[Note: Integrated as part of FoodData Central, April 2019.] The USDA National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States and provides the foundation for most food composition databases in the public and private sectors. This is the last release of the database in its current format. SR-Legacy will continue its preeminent role as a stand-alone food composition resource and will be available in the new modernized system currently under development. SR-Legacy contains data on 7,793 food items and up to 150 food components that were reported in SR28 (2015), with selected corrections and updates. This release supersedes all previous releases. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_DB.zipResource Description: Locally stored copy - The USDA National Nutrient Database for Standard Reference as a relational database using AcessResource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: Locally stored copy - ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.

Several USDA food composition databases, including the Food and Nutrient Database for Dietary Studies (FNDDS), Standard Reference (SR) Legacy, and the USDA Branded Food Products Database, have transitioned to FoodData Central, a new and harmonized USDA food and nutrient data system. FoodData Central also includes expanded nutrient content information as well as links to diverse data sources that offer related agricultural, environmental, food, health, dietary supplement, and other information. The new system is designed to strengthen the capacity for rigorous research and policy applications through its search capabilities, downloadable datasets, and detailed documentation. Application developers can incorporate the information into their applications and web sites through the application programming interface (API) REST access. The constantly changing and expanding food supply is a challenge to those who are interested in using food and nutrient data. Including diverse types of data in one data system gives researchers, policymakers, and other audiences a key resource for addressing vital nutrition and health issues. FoodData Central: Includes five distinct types of data containing information on food and nutrient profiles, each with a unique purpose: Foundation Foods; Experimental Foods; Standard Reference; Food and Nutrient Database for Dietary Studies; USDA Global Branded Food Products Database. Provides a broad snapshot in time of the nutrients and other components found in a wide variety of foods and food products. Presents data that come from a variety of sources and are updated as new information becomes available. Includes values that are derived through a variety of analytic and computational approaches, using state-of-the-art methodologies and transparent presentation. FoodData Central is managed by the Agricultural Research Service and hosted by the National Agricultural Library. Resources in this dataset:Resource Title: Website Pointer for FoodData Central. File Name: Web Page, url: https://fdc.nal.usda.gov/index.html Includes Search, Download data, API Guide, Data Type Documentation, and Help pages.

[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.

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 Nutrient Data Laboratory is responsible for developing authoritative nutrient databases that contain a wide range of food composition values of the nation's food supply. This requires updating and revising the USDA Nutrient Database for Standard Reference (SR) and developing various special interest databases. However, with over 7,000 food items in SR and a complete nutrient profile costing approximately $2,000 for one sample, analyzing every food item for every nutrient and meeting all user requirements is impossible. Consequently, priorities must be determined. Procedures using food consumption data and nutrient values for developing the Key Foods list are explained. Key Foods have been identified as those food items that contribute up to 75% of any one nutrient to the dietary intake of the US population. These Key Foods will be used to set priorities for nutrient analyses under the National Food and Nutrient Analysis Program. The tables describe key foods based on Continuing Survey Of Food Intakes By Individuals (CSFII, 1989-) and WWEIA-NHANES (What We Eat In America - National Health and Nutrition Examination Survey 2001-) survey data.

The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR. The current version, Survey-SR 2013-2014, is mainly based on the USDA National Nutrient Database for Standard Reference (SR) 28 (2) and contains sixty-six nutrientseach for 3,404 foods. These nutrient data will be used for assessing intake data from WWEIA, NHANES 2013-2014. Nutrient profiles were added for 265 new foods and updated for about 500 foods from the version used for the previous survey (WWEIA, NHANES 2011-12). New foods added include mainly commercially processed foods such as several gluten-free products, milk substitutes, sauces and condiments such as sriracha, pesto and wasabi, Greek yogurt, breakfast cereals, low-sodium meat products, whole grain pastas and baked products, and several beverages including bottled tea and coffee, coconut water, malt beverages, hard cider, fruit-flavored drinks, fortified fruit juices and fruit and/or vegetable smoothies. Several school lunch pizzas and chicken products, fast-food sandwiches, and new beef cuts were also added, as they are now reported more frequently by survey respondents. Nutrient profiles were updated for several commonly consumed foods such as cheddar, mozzarella and American cheese, ground beef, butter, and catsup. The changes in nutrient values may be due to reformulations in products, changes in the market shares of brands, or more accurate data. Examples of more accurate data include analytical data, market share data, and data from a nationally representative sample. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES 2013-14 (Survey SR 2013-14). File Name: SurveySR_2013_14 (1).zipResource Description: Access database downloaded on November 16, 2017. US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR), October 2015. Resource Title: Data Dictionary. File Name: SurveySR_DD.pdf

This dataset comes for the USDA, originating from MyFoodData.com.

File type has been updated to .csv for "MyFoodData_USDA_05142025.csv"

Essentially this is all boils down to: - 14,000+ food items - Broken up by serving size - calories - micro nutrients - macro-nutrients - Manufacturer - Food Type - Food Group

File contents were last updated in 2020, which I have not updated nor interfaced with since. This set is from the public domain for the public user. Happy eating.

[Note: Integrated as part of FoodData Central, April 2019.] USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is a database that is used to convert food and beverages consumed in What We Eat In America (WWEIA), National Health and Nutrition Examination Survey (NHANES) into gram amounts and to determine their nutrient values. Because FNDDS is used to generate the nutrient intake data files for WWEIA, NHANES, it is not required to estimate nutrient intakes from the survey. FNDDS is made available for researchers using WWEIA, NHANES to review the nutrient profiles for specific foods and beverages as well as their associated portions and recipes. Such detailed information makes it possible for researchers to conduct enhanced analysis of dietary intakes. FNDDS can also be used in other dietary studies to code foods/beverages and amounts eaten and to calculate the amounts of nutrients/food components in those items. FNDDS is released every two-years in conjunction with the WWEIA, NHANES dietary data release. The FNDDS is available for free download from the FSRG website. Resources in this dataset:Resource Title: Website Pointer to Food and Nutrient Database for Dietary Studies. File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fndds/ USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is a database that is used to convert food and beverages consumed in What We Eat In America (WWEIA), National Health and Nutrition Examination Survey (NHANES) into gram amounts and to determine their nutrient values.

Context

I needed to record our grocery intake using a standard nomenclature that supports access and tabulation of nutrients consumed. That is, a nomenclature that spans retail grocery using UPC to the identifiers used to access nutrition labels. I wanted to know if our household's intake of nutrients complied with the bounds set forth in the 2015-2020 USDA Dietary Guidelines. And if not, what foods will most efficiently remedy dietary deficiencies/excesses. Professionally, I have been devising countermeasures to fight chronic disease and see food more so than medication as the key.

Content

Downloaded from the USDA FoodData Central database: https://fdc.nal.usda.gov/download-datasets.html, this constellation of tables is centered on the food table. That table identifies foods broadly classed as Standard like what you might find in the periphery of the store, or Branded which typically occupies the center aisles. Each Standard Reference food identifier points to exactly one food label, where each label (in the set of all labels) is a set of entity attribute value (EAV) triples comprised of FoodId NutrientId and Quantity per hectogram (i.e., 100g). Standard Reference and Branded Reference foods each occupy their own tables. Since categories for Standard Reference foods are coded, a table providing their descriptions are also provided. Note that categories for Branded Reference foods are in verbose text.

Layouts

For field definitions and table layouts see: Download_&_API_Field_Descriptions_April_2021.pdf in this distribution.

Derived tables

Syntax for tables derived from USDA FoodData Central: - Filenames are prefixed by user initials 'JCS_' - The remaining portion of the filename is a hyphenated list of its domain names. - FoodId in derived files were zero-left padded for proper collation and joining.

Domains for derived tables from which individual column values are drawn: - SRCat: Standard Reference food category, zero-left padded to two places. - Applicable: A Boolean {YES,NO} indicating whether a standard reference food category applies to the current study. - CatDesc: The description of that food category. - FoodId: A unique and unchanging food identifier, zero-left padded to seven places. - Description: Text describing each Standard Reference food item. - FoodName: Text describing each Branded Reference food item. - BRCat: Branded Reference category expressed as verbose text. - UPC: Uniform Product Code that identifies a branded food item regardless of revision date. - BrandOwner: Corporate entity that owns that brand of foods. - SvgSizeHgs: Serving size of a branded food item in hectograms. - DateAvail: Revision date for a UPC's metadata or nutrition label. - VerCnt: Number of revisions that a food identified by its UPC code underwent. - NutrientId: Uniquely identifies a nutrient as a 4-digit number with cross-references stored in nutrient.csv - Per100g: Quantity of a nutrient per hectogram as a float but stored as plain text.

Acknowledgements

To my Chair, Prof. Taghi M. Khoshgoftaar, PhD.

Inspiration

Given a list of foods and when and how much of each were consumed: - aggregate quantities of each nutrient consumed, - norming the quantities of nutrients consumed to average daily calorie burn, and - comparing each nutrient consumed to the target range for that nutrient.

Nutrient-wise deficiencies and excesses relative to target ranges are fed to a recommender that identifies: - what foods most efficiently by weight remedies these deficiencies and excesses - while being foods most likely to be consumed.

Rankings presented by the recommender during one period are evaluated by foods consumed in the following period. What ranking is the most 'compact'? That is, what ranking has the most of its foods consumed among its top k foods?

Known Issues

• Columns for Brand, Sub-brand, and Descriptions are inconsistent in their formatting. (i.e. brand names found in Descriptions column instead of Brand column). Left as is from the original USDA datasets.

• Column Serving Size Unit is inconsistent in its descriptions. (Attempted to make this uniform, but found incorrect units alongside values (MG =? g)). Left as is from the original datasets.

• Column Amount does NOT equal to the nutrient amount per serving. To find the nutrient amount per serving, I used the following equation:

• (Nutrient amounts based on 100g or 100 ml according to USDA):

• [Nutrient Amount per Serving = (Amount)*(Serving Size) / 100]

This should be considered a tentative solution and may not apply to all listed values. For this reason, I excluded a column which uses this formula.

• This dataset does not include the household servings (servings per container).

• Column V1 can be ignored.

About Dataset

-Contains +20 million rows, 15 columns of nutritional data typically found on food labels for the various brand foods in the United States. -Combination of original datasets with minimal changes.

• Nutritional data includes: Calcium(Ca), Carbohydrates(by difference), Cholesterol, Calories (Energy kcal), Total Fat, Unsaturated (Mono and Poly) Fat, Saturated Fat, Trans Fat, Dietary Fiber, Iron(Fe), Potassium(K), Sodium(Na), Sugar Total, Sugar Added, and Vitamin D (D2 + D3) (see Known Issues).

Original Dataset

• FoodData Central (USDA) can be viewed and downloaded here in CSV or JSON format (Latest Downloads: Branded section).

License

• Public Domain, United States Department of Agriculture.

Final Thoughts

• First attempt of creating a viable dataset.
• I'm guessing the inconsistencies of the descriptions/brand names come from different people and/or older data combinations.

Insufficient data exist for accurate estimation of global nutrient supplies. Commonly used global datasets contain key weaknesses: 1) data with global coverage, such as the FAO food balance sheets, lack specific information about many individual foods and no information on micronutrient supplies nor heterogeneity among subnational populations, while 2) household surveys provide a closer approximation of consumption, but are often not nationally representative, do not commonly capture many foods consumed outside of the home, and only provide adequate information for a few select populations. Here, we attempt to improve upon these datasets by constructing a new model—the Global Expanded Nutrient Supply (GENuS) model—to estimate nutrient availabilities for 23 individual nutrients across 225 food categories for thirty-four age-sex groups in nearly all countries. Furthermore, the model provides historical trends in dietary nutritional supplies at the national level using data from 1961–2011. We determine supplies of edible food by expanding the food balance sheet data using FAO production and trade data to increase food supply estimates from 98 to 221 food groups, and then estimate the proportion of major cereals being processed to flours to increase to 225. Next, we estimate intake among twenty-six demographic groups (ages 20+, both sexes) in each country by using data taken from the Global Dietary Database, which uses nationally representative surveys to relate national averages of food consumption to individual age and sex-groups; for children and adolescents where GDD data does not yet exist, average calorie-adjusted amounts are assumed. Finally, we match food supplies with nutrient densities from regional food composition tables to estimate nutrient supplies, running Monte Carlo simulations to find the range of potential nutrient supplies provided by the diet. To validate our new method, we compare the GENuS estimates of nutrient supplies against independent estimates by the USDA for historical US nutrition and find very good agreement for 21 of 23 nutrients, though sodium and dietary fiber will require further improvement.

Climate variability, changing land use and management, and dynamic policy environments are the main reasons why long-term water quality data sets are needed to understand and predict possible water quality outcomes to alternative future scenarios. Such data sets were acquired by the USDA-ARS in three watersheds in Oklahoma: the Southern Great Plains Research Watershed (SGPRW), the Little Washita River Experimental Watershed (LWREW), and the Fort Cobb Reservoir Experimental Watershed (FCREW). Water quality data collection in the SGPRW began in the 1960s and continued through 1978, while that in the LWREW covered the 1960s to 1990 period. Data collection began in the FCREW in 2004 and continues through the present.

The data were collected from streams, unit source watersheds, groundwater wells, and reservoirs. It should be noted that various forms of P—reactive P, total P, soluble P, water-soluble P, particulate P, bioavailable P, total water-soluble P—were measured and are described here as given in the original data sets. No effort was made to determine the similarity of these variables.

• Resource Title: GeoData catalog record.

  File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/WashitaNutrient_jjm_2015-03-16_1025

The National Institute of Food and Agriculture is committed to serving its stakeholders, Congress, and the public by using new technologies to advance greater openness. To strengthen transparency and promote open government, NIFA is providing easy access to data and metrics on how the agency disseminates funding. NIFA is committed to increasing transparency and making technical advancements to ensure that data is easily accessible. The Data Gateway provides the ability to filter and export data. Recently added features to the Congressional District Map and Data Gateway Search make for an improved user experience when searching and reporting information on NIFA-administered grants and projects! New interactive features in the Congressional District Map allow users to see the total amount of funding by state and further to drill down to the individual awards. Funding information is available for awards made from 2011-2015. Simply click on a state listing on the right of the screen. No need to create your own search if you are looking for NIFA funding by Congressional District. Key enhancements in the Data Gateway Search tool include:

A project-based display of data Embedded help text within tool Drop down lists allowing you to choose the fields you want to search and display Expanded filter lists

The Current Research Information System (CRIS) provides documentation and reporting for ongoing agricultural, food science, human nutrition, and forestry research, education and extension activities for the United States Department of Agriculture; with a focus on the National Institute of Food and Agriculture (NIFA) grant programs. Projects are conducted or sponsored by USDA research agencies, state agricultural experiment stations, land-grant universities, other cooperating state institutions, and participants in NIFA-administered grant programs, including Small Business Innovation Research and Agriculture and Food Research Initiative. The Planning, Accountability, & Reporting Staff office of NIFA is responsible for maintaining CRIS. Resources in this dataset:Resource Title: NIFA Reporting Portal. File Name: Web Page, url: https://portal.nifa.usda.gov Main html page for the database

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

What We Eat in America (WWEIA) is the dietary intake interview component of the National Health and Nutrition Examination Survey (NHANES). WWEIA is conducted as a partnership between the U.S. Department of Agriculture (USDA) and the U.S. Department of Health and Human Services (DHHS). Two days of 24-hour dietary recall data are collected through an initial in-person interview, and a second interview conducted over the telephone within three to 10 days. Participants are given three-dimensional models (measuring cups and spoons, a ruler, and two household spoons) and/or USDA's Food Model Booklet (containing drawings of various sizes of glasses, mugs, bowls, mounds, circles, and other measures) to estimate food amounts. WWEIA data are collected using USDA's dietary data collection instrument, the Automated Multiple-Pass Method (AMPM). The AMPM is a fully computerized method for collecting 24-hour dietary recalls either in-person or by telephone. For each 2-year data release cycle, the following dietary intake data files are available: Individual Foods File - Contains one record per food for each survey participant. Foods are identified by USDA food codes. Each record contains information about when and where the food was consumed, whether the food was eaten in combination with other foods, amount eaten, and amounts of nutrients provided by the food. Total Nutrient Intakes File - Contains one record per day for each survey participant. Each record contains daily totals of food energy and nutrient intakes, daily intake of water, intake day of week, total number foods reported, and whether intake was usual, much more than usual or much less than usual. The Day 1 file also includes salt use in cooking and at the table; whether on a diet to lose weight or for other health-related reason and type of diet; and frequency of fish and shellfish consumption (examinees one year or older, Day 1 file only). DHHS is responsible for the sample design and data collection, and USDA is responsible for the survey’s dietary data collection methodology, maintenance of the databases used to code and process the data, and data review and processing. USDA also funds the collection and processing of Day 2 dietary intake data, which are used to develop variance estimates and calculate usual nutrient intakes. Resources in this dataset:Resource Title: What We Eat In America (WWEIA) main web page. File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/wweianhanes-overview/ Contains data tables, research articles, documentation data sets and more information about the WWEIA program. (Link updated 05/13/2020)

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The effect of plant community structure on nutrient cycling is fundamental to our understanding of ecosystem function. We examined the importance of plant species and plant cover (i.e. plant covered microsites vs bare soil) on nutrient cycling in shortgrass steppe of northeastern Colorado. We tested the effects of both plant species and cover on soils in an area of undisturbed shortgrass steppe and an area that had undergone nitrogen and water additions from 1971 to 1974, resulting in significant shifts in plant species composition. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83317.

• Resource Title: Website Pointer to html file.

  File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=121

  Webpage with information and links to data files for download

To help enhance USA soil health, and ensure a robust living soil component that sustains essential functions for healthy plants, animals, and environment, and ultimately provides food for a healthy society, the GRACEnet Soil Biology group are working together with the larger USDA-ARS GRACEnet community to provide soil biology component measurements across regions and to eliminate data gaps for GRACEnet and REAP efforts. The Soil Biology group is focused on efforts that foster method comparison and meta-analyses to allow researchers to better assess soil biology and soil health indicators that are most responsive to agricultural management and that reflect the ecosystems services associated with a healthy, functioning soil. The GRACEnet Soil Biology mission is to produce the soil biology data, including methods of identifying and quantifying specific organisms and processes they govern, that are needed to evaluate impacts on agroecosystems and sustainable agricultural practices. This data collection effort is being accomplished in a highly structured manner to support current and future soil health and antimicrobial resistance research initiatives. The outcomes of the efforts of this team will provide a common biological data platform for several ARS databases, including: GRACEnet/REAP, Nutrient Use and Outcome Network (NUOnet), Long-Term Agroecosystem Research (LTAR) network, soil biology (e.g., MyPhyloDB) databases, and others. Resources in this dataset:Resource Title: Soil Biology Data Search. File Name: Web Page, url: https://agcros-usdaars.opendata.arcgis.com/datasets?group_ids=091b86e9e44a4e948ef2aeae3c916ca5

Find the calorie content of any food or beverage using the Food-a-pedia, looking at the Nutrition Facts label, or checking product or restaurant websites

The data set contains concentration, load, and daily discharge data for Devils Icebox Cave and Hunters Cave from 1999 to 2002. The data are available in Microsoft Excel 2010 format. Sheet 1 (Cave Streams Metadata) contains supporting information regarding the length of record, site locations, parameters measured, parameter units, method detection limits, describes the meaning of zero and blank cells, and briefly describes unit area load computations. Sheet 2 (Devils Icebox Concentration Data) contains concentration data from all samples collected from 1999 to 2002 at the Devils Icebox site for 12 analytes and two computed nutrient parameters. Sheet 3 (Devils Icebox SS Conc Data) contains 15-minute suspended sediment (SS) concentrations estimated from turbidity sensor data for the Devils Icebox site. Sheet 4 (Devils Icebox Load & Discharge Data) contains daily data for discharge, load, and unit area loads for the Devils Icebox site. Sheet 5 (Hunters Cave Concentration Data) contains concentration data from all samples collected from 1999 to 2002 at the Hunters Cave site for 12 analytes and two computed nutrient parameters. Sheet 6 (Hunters Cave SS Conc Data) contains 15-minute SS concentrations estimated from turbidity sensor data for the Hunters Cave site. Sheet 7 (Hunters Cave Load & Discharge Data) contains daily data for discharge, load, and unit area loads for the Hunters Cave site.

Atrazine concentrations in Goodwater Creek Experimental Watershed (GCEW) were shown to be among the very highest of any watershed in the United States based on comparisons using the national Watershed Regressions for Pesticides (WARP) model and by direct comparison with the 112 watersheds used in the development of WARP. The herbicide data collected in GCEW are documented at plot, field, and watershed scales. This 20-yr-long (1991-2010) effort was augmented with a spatially broad effort within the Central Mississippi River Basin encompassing 12 related claypan watersheds in the Salt River Basin, two cave streams on the fringe of the Central Claypan Areas in the Bonne Femme watershed, and 95 streams in northern Missouri and southern Iowa. The research effort on herbicide transport has highlighted the importance of restrictive soil layers with smectitic mineralogy to the risk of transport vulnerability. Near-surface soil features, such as claypans and argillic horizons, result in greater herbicide transport than soils with high saturated hydraulic conductivities and low smectitic clay content.

• Resource Title: GeoData catalog record.

  File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/GoodwaterNutrient_jjm_2015-03-12_1533

These two data sets are collections of best quality data on vitamin D content in foods, available in Europe (hosted by EuroFIRTM) and in US (hosted by USDA- in SR26). Data was collected in period of 2014-2015. Search criteria included analytical and manufactures' data sources, and these are sorted in food groups and vitamin D forms- vitamin D total (expressed in ug/100g or IU, and converted to ug), vitamin D3 (ug/100g), vitamin D2 (ug/100g), vitamin 25OHD (ug/100g).

A manuscript describing creation process of these two dataset is uploaded as well.