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.

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.

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 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.

Years Available: Starting with 2004 and including annual updates.

Panel Size: 40,000–60,000 active panelists (varies by year), projectable to the total United States using household projection factors.

Panelists: Household demographic, geographic, and product ownership variables are included, as well as select demographics for the heads of household and other members.

• Demographic variables include household income range, size, composition, presence and age of children, marital status, type of residence, race, and Hispanic origin. Male and female heads of household also report age range, birth date, hours employed, education, and occupation. For other family members, birth date, employment, and relationship/sex are reported.
• Geographic variables include panelist zip code, FIPS state and county codes, region (East, Central, South, West), and Scantrack Market code (assigned by NielsenIQ, and called Syndicated Major Markets (SMM) starting in 2021).
• Product Ownership variables include kitchen appliances, TV items, and internet connection.

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Products: From 2004 to 2020, all 10 NielsenIQ food and nonfood Departments (~1.4 million UPC codes). These Departments are dry grocery, frozen foods, dairy, deli, packaged meat, fresh produce, nonfood grocery, alcohol, general merchandise, and health and beauty aids.

Starting in 2021 data, NielsenIQ implemented a new Product Hierarchy structure. The original Product Hierarchy of Departments, Product Groups, and Product Modules has been phased out. For now, Product Module Codes will still be available to make the transition smoother.

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). Starting in 2021 data, NielsenIQ expanded the number of additional product characteristics.

**Purchases: **Each shopping trip contains the date, retail chain code, retail channel, first three digits of store zip code, and total amount spent. For each product purchased, the UPC code, quantity, price, and any deals/coupons are recorded. Note that retailer names are not available.

Geographies: Entire United States, divided into 62 major markets.

Retail Channels: All retail channels—grocery, drug, mass merchandise, superstores, club stores, convenience, health, and others.

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

This database contains information on individual store characteristics for supermarkets, supercenters, superettes, convenience stores and grocery kiosks. The stores TDLinx code is hierarchical, which allows linking an individual store to its firm name, and to an ultimate parent, such as a foreign firm owner. Characteristics include store name, address, telephone number, location (longitude-latitude, state, county, and census tract), market area identifiers, annual sales, selling area, full-time equivalent employees, number of checkout registers, and many more.

PopFacts Premier Demographic Flat File.

Access updated Nielsen import data India with HS Code, price, importers list, Indian ports, exporting countries, and verified Nielsen buyers in India.

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.

Market Hotness: Nielsen Household in Maricopa County, AZ was 4.00000 Rank in July of 2024, according to the United States Federal Reserve. Historically, Market Hotness: Nielsen Household in Maricopa County, AZ reached a record high of 4.00000 in August of 2016 and a record low of 4.00000 in August of 2016. Trading Economics provides the current actual value, an historical data chart and related indicators for Market Hotness: Nielsen Household in Maricopa County, AZ - last updated from the United States Federal Reserve on November of 2025.

The food access data displayed in this theme are quantitative measures that illustrate the accessibility of nutritious, affordable, and culturally appropriate food in Florida’s communities. Food consumption is often influenced by the food environment and barriers that may inhibit an individual’s ability to make healthful food choices. In addition, food access has been studied as a contributing factor in diet and health outcomes. The Florida Department of Agriculture and Consumer Services has used the most current food access data from trusted sources, such as the U.S. Department of Agriculture, Centers for Disease Control and Prevention, Florida Department of Children and Families, Feeding Florida, Nielsen, and The Reinvestment Fund to build this visualization. The food access data listed in Florida’s Roadmap to Living Healthy includes important layers, such as nutrition programs, food banks, food deserts, retail market locations, Supplemental Nutrition Assistance Program (SNAP) statistics), low supermarket access areas, farmers’ markets, and limited service restaurants along with other vital statistics. This unique categorization of food access data can be used to better identify the specific food access needs of individual communities in Florida, and allow government agencies, nonprofit organizations, and other organizations to identify gaps so they may begin to improve access to those communities.

Replicators need to have access to Nielsen datasets through Kilts Center for Marketing at Chicago Booth. All Nielsen-relevant datasets are confidential, and therefore, not released to public.

Database of literature reviewed in the publication "What influences the implementation of natural climate solutions? A systematic map and review of the evidence" (Schulte et al., 2021).

rCRUX generated reference database using NCBI nt blast database and an additional custom blast database comprised of all Actinopterygii mitogenomes. Both blast databases were downloaded in December 2022.

Primer Name: MiFish Universal Gene: 12S Length of Target: 163–185 get_seeds_local() minimum length: 170 get_seeds_local() maximum length: 250 blast_seeds() minimum length: 140 blast_seeds() maximum length: 250 max_to_blast: 1000 Forward Sequence (5'-3'): GTGTCGGTAAAACTCGTGCCAGC Reverse Sequence (5'-3'): CATAGTGGGGTATCTAATCCCAGTTTG Reference: Miya, M., Sato, Y., Fukunaga, T., Sado, T., Poulsen, J. Y., Sato, K., ... & Kondoh, M. (2015). MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. Royal Society open science, 2(7), 150088. https://doi.org/10.1098/rsos.150088

We chose default rCRUX parameters for get_blast_seeds() of percent coverage of 70, percent identity of 70, evalue 3e+7, and max number of blast alignments = '100000000' and for blast_seeds() of coverage of 70, percent identity of 70, evalue 3e+7, rank of genus, and max number of blast alignments = '10000000'.

Licensed for Internal Use only: Foreign Language Database for use with Nielsen PrimeLocation Web and Desktop Software.

We present a compilation and analysis of 1099 Holocene relative shore-level (RSL) indicators including 867 relative sea-level data points and 232 data points from the Ancylus Lake and the following transitional phase from 10.7 to 8.5 ka BP located around the Baltic Sea. The spatial distribution covers the Baltic Sea and near-coastal areas fairly well, but some gaps remain mainly in Sweden. RSL data follow the standardized HOLSEA format and, thus, are ready for spatially comprehensive applications in, e.g., glacial isostatic adjustment (GIA) modelling. Sampling method The data set is a compilation of rather different samples from geological, geomorphological and archaeological studies. Most of the data was already published in different formats. In this compilation we homogenized the meta information of the available information according to the HOLSEA database format, https://www.holsea.org/archive-your-data, which is a modification of the recommendations given in Hijma et al. (2015). In addition to the reformatting, the majority of samples with radiocarbon dating were recalibrated with oxcal-software using the calib13 and marine13 curves. Furthermore, all sample descriptions were critically checked for consistency in positioning, levelling and indicative meaning by experts of the respective geographic region see Supplement 2. Analytical method In principle, it is a compilation, recalibration and revision of already published data. Data Processing Data of individual compilations were revised and imported into a relational database system. Therein, the data was transferred into the HOLSEA format by specified rules. By this procedure, a homogeneous categorisation was achieved without losing the original data. Also this is stored in the relational database system allowing for later updates of the transfer procedure or a recalibration of the data. Description of data table HOLSEA-baltic-yymmdd.xlsx The workbook in excel format contains 5 sheets, see https://www.holsea.org/archive-your-data: · Long-form, containing the complete information available for each sample · Short-form, a subset of attributes of the Long-form sheet · Radiocarbon, containing the radiocarbon dating information of the respective samples · U-series, a corresponding table containing the respective information of Uranium dating · References, a complete reference list of the primary publications in which the individual data sampling is described. All online sources for the compilation are included in the metadata. A full list of source references is provided in the data description file.

Market research companies have benefited from research and development (R&D) expenditure growth as companies develop new products to satisfy consumer demand. Downstream companies continue to rely on market research to create new products and campaigns that fit evolving consumer preferences. As companies strive to enhance consumer-centric strategies amid increased consumer spending, demand for tailored market research solutions has surged. A 10.7% surge in corporate profit over the past five years enabled businesses to outsource more of their research operations to professional market researchers. The digital shift has further transformed the landscape, with companies pioneering new research tools to tap into the vast potential of big data to enhance accessibility and participation. These trends have led to revenue growing at a CAGR of 3.8% to an estimated $36.4 billion over the past five years, including an estimated 2.1% boost in 2025 alone. Consumers' and advertisers' growing reliance on the internet has led to new metrics market researchers can use to better understand consumers. These have allowed new companies to enter the industry and driven providers to adjust services and implement new technologies. The rising use of social media to advertise and market new products across platforms like TikTok and Instagram also contributed to the growing demand for market research. These technological advancements improved data collection and analysis methods, offering actionable insights that helped companies refine marketing strategies and develop better products. New opportunities continue to drive revenue growth, but expansions to services and onboarding of new technology cut researchers’ profitability. Moving forward, the industry will benefit from acceleration in R&D budgets and technological and a data procurement evolution. Companies will strengthen their R&D budgets as economic conditions improve, further driving demand for advanced market research tools. The proliferation of online commerce and smart technologies will give researchers unprecedented access to consumer data. Technological developments, such as artificial intelligence (AI), are poised to create new metrics based on human reactions, which companies can leverage to better understand consumer behavior and preferences. Access to these metrics, however, will lead to tightening data privacy regulations, which may result in higher compliance costs that eat into profitability. Finally, growing emphasis on ethical practices, transparency and data security will shape consumer trust and research standards, creating new opportunities and challenges in a rapidly evolving marketplace. Revenue is poised to grow at a CAGR of 2.4% to an estimated $41.0 billion through the end of 2030.

This dataset includes over 49,000 well records from the state well drillers databases in Minnesota, Wisconsin, and Michigan. Each state well has at a minimum the well depth and a static water level. Static water levels were mostly determined when the well was constructed. Data included in this shapefile include the well construction date, well depth, well elevation (if determined), type of well, the methods used for determining the well location and elevation (if determined), casing and screen depths (where reported), the static water level and a date, and the year the well was constructed. The field names from each of the state databases were harmonized to merge the data, and a table of the original field names is included. Tabular files are included with codes describing the original field names mapped to the combined field names, and descriptions of codes used to describe the well location method, well depth method, and well type for each state. The USGS wells in this dat ...

Database of observations and specimens of Danish Lacewings(Neuroptera). Some of the observations are documented with photos. The specimens are mainly kept in a private collection, but some are deposited in the Natural History Museum, Aarhus, Denmark. The database includes a few records from other European countries.

Toxicant classifications extracted from 4 official published sources are applied to the current listing of industrial chemicals from the 2019 version of Canada's national database

Each record contains information for one unique version of a UPC code. In addition to the “core” attributes, some products also have “extra” attributes, reported with the BP prefix. Not every product has extra attributes. Banded Pack (BP) products are products where the manufacturer has shrink wrapped multiple products together.