Cost of food in the United States increased 3.10 percent in September of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Cost of food in Canada increased 3.40 percent in October of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Canada Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Cost of food in Ireland increased 4.50 percent in October of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Ireland Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Monthly average retail prices for food, household supplies, personal care items, cigarettes and gasoline. Prices are presented for the current month and previous four months. Prices are in Canadian current dollars.

This dataset provides estimated average retail prices for more than 150 fruits and vegetables by year.

The source of the header image is Scott Webb on Unsplash.

OVERVIEW

The United States Department of Agriculture's Economic Research Service (ERS) estimated average prices for over 150 commonly consumed fruits and vegetables, using 2013, 2016, 2020, and 2022 retail sales data from Circana. Circana obtained this data from a selection of retail establishments across the U.S., including grocery stores, supermarkets, convenience stores, drug stores, and liquor stores.

See ers.usda.gov for additional documentation.

LIMITATIONS & SOURCES OF ERROR

• Coding inconsistencies and market dynamics make this dataset unsuitable for inferring price trends over time.
• The sample of U.S. retail establishments from which Circana collected data may not be representative of the entire population.
• Estimates of a product’s inedible portion and weight change from cooking are based on samples of products. Actual amounts will differ for specific products bought by a household (e.g., the actual size of the inedible core of an apple varies between fruits).

Cost of food in Australia increased 3.20 percent in October of 2025 over the same month in the previous year. This dataset provides - Australia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Monthly average retail prices for selected products, for Canada, provinces, Whitehorse and Yellowknife. Prices are presented for the current month and the previous four months. Prices are based on transaction data from Canadian retailers, and are presented in Canadian current dollars.

What's in the Data?

This dataset tracks food insecurity across different demographics starting 4/23/2020 to 8/23/2021. It contains fields such as Race, Education, Sex, State, Income, etc. If you're looking for a dataset to examine Covid-19's impact on food insecurity for different demographics, then here you are!

Data Source

This data is from the United States Census Bureau's Pulse Survey. The Pulse Survey is a frequently updating survey designed to collect data on how people's lives have been impacted by the coronavirus. Specifically, this dataset is a cleaned up version of the ' Food Sufficiency for Households, in the Last 7 Days, by Select Characteristics" tables.

The original form of this data can be found at: https://www.census.gov/programs-surveys/household-pulse-survey/data.html

What was done to this data?

The original form of this data was split into 36 excel files containing ~67 sheets each. The data was in a non-tidy format, and questions were also not entirely standard. This dataset is my attempt to combine all these different files, tidy the data up, and combine slightly different questions together.

Why are there so many NA's?

The large amount of NA's are a consequence of how awful the data was originally/ forcing the data into a tidy format. Just filter the NA's out for the question you want to analyze and you'll be fine.

Introduction

The Free-living Food Intake Cycle (FreeFIC) dataset was created by the Multimedia Understanding Group towards the investigation of in-the-wild eating behavior. This is achieved by recording the subjects’ meals as a small part part of their everyday life, unscripted, activities. The FreeFIC dataset contains the (3D) acceleration and orientation velocity signals ((6) DoF) from (22) in-the-wild sessions provided by (12) unique subjects. All sessions were recorded using a commercial smartwatch ((6) using the Huawei Watch 2™ and the MobVoi TicWatch™ for the rest) while the participants performed their everyday activities. In addition, FreeFIC also contains the start and end moments of each meal session as reported by the participants.

Description

FreeFIC includes (22) in-the-wild sessions that belong to (12) unique subjects. Participants were instructed to wear the smartwatch to the hand of their preference well ahead before any meal and continue to wear it throughout the day until the battery is depleted. In addition, we followed a self-report labeling model, meaning that the ground truth is provided from the participant by documenting the start and end moments of their meals to the best of their abilities as well as the hand they wear the smartwatch on. The total duration of the (22) recordings sums up to (112.71) hours, with a mean duration of (5.12) hours. Additional data statistics can be obtained by executing the provided python script stats_dataset.py. Furthermore, the accompanying python script viz_dataset.py will visualize the IMU signals and ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below.

The script(s) and the pickle file must be located in the same directory.

Tested with Python 3.6.4

Requirements: Numpy, Pickle and Matplotlib

Calculate and echo dataset statistics

$ python stats_dataset.py

Visualize signals and ground truth

$ python viz_dataset.py

FreeFIC is also tightly related to Food Intake Cycle (FIC), a dataset we created in order to investigate the in-meal eating behavior. More information about FIC can be found here and here.

Publications

If you plan to use the FreeFIC dataset or any of the resources found in this page, please cite our work:

@article{kyritsis2020data,
title={A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches},
author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios},
journal={IEEE Journal of Biomedical and Health Informatics}, year={2020},
publisher={IEEE}}

@inproceedings{kyritsis2017automated, title={Detecting Meals In the Wild Using the Inertial Data of a Typical Smartwatch}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
year={2019}, organization={IEEE}}

Technical details

We provide the FreeFIC dataset as a pickle. The file can be loaded using Python in the following way:

import pickle as pkl import numpy as np

with open('./FreeFIC_FreeFIC-heldout.pkl','rb') as fh: dataset = pkl.load(fh)

The dataset variable in the snipet above is a dictionary with (5) keys. Namely:

'subject_id'

'session_id'

'signals_raw'

'signals_proc'

'meal_gt'

The contents under a specific key can be obtained by:

sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals gt = dataset['meal_gt'] # for the meal ground truth

The sub, ses, raw, proc and gt variables in the snipet above are lists with a length equal to (22). Elements across all lists are aligned; e.g., the (3)rd element of the list under the 'session_id' key corresponds to the (3)rd element of the list under the 'signals_proc' key.

sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take the following values: ([1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20]). It should be emphasized that the subjects with ids (15, 16, 17, 18, 19) and (20) belong to the held-out part of the FreeFIC dataset (more information can be found in ( )the publication titled "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al). Moreover, the subject identifier in FreeFIC is in-line with the subject identifier in the FIC dataset (more info here and here); i.e., FIC’s subject with id equal to (2) is the same person as FreeFIC’s subject with id equal to (2).

ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between (1) and (5). It should be noted that not all subjects have the same number of sessions.

raw: list Each element of this list is dictionary with the 'acc' and 'gyr' keys. The data under the 'acc' key is a (N_{acc} \times 4) numpy.ndarray that contains the timestamps in seconds (first column) and the (3D) raw accelerometer measurements in (g) (second, third and forth columns - representing the (x, y ) and (z) axis, respectively). The data under the 'gyr' key is a (N_{gyr} \times 4) numpy.ndarray that contains the timestamps in seconds (first column) and the (3D) raw gyroscope measurements in ({degrees}/{second})(second, third and forth columns - representing the (x, y ) and (z) axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info here and here). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different ((N_{acc} eq N_{gyr})). This behavior is predictable and is caused by the Android platform.

proc: list Each element of this list is an (M\times7) numpy.ndarray that contains the timestamps, (3D) accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the (x,y) and (z) accelerometer values in (g) and the fifth, sixth and seventh columns contain the (x,y) and (z) gyroscope values in ({degrees}/{second}). Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of (100) Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info here and here). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component).

meal_gt: list Each element of this list is a (K\times2) matrix. Each row represents the meal intervals for the specific in-the-wild session. The first column contains the timestamps of the meal start moments whereas the second one the timestamps of the meal end moments. All timestamps are in seconds. The number of meals (K) varies across recordings (e.g., a recording exist where a participant consumed two meals).

Ethics and funding

Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - BigO: Big data against childhood obesity.

Contact

Any inquiries regarding the FreeFIC dataset should be addressed to:

Dr. Konstantinos KYRITSIS

Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124

Tel: +30 2310 996359, 996365 Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

Cost of food in the United Kingdom increased 4.90 percent in October of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United Kingdom Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

[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.

This layer shows which parts of the United States and Puerto Rico fall within ten minutes" walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale. When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes" walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don"t own a car? How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards. The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access. Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population"s grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples). The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer. Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters. The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis. The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer"s block figures can be summarized further, to tract, county and state levels. The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer. Methodology Every census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway. A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle"s access to all types of roads was factored in. The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle). The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step. Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect. Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person"s commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point. Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

[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.

This statistic shows the share of individuals that used food pantries and emergency kitchens in the United States in 2021, by household food security status. At that time, a *** percent share of individuals from food secure households used food pantries in the United States. Compared to 2020, this figure has declined by *** percent. The dataset has been discontinued by the source. This content will not be updated. More up-to-date content on the share of people living in food insecurity who receive free meals or groceries can be found here. Data is provided by the same source.

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)

***** changes between version FooDrugs_v2 and FooDrugs_V3 (31st January 2023) are:

Increased the amount of text documents by 85.675 from PubMed and ClinicalTrials.gov, and the amount of Text Mining interactions by 168.826.

Increased the amount of transcriptomic studies by 32 GEO series.

Removed all rows in table cmap_foodrugs representing interactions with values of tau=0

Removed 43 GEO series that after manually checking didn't correspond to food compounds.

Added a new column to the table texts: citation to hold the citation of the text.

Added these columns to the table study: contributor to contain the authors of the study, publication_date to store the date of publication of the study in GEO and pubmed_id to reference the publication associated with the study if any.

Added a new column to topTable to hold the top 150 up-regulated and 150 down-regulated genes.

Cost of food in India decreased 5.02 percent in October of 2025 over the same month in the previous year. This dataset provides - India Food Inflation - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Solomon Islands and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For Solmon Islands, after five rounds of data collection from 2020-2020, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. Fieldwork took place in two non-consecutive weeks of each month. Data for April 2023-December 2023 were a repeated cross section, while January 2024 established the first month of a panel, the was continued to September 2024. Each month has approximately 550 households in the sample and is representative of urban and rural areas, but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in Solomon Islands. There is one date file for household level data with a unique household ID. and a separate file for individual level data within each household data, that can be matched to the household file using the household ID, and which also has a unique individual ID within the household data which can be used to track individuals over time within households, where the data is panel data.

Geographic coverage

Urban and rural areas of Solomon Islands.

Analysis unit

Household, individual.

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The initial sample was drawn from information provided by a major phone service provider in Solomon Islands, covering all the provinces in the country. It had a probability-based weighted design, with a proportionate stratification to achieve geographical representation. The geographical distribution compared to the 2019 Census is listed below for the first month of the HFPS monthly survey:

Choiseul : Census: 4.3%, HFPS: 5.2% Western : Census: 14.4%, HFPS: 13.7% Isabel : Census: 4.8%, HFPS: 4.7% Central : Census: 3.6%, HFPS: 5.2% Ren Bell : Census: 0.6%, HFPS: 1.4% Guadalcanal: Census: 19.8%, HFPS: 21.1% Malaita : Census: 23.1%, HFPS: 18.7% Makira : Census: 5.6%, HFPS: 5.6% Temotu: Census: 3.0%, HFPS: 3% Honiara: Census: 20.7%, HFPS: 21.3%

Source: Census of Population and Housing 2019

Note: The values in the HFPS column represent the proportion of survey participants residing in each province, based on the raw HFPS data from April.

In April 2023, the geographic distribution of World Bank HFPS participants was generally similar to that of the census data at the province level, though within provinces, areas with less mobile phone connectivity are likely to be underrepresented. One indication of this is that urban areas constituted 38.2 percent of the survey sample, which is a slight overrepresentation, compared to 32.5 percent in the Census 2019.

A monthly panel was established in January 2024, that is ongoing as of March 2025. In each subsequent month after January 2024, the survey firm would first attempt to contact all households from the previous month and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households. Across all months of the survey a total of, 9,926 interviews were completed.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire, which can be found in the External Resources of this documentation, is available in English, with Solomons Pijin translation. There were few changes to the questionnaire across the survey months, but some sections were only introduced in 2024, namely energy access questions and questions to inform the baseline data of the Solomon Islands Government Integrated Economic Development and Climate Resilience (IEDCR) project.

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The total number of observations is 9,926 in the household dataset and 62,054 in the individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Papua New Guinea and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For PNG, after five rounds of data collection from 2020-2022, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. This followed an initial pilot of the data collection from January 2023-March 2023. Data for April 2023-September 2023 were a repeated cross section, while October 2023 established the first month of a panel, which is ongoing as of March 2025. For each month, approximately 550-1000 households were interviewed. The sample is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in PNG. There is one date file for household level data with a unique household ID, and separate files for individual level data within each household data, and household food price data, that can be matched to the household file using the household ID. A unique individual ID within the household data which can be used to track individuals over time within households.

Geographic coverage

Urban and rural areas of Papua New Guinea

Analysis unit

Household, Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification from a large random sample of Digicel’s subscribers. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The resulting overall sample has a probability-based weighted design, with a proportionate stratification to achieve a proper geographical representation. More information on sampling for the cross-sectional monthly sample can be found in previous documentation for the PNG HFPS data.

A monthly panel was established in October 2023, that is ongoing as of March 2025. In each subsequent round of data collection after October 2024, the survey firm would first attempt to contact all households from the previous month, and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

he questionnaire, which can be found in the External Resources of this documentation, is in English with a Pidgin translation.

The survey instrument for Q1 2025 consists of the following modules: -1. Basic Household information, -2. Household Roster, -3. Labor, -4a Food security, -4b Food prices -5. Household income, -6. Agriculture, -8. Access to services, -9. Assets -10. Wellbeing and shocks -10a. WASH

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.

BackgroundTo limit the spread of COVID-19, a strict lockdown was imposed in France between March and May 2020. Mobility limitations and closure of non-essential public places (restaurants, open-air markets, etc.) affected peoples' food environment (FE) and thus their food purchasing practices (FPPs). This study aimed to explore changes in FPPs of French households during lockdown and associations with individual and environmental factors.MethodsIn April of 2020 households from the Mont'Panier cross-sectional study (n = 306), a quota sampling survey conducted in the south of France, were asked to complete an online questionnaire about their FPPs during lockdown and related factors, including perceived FE (distance to closest general food store, perception of increased food prices, etc.). Objective FE (presence, number, proximity, and density of food outlets) was assessed around participant's home using a geographical information system. Multiple correspondence analysis based on changes in frequency of use and quantity of food purchased by food outlet, followed by a hierarchical cluster analysis, resulted in the identification of clusters. Logistic regression models were performed to assess associations between identified clusters and household's sociodemographic characteristics, perceived, and objective FE.ResultsFive clusters were identified. Cluster “Supermarket” (38% of the total sample), in which households reduced frequency of trips, but increased quantity bought in supermarkets during lockdown, was associated with lower incomes and the perception of increased food prices. Cluster “E-supermarket” (12%), in which households increased online food shopping with pickup at supermarket, was associated with higher incomes. Cluster “Diversified” (22%), made up of households who reduced frequency of trips to diverse food outlet types, was associated with the perception of increased food prices. Cluster “Organic Food Store” (20%), in which households did not change frequency of trips, nor quantity purchased in organic food stores, was associated with being older (35–50 y vs.

COVID-19 Stats & Trends

Context

This dataset seeks to provide insights into what has changed due to policies aimed at combating COVID-19 and evaluate the changes in community activities and its relation to reduced confirmed cases of COVID-19. The reports chart movement trends, compared to an expected baseline, over time (from 2020/02/15 to 2020/02/05) by geography (across 133 countries), as well as some other stats about the country that might help explain the evolution of the disease.

Content

1. Grocery & Pharmacy: Mobility trends for places like grocery markets, food warehouses, farmers' markets, specialty food shops, drug stores, and pharmacies.
2. Parks: Mobility trends for places like national parks, public beaches, marinas, dog parks, plazas, and public gardens.
3. Residential: Mobility trends for places of residence.
4. Retail & Recreation: Mobility trends for places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.
5. Transit stations: Mobility trends for places like public transport hubs such as subway, bus, and train stations.
6. Workplaces: Mobility trends for places of work.
7. Total Cases: Total number of people infected with the SARS-CoV-2.
8. Fatalities: Total number of deaths caused by CoV-19.
9. Government Response Stringency Index: Additive score of nine indicators of government response to CoV-19: School closures, workplace closures, cancellation of public events, public information campaigns, stay at home policies, restrictions on internal movement, international travel controls, testing policy, and contact tracing.
10. COVID-19 Testing: Total number of tests performed.
11. Total Vaccinations: Total number of shots given.
12. Total People Vaccinated: Total number of people given a shot.
13. Total People Fully Vaccinated: Total number of people fully vaccinated (might require two shots of some vaccines).
14. Population: Total number of inhabitants.
15. Population Density per km2: Number of human inhabitants per square kilometer.
16. Health System Index: Overall performance of the health system.
17. Human Development Index (HDI): Summary index based on life expectancy at birth, expected years of schooling for children and mean years of schooling for adults, and GNI per capita.
18. GDP (PPP) per capita: Gross Domestic Product (GDP) per capita based on Purchasing Power Parity (PPP), taking into account the relative cost of local goods, services and inflation rates of the country, rather than using international market exchange rates, which may distort the real differences in per capita income.
19. Elderly Population (percentage): Percentage of the population above the age of 65 years old.

References & Acknowledgements

Bing COVID-19 data. Available at: https://github.com/microsoft/Bing-COVID-19-Data COVID-19 Community Mobility Report. Available at: https://www.google.com/covid19/mobility/ COVID-19: Government Response Stringency Index. Available at: https://ourworldindata.org/grapher/covid-stringency-index Coronavirus (COVID-19) Testing. Available at: https://github.com/owid/covid-19-data/blob/master/public/data/testing/covid-testing-all-observations.csv Coronavirus (COVID-19) Vaccination. Available at: https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv List of countries and dependencies by population. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries and dependencies by population density. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries by Human Development Index. Available at: http://hdr.undp.org/en/data Measuring Overall Health System Performance. Available at: https://www.who.int/healthinfo/paper30.pdf?ua=1 List of countries by GDP (PPP) per capita. Available at: https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD List of countries by age structure (65+). Available at: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS

Authors

• Diogo Silva, up201706892@fe.up.pt