According to forecasts, net sales of electrical products on Amazon are forecast at over 164 billion U.S. dollars. With a compound annual growth rate of 11.6 percent, this figure is expected to exceed 284 billion dollars by 2026. Yet, the category expected to grow the strongest on the e-commerce platform is health and beauty.

PromptCloud offers cutting-edge data extraction services that empower businesses with real-time, actionable intelligence from the vast expanses of the online marketplace. We are committed to putting data at the heart of your business. Reach out for a no-frills PromptCloud experience- professional, technologically ahead and reliable.

Our Amazon Best Seller Products Dataset is a key tool for businesses looking to understand and capitalize on market trends. It allows you to identify top-selling products and sellers, and track their performance across various categories and subcategories. This dataset is invaluable for competitive intelligence, monitoring trending products, and understanding customer sentiment. It also plays a crucial role in monitoring competitor prices and enhancing product inventory, ensuring that your business stays relevant and competitive.

Beyond Amazon, PromptCloud offers access to a diverse range of Ecommerce Product Data from various e-commerce websites. PromptCloud is a leading provider of advanced web scraping services, uniquely tailored to meet the dynamic needs of modern businesses. Our services are fully customizable, allowing clients to specify source websites, data collection frequencies, data points, and delivery mechanisms to fit their unique requirements​​. The data aggregation feature of our web crawler enables the extraction of data from multiple sources in a single stream, catering to a diverse range of ecommerce clients.

PromptCloud is a leading provider of advanced web scraping services, uniquely tailored to meet the dynamic needs of modern businesses. Our services are fully customizable, allowing clients to specify source websites, data collection frequencies, data points, and delivery mechanisms to fit their unique requirements​​. The data aggregation feature of our web crawler enables the extraction of data from multiple sources in a single stream, catering to a diverse range of clients, from news aggregators to job boards​​.

With over a decade of experience in extracting web data from any e-commerce website, PromptCloud stands as a seasoned veteran in the field. This extensive experience translates into high-quality, reliable data extraction, making PromptCloud your ideal product web data extraction partner. The reliability of our data is uncompromised, with a 100% verification process that ensures accuracy and trustworthiness.

Buy Amazon datasets and get access to over 300 million records from any Amazon domain. Get insights on Amazon products, sellers, and reviews.

From 2004 to 2024, the net revenue of Amazon e-commerce and service sales has increased tremendously. In the fiscal year ending December 31, the multinational e-commerce company's net revenue was almost 638 billion U.S. dollars, up from 575 billion U.S. dollars in 2023.Amazon.com, a U.S. e-commerce company originally founded in 1994, is the world’s largest online retailer of books, clothing, electronics, music, and many more goods. As of 2024, the company generates the majority of it's net revenues through online retail product sales, followed by third-party retail seller services, cloud computing services, and retail subscription services including Amazon Prime. From seller to digital environment Through Amazon, consumers are able to purchase goods at a rather discounted price from both small and large companies as well as from other users. Both new and used goods are sold on the website. Due to the wide variety of goods available at prices which often undercut local brick-and-mortar retail offerings, Amazon has dominated the retailer market. As of 2024, Amazon’s brand worth amounts to over 185 billion U.S. dollars, topping the likes of companies such as Walmart, Ikea, as well as digital competitors Alibaba and eBay. One of Amazon's first forays into the world of hardware was its e-reader Kindle, one of the most popular e-book readers worldwide. More recently, Amazon has also released several series of own-branded products and a voice-controlled virtual assistant, Alexa. Headquartered in North America Due to its location, Amazon offers more services in North America than worldwide. As a result, the majority of the company’s net revenue in 2023 was actually earned in the United States, Canada, and Mexico. In 2023, approximately 353 billion U.S. dollars was earned in North America compared to only roughly 131 billion U.S. dollars internationally.

• 500K+ Active Amazon Stores • 200K+ Seller Leads • Platforms USA, Germany, UK, Italy, France, Spain, CA • C-Suite/Marketing/Sales Contacts • FBA/Non-FBA Sellers • 15+ data points available for each prospect • Filter your leads by store size, niche, location, and many more • 100% manually researched and verified.

For over a decade, we have been manually collecting Amazon seller data from various data sources such as Amazon, Linkedin, Google, and others. We are specialized to get valid, and potential data so you may conduct ads and begin selling without hesitation.

We designed our data packages for all types of organizations, thus they are reasonably priced. We are always trying to reduce our prices to better suit all of your requirements.

So, if you’re looking to reach out to your targeted Amazon sellers, now is the greatest time to do so and offer your goods, services, and promotions. You can get your targeted Amazon Sellers List with seller contact information.

Alternatively, if you provide Amazon Seller Names or IDs, we will conduct Custom Research and deliver the customized list to you.

Data Points Available:

Full Name Linkedin URL Direct Email Generic Phone Number Business Name and Address Company Website Seller IDs and URLs Revenue Seller Review Count Niche FBA/Non-FBA Country and More

✔️ Easy-to-handle Excel Sheet ✔️ Human Researched and Verified leads with Direct contacts ✔️ Up to 🇩🇪25K German Active Amazon third-party private Label Sellers lead direct contact and info ✔️ Up to 30+ data points for each prospect ✔️ Sort your list by store size, product category, company location, and much more! ✔️ Enjoy a list that has been hand-researched and verified. No scrapped contacts!

Data includes: Seller ID Seller Business Model: Private Label Seller/Wholesaler Estimated Annual Revenue in $ ( accurate +/-30%) Annual Revenue Bracket [$] % of goods shipped in FBA Amazon Seller Page Seller Website Decision Maker First Name Decision Maker Last Name Direct Email Generic Email Office Phone Number Decision Maker Linkedin URL Seller Name Seller Storefront Link Seller Business Name Seller Business Full Address City State Zip Seller Business Country Number of Reviews [30 days] Number of Reviews [90 days] Number of Reviews [12 months] Number of Reviews [lifetime] Seller Rating Lifetime Total number of products Total number of brands Brands Top Product URL Top Product Shipping From Top Product Category Top Product SubCategory

About SellerDirectories: SellerDirectories.com provides human-researched and verified data on Amazon sellers and eCommerce Brands.

Our human-researched and verified data is trusted by companies like Walmart, Microsoft, Tencent, Helium10, and 250 more! ⭐ Check our verified 5-star reviews on Trustpilot ⭐ https://www.trustpilot.com/review/sellerdirectories.com ⭐

✅ Data Sources: Aggregated from 50+ sources

✅ Data Collection: Human Researched and verified (we don't like scrapped contact data)

✅ 98%+ accurate and up-to-date data (verified)

✅ Brand /seller Targeting Options: Multiple filters available (including revenue, location, business model, and product category)

✅ Customer Service: Lifetime support and accuracy guarantee on your list. Our lists include resources on how best to run outreach campaigns to turn a prospect list into actual business opportunities. Buy B2B contact database with SellerDirectories.com, and get your B2B contacts database sorted!

Amazon dataset was retrieved from data.world, an open-access repository. Created by @revanthkrishnaa.The DataSet contains historical sales data for 45 Amazon stores located in different regions.Dataset DescriptionThe DataSet contains historical sales data for 45 Amazon stores located in different regions. Each store contains a number of departments, and have to predict the department-wide sales for each store.In addition, Amazon runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the Dataset is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data.This file contains anonymized information about the 45 stores, indicating the type and size of store.This is the historical training data, which covers to 2019-02-05 to 2021-11-01. Within this file you following are the different fields:Store - the store numberDept - the department numberDate - the weekWeekly_Sales - sales for the given department in the given store IsHoliday - whether the week is a special holiday week Temperature - average temperature in the region Fuel_Price - cost of fuel in the regionMarkDown1-5 - anonymized data related to promotional markdowns that Amazon is running. MarkDown data is only available after Nov 2020 and is not available for all stores all the time. Any missing value is marked with an NA.CPI - the consumer price indexUnemployment - the unemployment rateFor convenience, the four holidays fall within the following weeks in the dataset (not all holidays are in the data):Super Bowl: 12-Feb-19, 11-Feb-20, 10-Feb-21, 8-Feb-18Labor Day: 10-Sep-19, 9-Sep-20, 7-Sep-21, 6-Sep-18Thanksgiving: 26-Nov-19, 25-Nov-20, 23-Nov-21, 29-Nov-18Christmas: 31-Dec-19, 30-Dec-20, 28-Dec-21, 27-Dec-18Show less

Capture all Amazon product listing details with confidence that you are getting complete and current data. Rainforest API offers comprehensive coverage of each of the product listings or search results in a cleanly structured output.

Rainforest API's advanced parsing means the results returned are exactly what a human user would see. You can request data from any Amazon domain and originate your request from any country in the world. The high-capacity, global infrastructure of the Rainforest API assures you the highest level of performance and reliability. For easy integration with your apps, data is delivered in JSON or CSV format. A convenient CSV Builder allows customization of data columns.

Data is retrieved in real time, by search term, or for single products, by global identifiers such as GTIN, ISBN, UPC and EAN rather than Amazon ASIN. The API automatically performs the ASIN conversion for each request. You can also submit a product page URL (product results), or a category ID (category search results) instead.

So what's in the data from Rainforest API?

Product: - Brand & manufacturer - Manufacturer & Amazon product descriptions - Specifications - Buy Box Winner: price, etc. - 1st party, 2nd party & 3rd party seller data - Additional product details (i.e. energy efficiency, add-ons) - A-Plus content - Imagery - Product videos - Category details (category, bestseller category) - Deals (types, states) - Bundles - Seller offers (including delivery options) - Frequently bought together / Also bought - Also viewed / Similar item to consider - Rating & reviews (incl. full review, top positive, top negative, manufacturer replies) - Stock estimation - Sales estimation (for select Amazon domains)

Search Results: - Product details per search result - Position - Related searches - Related brands

...and more, depending on your request parameters or the search result.

How can Traject Data: Amazon Product Results Data be used? - Product listing management - Price monitoring - Brand protection - Category & product trends monitoring - Market research & competitor intelligence - Location-specific & cross-border Amazon shipping data - Rank tracking on Amazon

Who uses Traject Data: Amazon Product Results Data? This data is leveraged by software developers, marketers, founders, sales & business development teams, researchers, and data analysts & engineers in ecommerce, other retail/wholesale business, agencies and SaaS platforms.

Anyone in your organization who works with your digital presence can develop business intelligence and strategy using this advanced product data.

Utilize our Amazon reviews dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset can aid in understanding customer behavior, product performance, and market trends, empowering organizations to refine their product and marketing strategies. Access the entire dataset or tailor a subset to fit your requirements. Popular use cases include: Product Performance Analysis: Analyze Amazon reviews to assess product performance, uncovering customer satisfaction levels, common issues, and highly praised features to inform product improvements and marketing messages. Customer Behavior Insights: Gain insights into customer behavior, purchasing patterns, and preferences, enabling more personalized marketing and product recommendations. Demand Forecasting: Leverage Amazon reviews to predict future product demand by analyzing historical review data and identifying trends, helping to optimize inventory management and sales strategies. Accessing and analyzing the Amazon reviews dataset supports market strategy optimization by leveraging insights to analyze key market trends and customer preferences, enhancing overall business decision-making.

Get the needed Amazon product review data right from the data extractor! Collect Amazon review information from 19 Amazon countries from the following domains: - amazon.com - amazon.com.au - amazon.com.br - amazon.ca - amazon.cn - amazon.fr - amazon.de - amazon.in - amazon.it - amazon.com.mx - amazon.nl - amazon.sg - amazon.es - amazon.com.tr

Request Ecommerce Product Review dataset by: - keyword - category - seller - product ID (ASIN)

Amazon E-commerce Reviews Data datasets gathered by keyword, seller, category, or ASIN contain: - Product ID (can be extended to the full product information) - Review content and rating - Review metadata

Amazon extraction results can be delivered by schedule or API request, so the data can be extracted in real-time.

DATAANT uses the in-house web scraping service with no concurrency limitations, so unlimited data extractions can be performed simultaneously.

Output can and attributes can be customized to fit your particular needs.

Merchant API will provide you with all essential data and metrics for conducting comprehensive competitor analysis, price monitoring, and market niche research.

With Google Shopping API you can get:

• Google Shopping Products listed for the specified keyword. The results include product title, description in Google Shopping SERP, product rank, price, reviews, and rating as well as the related domain. • Full detailed Google Shopping Product Specification. You will receive all product attributes and their content from the product specification page. • A list of Google Shopping Sellers of the specified product. The provided data for each seller includes related product base and total price, shipment and purchase details, and special offers. • Google Shopping Sellers Ad URL with all additional parameters set by the seller.

With Amazon API you can get:

• Results from Amazon product listings according to the specified keyword (product name), location, and language parameters. • A list of ASINs (unique product identifiers assigned by Amazon) of all modifications listed for the specified product and information about the product prices based on ASIN • Amazon Choice products

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

✔️ Easy-to-handle Excel Sheet ✔️ Human Researched and Verified leads with Direct contacts ✔️ Up to 🇬🇧30K Active Amazon third-party private Label Sellers lead direct contact and info ✔️ Up to 30+ data points for each prospect ✔️ Sort your list by store size, product category, company location, and much more! ✔️ Enjoy a list that has been hand-researched and verified. No scrapped contacts! Buy B2B contact database with SellerDirectories.com, and get your B2B contacts database sorted!

Data includes: Seller ID Seller Business Model: Private Label Seller/Wholesaler Estimated Annual Revenue in $ ( accurate +/-30%) Annual Revenue Bracket [$] % of goods shipped in FBA Amazon Seller Page Seller Website Decision Maker First Name Decision Maker Last Name Direct Email Generic Email Office Phone Number Decision Maker Linkedin URL Seller Name Seller Storefront Link Seller Business Name Seller Business Full Address City State Zip Seller Business Country Number of Reviews [30 days] Number of Reviews [90 days] Number of Reviews [12 months] Number of Reviews [lifetime] Seller Rating Lifetime Total number of products Total number of brands Brands Top Product URL Top Product Shipping From Top Product Category Top Product SubCategory

About SellerDirectories: SellerDirectories.com provides human-researched and verified data on Amazon sellers and eCommerce Brands.

Our human-researched and verified data is trusted by companies like Walmart, Microsoft, Tencent, Helium10, and 250 more! ⭐ Check our verified 5-star reviews on Trustpilot ⭐ https://www.trustpilot.com/review/sellerdirectories.com ⭐

✅ Data Sources: Aggregated from 50+ sources

✅ Data Collection: Human Researched and verified (we don't like scrapped contact data)

✅ 98%+ accurate and up-to-date data (verified)

✅ Brand /seller Targeting Options: Multiple filters available (including revenue, location, business model, and product category)

✅ Customer Service: Lifetime support and accuracy guarantee on your list. Our lists include resources on how best to run outreach campaigns to turn a prospect list into actual business opportunities.

OGBN-Products

Webpage: https://ogb.stanford.edu/docs/nodeprop/#ogbn-products

Usage in Python

import os.path as osp
import pandas as pd
import datatable as dt
import torch
import torch_geometric as pyg
from ogb.nodeproppred import PygNodePropPredDataset

class PygOgbnProducts(PygNodePropPredDataset):
  def _init_(self, meta_csv = None):
    root, name, transform = '/kaggle/input', 'ogbn-products', None
    if meta_csv is None:
      meta_csv = osp.join(root, name, 'ogbn-master.csv')
    master = pd.read_csv(meta_csv, index_col = 0)
    meta_dict = master[name]
    meta_dict['dir_path'] = osp.join(root, name)
    super()._init_(name = name, root = root, transform = transform, meta_dict = meta_dict)
  def get_idx_split(self, split_type = None):
    if split_type is None:
      split_type = self.meta_info['split']
    path = osp.join(self.root, 'split', split_type)
    if osp.isfile(os.path.join(path, 'split_dict.pt')):
      return torch.load(os.path.join(path, 'split_dict.pt'))
    if self.is_hetero:
      train_idx_dict, valid_idx_dict, test_idx_dict = read_nodesplitidx_split_hetero(path)
      for nodetype in train_idx_dict.keys():
        train_idx_dict[nodetype] = torch.from_numpy(train_idx_dict[nodetype]).to(torch.long)
        valid_idx_dict[nodetype] = torch.from_numpy(valid_idx_dict[nodetype]).to(torch.long)
        test_idx_dict[nodetype] = torch.from_numpy(test_idx_dict[nodetype]).to(torch.long)
        return {'train': train_idx_dict, 'valid': valid_idx_dict, 'test': test_idx_dict}
    else:
      train_idx = dt.fread(osp.join(path, 'train.csv'), header = None).to_numpy().T[0]
      train_idx = torch.from_numpy(train_idx).to(torch.long)
      valid_idx = dt.fread(osp.join(path, 'valid.csv'), header = None).to_numpy().T[0]
      valid_idx = torch.from_numpy(valid_idx).to(torch.long)
      test_idx = dt.fread(osp.join(path, 'test.csv'), header = None).to_numpy().T[0]
      test_idx = torch.from_numpy(test_idx).to(torch.long)
      return {'train': train_idx, 'valid': valid_idx, 'test': test_idx}

dataset = PygOgbnProducts()
split_idx = dataset.get_idx_split()
train_idx, valid_idx, test_idx = split_idx['train'], split_idx['valid'], split_idx['test']
graph = dataset[0] # PyG Graph object

Description

Graph: The ogbn-products dataset is an undirected and unweighted graph, representing an Amazon product co-purchasing network [1]. Nodes represent products sold in Amazon, and edges between two products indicate that the products are purchased together. The authors follow [2] to process node features and target categories. Specifically, node features are generated by extracting bag-of-words features from the product descriptions followed by a Principal Component Analysis to reduce the dimension to 100.

Prediction task: The task is to predict the category of a product in a multi-class classification setup, where the 47 top-level categories are used for target labels.

Dataset splitting: The authors consider a more challenging and realistic dataset splitting that differs from the one used in [2] Instead of randomly assigning 90% of the nodes for training and 10% of the nodes for testing (without use of a validation set), use the sales ranking (popularity) to split nodes into training/validation/test sets. Specifically, the authors sort the products according to their sales ranking and use the top 8% for training, next top 2% for validation, and the rest for testing. This is a more challenging splitting procedure that closely matches the real-world application where labels are first assigned to important nodes in the network and ML models are subsequently used to make predictions on less important ones.

Note 1: A very small number of self-connecting edges are repeated (see here); you may remove them if necessary.

Note 2: For undirected graphs, the loaded graphs will have the doubled number of edges because the bidirectional edges will be added automatically.

Summary

Open Graph Benchmark

Website: https://ogb.stanford.edu

The Open Graph Benchmark (OGB) [3] is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner.

References

[1] http://manikvarma.org/downloads/XC/XMLRepository.html [2] Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 257–266, 2019. [3] Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in Neural Information Processing Systems, pp. 22118–22133, 2020.

License: Amazon License

By accessing the Amazon Customer Reviews Library ("Reviews Library"), you agree that the Reviews Library is an Amazon Service subject to the Amazon.com Conditions of Use (https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions: In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Library for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Library or its contents, including use of the Reviews Library for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Library with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Library. If you violate any of the foregoing conditions, your license to access and use the Reviews Library will automatically terminate without prejudice to any of the other rights or remedies Amazon may have.

Disclaimer

I am NOT the author of this dataset. It was downloaded from its official website. I assume no responsibility or liability for the content in this dataset. Any questions, problems or issues, please contact the original authors at their website or their GitHub repo.

✔️ Easy-to-handle Excel Sheet ✔️ Human Researched and Verified leads with Direct contacts ✔️ Up to 🇨🇳 10K Chinese Active Amazon third-party private Label Sellers lead direct contact and info ✔️ Up to 30+ data points for each prospect ✔️ Sort your list by store size, product category, company location, and much more! ✔️ Enjoy a list that has been hand-researched and verified. No scrapped contacts!

Data includes: Seller ID Seller Business Model: Private Label Seller/Wholesaler Estimated Annual Revenue in $ ( accurate +/-30%) Annual Revenue Bracket [$] % of goods shipped in FBA Amazon Seller Page Seller Website Decision Maker First Name Decision Maker Last Name Direct Email Generic Email Office Phone Number Decision Maker Linkedin URL Seller Name Seller Storefront Link Seller Business Name Seller Business Full Address City State Zip Seller Business Country Number of Reviews [30 days] Number of Reviews [90 days] Number of Reviews [12 months] Number of Reviews [lifetime] Seller Rating Lifetime Total number of products Total number of brands Brands Top Product URL Top Product Shipping From Top Product Category Top Product SubCategory

About SellerDirectories: SellerDirectories.com provides human-researched and verified data on Amazon sellers and eCommerce Brands.

Our human-researched and verified data is trusted by companies like Walmart, Microsoft, Tencent, Helium10, and 250 more! ⭐ Check our verified 5-star reviews on Trustpilot ⭐ https://www.trustpilot.com/review/sellerdirectories.com ⭐

✅ Data Sources: Aggregated from 50+ sources

✅ Data Collection: Human Researched and verified (we don't like scrapped contact data)

✅ 98%+ accurate and up-to-date data (verified)

✅ Brand /seller Targeting Options: Multiple filters available (including revenue, location, business model, and product category)

✅ Customer Service: Lifetime support and accuracy guarantee on your list. Our lists include resources on how best to run outreach campaigns to turn a prospect list into actual business opportunities. Buy B2B contact database with SellerDirectories.com, and get your B2B contacts database sorted!

Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset. This dataset mainly includes reviews (ratings, text) and item metadata (desc- riptions, category information, price, brand, and images). Compared to the pre- vious versions, the 2023 version features larger size, newer reviews (up to Sep 2023), richer and cleaner meta data, and finer-grained timestamps (from day to milli-second).

Context

This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories.

Contents

• Reviews.csv: Pulled from the corresponding SQLite table named Reviews in database.sqlite
  
• database.sqlite: Contains the table 'Reviews'
  
  

Data includes:
- Reviews from Oct 1999 - Oct 2012
- 568,454 reviews
- 256,059 users
- 74,258 products
- 260 users with > 50 reviews

Acknowledgements

See this SQLite query for a quick sample of the dataset.

If you publish articles based on this dataset, please cite the following paper:

• J. McAuley and J. Leskovec. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. WWW, 2013.

Global MODIS vegetation indices are designed to provide consistent spatial and temporal comparisons of vegetation conditions. Blue, red, and near-infrared reflectances, centered at 469-nanometers, 645-nanometers, and 858-nanometers, respectively, are used to determine the MODIS daily vegetation indices. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes a new Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI also uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI and EVI products are computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows. Global MOD13Q1 data are provided every 16 days at 250-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Lacking a 250m blue band, the EVI algorithm uses the 500m blue band to correct for residual atmospheric effects, with negligible spatial artifacts. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes, including primary production and land cover conversion.

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

These datasets contain reviews from the Steam video game platform, and information about which games were bundled together.

Metadata includes

• reviews

• purchases, plays, recommends (likes)

• product bundles

• pricing information

Basic Statistics:

• Reviews: 7,793,069

• Users: 2,567,538

• Items: 15,474

• Bundles: 615

✔️ Easy-to-handle Excel Sheet ✔️ Human Researched and Verified leads with Direct contacts ✔️ Up to 200K Active Amazon third-party private Label Sellers lead direct contact and info ✔️ Up to 30+ data points for each prospect ✔️ Sort your list by store size, product category, company location, and much more! ✔️ Enjoy a list that has been hand-researched and verified. No scrapped contacts! Buy a B2B contact database with SellerDirectories.com, and get your B2B contacts database sorted!

Data includes: Seller ID Seller Business Model: Private Label Seller/Wholesaler Estimated Annual Revenue in $ ( accurate +/-30%) Annual Revenue Bracket [$] % of goods shipped in FBA Amazon Seller Page Seller Website Decision Maker First Name Decision Maker Last Name Direct Email Generic Email Office Phone Number Decision Maker Linkedin URL Seller Name Seller Storefront Link Seller Business Name Seller Business Full Address City State Zip Seller Business Country Number of Reviews [30 days] Number of Reviews [90 days] Number of Reviews [12 months] Number of Reviews [lifetime] Seller Rating Lifetime Total number of products Total number of brands Brands Top Product URL Top Product Shipping From Top Product Category Top Product SubCategory

About SellerDirectories: SellerDirectories.com provides human-researched and verified data on Amazon sellers and eCommerce Brands.

Our human-researched and verified data is trusted by companies like Walmart, Microsoft, Tencent, Helium10, and 250 more! ⭐ Check our verified 5-star reviews on Trustpilot ⭐ https://www.trustpilot.com/review/sellerdirectories.com ⭐

✅ Data Sources: Aggregated from 50+ sources

✅ Data Collection: Human Researched and verified (we don't like scrapped contact data)

✅ 98%+ accurate and up-to-date data (verified)

✅ Brand /seller Targeting Options: Multiple filters available (including revenue, location, business model, and product category)

✅ Customer Service: Lifetime support and accuracy guarantee on your list. Our lists include resources on how best to run outreach campaigns to turn a prospect list into actual business opportunities.