[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

Following a request from the European Commission for a review of European dietary reference values (DRVs), the EFSA’s Panel on Dietetic Products, Nutrition and Allergies (NDA) has prepared a number of Scientific Opinions on DRVs for micronutrients. The DATA Unit supported this activity by estimating the nutrient intake of a number of micronutrients in nine selected European countries and different age groups. In addition, the DATA Unit also provided information on average content of food sources of the respective nutrients per country based on the composition database, as well as main food group contributors to nutrient intakes and assessed the comparability of the provided data with pertinent published intake data.

Intake estimates have been assessed using food consumption data from the EFSA Comprehensive Food Consumption Database (EFSA, 2011a) and the EFSA Nutrient composition database. Food composition data used to populate the Nutrient composition database were provided to EFSA through the EFSA procurement project ‘Updated food composition database for nutrient intake’ (Roe at al., 2013). Data were provided following the EFSA specification for standard sample description for food and feed and were classified according to the FoodEx2 classification system of EFSA (EFSA, 2011b).

The food composition data used in these assessments and here published cover the following vitamins and minerals: calcium (Ca); copper (Cu); cobalamin (vitamin B12); magnesium (Mg); niacin; phosphorus (P); potassium (K); riboflavin; thiamin; iron (Fe); selenium (Se); vitamin B6; vitamin K, zinc (Zn), and vitamin E(1). The food composition dataset contains data from seven(2) countries: Finland, France, Germany, Italy, Netherlands, Sweden, and United Kingdom. This dataset version has been checked for outliers but is prior to data completion for missing foods and nutrient values.

(1) Vitamin E is defined as alpha-tocopherol (AT) only, however as most food composition databases in the EU contain values as alpha-tocopherol equivalents (TE), data on TE are also provided

(2) For the nutrient intake estimates of Ireland and Latvia present in the opinions of the EFSA Panel on Dietetic Products, Nutrition and Allergies (NDA), food composition data from UK and Germany were respectively used

The United States Department of Agriculture (USDA) Nutrient Data Laboratory (NDL), in collaboration with the National Cattlemen's Beef Association, National Pork Board, American Lamb Board, and meat scientists at selected universities, has conducted several research studies designed to update and expand nutrient data on retail meat cuts in the USDA National Nutrient Database for Standard Reference (SR). These studies have provided current and accurate estimates of data to update SR, and the study results have been incorporated into data sets that can be used for nutrient labeling. NDL has developed these data sets, presented in an easy-to-use table format. Each data set provides retailers with easier access to the most accurate nutrient data for the purpose of on-pack nutrition labeling and for nutrition claims. These data sets focus on the cuts identified by USDA Food Safety and Inspection Service (FSIS) proposed labeling regulations for fresh, single-ingredient meats. The FSIS, an agency of the USDA, is the public health agency responsible for ensuring that the nation's commercial supply of meat, poultry and egg products is safe, wholesome, and correctly labeled and packaged. Resources in this dataset:Resource Title: The USDA Nutrient Data Set for Retail Beef Cuts, Release 3.0. File Name: Retail_Beef_Cuts03.pdfResource Description: Each data set provides retailers with easier access to the most accurate nutrient data for the purpose of on-pack nutrition labeling and for nutrition claims. These data sets focus on the cuts identified by USDA Food Safety and Inspection Service (FSIS) proposed labeling regulations for fresh, single-ingredient meats. The online version of this document can be found at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Retail_Beef_Cuts03.pdfResource Title: The USDA Nutrient Data Set for Retail Beef Cuts, Release 3.0 (MS Excel download). File Name: Beef_Labelling_Table03.xlsxResource Description: The tables in "The USDA Nutrient Data Set for Retail Beef Cuts" imported into a Microsoft Excel spreadsheet. The online version of this spreadsheet can be found at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Beef_Labelling_Table03.xlsxResource Title: USDA Nutrient Data Set for Retail Pork Cuts, Release 2. File Name: Pork09.pdfResource Description: Each data set provides retailers with easier access to the most accurate nutrient data for the purpose of on-pack nutrition labeling and for nutrition claims. These data sets focus on the cuts identified by USDA Food Safety and Inspection Service (FSIS) proposed labeling regulations for fresh, single-ingredient meats. Find the online version of this document at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Pork09.pdfResource Title: USDA Nutrient Data Set for Retail Pork Cuts, Release 2 (MS Excel download). File Name: Pork09_Tbl.xlsResource Description: The tables in "The Revised USDA Nutrient Data Set for Fresh Pork" imported into a Microsoft Excel spreadsheet. Find the online version of this spreadsheet at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Pork09_Tbl.xlsResource Title: Raw Ground Pork (MS Excel download). File Name: EstNutrRawGrndPork4_28.xlsResource Description: These tables provide nutrient profiles for raw ground pork from 4-28% fat, in increments of 1% fat, as determined by regression equations. Find the online version of this spreadsheet at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/EstNutrRawGrndPork4_28.xlsResource Title: USDA Nutrient Data Set for Retail Veal Cuts. File Name: Retail_Veal_Cuts.pdfResource Description: Each data set provides retailers with easier access to the most accurate nutrient data for the purpose of on-pack nutrition labeling and for nutrition claims. These data sets focus on the cuts identified by USDA Food Safety and Inspection Service (FSIS) proposed labeling regulations for fresh, single-ingredient meats. Find the online version of this document at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Retail_Veal_Cuts.pdfResource Title: Veal Labeling Table (MS Excel download). File Name: Veal_Labeling_Table.xlsxResource Description: The tables in "The USDA Nutrient Data Set for Retail Veal Cuts" imported into a Microsoft Excel spreadsheet. Find the online version of this spreadsheet at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Veal_Labeling_Table.xlsxResource Title: USDA Nutrient Data Set for Retail Lamb Cuts. File Name: Lamb_Labeling_Doc.pdfResource Description: Each data set provides retailers with easier access to the most accurate nutrient data for the purpose of on-pack nutrition labeling and for nutrition claims. These data sets focus on the cuts identified by USDA Food Safety and Inspection Service (FSIS) proposed labeling regulations for fresh, single-ingredient meats. Find the online version of this document at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Lamb_Labeling_Doc.pdfResource Title: Lamb Labeling Table (MS Excel download). File Name: Lamb_Labeling_Table.xlsxResource Description: The tables in "The USDA Nutrient Data Set for Retail Lamb Cuts" imported into a Microsoft Excel spreadsheet. Find the online version of this spreadsheet at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Lamb_Labeling_Table.xlsx

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.

Abstract

Household Income and Expenditure Surveys (HIES) are implemented to rebase consumer price indices and estimates of household contribution to national gross domestic product. More recently, HIES data have been used in poverty analyses and to conduct nutrition and food security oriented analyses. The more recent applications of HIES data – poverty, nutrition and food security – require the use of edible-portion conversion factors to convert the reported acquisition of wholefoods into edible portions so estimates can be made of what people apparently ingest. These data then require the use of food composition tables (FCTs) to convert the edible portion into caloric and nutrient consumption values, so total energy and nutrient consumption can be estimated. HIES data in the Pacific region are coded using the United Nations Statistics Division’s Classification of Individual Consumption According to Purpose (COICOP); however, there is no regionally standardised linkage between COICOP and the Pacific Islands Food Composition Tables Second Edition (PIFCT). Furthermore, the PIFCTs do not have edible-portion conversion factors and are insufficient to cover the full list of foods reported in the HIES. To address this, the Pacific Nutrient Database (PNDB) was developed to provide the Pacific region with a standard set of conversion factors and food composition data that are mapped to COICOP (1999). To add more value to the database, each food item is also mapped to COICOP 2018, classified into FAO Commodity Groups and food groups to compute Household Dietary Diversity Scores (HDDS). The PNDB includes 26 components plus edible and inedible portions for a total of 822 foods.

Geographic coverage

Pacific Region.

Analysis unit

COICOP commodity

Universe

Not applicable.

Kind of data

Aggregate data [agg]

Sampling procedure

Not applicable.

Sampling deviation

Not applicable.

Mode of data collection

Other [oth]

Research instrument

Questionnaires used were those from Household Income and Expenditure Surveys (HIES) in the Pacific Region.

Cleaning operations

Data editing was done using the software Excel.

Response rate

Not applicable.

Sampling error estimates

Not applicable.

Data appraisal

Not applicable.

This Australian and New Zealand food category cost dataset was created to inform diet and economic modelling for low and medium socioeconomic households in Australia and New Zealand. The dataset was created according to the INFORMAS protocol, which details the methods to systematically and consistently collect and analyse information on the price of foods, meals and affordability of diets in different countries globally. Food categories were informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods.

Methods The dataset was created according to the INFORMAS protocol [1], which detailed the methods to collect and analyse information systematically and consistently on the price of foods, meals, and affordability of diets in different countries globally.

Cost data were collected from four supermarkets in each country: Australia and New Zealand. In Australia, two (Coles Merrylands and Woolworths Auburn) were located in a low and two (Coles Zetland and Woolworths Burwood) were located in a medium metropolitan socioeconomic area in New South Wales from 7-11th December 2020. In New Zealand, two (Countdown Hamilton Central and Pak ‘n Save Hamilton Lake) were located in a low and two (Countdown Rototuna North and Pak ‘n Save Rosa Birch Park) in a medium socioeconomic area in the North Island, from 16-18th December 2020.

Locations in Australia were selected based on the Australian Bureau of Statistics Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) [2]. The index ranks areas from most disadvantaged to most advantaged using a scale of 1 to 10. IRSAD quintile 1 was chosen to represent low socio-economic status and quintile 3 for medium SES socio-economic status. Locations in New Zealand were chosen using the 2018 NZ Index of Deprivation and statistical area 2 boundaries [3]. Low socio-economic areas were defined by deciles 8-10 and medium socio-economic areas by deciles 4-6. The supermarket locations were chosen according to accessibility to researchers. Data were collected by five trained researchers with qualifications in nutrition and dietetics and/or nutrition science.

All foods were aggregated into a reduced number of food categories informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods. Nutrient data for each food category can therefore be linked to the Australian Food and Nutrient (AUSNUT) 2011-13 database [4] and NZ Food Composition Database (NZFCDB) [5] using the 8-digit codes provided for Australia and New Zealand, respectively.

Data were collected for three representative foods within each food category, based on criteria used in the INFORMAS protocol: (i) the lowest non-discounted price was chosen from the most commonly available product size, (ii) the produce was available nationally, (iii) fresh produce of poor quality was omitted. One sample was collected per representative food product per store, leading to a total of 12 food price samples for each food category. The exception was for the ‘breakfast cereal, unfortified, sugars ≤15g/100g’ food category in the NZ dataset, which included only four food price samples because only one representative product per supermarket was identified.

Variables in this dataset include: (i) food category and description, (ii) brand and name of representative food, (iii) product size, (iv) cost per product, and (v) 8-digit code to link product to nutrient composition data (AUSNUT and NZFCDB).

References

Vandevijvere, S.; Mackay, S.; Waterlander, W. INFORMAS Protocol: Food Prices Module [Internet]. Available online: https://auckland.figshare.com/articles/journal_contribution/INFORMAS_Protocol_Food_Prices_Module/5627440/1 (accessed on 25 October).
2071.0 - Census of Population and Housing: Reflecting Australia - Stories from the Census, 2016 Available online: https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by Subject/2071.0~2016~Main Features~Socio-Economic Advantage and Disadvantage~123 (accessed on 10 December).
Socioeconomic Deprivation Indexes: NZDep and NZiDep, Department of Public Health. Available online: https://www.otago.ac.nz/wellington/departments/publichealth/research/hirp/otago020194.html#2018 (accessed on 10 December)
AUSNUT 2011-2013 food nutrient database. Available online: https://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/foodnutrient.aspx (accessed on 15 November).
NZ Food Composition Data. Available online: https://www.foodcomposition.co.nz/ (accessed on 10 December)

Usage Notes The uploaded data includes an Excel spreadsheet where a separate worksheet is provided for the Australian food price database and New Zealand food price database, respectively. All cost data are presented to two decimal points, and the mean and standard deviation of each food category is presented. For some representative foods in NZ, the only NFCDB food code available was for a cooked product, whereas the product is purchased raw and cooked prior to eating, undergoing a change in weight between the raw and cooked versions. In these cases, a conversion factor was used to account for the weight difference between the raw and cooked versions, to ensure that nutrient information (on accessing from the NZFCDB) was accurate. This conversion factor was developed based on the weight differences between the cooked and raw versions, and checked for accuracy by comparing quantities of key nutrients in the cooked vs raw versions of the product.

Background/Objectives: Advances in artificial intelligence now allow combined use of large language and vision models; however, there has been limited evaluation of their potential in dietary assessment. This data arose from a study that aimed to evaluate the accuracy of ChatGPT-4 in estimating nutritional content of commonly consumed meals from meal photographs.Methods: Meal photographs (n=114) were uploaded to ChatGPT, and it was asked to identify the foods in each meal, estimate their weight, and estimate the nutrient content of the meals for 16 nutrients for comparison with the known values. There were a total of 39 unique meals with each one photographed 3 times for 3 different portion sizes giving rise to 114 photographs. This dataset is in the form of an excel workbook containing four worksheets. The worksheet titled "ChatGPT Foods & Weights" contains the foods identified by ChatGPT in each of the 114 meal photographs as well as its estimate for the weight of each of those foods. The worksheet titled "Actual Foods & Weights" contains the true foods and weights for each of the meal photographs. The worksheet "ChatGPT Nutrition Estimates" contains ChatGPT's estimates of the nutrition content of each of the 114 meal photographs for 16 different nutrients. The worksheet "Actual Nutrition Content" contains the true nutrition content of the meals in the photographs.

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

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 documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: SNAP Grocers March 2022 - Microsoft Excel VersionItem Type: Microsoft ExcelSummary: SNAP Grocers Retail Locations according to the USDA as of March 2022Notes: Excel version of this feature service: https://nmcdc.maps.arcgis.com/home/item.html?id=79018ad311f94af98e1523c47256599ePrepared by: Uploaded by EMcRae_NMCDCSource: USDAFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=018afa2174524fc78639c108f89107bdUID: 85Data Requested: SNAPMethod of Acquisition: USDA downloadDate Acquired: April 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 3Tags: PENDING_ Title SNAP Grocers in New Mexico as of March 17, 2022 - SNAP_Grocers_March_2022

Summary SNAP Grocer Retail Locations According to the USDA as of March 2022

Notes

Download Data April 4 2022

Date represents March 17 2022

Source US Department of Agriculture, Food and Nutrition Services - https://www.fns.usda.gov/snap/retailer-locator

Feature Service https://nmcdc.maps.arcgis.com/home/item.html?id=79018ad311f94af98e1523c47256599e#overview

Prepared by EMcRae_NMCDC

Alias Definition

X Long

Y Lat

ObjectId Object ID

Name Store Name

Address Address

Address_Line_2 Address 2

City City

State State

Zip5 Five Digit Zip Code

Zip4 Four Digit Zip Code

County County

Longitude Longitude

Latitude Latitude

Abstract copyright UK Data Service and data collection copyright owner.

The National Diet and Nutrition Survey (NDNS) Rolling Programme (RP) began in 2008 and is designed to assess the diet, nutrient intake and nutritional status of the general population aged 1.5 years and over living in private households in the UK. (For details of the previous NDNS series, which began in 1992, see the documentation for studies 3481, 4036, 4243 and 5140.)

The programme is funded by Public Health England (PHE), an executive agency of the Department of Health, and the UK Food Standards Agency (FSA).

The NDNS RP is currently carried out by a consortium comprising NatCen Social Research (NatCen) (NatCen, contract lead) and the MRC Epidemiology Unit, University of Cambridge (scientific lead). The MRC Epidemiology Unit joined the consortium in November 2017. Until December 2018, the consortium included the MRC Elsie Widdowson Laboratory, Cambridge (former scientific lead). In Years 1 to 5 (2008/09 – 2012/13) the consortium also included the University College London Medical School (UCL).

Survey activities at the MRC Epidemiology Unit are delivered with the support of the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (IS-BRC-1215- 20014), comprising the NIHR BRC Nutritional Biomarker Laboratory and NIHR BRC Dietary Assessment and Physical Activity Group. The NIHR Cambridge Biomedical Research Centre is a partnership between Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, funded by the NIHR.

The NDNS RP provides the only source of high quality, nationally representative UK data on the types and quantities of foods consumed by individuals, from which estimates of nutrient intake for the population are derived. Results are used by Government to develop policy and monitor progress toward diet and nutrition objectives of UK Health Departments, for example work to tackle obesity and monitor progress towards a healthy, balanced diet as visually depicted in the Eatwell Guide. The NDNS RP provides an important source of evidence underpinning the Scientific Advisory Committee on Nutrition (SACN) work relating to national nutrition policy. The food consumption data are also used by the FSA to assess exposure to chemicals in food, as part of the risk assessment and communication process in response to a food emergency or to inform negotiations on setting regulatory limits for contaminants.

Further information is available from the gov.uk National Diet and Nutrition Survey webpage.

This study was a follow-up of National Diet and Nutrition Survey Rolling Programme (NDNS RP) participants and aimed to describe, and assess the impact of the COVID-19 pandemic on, the diet and physical activity of people in the UK in 2020. Self-reported diet and physical activity data was collected between August and October 2020 for around 1,000 adults and children which was compared with their diet and activity data obtained at the time of their original NDNS RP interview. Data on food security, financial security and changes in dietary and health-related behaviours since the start of the COVID-19 pandemic in the UK in February 2020 were also collected in this study (but not previously in the NDNS RP) through a web questionnaire with the aim of helping to understand the context for any changes in diet and activity. Participants were also asked to complete 4 online dietary recalls over a 2 to 3 week period to assess their current diet. This was compared with their reported diet when originally assessed in the NDNS RP (on average 2 years 7 months earlier). Adults were also asked to complete a Recent Physical Activity Questionnaire (RPAQ), again to compare with their reported physical activity when originally assessed in the NDNS RP.

Latest edition information

For the second edition (September 2022), the Food Level dietary data file has been replaced with a new version, with the variable 'FoodNumber' added. An Excel format nutrient database has also been added to the study, and the documentation updated accordingly.

The main NDNS study can be found under SN 6533.

Main Topics:

The study focuses on diet and physical activity during Covid-19, following up participants who completed the main NDNS between 2016 and 2020.

Assessment of fluoride intake is paramount in understanding the mechanisms of fluoride metabolism, specifically the prevention of dental caries, dental fluorosis, and skeletal fluorosis. A database for fluoride is needed for epidemiologists and health researchers to estimate the intakes, and to investigate the relationships between intakes and human health. Resources in this dataset:Resource Title: READ ME - USDA National Fluoride Database of Selected Beverages and Foods, Release 2. File Name: F02.pdfResource Description: Describes methods and procedures, data generation and evaluation, formats of tables, data dissemination, references cited in the documentation and database, and other miscellaneous information about this database. Resource Title: Fluoride Database of Selected Beverages and Foods (Release 2). File Name: F02.xlsResource Description: This file contains the Fluoride Database imported into a Microsoft Excel spreadsheet. You need Excel 2000 or later to use this file.

WFP Nutrition Sites. The Microsoft Excel spread sheet contains a record of the nutrition sites in the states of Central Darfur, East Darfur, Kassala, North Darfur, Red Sea, South Darfur and West Darfur. It includes details of the Cooperating Partners, States, Localities, Sites, Programs by Type and XY coordinates (in Decimal Degrees).

Analysis of ‘US Public Food Assistance’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jpmiller/publicassistance on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset focuses on public assistance programs in the United States that provide food, namely SNAP and WIC. If you are interested in a broader picture of food security across the world, please see Food Security Indicators for the World 2016-2020.

Initial coverage was for the Special Supplemental Nutrition Program for Women, Infants, and Children Program, or simply WIC. The program allocates Federal and State funds to help low-income women and children up to age five who are at nutritional risk. Funds are used to provide supplemental foods, baby formula, health care, and nutrition education.

Starting with version 5, the dataset also covers the US Supplemental Nutrition Assistance Program, more commonly known as SNAP. The program is the successor to the Food Stamps program previously in place. The program provides food assistance to low-income families in the form of a debit card. A 2016 study using POS data from SNAP-eligible vendors showed the three most purchased types of food to be meats, sweetened beverages, and vegetables.

Content

Files may include participation data and spending for state programs, and poverty data for each state. Data for WIC covers fiscal years 2013-2016, which is actually October 2012 through September 2016. Data for SNAP covers 2015 to 2020.

Motivation

My original purpose here is two-fold:

• Explore various aspects of US Public Assistance. Show trends over recent years and better understand differences across state agencies. Although the federal government sponsors the program and provides funding, program are administered at the state level and can widely vary. Indian nations (native Americans) also administer their own programs.

• Share with the Kaggle Community the joy - and pain - of working with government data. Data is often spread across numerous agency sites and comes in a variety of formats. Often the data is provided in Excel, with the files consisting of multiple tabs. Also, files are formatted as reports and contain aggregated data (sums, averages, etc.) along with base data.

As of March 2nd, I am expanding the purpose to support the M5 Forecasting Challenges here on Kaggle. Store sales are partly driven by participation in Public Assistance programs. Participants typically receive the items free of charge. The store then recovers the sale price from the state agencies administering the program.

Additional Content Ideas

The dataset can benefit greatly from additional content. Economics, additional demographics, administrative costs and more. I'd like to eventually explore the money trail from taxes and corporate subsidies, through the government agencies, and on to program participants. All community ideas are welcome!

--- Original source retains full ownership of the source dataset ---

The Stata data file "cap_dietary_combined.dta” and equivalent excel file of the same name comprises nutrition-related data collected by adolescent secondary school students during a "Citizen Science" project in the districts of Kavre and Jumla, Nepal during April to June 2022. The project was part of a CIFF-funded Children in All Policies 2030 (CAP2030) project . The data were generated by the students using a mobile device data collection form developed using "Open Data Kit (ODK) Collect" electronic data collection platform by Kathmandu Living Labs (KLL) and University College London (UCL) for the purposes of this study. The data collected were part of a learning exercise for students to raise awareness of under- and over- nutrition and poor diets in their locale. The form could be used to record 24-hour dietary recall of different healthy food groups and unhealthy sentinel foods amongst children under 5 years, older children and adolescents and adults. Height and weight could also be recorded where stadiometers and weighing scales were available. The form provided feedback to the data collector / respondent in terms of saying what their dietary diversity and unhealthy eating scores were and suggested what foods should be consumed for a healthy diet. In the case of under- or over- nutrition a message appeared with dietary / health advice. Since the students were using 10 android tablets to record information in and around their schools, the dataset may contain several copies of the same respondent recorded by different individuals, so should not be relied upon for precise prevalence of nutritional status. Rather, the data serve to demonstrate the potential of citizen science methods with Nepali school students to record such information. The nutrition app and the process of gathering the data are described in a paper entitled "Citizen science for climate change resilience: engaging adolescents to study climate hazards, biodiversity and nutrition in rural Nepal" submitted to Wellcome Open Research in Feb 2023. The data contributed to Table 6, Figure 7 and Annex 6 of this paper.

This dataset represents the list of CA WIC authorized food items identified by food category and subcategory. Each item is uniquely identified by a Universal Product Code (UPC) or Price Look-Up code (PLU) for WIC electronic benefit transfer (EBT). The WIC Authorized vendors use the CA Authorized Product List (APL) to transact WIC food items at cash registers. The APL plays a crucial role in supporting WIC EBT purchases. WIC EBT requires vendor systems to maintain the APL to match the scanned food items' UPCs to ensure they are on the APL. The food items identified by UPC and PLU can be found in the data files below. When you download the files, Excel may prompt you to automatically format the data. If prompted, you may want to hit ‘Don’t Convert’ so that Excel leaves the data as is without any formatting or data conversions.

The Women, Infants and Children (WIC) Program is a federally-funded health and nutrition program that provides assistance to pregnant women, new mothers, infants, and children under age five. WIC helps California families by providing food benefits to individual participants based on their nutritional need and risk assessment. The food benefits can be used to purchase healthy supplemental foods from approximately 3,800 WIC authorized vendor stores throughout the State. WIC also provides nutritional education, breastfeeding support, healthcare referrals, and other community services. Participants must meet income guidelines and other criteria. Currently, 84 WIC agencies provide services monthly to approximately one million participants at several hundred sites in local communities throughout the State.

This dataset measures food availability and access for 76 low- and middle-income countries. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources. This dataset is the basis for the International Food Security Assessment 2015-2025 released in June 2015. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. Countries (Spatial Description, continued): Democratic Republic of the Congo, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Korea, Pakistan, Peru, Philippines, Rwanda, Senegal, Sierra Leone, Somalia, Sri Lanka, Sudan, Swaziland, Tajikistan, Tanzania, Togo, Tunisia, Turkmenistan, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, and Zimbabwe. Resources in this dataset:Resource Title: CSV File for all years and all countries. File Name: gfa25.csvResource Title: International Food Security country data. File Name: GrainDemandProduction.xlsxResource Description: Excel files of individual country data. Please note that these files provide the data in a different layout from the CSV file. This version of the data files was updated 9-2-2021

More up-to-date files may be found at: https://www.ers.usda.gov/data-products/international-food-security.aspx

There are two Excel files with food information based on findings in the preparatory phase of the study: " Does food biodiversity protect against malnutrition and favour the resilience to climate change-related events in Amazon Indigenous communities? A protocol for a mixed methods study". During the pilot phase we completed four cooking workshop to identify food preparations and validate a food list to be used posteriorly during diet evaluation. As per suggestion of our Indigenous collaborators, the cooking workshops were conducted with four families selected based on two criteria: geographical location and have different number of family members, in order to represent different altitudes in Shawi territory and diversity of Shawi families, respectively. In each family, husband and wife provided their opinion regarding the list of foods, except in family fourth were woman did not have husband. First family was visited in Feb 2021 at 10 de Agosto community (high geographical location), nine family members Second family was visited in March 2021 at Nueva Luz community (high geographical location) , 10 family members Third family was visited in May 2021 at Irapay community (low geographical location), six family members Fourth family was visited in June 2021 at Maranata community (low geographical location), eleven family members