated_HV.csvResource Description: Condensed data for each site by year for plant size, weevil density, vegetation cover and meteorological data, 2008-2019.Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: Cover_2008 data dictionary. File Name: cover_2008_HV_meta.csvResource Description: description of variables in the file cover_2008_HVResource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: vegetation cover 2008. File Name: cover_2008_HV.csvResource Description: vegetation cover 2008-2011Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: vegetation cover 2013 data dictionary. File Name: cover_2013_HV_meta.csvResource Description: description of variables in the file cover_2013_HV, which contains vegetation cover 2013-2019Resource Title: vegetation cover 2013. File Name: cover_2013_HV.csvResource Description: vegetation cover 2013-2019Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: dissections 2009 data dictionary. File Name: dissections_2009_HV_meta.csvResource Description: Description of variables in the file dissections_2009_HV, containing dissections of toadflax stems 2009-2011Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: dissections 2009-2011. File Name: dissections_2009_HV.csvResource Description: Data from dissection of toadflax stems 2009-2011Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: dissections 2012 data dictionary. File Name: dissections_2012_HV_meta.csvResource Description: description of variables in the file dissections_2012_HV, containing data from dissections of toadflax stems 2012-2019Resource Title: dissections 2012-2019. File Name: dissections_2012_HV.csvResource Description: data from dissection of toadflax stems 2012-2019Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: stem_counts data dictionary. File Name: stem_counts_HV_meta.csvResource Description: description of variables in the file stem_counts_HV, which contains data on number of toadflax stems in 25 x 50 cm quadrats, 2008-2013Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com

We monitored populations of the stem weevil, Mecinus janthiniformis, the invasive alien weed Dalmatian toadflax (Linaria dalmatica) and other vegetation to document the impact of using M. janthiniformis as a biological control agent of L. dalmatica. Weevils were released in 2008 and again in 2014 after a wild fire. The results document increases and spread of weevil populations, decrease in Dalmatian toadflax and changes in cover of some vegetation classes. Resources in this dataset:Resource Title: Consolidated data dictionary. File Name: consolidated_HV_meta.csvResource Description: Description of variables in Condensed_HV data fileResource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: Consolidated data. File Name: consolidated_HV.csvResource Description: Condensed data for each site by year for plant size, weevil density, vegetation cover and meteorological data, 2008-2019.Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: Cover_2008 data dictionary. File Name: cover_2008_HV_meta.csvResource Description: description of variables in the file cover_2008_HVResource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: vegetation cover 2008. File Name: cover_2008_HV.csvResource Description: vegetation cover 2008-2011Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: vegetation cover 2013 data dictionary. File Name: cover_2013_HV_meta.csvResource Description: description of variables in the file cover_2013_HV, which contains vegetation cover 2013-2019Resource Title: vegetation cover 2013. File Name: cover_2013_HV.csvResource Description: vegetation cover 2013-2019Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: dissections 2009 data dictionary. File Name: dissections_2009_HV_meta.csvResource Description: Description of variables in the file dissections_2009_HV, containing dissections of toadflax stems 2009-2011Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: dissections 2009-2011. File Name: dissections_2009_HV.csvResource Description: Data from dissection of toadflax stems 2009-2011Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: dissections 2012 data dictionary. File Name: dissections_2012_HV_meta.csvResource Description: description of variables in the file dissections_2012_HV, containing data from dissections of toadflax stems 2012-2019Resource Title: dissections 2012-2019. File Name: dissections_2012_HV.csvResource Description: data from dissection of toadflax stems 2012-2019Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: stem_counts data dictionary. File Name: stem_counts_HV_meta.csvResource Description: description of variables in the file stem_counts_HV, which contains data on number of toadflax stems in 25 x 50 cm quadrats, 2008-2013Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: stem_counts. File Name: stem_counts_HV.csvResource Description: Number of toadflax stems in 25 x 50 cm quadrats, 2008-2013Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: stem_height data dictionary. File Name: stem_height_HV_meta.csvResource Description: description of variables in the file stem_height_HV.csv, which contains data on height of live Linaria dalmatica stems, 2008-2019Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com Resource Title: stem_height. File Name: stem_height_HV.csvResource Description: height of live stems of Linaria dalmatica, 2008-2019Resource Software Recommended: Microsoft Excel,url: http://www.microsoft.com

The National Health and Nutrition Examination Survey (NHANES) provides data and have considerable potential to study the health and environmental exposure of the non-institutionalized US population. However, as NHANES data are plagued with multiple inconsistencies, processing these data is required before deriving new insights through large-scale analyses. Thus, we developed a set of curated and unified datasets by merging 614 separate files and harmonizing unrestricted data across NHANES III (1988-1994) and Continuous (1999-2018), totaling 135,310 participants and 5,078 variables. The variables conveydemographics (281 variables),dietary consumption (324 variables),physiological functions (1,040 variables),occupation (61 variables),questionnaires (1444 variables, e.g., physical activity, medical conditions, diabetes, reproductive health, blood pressure and cholesterol, early childhood),medications (29 variables),mortality information linked from the National Death Index (15 variables),survey weights (857 variables),environmental exposure biomarker measurements (598 variables), andchemical comments indicating which measurements are below or above the lower limit of detection (505 variables).csv Data Record: The curated NHANES datasets and the data dictionaries includes 23 .csv files and 1 excel file.The curated NHANES datasets involves 20 .csv formatted files, two for each module with one as the uncleaned version and the other as the cleaned version. The modules are labeled as the following: 1) mortality, 2) dietary, 3) demographics, 4) response, 5) medications, 6) questionnaire, 7) chemicals, 8) occupation, 9) weights, and 10) comments."dictionary_nhanes.csv" is a dictionary that lists the variable name, description, module, category, units, CAS Number, comment use, chemical family, chemical family shortened, number of measurements, and cycles available for all 5,078 variables in NHANES."dictionary_harmonized_categories.csv" contains the harmonized categories for the categorical variables.“dictionary_drug_codes.csv” contains the dictionary for descriptors on the drugs codes.“nhanes_inconsistencies_documentation.xlsx” is an excel file that contains the cleaning documentation, which records all the inconsistencies for all affected variables to help curate each of the NHANES modules.R Data Record: For researchers who want to conduct their analysis in the R programming language, only cleaned NHANES modules and the data dictionaries can be downloaded as a .zip file which include an .RData file and an .R file.“w - nhanes_1988_2018.RData” contains all the aforementioned datasets as R data objects. We make available all R scripts on customized functions that were written to curate the data.“m - nhanes_1988_2018.R” shows how we used the customized functions (i.e. our pipeline) to curate the original NHANES data.Example starter codes: The set of starter code to help users conduct exposome analysis consists of four R markdown files (.Rmd). We recommend going through the tutorials in order.“example_0 - merge_datasets_together.Rmd” demonstrates how to merge the curated NHANES datasets together.“example_1 - account_for_nhanes_design.Rmd” demonstrates how to conduct a linear regression model, a survey-weighted regression model, a Cox proportional hazard model, and a survey-weighted Cox proportional hazard model.“example_2 - calculate_summary_statistics.Rmd” demonstrates how to calculate summary statistics for one variable and multiple variables with and without accounting for the NHANES sampling design.“example_3 - run_multiple_regressions.Rmd” demonstrates how run multiple regression models with and without adjusting for the sampling design.

The Agricultural Research Service of the US Department of Agriculture (USDA) in collaboration with other government agencies has a program to track changes in the sodium content of commercially processed and restaurant foods. This monitoring program includes these activities: Tracking sodium levels of ~125 popular foods, called "Sentinel Foods," by periodically sampling them at stores and restaurants around the country, followed by laboratory analyses. Tracking levels of "related" nutrients that could change when manufacturers reformulate their foods to reduce sodium; these related nutrients are potassium, total and saturated fat, total dietary fiber, and total sugar. Sharing the results of these monitoring activities to the public periodically in the Sodium Monitoring Dataset and USDA National Nutrient Database for Standard Reference and once every two years in the Food and Nutrient Database for Dietary Studies. The Sodium Monitoring Dataset is downloadable in Excel spreadsheet format. Resources in this dataset:Resource Title: Data Dictionary. File Name: SodiumMonitoringDataset_datadictionary.csvResource Description: Defines variables, descriptions, data types, character length, etc. for each of the spreadsheets in this Excel data file: Sentinel Foods - Baseline; Priority-2 Foods - Baseline; Sentinel Foods - Monitoring; Priority-2 Foods - Monitoring.Resource Title: Sodium Monitoring Dataset (MS Excel download). File Name: SodiumMonitoringDatasetUpdatedJuly2616.xlsxResource Description: Microsoft Excel : Sentinel Foods - Baseline; Priority-2 Foods - Baseline; Sentinel Foods - Monitoring; Priority Foods - Monitoring.

These are Excel outputs of each of the USEEIO State Models v1.0 for year 2020. They were developed using useeior v1.4.0 (https://github.com/USEPA/useeior/releases/tag/v1.4.0). See the referenced USEPA report for a full description. See the Data Dictionary for interpretation of the sheets in each Excel file.

[NOTE - 2022-09-07: this dataset is superseded by an updated version https://doi.org/10.15482/USDA.ADC/1526332 ] This dataset contains soil water content data developed from neutron probe readings taken in access tubes in each of the four large, precision weighing lysimeters and in the fields surrounding each lysimeter at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) beginning in 1989. Readings were taken periodically with a field-calibrated neutron probe at depths from 10 cm to 230 cm (maximum of 190 cm depth in the lysimeters) in 20-cm depth increments. Periods between readings were typically one to two weeks, sometimes longer according to experimental design and need for data. Field calibrations in the Pullman soil series were done every few years. Calibrations typically produced a regression equation with RMSE <= 0.01 m3 m-3 (e.g., Evett and Steiner, 1995). Data were used to guide irrigation scheduling to achieve full or deficit irrigation as required by the experimental design. Data may be used to calculate the soil profile water content in mm of water from the surface to the maximum depth of reading. Profile water content differences between reading times in the same access tube are considered the change in soil water storage during the period in question and may be used to compute evapotranspiration (ET) using the soil water balance equation: ET = (change in storage + P + I + F + R, where P is precipitation during the period, I is irrigation during the period, F is soil water flux (drainage) out of the bottom of the soil profile during the period, and R is the sum of runon and runoff during the period. Typically, R is taken as zero because the fields were furrow diked to prevent runon and runoff during most of each growing season. Resources in this dataset:Resource Title: 1995 Bushland, TX, east alfalfa volumetric soil water content data. File Name: 1995_East_Alfalfa_Soil-water.xlsxResource Description: Contains periodic volumetric soil water content data from neutron probe readings in 20-cm depth increments from 10-cm depth to 230-cm depth in access tubes in fields around each of the Bushland, TX, large, precision weighing lysimeters, and to 190-cm depth in each lysimeter. The excel file contains a data dictionary for each tab containing data. There is also a tab named Introduction that lists the authors, equipment used, relevant citations, and explains the other tabs, which contain either data dictionaries, data, geographical coordinates of access tube locations, or data visualization tools. Tab names are unique so that tabs may be saved as individual CSV files.Resource Title: 1996 Bushland, TX, east alfalfa volumetric soil water content data. File Name: 1996_East_Alfalfa_Soil-water.xlsxResource Description: Contains periodic volumetric soil water content data from neutron probe readings in 20-cm depth increments from 10-cm depth to 230-cm depth in access tubes in fields around each of the Bushland, TX, large, precision weighing lysimeters, and to 190-cm depth in each lysimeter. The excel file contains a data dictionary for each tab containing data. There is also a tab named Introduction that lists the authors, equipment used, relevant citations, and explains the other tabs, which contain either data dictionaries, data, geographical coordinates of access tube locations, or data visualization tools. Tab names are unique so that tabs may be saved as individual CSV files.Resource Title: 1997 Bushland, TX, east alfalfa volumetric soil water content data. File Name: 1997_East_Alfalfa_Soil-water.xlsxResource Description: Contains periodic volumetric soil water content data from neutron probe readings in 20-cm depth increments from 10-cm depth to 230-cm depth in access tubes in fields around each of the Bushland, TX, large, precision weighing lysimeters, and to 190-cm depth in each lysimeter. The excel file contains a data dictionary for each tab containing data. There is also a tab named Introduction that lists the authors, equipment used, relevant citations, and explains the other tabs, which contain either data dictionaries, data, geographical coordinates of access tube locations, or data visualization tools. Tab names are unique so that tabs may be saved as individual CSV files.Resource Title: 1998 Bushland, TX, east alfalfa volumetric soil water content data. File Name: 1998_East_Alfalfa_Soil-water.xlsxResource Description: Contains periodic volumetric soil water content data from neutron probe readings in 20-cm depth increments from 10-cm depth to 230-cm depth in access tubes in fields around each of the Bushland, TX, large, precision weighing lysimeters, and to 190-cm depth in each lysimeter. The excel file contains a data dictionary for each tab containing data. There is also a tab named Introduction that lists the authors, equipment used, relevant citations, and explains the other tabs, which contain either data dictionaries, data, geographical coordinates of access tube locations, or data visualization tools. Tab names are unique so that tabs may be saved as individual CSV files.Resource Title: 1999 Bushland, TX, east alfalfa volumetric soil water content data. File Name: 1999_East_Alfalfa_Soil-water.xlsxResource Description: Contains periodic volumetric soil water content data from neutron probe readings in 20-cm depth increments from 10-cm depth to 230-cm depth in access tubes in fields around each of the Bushland, TX, large, precision weighing lysimeters, and to 190-cm depth in each lysimeter. The excel file contains a data dictionary for each tab containing data. There is also a tab named Introduction that lists the authors, equipment used, relevant citations, and explains the other tabs, which contain either data dictionaries, data, geographical coordinates of access tube locations, or data visualization tools. Tab names are unique so that tabs may be saved as individual CSV files.

SDR 2.0 Cotton File: Cumulative List of Variables in the Surveys of the SDR Database is a comprehensive data dictionary, in Microsoft Excel format. Its main purpose is to facilitate the overview of 88118 variables (i.e. variable names, values, and labels) available in the original (source) data files that we retrieved automatically for harmonization purposes in the SDR Project. Information in the Cotton File comes from 215 source data files that comprise ca. 3500 national surveys administered between 1966 and 2017 in 169 countries or territories, as part of 23 international survey projects.