Unlock the full potential of your data-driven projects with our comprehensive Grainger products dataset. This meticulously curated dataset includes detailed information on a wide range of products available on Grainger, one of the leading industrial supply companies.

This dataset is perfect for eCommerce platforms, market analysis, competitive analysis, product comparison, and more. Leverage the power of high-quality, structured data to enhance your business strategies and decision-making processes.

Versions:

Available latest version of the Grainger dataset with 1.2 Million records and last extracted on Jan 2025.

Reach out to contact@crawlfeeds.com

Use Cases:

• eCommerce Platforms: Integrate detailed product information to enhance your product listings.
• Market Analysis: Analyze product trends, pricing, and competition in the industrial supply market.
• Inventory Management: Utilize SKUs and unique identifiers for efficient inventory tracking.
• Data-Driven Projects: Incorporate rich product data into your data science and machine learning models.

Explore the vast collection of Grainger products and elevate your business insights with this high-quality dataset.

The Product Catalog Data provides a comprehensive overview of products across various categories. This dataset includes detailed product titles, descriptions, barcodes, category-specific attributes, weight, measurements, and imagery. It's tailored for marketplaces, eCommerce sites, and data analysts who require in-depth product information to enhance user experience, SEO, and product categorization.

Popular Attributes:

✔ Detailed product information

✔ High-quality imagery

✔ Extensive attribute coverage

✔ Ideal for UX and SEO optimization

✔ Comprehensive product categorization

Key Information:

Rich dataset with 30+ attributes per product

Pricing: Flexible subscription models

Update Frequency: Daily updates

Coverage: Global and specific markets

Historical Data: 12 Months +

LMOS_Miscellaneous_Data is the supplementary and ancillary data to support the Lake Michigan Ozone Study (LMOS). This data product currently features supplementary satellite data. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete.Elevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors recording levels/amounts exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime “lake breeze” airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.

This dataset contains images (scenes) containing fashion products, which are labeled with bounding boxes and links to the corresponding products.

Metadata includes

• product IDs

• bounding boxes

Basic Statistics:

• Scenes: 47,739

• Products: 38,111

• Scene-Product Pairs: 93,274

Data products generated from StEWI v1.0.5. The source code and release notes can be found at https://github.com/USEPA/standardizedinventories/releases/tag/v1.0.5 Datasets include Flow-By-Facility, Flow-By-Process, Facility, and Flow data files for the EPA inventory sources and years listed in Table 7 of the associated publication. The dataset link directs to a page with a table with direct links to each dataset. These are the same datasets returned from the basic 'get...' functions using StEWI. Dataset are in Apache parquet format. This dataset is associated with the following publication: Young, B., W.W. Ingwersen, M. Bergmann , J.D. Hernandez-Betancur , T. Ghosh, E. Bell, and S. Cashman. A System for Standardizing and Combining U.S. Environmental Protection Agency Emissions and Waste Inventory Data. Applied Sciences. MDPI AG, Basel, SWITZERLAND, 3447, (2022).

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

The Shopee Products Dataset is a comprehensive resource that empowers businesses, researchers, and analysts to gain a holistic view of the Shopee e-commerce ecosystem. Whether your goal is to conduct market analysis, optimize pricing strategies, understand customer behavior, or evaluate competitors, this dataset offers the essential information you need to make informed decisions and succeed in the dynamic world of Shopee. At its core, this dataset provides key attributes such as product ID, title, ratings, reviews, pricing details, and seller information, among others. These fundamental data elements offer insights into product performance, customer sentiment, and seller credibility.

The Amazon-Google dataset for entity resolution derives from the online retailers Amazon.com and the product search service of Google accessible through the Google Base Data API. The dataset contains 1363 entities from amazon.com and 3226 google products as well as a gold standard (perfect mapping) with 1300 matching record pairs between the two data sources. The common attributes between the two data sources are: product name, product description, manufacturer and price.

The dataset was initially published in the repository of the Database Group of the University of Leipzig: https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolution

To enable the reproducibility of the results and the comparability of the performance of different matchers on the Amazon-Google matching task, the dataset was split into fixed train, validation and test sets. The fixed splits are provided in the CompERBench repository:

http://data.dws.informatik.uni-mannheim.de/benchmarkmatchingtasks/index.html

TheLook is a fictitious eCommerce clothing site developed by the Looker team. The dataset contains information about customers, products, orders, logistics, web events and digital marketing campaigns. The contents of this dataset are synthetic, and are provided to industry practitioners for the purpose of product discovery, testing, and evaluation. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets.What is BigQuery .

Provide government open data platform departmental dataset statistics on and off the shelf

SHAPE - SHelf mAnagement Product datasEtSHAPE (SHelf mAnagement Product datasEt) contains ~46K images of ~16K different SKU (Stock Keeping Unit) belonging to 62 different categories, fine-grained labeled with their category and European Article Number (EAN). Category and EAN are anonymized, real values could be released under commercial agreement.Dataset is structured as follow:First level folders are categories (anonymized with numbers 1,2,3...), second level folders are SKU (EANs are anonymized with numbers 1,2,3).Please refer to the original publication for any detail. Also when using the data, please cite the original paper:https://doi.org/10.1016/j.eswa.2024.124635

High rate data processed to single-look complex SAR images for each antenna. Gridded tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.

Download or connect to open data endpoints Get data Download data as spreadsheet, kml, shapefile or connect to service APIs to stay up to date. Create maps Create maps, analyse and discover trends. Watch video instructions. Code apps Make applications with our data.ArGIS API for Javascript. Categories City Council Assets, amenities and public space Council services and facilities Culture, leisure and sport Economy and business Environment and climate Planning Transport and access View all Terms Unless otherwise stated, data products available from the data hub are published under Creative Commons licences. For terms of use and more information see site Disclaimer. Contact If you have a question, comments, or requests for interactive maps and data, we would love to hear from you. Council business For information on rates, development applications, strategies, reports and other council business, see the City of Sydney's main website.

Data.AustinTexas.gov is the official portal for Open Data from the City of Austin (COA). The City of Austin’s GIS/Map Downloads page is the official portal for COA GIS data and map products that do not reside on Data.AustinTexas.gov. Both are public domain websites, which means you may link to Data.AustinTexas.gov and ftp://ftp.ci.austin.tx.us/GIS-Data/Regional/coa_gis.html at no cost. When you link to Data.AustinTexas.gov or ftp://ftp.ci.austin.tx.us/GIS-Data/Regional/coa_gis.html, please do it in an appropriate context as a service to people when they need to find official City of Austin data. We encourage you to use our logo, which we’ve provided below. Placement of the Data.AustinTexas.gov logo is to be used only as a marker and link to the home page. It is not meant as a form of endorsement or approval from the City of Austin. City of Austin Open Data Terms of Use - https://data.austintexas.gov/stories/s/ranj-cccq

NEW GOES-19 Data!! On April 4, 2025 at 1500 UTC, the GOES-19 satellite will be declared the Operational GOES-East satellite. All products and services, including NODD, for GOES-East will transition to GOES-19 data at that time. GOES-19 will operate out of the GOES-East location of 75.2°W starting on April 1, 2025 and through the operational transition. Until the transition time and during the final stretch of Post Launch Product Testing (PLPT), GOES-19 products are considered non-operational regardless of their validation maturity level. Shortly following the transition of GOES-19 to GOES-East, all data distribution from GOES-16 will be turned off. GOES-16 will drift to the storage location at 104.7°W. GOES-19 data should begin flowing again on April 4th once this maneuver is complete.

NEW GOES 16 Reprocess Data!! The reprocessed GOES-16 ABI L1b data mitigates systematic data issues (including data gaps and image artifacts) seen in the Operational products, and improves the stability of both the radiometric and geometric calibration over the course of the entire mission life. These data were produced by recomputing the L1b radiance products from input raw L0 data using improved calibration algorithms and look-up tables, derived from data analysis of the NIST-traceable, on-board sources. In addition, the reprocessed data products contain enhancements to the L1b file format, including limb pixels and pixel timestamps, while maintaining compatibility with the operational products. The datasets currently available span the operational life of GOES-16 ABI, from early 2018 through the end of 2024. The Reprocessed L1b dataset shows improvement over the Operational L1b products but may still contain data gaps or discrepancies. Please provide feedback to Dan Lindsey (dan.lindsey@noaa.gov) and Gary Lin (guoqing.lin-1@nasa.gov). More information can be found in the GOES-R ABI Reprocess User Guide.


NOTICE: As of January 10th 2023, GOES-18 assumed the GOES-West position and all data files are deemed both operational and provisional, so no ‘preliminary, non-operational’ caveat is needed. GOES-17 is now offline, shifted approximately 105 degree West, where it will be in on-orbit storage. GOES-17 data will no longer flow into the GOES-17 bucket. Operational GOES-West products can be found in the GOES-18 bucket.

GOES satellites (GOES-16, GOES-17, GOES-18 & GOES-19) provide continuous weather imagery and monitoring of meteorological and space environment data across North America. GOES satellites provide the kind of continuous monitoring necessary for intensive data analysis. They hover continuously over one position on the surface. The satellites orbit high enough to allow for a full-disc view of the Earth. Because they stay above a fixed spot on the surface, they provide a constant vigil for the atmospheric "triggers" for severe weather conditions such as tornadoes, flash floods, hailstorms, and hurricanes. When these conditions develop, the GOES satellites are able to monitor storm development and track their movements. SUVI products available in both NetCDF and FITS.

The Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) activity originated in support of the NASA Earth System Observatory's Surface Biology and Geology (SBG) mission to develop prototype workflows with community algorithms and generate prototype data products envisioned for SBG. SISTER focused on developing a data system that is open, portable, scalable, standards-compliant, and reproducible. This collection contains EXPERIMENTAL workflows and sample data products, including (a) the Common Workflow Language (CWL) process file and a Jupyter Notebook that run the entire SISTER workflow capable of generating experimental sample data products spanning terrestrial ecosystems, inland and coastal aquatic ecosystems, and snow, (b) the archived algorithm steps (as OGC Application Packages) used to generate products at each step of the workflow, (c) a small number of experimental sample data products produced by the workflow which are based on the Airborne Visible/Infrared Imaging Spectrometer-Classic (AVIRIS or AVIRIS-CL) instrument, and (d) instructions for reproducing the sample products included in this dataset. DISCLAIMER: This collection contains experimental workflows, experimental community algorithms, and experimental sample data products to demonstrate the capabilities of an end-to-end processing system. The experimental sample data products provided have not been fully validated and are not intended for scientific use. The community algorithms provided are placeholders which can be replaced by any user's algorithms for their own science and application interests. These algorithms should not in any capacity be considered the algorithms that will be implemented in the upcoming Surface Biology and Geology mission.

See description and link below to datasets.

This file geodatabase consists of all publicly available (Open) products extracted from the Land Information Ontario (LIO) Warehouse excluding Wetlands, Contours and OHN products. This data represents the data housed in the LIO Warehouse as of the date the extraction occurred. The file geodatabase will be refreshed on a bi-weekly basis and has been prepared as a convenience to users wanting access to all LIO Warehouse Open Data products in file geodatabase structure. Metadata for each layer is available in the Ontario GeoHub and can be found by searching for the various layers individually.StatusOn going: Data is continually being updatedMaintenance and Update FrequencyFortnightly: Data is updated every two weeksContactGeospatial Ontario Support, Ministry of Natural Resources and Forestry, geospatial@ontario.ca

Contains 10,000 fine-grained SKU-level products frequently bought by online customers in JD.com.

Introducing a comprehensive dataset of the world-renowned furniture retailer, Ikea. This dataset contains all the latest product information, including product names, descriptions, prices, and images, directly sourced from the official Ikea website. With this information, you can analyze and understand the market trends and consumer preferences for Ikea products, helping you make informed business decisions. Whether you're a market researcher, data analyst, or simply a fan of Ikea products, this dataset is a must-have for anyone looking to gain insights into the brand and its offerings. Get your hands on this valuable resource today.