The same product could have different titles, descriptions and product ID'S on different sites, depending on the structure of each site.

Our algorithm allows our clients to automatically match and track the performance of the same products across multiple platforms such as eBay, Amazon, and DTC sites.

Innovation is the engine of long-term growth. Value of innovation data is tied to aligning innovation behavior with product offerings.

Moat provides structured proprietary product data rolled up to an ultimate parent and mapped to ticker symbols. Patent ownership portion of data is time aware of asset transfers and corporate hierarchy changes. Data is mapped to actual markets and products (not a CPC schema).

Dataset can be combined with other data sets to create queryable relationships among products, technologies, patents, entities, investment, risk, talent, and value.

Datasets can be used for such things as: - Innovation Informed Financial Metrics - Evaluate a company based on a comparison of innovation informed financial metrics to peers. - Enterprise Valuation - Validate or ascertain enterprise value through intangible asset aligned enterprise values. - Patent Valuation - Estimate of the dollar value of the cost to rebuild a patent portfolio - IP Risk and Litigation - Quantifies risks to each patent and patent portfolio through strength, validity, and litigation metrics. - Innovation Tracking and Analysis - Maps financial, product, and risk data to patents to facilitate comparative analysis and to reveal demonstrated innovation behavior. - Patent Lifecycle and Expiration - Data that estimates the lifecycle and expirations of technology areas and products protected by complex patent strategies.

Patent data portion of proprietary market data is time-aware and 20 years of historical data is available. Data is updated daily. In depth usage examples can be provided on request.

Access NYSE Arca Integrated market data feed for ETPs and ETFs with enhanced granularity and determinism not available via the SIPs or the Openbook feed.

NYSE Arca Integrated is a proprietary data feed that provides full order book updates, including every quote and order at each price level, on the Arca market (formerly ArcaEX, the Archipelago Exchange). It operates on NYSE's Pillar platform and disseminates all order book activity in an order-by-order view of events, including trade executions, order modifications, cancellations, and other book updates.

NYSE Arca is the leading US exchange for listing and trading exchange-traded funds (ETFs), offering the narrowest quoted spreads and maintaining the highest percentage of time (71.1%) at the NBBO for all U.S. ETFs. As of January 2025, it represented approximately 9.96% of the average daily volume (ADV) across all exchange-listed US securities, including those listed on Nasdaq, other NYSE venues, and Cboe exchanges.

With L3 granularity, NYSE Arca Integrated captures information beyond the L1, top-of-book data available through SIP feeds, enabling accurate modeling of book imbalances, quote lifetimes, and queue dynamics. This data includes explicit trade aggressor side, odd lots, and imbalances. Auction imbalances offer valuable insights into NYSE Arca’s opening and closing auctions by providing details like imbalance quantity, paired quantity, imbalance reference price, and book clearing price.

Full depth of book data on Arca is particularly valuable over the SIPs for modeling pre-market, after-market and sweep-to-fill liquidity on U.S. exchange-traded products (ETPs) and ETFs.

Historical data is available for usage-based rates or with any Databento US Equities subscription. Visit our pricing page for more details.

Asset class: Equities

Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.

Supported data encodings: DBN, CSV, JSON (Learn more)

Supported market data schemas: MBO, MBP-1, MBP-10, TBBO, Trades, BBO-1s, BBO-1m, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Imbalance, Statistics, Status (Learn more)

Resolution: Immediate publication, nanosecond-resolution timestamps

A proprietary, data-science derived Phone Quality Level (PQL) score that measures the quality of the phone number(s) associated with an individual in our US Consumer database to ensure the highest levels of deliverability at a fraction of the cost compared to our competitors.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Dataset from Health Sciences Authority. For more information, visit https://data.gov.sg/datasets/d_2ae2e6beb458d059318c1c14ad899f98/view

CE Scanner US provides financial services investors with point-of-sale transaction data. Proprietary M&A attribution and volume equivalency offer rollup views to ticker and brand level with comparative detailed category/subcategory views into retail sales, volumes, distribution, and trends.

Altosight | AI-Powered Amazon Data, eBay Data & More | Global Marketplace Insights

✦ Altosight offers robust, AI-powered Amazon Data services that provide deep insights into product listings, reviews, prices, and sales trends.

✦ Amazon Reviews Data, eBay Data, Alibaba Data, and AliExpress Data are also covered, giving businesses the tools they need to make data-driven decisions across the world’s largest marketplaces.

Our Amazon Data encompasses a broad range of publicly available information from Amazon’s marketplace, which can be used to improve customer experience, personalize recommendations, optimize operations, and drive business success.

With unlimited free data points, fast delivery, and no setup costs, Altosight provides unparalleled flexibility and efficiency.

➤ We offer multiple data delivery options including API, CSV, JSON, and FTP, ensuring seamless integration into your business processes at no additional charge.

― Key Use Cases ―

➤ Marketplace Expansion & Product Assortment Optimization

🔹 Identify gaps in your product offerings by comparing competitor inventories with Alibaba Data, Amazon Data, and eBay Data.

🔹 Expand your product catalog by analyzing trends in best-sellers, emerging products, and market demand.

🔹 Use Digital Shelf Data to track product placements, best-seller rankings, and availability across major marketplaces to optimize your digital shelf space.

➤ Customer Sentiment & Product Review Analysis

🔹 Leverage Amazon Reviews Data to understand customer feedback, identify common complaints, and highlight product strengths.

🔹 Analyze AliExpress Data to track seller ratings and customer reviews, providing insights into consumer sentiment across different marketplaces.

🔹 Use these insights to refine product offerings, improve customer satisfaction, and enhance your brand’s reputation.

➤ Competitive Price Monitoring & Dynamic Repricing

🔹 Track product prices across Amazon, eBay, Alibaba, and AliExpress to ensure you remain competitive in the marketplace.

🔹 Use Amazon Data and eBay Data for real-time insights into competitor pricing and discounts.

🔹 Implement dynamic repricing strategies to react to price changes in real-time, ensuring your products always stay competitively priced.

➤ Product Sourcing & Wholesaler Opportunities

🔹 Use Alibaba Data and AliExpress Data to uncover new product opportunities and identify potential wholesalers.

🔹 Discover trending products to source for your business and form partnerships with reliable suppliers, streamlining your supply chain and business growth.

➤ Market Trend Identification & Forecasting

🔹 Use Alibaba Data and AliExpress Data to identify emerging trends in consumer behavior, product categories, and price fluctuations.

🔹 Conduct comprehensive market research to forecast product demand and industry trends based on historical data from Amazon and other marketplaces.

🔹 Stay ahead of market changes by leveraging real-time data for strategic decision-making, product launches, and marketing initiatives.

➤ Retailer & Brand Performance Tracking

🔹 Track the performance of specific retailers or brands across Amazon, eBay, Alibaba, and AliExpress using detailed sales and review data.

🔹 Monitor how frequently products move up or down in rankings, providing valuable insights for brand positioning and marketing effectiveness.

🔹 Analyze which retailers sell particular brands and products, helping businesses identify new partnerships or distribution opportunities.

― Data Collection & Quality ―

✔ Publicly Sourced Data: Altosight collects Amazon Data, Amazon Reviews Data, eBay Data, Alibaba Data, and AliExpress Data from publicly available sources. This includes product information, transaction data, reviews, and other valuable data points that are essential for making informed business decisions.

✔ AI-Powered Scraping: Our AI-driven technology handles CAPTCHAs, dynamic content, and JavaScript-heavy websites to ensure continuous and accurate data collection. We extract and structure Amazon Reviews Data, Digital Shelf Data, and other relevant marketplace data for easy integration into your existing systems.

✔ High-Quality Data: Altosight ensures all data is cleaned, structured, and ready for use, with high accuracy and reliability. Our solutions are ideal for market research, competitor analysis, and operational optimization.

― Why Choose Altosight? ―

✔ Unlimited Data Points: Altosight offers unlimited free data points, allowing you to extract as many product attributes or sales data as needed without additional charges. This ensures cost-effectiveness while maintaining access to all the insights you require.

✔ Proprietary Anti-Blocking Technology: Our proprietary scraping technology ensures continuous access to Amazon Data, eBay Data, Alibaba Data, and AliExpress Data by bypassing CAPTCHAs, Cloudflare, and other blocking mechanisms.

✔ Custom & R...

Access United Healthcare Transparency in Coverage data for 76,000 employers. Analyze costs across providers, plans, and employers. Includes in-network rates, out-of-network amounts, and cost-sharing info. 400TB+ monthly data. Ideal for pricing insights and cost strategies.

Hong Kong CPI (B): Weights: Misc Goods: Proprietary Medicine & Supplies data was reported at 0.590 % in Mar 2011. This stayed constant from the previous number of 0.590 % for Feb 2011. Hong Kong CPI (B): Weights: Misc Goods: Proprietary Medicine & Supplies data is updated monthly, averaging 0.590 % from Apr 2006 (Median) to Mar 2011, with 60 observations. The data reached an all-time high of 0.590 % in Mar 2011 and a record low of 0.590 % in Mar 2011. Hong Kong CPI (B): Weights: Misc Goods: Proprietary Medicine & Supplies data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong – Table HK.I036: Consumer Price Index (B): 10/04-9/05=100: Weights.

Hong Kong CPI (A): Miscellaneous Goods: Proprietary Medicines and Supplies data was reported at 101.900 Oct1999-Sep2000=100 in Mar 2006. This records an increase from the previous number of 101.800 Oct1999-Sep2000=100 for Feb 2006. Hong Kong CPI (A): Miscellaneous Goods: Proprietary Medicines and Supplies data is updated monthly, averaging 100.450 Oct1999-Sep2000=100 from Oct 1999 (Median) to Mar 2006, with 78 observations. The data reached an all-time high of 101.900 Oct1999-Sep2000=100 in Mar 2006 and a record low of 99.000 Oct1999-Sep2000=100 in Oct 1999. Hong Kong CPI (A): Miscellaneous Goods: Proprietary Medicines and Supplies data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong – Table HK.I027: Consumer Price Index (A): 10/99-9/00=100.

Need a cost-effective, real-time U.S. equity quote and trade solution? Nasdaq Basic is the leading exchange-provided alternative for real-time Best Bid and Offer and Last Sale information for all U.S. exchange-listed stocks. With Basic, investors access a proprietary data product that provides accuracy, liquidity, instrument coverage and accessibility with significant cost-savings.

NOAA NMFS does not approve, recommend, or endorse any proprietary product or proprietary material mentioned in this publication. No reference shall be made to NMFS, or to this publication furnished by NMFS, in any advertising or sales promotion which would indicate or imply that NMFS approves, recommends, or endorses any proprietary product or proprietary material mentioned herein or which has as its purpose any intent to cause directly or indirectly the advertised product to be used or purchased because of this NMFS publication. NMFS is not responsible for any uses of these datasets beyond those for which they were intended, and NMFS makes no claims regarding the accuracy of any data provided by agencies or individuals outside NMFS. Acknowledgment of NOAA NMFS and SEAMAP would be appreciated in products derived or publications generated from this data.

We collect, validate, model, and segment raw data signals from over 900+ sources globally to deliver thousands of mobile audience segments. We then combine that data with other public and private data sources to derive interests, intent, and behavioral attributes. Our proprietary algorithms then clean, enrich, unify and aggregate these data sets for use in our products. We have categorized our audience data into consumable categories such as interest, demographics, behavior, geography, etc. Audience Data Categories:Below mentioned data categories include consumer behavioral data and consumer profiles (available for the US and Australia) divided into various data categories. Brand Shoppers:Methodology: This category has been created based on the high intent of users in terms of their visits to Brand outlets in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Place Category Visitors:Methodology: This category has been created based on the high intent of users visiting specific places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Demographics:This category has been created based on deterministic data that we receive from apps based on the declared gender and age data. Marital Status, Education, Party affiliation, and State residency are available in the US. Geo-Behavioural:This category has been created based on the high intent of users in terms of the frequency of their visits to specific granular places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Interests:This segment is created based on users' interest in a specific subject while browsing the internet when the visited website category is clearly focused on a specific subject such as cars, cooking, traveling, etc. We use a deterministic model to assign a proper profile and time that information is valid. The recency of data can range from 14 to 30 days, depending on the topic. Intent:Factori receives data from many partners to deliver high-quality pieces of information about users’ shopping intent. We collect data from sources connected to the eCommerce sector and we also receive data connected to online transactions from affiliate networks to deliver the most accurate segments with purchase intentions, such as laptops, mobile phones, or cars. The recency of data can range from 7 to 14 days depending on the product category. Events:This category was created based on the high interest of users in terms of content related to specific global events - sports, culture, and gaming. Among the event segments, we also distinguish categories related to the interest in certain lifestyle choices and behaviors. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. App Usage:Mobile category is a branch of the taxonomy that is dedicated only to the data that is based on mobile advertising IDs. It is based on the categorization of the mobile apps that the user has installed on the device. Auto Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of automobile and other automotive attributes via a survey or registration. These audiences are currently available in the USA. Motorcycle Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of motorcycle and other motorcycle-based attributes via a survey or registration. These audiences are currently available for the USA. Household:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on users' declaring their marital status, parental status, and the overall number of children via a survey or registration. These audiences are currently available in the USA. Financial:Consumer Profiles - Available for the US and Australia this audience has been created based on their behavior in different financial services like property ownership, mortgage, investing behavior, and wealth and declaring their estimated net worth via a survey or registration. Purchase/ Spending Behavior:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on their behavior in different spending behaviors in different business verticals available in the USA. Clusters:Consumer Profiles - Available for the US and AustraliaClusters are groups of consumers who exhibit similar demographic, lifestyle, and media consumption characteristics, empowering marketers to understand the unique attributes that comprise their most profitable consumer segments. Armed with this rich data, data scientists can drive analytics and modeling to power their brand’s unique marketing initiatives. B2B Audiences;Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring their employee credentials, designations, and companies they work in, further specifying business verticals, revenue breakdowns, and headquarters locations. Customizable Audiences Data Segment:Brands can choose the appropriate pre-made audience segments or ask our data experts about creating a custom segment that is precisely tailored to your brief in order to reach their target customers and boost the campaign's effectiveness. Location Query Granularity:Minimum area: HEX 8Maximum area: QuadKey 17/City

Analysis of ‘Retail Transaction Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/michalfr/retail-transaction-data on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset contains transactions and the products they contain, which were obtained by scanning receipts from retail establishments by numerous users. Products were categorized by our proprietary NLP model.

Content

Data was collected over a one-year period and contains product information from purchases made within that period, product category inferred from product name, information about organization, transaction to which products belong to and user that uploaded receipt.

The total user count is 22. The total retail organization count is 179. The total transaction count is 805. The total product count is 7477.

Acknowledgements

@kserno

Inspiration

Product categorization, User Behaviour Analysis, Product Analysis, Product Price Comparison between Various Retail Stores, Prediction of Next Transaction

--- Original source retains full ownership of the source dataset ---

Hong Kong Composite Consumer Price Index (CPI): Weights: Misc Goods: Proprietary Medicine & Supplies data was reported at 0.570 % in 2010. This stayed constant from the previous number of 0.570 % for 2009. Hong Kong Composite Consumer Price Index (CPI): Weights: Misc Goods: Proprietary Medicine & Supplies data is updated yearly, averaging 0.570 % from Dec 2006 (Median) to 2010, with 5 observations. The data reached an all-time high of 0.570 % in 2010 and a record low of 0.570 % in 2010. Hong Kong Composite Consumer Price Index (CPI): Weights: Misc Goods: Proprietary Medicine & Supplies data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong – Table HK.I010: Composite Consumer Price Index: 10/04-9/05=100: Weights: Annual.

Lucror Analytics: Fundamental Fixed Income Data and Financial Models for High-Yield Bond Issuers

At Lucror Analytics, we deliver expertly curated data solutions focused on corporate credit and high-yield bond issuers across Europe, Asia, and Latin America. Our data offerings integrate comprehensive fundamental analysis, financial models, and analyst-adjusted insights tailored to support professionals in the credit and fixed-income sectors. Covering 400+ bond issuers, our datasets provide a high level of granularity, empowering asset managers, institutional investors, and financial analysts to make informed decisions with confidence.

By combining proprietary financial models with expert analysis, we ensure our Fixed Income Data is actionable, precise, and relevant. Whether you're conducting credit risk assessments, building portfolios, or identifying investment opportunities, Lucror Analytics offers the tools you need to navigate the complexities of high-yield markets.

What Makes Lucror’s Fixed Income Data Unique?

Comprehensive Fundamental Analysis Our datasets focus on issuer-level credit data for complex high-yield bond issuers. Through rigorous fundamental analysis, we provide deep insights into financial performance, credit quality, and key operational metrics. This approach equips users with the critical information needed to assess risk and uncover opportunities in volatile markets.

Analyst-Adjusted Insights Our data isn’t just raw numbers—it’s refined through the expertise of seasoned credit analysts with 14 years average fixed income experience. Each dataset is carefully reviewed and adjusted to reflect real-world conditions, providing clients with actionable intelligence that goes beyond automated outputs.

Focus on High-Yield Markets Lucror’s specialization in high-yield markets across Europe, Asia, and Latin America allows us to offer a targeted and detailed dataset. This focus ensures that our clients gain unparalleled insights into some of the most dynamic and complex credit markets globally.

How Is the Data Sourced? Lucror Analytics employs a robust and transparent methodology to source, refine, and deliver high-quality data:

• Public Sources: Includes issuer filings, bond prospectuses, financial reports, and market data.
• Proprietary Analysis: Leveraging proprietary models, our team enriches raw data to provide actionable insights.
• Expert Review: Data is validated and adjusted by experienced analysts to ensure accuracy and relevance.
• Regular Updates: Models are continuously updated to reflect market movements, regulatory changes, and issuer-specific developments.

This rigorous process ensures that our data is both reliable and actionable, enabling clients to base their decisions on solid foundations.

Primary Use Cases 1. Fundamental Research Institutional investors and analysts rely on our data to conduct deep-dive research into specific issuers and sectors. The combination of raw data, adjusted insights, and financial models provides a comprehensive foundation for decision-making.

1. Credit Risk Assessment Lucror’s financial models provide detailed credit risk evaluations, enabling investors to identify potential vulnerabilities and mitigate exposure. Analyst-adjusted insights offer a nuanced understanding of creditworthiness, making it easier to distinguish between similar issuers.

2. Portfolio Management Lucror’s datasets support the development of diversified, high-performing portfolios. By combining issuer-level data with robust financial models, asset managers can balance risk and return while staying aligned with investment mandates.

3. Strategic Decision-Making From assessing market trends to evaluating individual issuers, Lucror’s data empowers organizations to make informed, strategic decisions. The regional focus on Europe, Asia, and Latin America offers unique insights into high-growth and high-risk markets.

Key Features of Lucror’s Data - 400+ High-Yield Bond Issuers: Coverage across Europe, Asia, and Latin America ensures relevance in key regions. - Proprietary Financial Models: Created by one of the best independent analyst teams on the street. - Analyst-Adjusted Data: Insights refined by experts to reflect off-balance sheet items and idiosyncrasies. - Customizable Delivery: Data is provided in formats and frequencies tailored to the needs of individual clients.

Why Choose Lucror Analytics? Lucror Analytics and independent provider free from conflicts of interest. We are committed to delivering high-quality financial models for credit and fixed-income professionals. Our proprietary approach combines proprietary models with expert insights, ensuring accuracy, relevance, and utility.

By partnering with Lucror Analytics, you can: - Safe costs and create internal efficiencies by outsourcing a highly involved and time-consuming processes, including financial analysis and modelling. - Enhance your credit risk ...

Overview Long-range scanning Doppler lidar located on Gordon Ridge. The WindTracer provides high-resolution, long-range lidar data for use in the WFIP2 program. Data Details The system is configured to take data in three different modes. All three modes take 15 minutes to complete and are started at 00, 15, 30, and 45 minutes after the hour. The first nine minutes of the period are spent performing two high-resolution, long-range Plan Position Indicator (PPI) scans at 0.0 and -1.0 degree elevation angles (tilts). These data have file names annotated with HiResPPI noted in the "optional fields" of the file name; for example: lidar.z09.00.20150801.150000.HiResPPI.prd. The next six minutes are spent performing higher altitude PPI scans and Range Height Indicator (RHI) scans. The PPI scans are completed at 6.0- and 30.0-degree elevations, and the RHI scans are completed from below the horizon (down into valleys, as able), up to 40 degrees elevation at 010-, 100-, 190-, and 280-degree azimuths. These files are annotated with PPI-RHI noted in the optional fields of the file name; for example: lidar.z09.00.20150801.150900.PPI-RHI.prd *The last minute is spent measuring a high-altitude vertical wind profile. Generally, this dataset will include data from near ground level up to the top of the planetary boundary layer (PBL), and higher altitude data when high-level cirrus or other clouds are present. The Velocity Azimuth Display (VAD) is measured using six lines of sight at an elevation angle of 75 degrees at azimuth angles of 000, 060, 120, 180, 240, and 300 degrees from True North. The files are annotated with VAD in the optional fields of the file name; for example: lidar.z09.00.20150801.151400.VAD.prd. LMCT does have a data format document that can be provided to users who need programming access to the data. This document is proprietary information but can be supplied to anyone after signing a non-disclosure agreement (NDA). To initiate the NDA process, please contact Keith Barr at keith.barr@lmco.com. The data are not proprietary, only the manual describing the data format. Data Quality Lockheed Martin Coherent Technologies (LMCT) has implemented and refined data quality analysis over the last 14 years, and this installation uses standard data-quality processing procedures. Generally, filtered data products can be accepted as fully data qualified. Secondary processing, such as wind vector analysis, should be used with some caution as the data-quality filters still are "young" and incorrect values can be encountered. Uncertainty Uncertainty in the radial wind measurements (the system's base measurement) varies slightly with range. For most measurements, accuracy of the filtered radial wind measurements have been shown to be within 0.5 m/s with accuracy better than 0.25 m/s not uncommon for ranges less than 10 km. Constraints Doppler lidar is dependent on aerosol loading in the atmosphere, and the signal can be significantly attenuated in precipitation and fog. These weather situations can reduce range performance significantly, and, in heavy rain or thick fog, range performance can be reduced to zero. Long-range performance depends on adequate aerosol loading to provide enough backscattered laser radiation so that a measurement can be made.

Bright Data’s datasets are created by utilizing proprietary technology for retrieving public web data at scale, resulting in fresh, complete, and accurate datasets. CrunchBase datasets provide unique insights into the latest industry trends. They enable the tracking of company growth, identifying key businesses and professionals, tracking employee movement between companies, as well as enabling more efficient competitive intelligence. Easily define your Crunchbase dataset using our smart filter capabilities, enabling you to customize pre-existing datasets, ensuring the data received fits your business needs. Bright Data’s Crunchbase company data includes over 2.8 million company profiles, with subsets available by industry, region, and any other parameters according to your requirements. There are over 70 data points per company, including overview, details, news, financials, investors, products, people, and more. Choose between full coverage or a subset. Get your Crunchbase dataset Today!