Discover the Walmart Products Free Dataset, featuring 2,000 records in CSV format. This dataset includes detailed information about various Walmart products, such as names, prices, categories, and descriptions.

It’s perfect for data analysis, e-commerce research, and machine learning projects. Download now and kickstart your insights with accurate, real-world data.

A computer program for accessing and visualization of thermodynamic and transport property data for chemical compounds and mixtures available at the TRC/NIST ThermoML archive https://data.nist.gov/od/id/mds2-2422. The data collection contains 2.2 million distinct property values (the whole archive can also be downloaded from that link, stored, and accessed from a local storage). The program has been compiled for Windows OS and tested under Windows 10. The operation procedures are described in the embedded Help.

The Free Energy and Advanced Sampling Simulation Toolkit (FEASST) is a free, open-source, modular program to conduct molecular and particle-based simulations with flat-histogram Monte Carlo and molecular dynamics methods. It is a software written in C++ and python which is made publicly available to aid in reproducibility. It is also provided as a service to the scientific community in which there are few , if any, Monte Carlo programs that support flat histogram methods and advanced sampling algorithms. This software is expected to be updated frequently with new methods.

Our NFL Data product offers extensive access to historic and current National Football League statistics and results, available in multiple formats. Whether you're a sports analyst, data scientist, fantasy football enthusiast, or a developer building sports-related apps, this dataset provides everything you need to dive deep into NFL performance insights.

Key Benefits:

Comprehensive Coverage: Includes historic and real-time data on NFL stats, game results, team performance, player metrics, and more.

Multiple Formats: Datasets are available in various formats (CSV, JSON, XML) for easy integration into your tools and applications.

User-Friendly Access: Whether you are an advanced analyst or a beginner, you can easily access and manipulate data to suit your needs.

Free Trial: Explore the full range of data with our free trial before committing, ensuring the product meets your expectations.

Customizable: Filter and download only the data you need, tailored to specific seasons, teams, or players.

API Access: Developers can integrate real-time NFL data into their apps with API support, allowing seamless updates and user engagement.

Use Cases:

Fantasy Football Players: Use the data to analyze player performance, helping to draft winning teams and make better game-day decisions.

Sports Analysts: Dive deep into historical and current NFL stats for research, articles, and game predictions.

Developers: Build custom sports apps and dashboards by integrating NFL data directly through API access.

Betting & Prediction Models: Use data to create accurate predictions for NFL games, helping sportsbooks and bettors alike.

Media Outlets: Enhance game previews, post-game analysis, and highlight reels with accurate, detailed NFL stats.

Our NFL Data product ensures you have the most reliable, up-to-date information to drive your projects, whether it's enhancing user experiences, creating predictive models, or simply enjoying in-depth football analysis.

Co-infection data in the form of colony forming units and amoeba cell counts. This dataset is associated with the following publication: Buse , H., F. Schaefer, and G. Rice. Enhanced survival but not amplification of Francisella spp. in the presence of free-living amoebae. Acta Microbiologica et Immunologica Hungarica. Akademiai Kiado, Budapest, HUNGARY, 64(1): 17-36, (2016).

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Free Soil. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Free Soil. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Free Soil, the median household income stands at $57,813 for householders within the 45 to 64 years age group, followed by $56,250 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $39,583.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Free Soil median household income by age. You can refer the same here

The Label-Free Detection Market report segments the industry into By Product (Consumables, Instruments), By Technology (Mass Spectrometry, Surface Plasmon Resonance (SPR), Bio-Layer Interferometry, and more), By Application (Binding Kinetics, Binding Thermodynamics, and more), By End-User (Pharmaceutical & Biotechnology Companies, and more), and Geography (North America, Europe, and more).

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

The demand for global Gluten-free Product market is expected to be valued at USD 6.28 Billion in 2025, forecasted at a CAGR of 7.0% to have an estimated value of USD 12.36 Billion from 2025 to 2035. From 2020 to 2025 a CAGR of 6.7% was registered for the market.

Attributes	Description
Estimated Global Industry Size (2025E)	USD 6.28 Billion
Projected Global Industry Value (2035F)	USD 12.36 Billion
Value-based CAGR (2025 to 2035)	7.0%

Country wise Insights

Countries	CAGR, 2025 to 2035
United States	5.7%
Germany	4.6%
India	8.9%

Category-wise Insights

Segment	Value Share (2025)
Ready Meals (Product Type)	42%

Segment	Value Share (2025)
Convenience Store (Distribution Channel)	58%

This dataset has been employed in the following articles:https://ieeexplore.ieee.org/document/9682692https://ieeexplore.ieee.org/document/9871051https://content.iospress.com/articles/technology-and-health-care/thc202198

This collection is composed of a subset of ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) OB1 L1C products from the ALOS PRISM L1C collection (DOI: 10.57780/AL1-ff3877f) which have been chosen so as to provide a cloud-free coverage over Europe. 70% of the scenes contained within the collection have a cloud cover percentage of 0%, while the remaining 30% of the scenes have a cloud cover percentage of no more than 20%. The collection is composed of PSM_OB1_1C EO-SIP products, with the PRISM sensor operating in OB1 mode with three views (Nadir, Forward and Backward) at 35 km width.

In 2024, the most popular free mobile application to download in Poland was the online shopping platform — Temu.

The global allergen-free food market is projected to grow from USD 50,365.7 million in 2025 to USD 102,843.7 million by 2035, reflecting a CAGR of 7.4% over the forecast period.

Attributes	Description
Estimated Global Industry Size (2025E)	USD 50,365.7 million
Projected Global Industry Value (2035F)	USD 102,843.7 million
Value-based CAGR (2025 to 2035)	7.4%

Semi Annual Market Update

Particular	Value CAGR
H1 2024	6.9% (2024 to 2034)
H2 2024	7.3% (2024 to 2034)
H1 2025	7.2% (2025 to 2035)
H2 2025	7.5% (2025 to 2035)

Country wise Insights

Countries	CAGR 2025 to 2035
United States	3.8%
United Kingdom	4.5%
Germany	3.2%

Category-wise Insights

Segment	Value Share (2025)
Beverages (Product Type)	40%

Segment	Value Share (2025)
Sugar Free (Claim)	40%

Dairy-free cream cheese products are available in a wide range of flavors and textures, catering to diverse consumer preferences. Flavored varieties, such as herb and garlic, chive, and smoked salmon, are gaining popularity, while plain and unflavored options remain a staple in many kitchens. The use of innovative ingredients, such as cashew, coconut, and almond, is expanding the product portfolio and appealing to consumers seeking unique and flavorful alternatives. Key drivers for this market are: . Increasing demand for Plant-based food products, . High demand for clean label products across the globe. Potential restraints include: . Increased ingredient development costs and stringent government regulations. Notable trends are: Rising Investment in R&D leading to innovation and new product developments.

free-law/md_embeddings dataset hosted on Hugging Face and contributed by the HF Datasets community

Cell free protein expression market to grow from USD 298 million in 2024 to USD 322 million in 2025 and USD 627 million by 2035, representing a CAGR of 6.9%

The Sugar Free Energy Drinks Market is segmented by Packaging Type (Glass Bottles, Metal Can, PET Bottles), by Distribution Channel (Off-trade, On-trade) and by Region (Africa, Asia-Pacific, Europe, Middle East, North America, South America). Market Value in USD and Volume in Liters are both presented. Key data points observed include market segmental split by soft drink category, packaging type, distribution channel, and region

This dataset is about companies. It has 1 row and is filtered where the company is Sumitomo Bakelite Company. It features 3 columns: country, and free cash flow.

The Free Universal Sound Separation (FUSS) Dataset is a database of arbitrary sound mixtures and source-level references, for use in experiments on arbitrary sound separation.

This is the official sound separation data for the DCASE2020 Challenge Task 4: Sound Event Detection and Separation in Domestic Environments.

Citation: If you use the FUSS dataset or part of it, please cite our paper describing the dataset and baseline [1]. FUSS is based on FSD data so please also cite [2]:

Overview: FUSS audio data is sourced from a pre-release of Freesound dataset known as (FSD50k), a sound event dataset composed of Freesound content annotated with labels from the AudioSet Ontology. Using the FSD50K labels, these source files have been screened such that they likely only contain a single type of sound. Labels are not provided for these source files, and are not considered part of the challenge. For the purpose of the DCASE Task4 Sound Separation and Event Detection challenge, systems should not use FSD50K labels, even though they may become available upon FSD50K release.

To create mixtures, 10 second clips of sources are convolved with simulated room impulse responses and added together. Each 10 second mixture contains between 1 and 4 sources. Source files longer than 10 seconds are considered "background" sources. Every mixture contains one background source, which is active for the entire duration. We provide: a software recipe to create the dataset, the room impulse responses, and the original source audio.

Motivation for use in DCASE2020 Challenge Task 4: This dataset provides a platform to investigate how source separation may help with event detection and vice versa. Previous work has shown that universal sound separation (separation of arbitrary sounds) is possible [3], and that event detection can help with universal sound separation [4]. It remains to be seen whether sound separation can help with event detection. Event detection is more difficult in noisy environments, and so separation could be a useful pre-processing step. Data with strong labels for event detection are relatively scarce, especially when restricted to specific classes within a domain. In contrast, source separation data needs no event labels for training, and may be more plentiful. In this setting, the idea is to utilize larger unlabeled separation data to train separation systems, which can serve as a front-end to event-detection systems trained on more limited data.

Room simulation: Room impulse responses are simulated using the image method with frequency-dependent walls. Each impulse corresponds to a rectangular room of random size with random wall materials, where a single microphone and up to 4 sources are placed at random spatial locations.

Recipe for data creation: The data creation recipe starts with scripts, based on scaper, to generate mixtures of events with random timing of source events, along with a background source that spans the duration of the mixture clip. The scipts for this are at this GitHub repo.

The data are reverberated using a different room simulation for each mixture. In this simulation each source has its own reverberation corresponding to a different spatial location. The reverberated mixtures are created by summing over the reverberated sources. The dataset recipe scripts support modification, so that participants may remix and augment the training data as desired.

The constituent source files for each mixture are also generated for use as references for training and evaluation. The dataset recipe scripts support modification, so that participants may remix and augment the training data as desired.

Note: no attempt was made to remove digital silence from the freesound source data, so some reference sources may include digital silence, and there are a few mixtures where the background reference is all digital silence. Digital silence can also be observed in the event recognition public evaluation data, so it is important to be able to handle this in practice. Our evaluation scripts handle it by ignoring any reference sources that are silent.

Format: All audio clips are provided as uncompressed PCM 16 bit, 16 kHz, mono audio files.

Data split: The FUSS dataset is partitioned into "train", "validation", and "eval" sets, following the same splits used in FSD data. Specifically, the train and validation sets are sourced from the FSD50K dev set, and we have ensured that clips in train come from different uploaders than the clips in validation. The eval set is sourced from the FSD50K eval split.

Baseline System: A baseline system for the FUSS dataset is available at dcase2020_fuss_baseline.

License: All audio clips (i.e., in FUSS_fsd_data.tar.gz) used in the preparation of Free Universal Source Separation (FUSS) dataset are designated Creative Commons (CC0) and were obtained from freesound.org. The source data in FUSS_fsd_data.tar.gz were selected using labels from the FSD50K corpus, which is licensed as Creative Commons Attribution 4.0 International (CC BY 4.0) License.

The FUSS dataset as a whole, is a curated, reverberated, mixed, and partitioned preparation, and is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) License. This license is specified in the `LICENSE-DATASET` file downloaded with the `FUSS_license_doc.tar.gz` file.

Note the links to the github repo in FUSS_license_doc/README.md are currently out of date, so please refer to FUSS_license_doc/README.md in this GitHub repo which is more recently updated.

Learn about the anticipated growth in the demand for uncoated wood free printing and writing paper in the United States, with market volume expected to reach 5.2M tons and market value projected to increase to $9.2B by 2035.