[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 Data have been compiled from published and unpublished sources.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 Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research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8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963).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.

[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.

FooDrugs database is a development done by the Computational Biology Group at IMDEA Food Institute (Madrid, Spain), in the context of the Food Nutrition Security Cloud (FNS-Cloud) project. Food Nutrition Security Cloud (FNS-Cloud) has received funding from the European Union's Horizon 2020 Research and Innovation programme (H2020-EU.3.2.2.3. – A sustainable and competitive agri-food industry) under Grant Agreement No. 863059 – www.fns-cloud.eu (See more details about FNS-Cloud below)

FooDrugs stores information extracted from transcriptomics and text documents for foo-drug interactiosn and it is part of a demonstrator to be done in the FNS-Cloud project. The database was built using MySQL, an open source relational database management system. FooDrugs host information for a total of 161 transcriptomics GEO series with 585 conditions for food or bioactive compounds. Each condition is defined as a food/biocomponent per time point, per concentration, per cell line, primary culture or biopsy per study. FooDrugs includes information about a bipartite network with 510 nodes and their similarity scores (tau score; https://clue.io/connectopedia/connectivity_scores) related with possible drug interactions with drugs assayed in conectivity map (https://www.broadinstitute.org/connectivity-map-cmap). The information is stored in eight tables:

• Table “study” : This table contains basic information about study identifiers from GEO, pubmed or platform, study type, title and abstract

• Table “sample”: This table contains basic information about the different experiments in a study, like the identifier of the sample, treatment, origin type, time point or concentration.

• Table “misc_study”: This table contains additional information about different attributes of the study.

• Table “misc_sample”: This table contains additional information about different attributes of the sample.

• Table “cmap”: This table contains information about 70895 nodes, compromising drugs, foods or bioactives, overexpressed and knockdown genes (see section 3.4). The information includes cell line, compound and perturbation type.

• Table “cmap_foodrugs”: This table contains information about the tau score (see section 3.4) that relates food with drugs or genes and the node identifier in the FooDrugs network.

• Table “topTable”: This table contains information about 150 over and underexpressed genes from each GEO study condition, used to calculate the tau score (see section 3.4). The information stored is the logarithmic fold change, average expression, t-statistic, p-value, adjusted p-value and if the gene is up or downregulated.

• Table “nodes”: This table stores the information about the identification of the sample and the node in the bipartite network connecting the tables “sample”, “cmap_foodrugs” and “topTable”.

In addition, FooDrugs database stores a total of 6422 food/drug interactions from 2849 text documents, obtained from three different sources: 2312 documents from PubMed, 285 from DrugBank, and 252 from drugs.com. These documents describe potential interactions between 1464 food/bioactive compounds and 3009 drugs. The information is stored in two tables:

• Table “texts”: This table contains all the documents with its identifiers where interactions have been identified with strategy described in section 4.

• Table “TM_interactions”: This table contains information about interaction identifiers, the food and drug entities, and the start and the end positions of the context for the interaction in the document.

FNS-Cloud will overcome fragmentation problems by integrating existing FNS data, which is essential for high-end, pan-European FNS research, addressing FNS, diet, health, and consumer behaviours as well as on sustainable agriculture and the bio-economy. Current fragmented FNS resources not only result in knowledge gaps that inhibit public health and agricultural policy, and the food industry from developing effective solutions, making production sustainable and consumption healthier, but also do not enable exploitation of FNS knowledge for the benefit of European citizens.
FNS-Cloud will, through three Demonstrators; Agri-Food, Nutrition & Lifestyle and NCDs & the Microbiome to facilitate:
(1) Analyses of regional and country-specific differences in diet including nutrition, (epi)genetics, microbiota, consumer behaviours, culture and lifestyle and their effects on health (obesity, NCDs, ethnic and traditional foods), which are essential for public health and agri-food and health policies;
(2) Improved understanding agricultural differences within Europe and what these means in terms of creating a sustainable, resilient food systems for healthy diets; and
(3) Clear definitions of boundaries and how these affect the compositions of foods and consumer choices and, ultimately, personal and public health in the future.
Long-term sustainability of the FNS-Cloud will be based on Services that have the capacity to link with new resources and enable cross-talk amongst them; access to FNS-Cloud data will be open access, underpinned by FAIR principles (findable, accessible, interoperable and re-useable). FNS-Cloud will work closely with the proposed Food, Nutrition and Health Research Infrastructure (FNHRI) as well as METROFOOD-RI and other existing ESFRI RIs (e.g. ELIXIR, ECRIN) in which several FNS-Cloud Beneficiaries are involved directly. (https://cordis.europa.eu/project/id/863059)

a Food Standards Australia and New Zealand,b United States Department of Agriculture,c Food Standards Agency,d Separate databases for flavonoids, carotenoids, proanthocyanidins and isoflavones,e Eurofir EBASIS contains bioactive data for UK and Europe,f National Health Survey,ghttps://www.xyris.com.au/foodworks/fw_pro.html,hhttp://www.nutribase.com/highend.html,ihttp://www.foodresearch.ca/wp-content/uploads/2013/06/candat-features-1.pdf,j Tinuviel Software,i Downlees Systems,k Forestfield Software,l Kelicomp,mhttp://www.tinuvielsoftware.com/faqs.htm,nhttp://www.dietsoftware.com/canada.html,o Text file: a file that only contains text,p A file containing tables of information stored in columns and separated by tabs (can be exported into almost any spreadsheet program),q Microsoft Excel spreadsheet,r Microsoft Access Database file: is a database file with automated functions and queries,s American Standard Code for Information Interchange (a standard file type that can be used by many programs),t Database File Format (this file type can be opened with Microsoft Excel and Access),u information to create Excel or PDF available,v Composition of Foods, Australia,w International Network of Food Data System,x Users guide states food name is most descriptive & recognisable of food referencedyhttp://www.foodstandards.gov.au/science/monitoringnutrients/nutrientables/nuttab/Pages/NUTTAB-2010-electronic-database-files.aspx,zhttp://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/default.aspx,aahttp://ndb.nal.usda.gov/ndb/search/list,bbhttp://tna.europarchive.org/20110116113217/http://www.food.gov.uk/science/dietarysurveys/dietsurveys/,cchttp://webprod3.hc-sc.gc.ca/cnf-fce/index-eng.jspDesktop analysis and examination of six key food composition databases format.

The Canadian Nutrient File (CNF) is the standard reference food composition database reporting the amount of nutrients in foods commonly consumed in Canada.

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. Resources in this dataset:Resource Title: List of Key Foods based on CSFII 1989-91. File Name: KeyFoods_key_ls91.txtResource Description: Key Foods based on CSFII 1989-91 https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/key_ls91.txtResource Title: List of Key Foods based on CSFII 1994-96 . File Name: KeyFoods_key_ls9496.txtResource Description: List of Key Foods based on CSFII 1994-96 https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/key_ls9496.txtResource Title: List of Key Foods based on WWEIA-NHANES 2001-02. File Name: KeyFoods_key_ls0102.txtResource Description: List of Key Foods based on WWEIA-NHANES 2001-02 https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/key_ls0102.txtResource Title: List of Key Foods based on WWEIA-NHANES 2003-04 . File Name: KeyFoods_key_ls0304.txtResource Description: https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/key_ls0304.txtResource Title: List of Key Foods based on WWEIA-NHANES 2007-08. File Name: Keyfoods_0708.xlsxResource Description: List of Key Foods based on WWEIA-NHANES 2007-08 https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/Keyfoods_0708.xlsxResource Title: List of Key Foods based on WWEIA-NHANES 2009-10. File Name: Keyfoods_0910.xlsxResource Description: List of Key Foods based on WWEIA-NHANES 2009-10 https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/Keyfoods_0910.xlsxResource Title: List of Key Foodsbased on WWEIA-NHANES 2011-12. File Name: Keyfoods_1112.xlsxResource Description: List of Key Foodsbased on WWEIA-NHANES 2011-12 https://www.ars.usda.gov/ARSUserFiles/80400525/Data/KeyFoods/Keyfoods_1112.xlsx

[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.

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)

The database contains values for 283 food items for the following proanthocyanidins groups: Dimers Trimers 4-6 mers (tetramers, pentamers and hexamers) 7-10 mers (heptamers, octamers, nonamers and decamers Polymers (DP>10) Resources in this dataset:Resource Title: READ ME - USDA Database for the Proanthocyanidin Content of Selected Foods. File Name: PA02.pdfResource Description: Information regarding the documentation, data sources, data management, data quality evaluation, aggregation and format, sources of data, and references cited.Resource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: Data Dictionary. File Name: PA02_DD.pdfResource Title: PA02.accdb. File Name: PA02.zipResource Description: This file contains the Proanthocyanidin Database imported into a MS Access database version 2007 or later. The file structure is the same as that of the USDA National Nutrient Database for Standard Reference.

EFSA is regularly evaluating pesticide occurrence data in food generated under the official monitoring programs of Member States with respect to consumer exposure and risk assessment. Most of these data refer to raw commodities (RAC) because maximum residue levels established under European legislation reflect pesticide residues only in the RAC. However, food processing operations can have decisive effects on pesticide residue levels and therefore consumer exposure. This database has been developed to compile validated processing factors for pesticide residues in food in line with the EFSA food classification and description system (FoodEx2).

This update fixes a problem that caused some median processing factors to display a "<" qualifier when it was not necessary. The calculated values for the processing factors were not affected by this error and remain unchanged from the previous release

The database is complemented by the following publications:

Background Document on the EU Database of Processing Factors for Pesticide Residues

Compendium of Representative Processing Techniques Investigated in Regulatory Studies for Pesticides

Linking Processed Foods and Processing Techniques to the FoodEx2 Coding System

Fermented Foods Microbial Genomes Database

This database contains 13,850 microbial genomes assembled from various fermented foods and associated curated metadata.

This GitHub repository documents how publicly available genomes and metagenome-assembled genomes were sourced and curated. This GitHub repository documents how the associated metadata was curated.

This database largely pulls from existing genome resources, and we curated this database specifically for fermented foods. If you use this database, please cite the following genome databases/resources:

1. MiFoDB, a workflow for microbial food metagenomic characterization, enables high-resolution analysis of fermented food microbial dynamics. Elisa B. Caffrey, Matthew R. Olm, Caroline Isabel Kothe, Joshua Evans, Justin L. Sonnenburg . bioRxiv 2024.03.29.587370; doi: https://doi.org/10.1101/2024.03.29.587370
2. Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome. Carlino, Niccolo Alvarez-Ordonez, Avelino et al. Cell, Volume 187, Issue 20, 5775-5795.e15 AND the associated Zenodo release: Master Consortium. (2024). Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome. Zenodo. https://doi.org/10.5281/zenodo.13285428

We are incredibly grateful for these groups and countless others taking the time to make their data publicly available. Included in the metadata is the original DOI and study link from which the genome was generated, in addition to if they were collated into one of the above two larger databases. If you specifically use/analyze a subset of genomes, please cite those studies to credit those that generate data and make it publicly available.

Subsetting the Database to Species/Strain-Resolved Representatives or a Custom Set

We have provided the entire set of 13,850 microbial genomes in a single tar archive for download. If you wish to only use a subset of the database, such as species-representative (clustered at 95% ANI) or "strain-representative" (clustered at 99% ANI) genomes, you can use our helper script for subsetting genomes that are representatives or a custom list that you provide. You will still have to download the entire ~13GB tar archive and decompress it, but this helper script automates splitting the genomes into subset directories of your choosing.

usage: subset_genomes.py [-h] [--rep-column {rep_95id,rep_99id}]
[--id-column ID_COLUMN] [--dry-run]
[--genome-list GENOME_LIST]
metadata_tsv all_genomes_dir output_dir

Subset representative genomes (species/strain) from a genome set using
metadata.

positional arguments:
metadata_tsv Path to metadata TSV file
all_genomes_dir Directory containing all .fa genome files
output_dir Directory to copy representative genomes to

optional arguments:
-h, --help show this help message and exit
--rep-column {rep_95id,rep_99id}
Column in metadata to use for representatives (e.g.,
rep_95id or rep_99id)
--id-column ID_COLUMN
Column in metadata with genome file IDs (default:
mag_id)
--dry-run Only print what would be copied, don't actually copy
--genome-list GENOME_LIST
Optional: Path to file with list of genome IDs or
filenames to subset (one per line)

KBase Fermented Foods Microbial Genomes Database Narrative

We have uploaded the "strain-representative" set of ~4300 genomes to KBase as a public narrative.

SuperTracker was an online tool offered by USDA (2011-2018) that helped users track diet, physical activity and weight. SuperTracker provided a personalized plan based on the 2015-2020 Dietary Guidelines for Americans for what you should eat and drink and guided users to making better choices. This dataset includes the SuperTracker source code (latest update April 2018), including: front end application, database schema, documentation, deployment scripts and a ReadMe.txt file that provides high level instructions for the source code. Database connection strings and actual data are not included. The full foods database spreadsheet is attached as well; these foods are based on the Food and Nutrient Database for Dietary Studies (FNDDS), and the Food Patterns Equivalents Database (FPED), both from the USDA/ARS Food Surveys Research Group. It is important to note that the code is based on 2015-2020 Dietary Guidelines for Americans and will not be updated to reflect future guidance. In addition, the food database is based on FNDDS from 2011-2012 (FNDDS 6.0) and FPED from 2011-2012 and will not be updated with future data releases.

The Canadian Nutrient File (CNF) is the standard reference food composition database reporting the amount of nutrients in foods commonly consumed in Canada.

Step-by-step method for determining which FID food profile was most appropriate to match with each FLIP product and the number and percent (%) of foods matched at each step (n = 16,136) a.

Resource Description: Information regarding the documentation, data sources, data management, data quality evaluation, aggregation and format, sources of data, and references cited.Resource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: Data Dictionary. File Name: PA02_DD.pdfResource Title: PA02.accdb. File Name: PA02.zipResource Description: This file contains the Proanthocyanidin Database imported into a MS Access database version 2007 or later. The file structure is the same as that of the USDA National Nutrient Database for Standard Reference.

Resource Description: This file contains the Flavonoid Database imported into a MS Access database. In addition to the summary values, a separate file containing the individual values used to calculate the summary values is included, along with all information on conversion factors used to calculate the individual aglycone values, analytical method, country of origin, etc. It also includes relationships between files. You need MS Access 2007 or later to use this file. The file structure is the same as that of the USDA National Nutrient Database for Standard Reference. Resource Title: USDA Database for the Flavonoid Content of Selected Foods Release 3.3 Documentation .

Number and proportion (%) of FID food profiles that were matched with a FLIP product and the average number and range of FLIP matches per FID food profile, overall and by major food group (n = 2785 FID food profiles) a.

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.

Supplementary material file 1: Table S1: List of 30 surveyed food groups and components included in each group. Table S2: Description of the evaluated diet indices and included components. The symbol “(+)” denotes healthy food groups, with increasing scores assigned to higher rank classes. The symbol “(-)” indicates inverse scoring where higher ranks receive lower scores. Table S3: Median daily intakes with 25 and 75 percentile limits in 30 food groups as estimated from the FFQ or the 24HDR in the validation group (n = 244) together with Spearman correlation coefficients and ratios between intakes assessed by the two recording methods. Table S4: Proportion (%) classified to food group tertiles (1, 2, 3) based on ranking of increasing intakes by the FFQ2020 (test method) and 24HDR (reference) recordings among the 244 participants with at least four 24HDR recordings. For food groups indicated in bold, italic text more than 50% of the participants had not eaten the product in any of the recorded 24HDR days. Table S5: Daily mean (sd) intakes of energy and 14 nutrients based on FFQ2020 and 24hDR recordings in the validation group (n = 244) and after exclusion of the lowest 5% and highest 1% based on PAL values (n = 231) together with Pearson and Spearman correlation coefficients for absolute and energy-adjusted (residual and energy density methods) data. Table S6: Proportion (%) of energy and nutrients in tertiles (1, 2, 3) based on a ranking of increasing intakes by the FFQ2020 and 24HDR recordings, respectively. Table S7: Central measures (variations) of food index scores and intake in the 30 surveyed foods groups and the repeatability of the estimated intakes as assessed by intraclass correlation coefficient with 95% CI using a two-way mixed-effects model, absolute agreement, and average measures. Table S8: Mean (sd) daily intake of energy and 14 nutrients as estimated from the baseline FFQ2020 and the follow-up FFQ2020, Pearson correlation coefficients, and intraclass correlation coefficient with 95% CI using a two-way mixed-effects model, absolute agreement, and average measures. Table S9: Spearman correlation coefficients in 30 food groups and 3 diet indexes as estimated from the FFQ or the 24HDR in the validation group for men (n = 95) and women (n = 149), respectively. Figure S1: Scatter plot for (A) PAL values estimated from 24HDRs versus FFQ2020 (the dotted lines represent lower cut-off, i.e., PAL 0.7), and (B) daily energy intake estimated from 24HDRs versus FFQ2020

Canada's national food composition database reporting the amount of nutrients in foods commonly consumed in Canada.