Retail Scanner Data consist of weekly pricing, volume, and store environment information generated by point-of-sale systems from more than 90 participating retail chains across all US markets.

Store Demographics: Includes store chain code, channel type, and area location. Retailer names are masked to protect identity.

Weekly Product Data: For each UPC code, participating stores report units, price, price multiplier, baseline units, baseline price, feature indicator, and display indicator. Products: Weekly product data for 2.6-4.5* million UPCs including food, nonfood grocery items, health and beauty aids, and select general merchandise aggregated into 1,100 product categories store environment variables (i.e., feature and display indicators) from a subset of stores. The 1,100 product categories are categorized into 125 product groups and 10 departments. The structure matches that of the consumer panel data. All private-label goods have a masked UPC to protect the identity of the retailers.

Product Characteristics: All products include UPC code and description, brand, multipack, and size, as well as NielsenIQ codes for department, product group, and product module. Some products contain additional characteristics (e.g., flavor).

Geographies: Scanner Data from 35,000-50,000* participating grocery, drug, mass merchandiser, and other stores, covering more than half the total sales volume of US grocery and drug stores and more than 30 percent of all US mass merchandiser sales volume. Data cover the entire United States, divided into 52 major markets, and include the same codes as those used in the consumer panel data.

Retail Channels: Food, drug, mass merchandise, convenience, and liquor.

Retail sales of specific packaged goods (coffee, laundry detergent, shampoo) broken out by U.S. region, brand, size, packaging material, UPC, and price.

Abstract

NielsenIQ Retail Scanner Data

Methodology

Through a relationship with NielsenIQ, the Kilts Center at the University of Chicago Booth School of Business provides multiple consumer datasets to academic researchers around the world.

Columbia has an agreement with the Kilts Center. Authorized faculty, graduate students, and research staff can apply to access this dataset.

Annual funding of the dataset is shared between the Program for Economic Research and the Libraries.

Funding for data analysis using the Columbia Data Platform is provided by the Program for Economic Research.

Usage

The Retail Scanner Data (also referred to as RMS data) consists of weekly pricing, volume, and store merchandising conditions generated by participating retail store point-of-sale systems in all US markets. Depending on the year, data are included from approximately 30,000-50,000 participating grocery, drug, mass merchandiser, and other stores. Products from all NielsenIQ-tracked categories are included in the data, such as food, non-food grocery items, health and beauty aids, and select general merchandise. Currently, the years 2006-2021 are included. We expect to update the data on an annual basis. Updates are expected to be available in the first quarter of each calendar year, and will always lag by 2 years (e.g. 2022 Retail Scanner data is expected to be released in Q1 of 2024).

There are three major types of files associated with the Retail Scanner Data: Stores, Product Description, and Movement (i.e., weekly sales and pricing). The Stores file contains information about each individual store location. The Product Description file contains information about each UPC. The Movement files contain the price and quantity of goods sold at specific stores on a specific week.

PopFacts Premier Demographic Flat File.

The Consumer Panel Data comprise a representative panel of households that continually provide information about their purchases in a longitudinal study in which panelists stay on as long as they continue to meet NielsenIQ's criteria. NielsenIQ consumer panelists use in-home scanners to record all of their purchases (from any outlet) intended for personal, in-home use. Consumers provide information about their households and what products they buy, as well as when and where they make purchases.

This dataset is about book series. It has 1 row and is filtered where the authors is David Nielsen. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

These data contain store-level information on UPC coded products, including store characteristics, sales, and detailed information on the products. There are three separate data series: the New England data, U.S. beverage data, and the WIC/Infant formula data. The New England data contain information on all food and beverage products sold in 8 major chains in New England. The U.S. beverage data include data on all non-alcoholic beverage products sold in grocery stores across the U.S. The WIC/Infant formula includes data on infant formula, ready-to-eat cereal, baby food, and peanut butter as well as demographics such as household size, income, race, and age and presence of children.

Nielsen PrimeLocation Web and Desktop Software Licensed for Internal Use only: Pop-Facts Demographics Database, Geographic Mapping Data Layers, Geo-Coding locations.

The Quarterly Food-at-Home Price Database provides food price data to support research on the economic determinants of food consumption, diet quality, and health outcomes.

This dataset is about books. It has 1 row and is filtered where the author is Arne Erik Nielsen. It features 7 columns including author, publication date, language, and book publisher.

The goal of sugar-sweetened beverage (SSB) taxes is to raise the prices of SSBs to decrease consumption. Price promotions play an important role in the sales of SSBs and could potentially be used by manufacturers to weaken the impact of such taxes. The purpose of this study is to determine how price promotions changed after the introduction of the 2017 Oakland SSB tax. A difference-in-differences study design was used to compare changes in prices and the prevalence and amount of price promotions for beverages in Oakland, California, relative to Sacramento, California, using two different datasets. Nielsen Retail Scanner data included price promotions for beverages sold and store audit data included price promotions offered by retailers. Changes were analyzed for SSBs, noncalorically sweetened beverages, and unsweetened beverages. After the implementation of the tax, the prevalence of price promotions for SSBs did not change significantly in Oakland relative to the comparison site of Sacramento. However, the depth of price promotions increased by an estimated 0.35 cents per ounce (P

This dataset is about book subjects. It has 1 row and is filtered where the authors is Helen Nielsen. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

• The dataset consists of Excel (.xlsx) files with data on sales of plant-based food products between 2017 and 2020 in a number of European countries (i.e. Austria, Belgium, Denmark, France, Germany, Italy, the Netherlands, Poland, Romania, Spain and the UK.)

• The data are clearly labelled within each file. The key variables (common across datasets) are Value in Euros, Volume in KG/LIT and Volume in Selling Units for a number of meat and dairy substitute food products specific to the retail region.

• The data were originally collected by Nielsen Market Track. They were analysed on the Smart Protein project in 2021 and used to publish an extensive market data report and to host a public webinar, both entitled Plant-based foods in Europe: how big is the market?

This database has been established as a central repository for spatially referenced biogeographic accounts of introduced aquatic species. The program provides scientific reports, online/realtime queries, spatial data sets, regional contact lists, and general information. The data is made available for use by biologists, interagency groups, and the general public.

Database on the recordings of Lepidoptera and Trichoptera by the Lepidopterological Society of Denmark (https://www.lepidoptera.dk). Occurrences are based on observations and collecting by any means, trapping, photos etc. Society specialist groups check data for errors and unusual records, mostly by contacting the observer/recorder. To assure quality of data, only members of the society are given access to enter records in the database.

The Product Description table contains information about each UPC.

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The Movement table contains the price and quantity of goods sold at specific stores on a specific week.

This dataset is about books. It has 2 rows and is filtered where the author is Laura Beth Nielsen. It features 7 columns including author, publication date, language, and book publisher.

Leaf counting dataset Dataset containing 9372 RGB images of weeds with the number of leaves counted. The images are collected in fields across Denmark using Nokia and Samsung cell phone cameras; Samsung, Nikon, Canon and Sony consumer cameras; and a Point Grey industrial camera. ## Citation If you use this dataset in your research or elsewhere, please cite/reference the following paper: PAPER: Weed Growth Stage Estimator Using Deep Convolutional Neural Networks Bibtex