Note:- Only publicly available data can be worked upon

In today's ever-evolving Ecommerce landscape, success hinges on the ability to harness the power of data. APISCRAPY is your strategic ally, dedicated to providing a comprehensive solution for extracting critical Ecommerce data, including Ecommerce market data, Ecommerce product data, and Ecommerce datasets. With the Ecommerce arena being more competitive than ever, having a data-driven approach is no longer a luxury but a necessity.

APISCRAPY's forte lies in its ability to unearth valuable Ecommerce market data. We recognize that understanding the market dynamics, trends, and fluctuations is essential for making informed decisions.

APISCRAPY's AI-driven ecommerce data scraping service presents several advantages for individuals and businesses seeking comprehensive insights into the ecommerce market. Here are key benefits associated with their advanced data extraction technology:

1. Ecommerce Product Data: APISCRAPY's AI-driven approach ensures the extraction of detailed Ecommerce Product Data, including product specifications, images, and pricing information. This comprehensive data is valuable for market analysis and strategic decision-making.

2. Data Customization: APISCRAPY enables users to customize the data extraction process, ensuring that the extracted ecommerce data aligns precisely with their informational needs. This customization option adds versatility to the service.

3. Efficient Data Extraction: APISCRAPY's technology streamlines the data extraction process, saving users time and effort. The efficiency of the extraction workflow ensures that users can obtain relevant ecommerce data swiftly and consistently.

4. Realtime Insights: Businesses can gain real-time insights into the dynamic Ecommerce Market by accessing rapidly extracted data. This real-time information is crucial for staying ahead of market trends and making timely adjustments to business strategies.

5. Scalability: The technology behind APISCRAPY allows scalable extraction of ecommerce data from various sources, accommodating evolving data needs and handling increased volumes effortlessly.

Beyond the broader market, a deeper dive into specific products can provide invaluable insights. APISCRAPY excels in collecting Ecommerce product data, enabling businesses to analyze product performance, pricing strategies, and customer reviews.

To navigate the complexities of the Ecommerce world, you need access to robust datasets. APISCRAPY's commitment to providing comprehensive Ecommerce datasets ensures businesses have the raw materials required for effective decision-making.

Our primary focus is on Amazon data, offering businesses a wealth of information to optimize their Amazon presence. By doing so, we empower our clients to refine their strategies, enhance their products, and make data-backed decisions.

[Tags: Ecommerce data, Ecommerce Data Sample, Ecommerce Product Data, Ecommerce Datasets, Ecommerce market data, Ecommerce Market Datasets, Ecommerce Sales data, Ecommerce Data API, Amazon Ecommerce API, Ecommerce scraper, Ecommerce Web Scraping, Ecommerce Data Extraction, Ecommerce Crawler, Ecommerce data scraping, Amazon Data, Ecommerce web data]

Dataset Card for "amazon-product-data-filter"

  Dataset Summary

The Amazon Product Dataset contains product listing data from the Amazon US website. It can be used for various NLP and classification tasks, such as text generation, product type classification, attribute extraction, image recognition and more. NOTICE: This is a sample of the full Amazon Product Dataset, which contains 1K examples. Follow the link to gain access to the full dataset.

  Languages… See the full description on the dataset page: https://huggingface.co/datasets/iarbel/amazon-product-data-sample.

Zoro.com Product Data Sample – Explore Structured E-commerce Product Listings

This dataset is a sample extraction of product listings from Zoro.com, a leading industrial supply e-commerce platform. It provides structured product-level data that can be used for market research, price comparison engines, product matching models, and e-commerce analytics.

The sample includes a variety of products across tools, hardware, safety equipment, and industrial supplies — with clean, structured fields suitable for both analysis and model training.

Also available: Grainger Product Datasets – structured data from a top industrial supplier.

Ideal for previewing before requesting larger or full Zoro datasets

Use Cases:

• Building product comparison or search engines

• Price intelligence and competitor monitoring

• Product classification and attribute extraction

• Training data for e-commerce AI models

Want More?

This is a sample of a much larger dataset extracted from Zoro.com.
👉 Contact us to access full datasets or request custom category extractions.

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.

nfinite-product-masks-sample

Version of the release: 1.0.0-alphaRelease date: 2025/08/30

  Dataset Summary

The nfinite-product-masks-sample dataset is a dataset of images from 3D models for objects usually found in the home & living room space. Each image has been rendered photo-realistically from 3D models.Those 3D models are generic models, from any IP (as explained in the Personal and Sensitive Information part, any resemblance to an object from real life is purely… See the full description on the dataset page: https://huggingface.co/datasets/Nfiniteai/product-masks-sample.

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

Sample purchasing data containing information on suppliers, the products they provide, and the projects those products are used for. Data created or adapted from publicly available sources.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Salient Features of Dentists Email Addresses

So make sure that you don’t find excuses for failing at global marketing campaigns and in reaching targeted medical practitioners and healthcare specialists. With our Dentists Email Leads, you will seldom have a reason not to succeed! So make haste and take action today!

1. 1.2 million phone calls per month as a part of a data verification
2. 85% telephone and email verified Dentist Mailing Lists
3. Quarterly SMTP and NCOA verified to keep data fresh and active
4. 15 million verification messages sent every month to validate email addresses
5. Connect with top Dentists across the US, Canada, UK, Europe, EMEA, Australia, APAC and many more countries.
6. egularly updated and cleansed databases to keep it free of duplicate and inaccurate data

How Can Our Dentists Data Help You to Market to Dentists?

We provide a variety of methods for marketing your dental appliances or products to the top-rated dentists in the United States. Take a glance at some of the available channels:

• Email blast • Marketing viability • Test campaigns • Direct mail • Sales leads • Drift campaigns • ABM campaigns • Product launches • B2B marketing

Data Sources

The contact details of your targeted healthcare professionals are compiled from highly credible resources like: • Websites • Medical seminars • Medical records • Trade shows • Medical conferences

What’s in for you? Over choosing us, here are a few advantages we authenticate- • Locate, target, and prospect leads from 170+ countries • Design and execute ABM and multi-channel campaigns • Seamless and smooth pre-and post-sale customer service • Connect with old leads and build a fruitful customer relationship • Analyze the market for product development and sales campaigns • Boost sales and ROI with increased customer acquisition and retention

Our security compliance

We use of globally recognized data laws like –

GDPR, CCPA, ACMA, EDPS, CAN-SPAM and ANTI CAN-SPAM to ensure the privacy and security of our database. We engage certified auditors to validate our security and privacy by providing us with certificates to represent our security compliance.

Our USPs- what makes us your ideal choice?

At DataCaptive™, we strive consistently to improve our services and cater to the needs of businesses around the world while keeping up with industry trends.

• Elaborate data mining from credible sources • 7-tier verification, including manual quality check • Strict adherence to global and local data policies • Guaranteed 95% accuracy or cash-back • Free sample database available on request

Guaranteed benefits of our Dentists email database!

85% email deliverability and 95% accuracy on other data fields

We understand the importance of data accuracy and employ every avenue to keep our database fresh and updated. We execute a multi-step QC process backed by our Patented AI and Machine learning tools to prevent anomalies in consistency and data precision. This cycle repeats every 45 days. Although maintaining 100% accuracy is quite impractical, since data such as email, physical addresses, and phone numbers are subjected to change, we guarantee 85% email deliverability and 95% accuracy on other data points.

100% replacement in case of hard bounces

Every data point is meticulously verified and then re-verified to ensure you get the best. Data Accuracy is paramount in successfully penetrating a new market or working within a familiar one. We are committed to precision. However, in an unlikely event where hard bounces or inaccuracies exceed the guaranteed percentage, we offer replacement with immediate effect. If need be, we even offer credits and/or refunds for inaccurate contacts.

Other promised benefits

• Contacts are for the perpetual usage • The database comprises consent-based opt-in contacts only • The list is free of duplicate contacts and generic emails • Round-the-clock customer service assistance • 360-degree database solutions

Envestnet®| Yodlee®'s Bank Statement Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Public Health – Seattle & King County and the Hazardous Waste Management Program are providing data describing the lead content of consumer products. This data is collected from several sources, including community product testing events, in-home investigations of lead-poisoned children, and products purchased for testing for research projects. Data are presented using two types of testing methods: product screening using X-ray fluorescence (XRF) analysis and laboratory analysis. Because XRF screening can be conducted without destroying the object to be tested, this method was used to test products that could not be submitted to the laboratory for analysis. Examples of the types of products tested via XRF analysis include keys, jewelry, cookware, dishware, toys, and other essential or valuable items. However, it is important to note that XRF analysis is only a screening method that gives approximate results and may have high detection limits for some products. Although XRF analysis is very useful for identifying products that could contain relatively high lead levels, it cannot be used to compare lead results to regulatory limits or standards. Laboratory analysis is the “gold standard” for product testing. Laboratory analysis can theoretically achieve detection limits for lead in the part per billion (ppb) range, although the detection limits can be higher if not enough sample is provided for analysis and/or the sample is chemically very complex (causing “matrix interference”). Examples of the types of products tested via laboratory analysis include seasonings, cosmetics, candy, dietary supplements, and other items that can be destroyed for analysis. Laboratory results can be used to compare lead concentrations to regulatory limits or standards. Laboratory methods used to analyze consumer products for lead content include graphite furnace atomic absorption (GFAA), inductively coupled plasma mass spectrometry (ICP-MS), and inductively coupled plasma optical emission spectroscopy (ICP-OES). Technical notes: Regardless of the method used, the lead concentrations are provided in parts per million (ppm). One part per million is equivalent to 0.0001 percent. One percent is 10,000 ppm. See the Lead Limits for Consumer Products table below for how we compared the lead concentrations in the tested products to acceptable limits. Every testing method has limitations in the smallest amount of lead that can be detected in a product. For the XRF analyzer, the instrument’s limit of detection (or LOD) varies depending on the sample’s chemical composition, shininess, curvature, positioning, and operator factors. For laboratory analysis, the relevant detection limit – the limit of quantitation (or LOQ) - can also vary, depending on the chemical complexity of the sample, the amount of sample collected, how the sample is prepared, and how it is analyzed. Therefore, where results are presented with the &quot<&quot (less than) symbol, it means the lead concentration is some number less than the reported value (i.e., the LOD or LOQ). It is not possible to compare results presented as below the LOQ or LOD to lead limits. For some research projects, the median XRF result may also be represented with a qualifier if more than 50% of the measurements were below the LOD. For research projects, the XRF value presented is the median of all measurements taken on the product. For all other data sources, the XRF value presented is the maximum of all measurements taken. Disclaimer: Staff collect information as it appears on product labels or as reported to staff during community events and investigations. Factors such as language barriers and terminology variations may result in misspelling and mislabeling of some products. The amount of lead found in a consumer product can also vary greatly because of variation

Find details of Sample Receiving Sgs North America Buyer/importer data in US (United States) with product description, price, shipment date, quantity, imported products list, major us ports name, overseas suppliers/exporters name etc. at sear.co.in.

Level-1A Data Products are the result of a non-destructive processing applied to the Level-0 data at NASA/JPL. The sensor calibration factors are applied in order to convert the binary encoded measurements to engineering units. Where necessary, time tag integer second ambiguity is resolved and data are time tagged to the respective satellite receiver clock time. Editing and quality control flags are added, and the data is reformatted for further processing. The Level-1A data are reversible to Level-0, except for the bad data packets. This level also includes the ancillary data products needed for processing to the next data level. The Level-1B Data Products are the result of a possibly destructive, or irreversible, processing applied to both the Level-1A and Level-0 data at NASA/JPL. The data are correctly time-tagged, and data sample rate is reduced from the higher rates of the previous levels. Collectively, the processing from Level-0 to Level-1B is called the Level-1 Processing. This level also includes the ancillary data products generated during this processing, and the additional data needed for further processing. The Level-2 data products include the static and time-variable (monthly) gravity field and related data products derived from the application of Level-2 processing at GFZ, UTCSR and JPL to the previous level data products. This level also includes the ancillary data products such as GFZ's Level-1B short-term atmosphere and ocean de-aliasing product (AOD1B) generated during this processing. GRACE-A and GRACE-B Level-1B Data Product • Satellite clock solution [GA-OG-1B-CLKDAT, GB-OG-1B-CLKDAT, GRACE CLKDAT]: Offset of the satellite receiver clock relative to GPS time, obtained by linear fit to raw on-board clock offset estimates. • GPS flight data [GA-OG-1B-GPSDAT, GB-OG-1B-GPSDAT, GRACE GPSDAT]: Preprocessed and calibrated GPS code and phase tracking data edited and decimated from instrument high-rate (10 s (code) or 1 s (phase)) to low-rate (10 s) samples for science use (1 file per day, level-1 format) • Accelerometer Housekeeping data [GA-OG-1B-ACCHKP, GB-OG-1B-ACCHKP, GRACE ACCHKP]: Accelerometer proof-mass bias voltages, capacitive sensor outputs, instrument control unit (ICU) and sensor unit (SU) temperatures, reference voltages, primary and secondary power supply voltages (1 file per day, level-1 format). • Accelerometer data [GA-OG-1B-ACCDAT, GB-OG-1B-ACCDAT, GRACE ACCDAT]: Preprocessed and calibrated Level-1B accelerometer data edited and decimated from instrument high-rate (0.1 s) to low-rate (1s) samples for science use (1 file per day, level-1 format). • Intermediate clock solution [GA-OG-1B-INTCLK, GB-OG-1B-INTCLK, GRACE INTCLK]: derived with GIPSY POD software (300 s sample rate) (1 file per day, GIPSY format) • Instrument processing unit (IPU) Housekeeping data [GA-OG-1B-IPUHKP, GB-OG-1B-IPUHKP, GRACE IPUHKP]: edited and decimated from high-rate (TBD s) to low-rate (TBD s) samples for science use (1 file per day, level-1 format) • Spacecraft Mass Housekeeping data [GA-OG-1B-MASDAT, GB-OG-1B-MASDAT, GRACE MASDAT]: Level 1B Data as a function of time • GPS navigation solution data [GA-OG-1B-NAVSOL, GB-OG-1B-NAVSOL, GRACE NAVSOL]: edited and decimated from instrument high-rate (60 s) to low-rate (30 s) samples for science use (1 file per day, level-1 format) • OBDH time mapping to GPS time Housekeeping data [GA-OG-1B-OBDHTM, GB-OG-1B-OBDHTM, GRACE OBDHTM]: On-board data handling (OBDH) time mapping data (OBDH time to receiver time • Star camera data [GA-OG-1B-SCAATT, GB-OG-1B-SCAATT, GRACE SCAATT]: Preprocessed and calibrated star camera quaternion data edited and decimated from instrument high-rate (1 s) to low-rate (5 s) samples for science use (1 file per day, level-1 format) • Thruster activation Housekeeping data [GA-OG-1B-THRDAT, GB-OG-1B-THRDAT, GRACE THRDAT]: GN2 thruster data used for attitude (10 mN) and orbit (40 mN) control • GN2 tank temperature and pressure Housekeeping data [GA-OG-1B-TNKDAT, GB-OG-1B-TNKDAT, GRACE TNKDAT]: GN2 tank temperature and pressure data • Oscillator frequency data [GA-OG-1B-USODAT, GB-OG-1B-USODAT, GRACE USODAT]: derived from POD productGRACE-A and GRACE-B Combined Level-1B Data Product • Preprocessed and calibrated k-band ranging data [GA-OG-1B-KBRDAT, GB-OG-1B-KBRDAT, GRACE KBRDAT]: range, range-rate and range-acceleration data edited and decimated from instrument high-rate (0.1 s) to low-rate (5 s) samples for science use (1 file per day, level-1 format) • Atmosphere and Ocean De-aliasing Product [GA-OG-1B-ATMOCN, GB-OG-1B-ATMOCN, GRACE ATMOCN]: GRACE Atmosphere and Ocean De-aliasing Product GRACE Level-2 Data Product • GAC [GA-OG-_2-GAC, GB-OG-_2-GAC, GRACE GAC]: Combination of non-tidal atmosphere and ocean spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on level-1 AOD1B product (1file per time span, level-2 format) • GCM [GA-OG-_2-GCM, GB-OG-_2-GCM, GRACE GCM]: Spherical harmonic coefficients and standard deviations of the long-term static gravity field estimated by combination of GRACE satellite instrument data and other information for a dedicated time span (multiple years) and spatial resolution (1 file per time span, level-2 format) • GAB [GA-OG-_2-GAB, GB-OG-_2-GAB, GRACE GAB]: Non-tidal ocean spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on level-1 AOD1B product (1file per time span, level-2 format) • GAD [GA-OG-_2-GAD, GB-OG-_2-GAD, GRACE GAD]: bottom pressure product - combination of surface pressure and ocean (over the oceans, and zero over land). Spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on level-1 AOD1B product (1file per time span, level-2 format) • GSM [GA-OG-_2-GSM, GB-OG-_2-GSM, GRACE GSM]: Spherical harmonic coefficients and standard deviations of the static gravity field estimated from GRACE satellite instrument data only for a dedicated time span (e.g. weekly, monthly, multiple years) and spatial resolution (1 file per time span, level-2 format).

The data set contains the details on the silver speciation in silver nanoparticle consumer products and the transformation of the silver during their usage and disposal. Synthetic stomach fluid and wastewater sludge are used to create a model for the lifecycle of silver nanoparticle dietary supplements. This dataset is associated with the following publication: Potter, P., J. Navratilova, K. Rogers, and S. Al-Abed. Transformation of silver nanoparticle consumer products during simulated usage and disposal. Environmental Science: Nano. RSC Publishing, Cambridge, UK, 6(2): 592-598, (2019).

Concentration of dissolved phosphate in a pump bottle water sample measured via In-Situ in umol/kg. Part of dataset GEOTRACES Intermediate Data Product 2021 v2 - Seawater

Product Review Datasets: Uncover user sentiment

Harness the power of Product Review Datasets to understand user sentiment and insights deeply. These datasets are designed to elevate your brand and product feature analysis, help you evaluate your competitive stance, and assess investment risks.

Data sources:

• Trustpilot: datasets encompassing general consumer reviews and ratings across various businesses, products, and services.

Leave the data collection challenges to us and dive straight into market insights with clean, structured, and actionable data, including:

• Product name;
• Product category;
• Number of ratings;
• Ratings average;
• Review title;
• Review body;

Choose from multiple data delivery options to suit your needs:

1. Receive data in easy-to-read formats like spreadsheets or structured JSON files.
2. Select your preferred data storage solutions, including SFTP, Webhooks, Google Cloud Storage, AWS S3, and Microsoft Azure Storage.
3. Tailor data delivery frequencies, whether on-demand or per your agreed schedule.

Why choose Oxylabs?

1. Fresh and accurate data: Access organized, structured, and comprehensive data collected by our leading web scraping professionals.

2. Time and resource savings: Concentrate on your core business goals while we efficiently handle the data extraction process at an affordable cost.

3. Adaptable solutions: Share your specific data requirements, and we'll craft a customized data collection approach to meet your objectives.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA standards.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Join the ranks of satisfied customers who appreciate our meticulous attention to detail and personalized support. Experience the power of Product Review Datasets today to uncover valuable insights and enhance decision-making.

Overview

The EarthCARE satellite combines four instruments, a Cloud Profiling Radar (CPR), an Atmospheric Lidar (ATLID), a Multispectral Imager (MSI) and a Broadband Radiometer (BBR), from which many products will be generated on the properties of clouds, aerosols, precipitation and radiation. The dataset in this repository consists of test products generated from simulated 3D scenes produced by the Canadian Global Environmental Multiscale (GEM) model. This includes both Level 1 (L1) "input" products containing simulated satellite measurements, and Level 2 (L2) "output" products produced by running the various European retrieval algorithms on the inputs. The dataset has been produced as part of the European Space Agency funded "CARDINAL" project involving numerous European and Canadian scientists, and there are several versions representing the evolution of the algorithms during preparation for the launch of EarthCARE. The scenes and algorithms are discussed in detail in a Special Issue of the journal Atmospheric Modelling Techniques (AMT), so the overview here is limited to a summary of what is contained in this dataset.

Directory structure

The four main top-level directories inside the zip file are for the three simulated scenes (except for the C-APC directory, described in a separate section below), each of which represent a single 6000-km long EarthCARE granule:

• Halifax: A swath over the Atlantic Ocean from the Caribbean to the Labrador Sea, passing close to Halifax in Nova Scotia
• Baja: A swath passing over the Rocky Mountains and the Baja California peninsula in Mexico
• Hawaii: A swath over the Pacific Ocean passing close to Hawaii
• Halifax_aerosol (added in version 10.10): A modified version of the Halifax scene with clouds removed and modified aerosols, to provided additional testing of the ATLID and MSI retrieval algorithms (note that algorithms involving CPR and BBR have not been run on this scene)

Each of these directories contains three further directories:

• input: simulated level-1 instrument data (note that the data formats, e.g. attributes and variable names, do not exactly match those of real EarthCARE data, although they are close)
• output: level-2 meteorological products generated from the input data (note that updates to the algorithms in future may lead to minor changes to the data formats, so always use the most up-to-date Zenodo release)
• logs: text files logging the progress of the algorithms as they generated the level-2 data

The input and output directories contain subdirectories for each product, and these each contain two files:

• A NetCDF4/HDF5 file with the suffix "h5" containing the retrieved data
• An XML file with the suffix "HDR" containing a description of the variables in the file (reproducing metadata already in the NetCDF4/HDF5 file)

Input products (L1)

The level-1 products are named Y-ZZZ where Y indicates the source of the data (A=ATLID, C=CPR, M=MSI, B=BBR and X=auxiliary) and ZZZ is a shortened version of the product name:

• A-NOM: Nominal ATLID measurements
• C-NOM: Nominal CPR measurements
• M-RGR: Regridded MSI measurements
• B-NOM: Nominal BBR measurements averaged to various scales
• B-SNG: Single-pixel BBR measurements
• X-JSG: Definition of the Joint Standard Grid on to which several of the observations are interpolated
• X-MET: Meteorological data from ECMWF

Single-instrument output products (L2a)

The naming convention is the same as the L1 data products.

• A-AER: ATLID aerosol profiles
• A-ALD: ATLID aerosol layer descriptor
• A-CTH: ATLID cloud top height
• A-EBD: ATLID extinction, backscatter and depolarization
• A-FM: ATLID feature mask
• A-ICE: ATLID ice cloud properties
• A-TC: ATLID target classification
• C-CD: CPR corrected Doppler velocity
• C-CLD: CPR cloud properties
• C-FMR: CPR feature mask and reflectivity
• C-TC: CPR target classification
• M-AOT: MSI aerosol optical thickness
• M-CM: MSI cloud mask
• M-COP: MSI cloud optical and physical properties

Multi-instrument output products (L2b)

The level-2b output products are named YY-ZZZ where YY is 2-4 character code conveying which of the four instruments were used and ZZZ is the shortened version of the product name.

• AC-TC: ATLID-CPR target classification
• AM-ACD: ATLID-MSI aerosol column descriptor
• AM-CTH: ATLID-MSI cloud top height
• BM-RAD: BBR-MSI broadband radiances (unfiltered)
• ACM-3D: ATLID-CPR-MSI constructed 3D scene
• ACM-CAP: ATLID-CPR-MSI synergistic retrieval of cloud, aerosol and precipitation
• ACM-COM: ATLID-CPR-MSI composite of single-instrument cloud and aerosol retrievals
• ACM-RT: ATLID-CPR-MSI radiative fluxes and heating rates computed on the retrievals
• BMA-FLX: BBR-MSI-ATLID broadband fluxes
• ACMB-DF: ATLID-CPR-MSI-BBR difference between radiances and fluxes computed from retrievals (ACM-RT) and measurements (BM-RAD, BMA-FLX)

CPR Antenna Pointing Correction (C-APC) product

From version 10.01 of the dataset, an additional top-level "C-APC" directory contains test data for the CPR Antenna Pointing Correction product, which will be generated via statistical analysis of a larger sample of C-NOM data. The generated information about mis-pointing of the radar antenna will then be used to improve interpretation of subsequent radar Doppler observations. There are two subdirectories:

• GEM_scenes: contains a file used in subsequent radar processing of the three GEM scenes, although in these scenes it has been assumed that there is no mis-pointing to correct, indicated by the file containing missing data and zeros.
• Antenna_mispointing_example: contains input and output files (in further subdirectories) illustrating what the data would look like with mis-pointing present. It was generated by artificially modifying the Doppler veclocities in the original Baja, Halifax and Hawaii scenes, concatenating them to generate a full orbit, and then running the C-APC algorithm to characterize the mis-pointing.

Product definition documents

From version 10.10 of the dataset, an additional top-level directory "Product_definition_documents" is provided containing documents that describe the variables contained in each of the level-2 product files.

Filename format

The data filenames and directories have the following name format:

ECA_EXAA_YYY_ZZZ_LL_OBSERVATION-TIME_GENERATION-TIME_VVVVVV

where:

• "ECA" indicates EarthCARE.
• EXAA indicates the file class: ESA, Latency N/A, Simulator, Baseline N/A.
• YYY is a three-character code indicating the instrumental source of the data. The 1-4 character codes defined in the sections above are converted to 3 character codes as follows: A -> ATL, C -> CPR, M -> MSI, B -> BBR, X -> AUX, AM -> AM_, AC -> AC_, BM -> BM_, ACMB -> ALL.
• ZZZ is a three-character code abbreviating the full name of the product, as defined in the sections above.
• LL represents the level of the data from 1B, 1C, 1D, 2A and 2B.
• OBSERVATION-TIME is a code representing the date and time the observations were taken in the form yyyymmddThhmmssZ. Note that for the present datasets the dates are fictional future dates.
• GENERATION-TIME is a code of the same form representing the time that the processor was run to produce the data product.
• VVVVVV indicates the orbit number and frame letter.

Further information may be obtained from the papers in the special issue of AMT. The description here may be expanded in future.

Contacts: Gerd-Jan van Zadelhoff

The Delta Neighborhood Physical Activity Study was an observational study designed to assess characteristics of neighborhood built environments associated with physical activity. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns and neighborhoods in which Delta Healthy Sprouts participants resided. The 12 towns were located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys between August 2016 and September 2017 using the Rural Active Living Assessment (RALA) tools and the Community Park Audit Tool (CPAT). Scale scores for the RALA Programs and Policies Assessment and the Town-Wide Assessment were computed using the scoring algorithms provided for these tools via SAS software programming. The Street Segment Assessment and CPAT do not have associated scoring algorithms and therefore no scores are provided for them. Because the towns were not randomly selected and the sample size is small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one contains data collected with the RALA Programs and Policies Assessment (PPA) tool. Dataset two contains data collected with the RALA Town-Wide Assessment (TWA) tool. Dataset three contains data collected with the RALA Street Segment Assessment (SSA) tool. Dataset four contains data collected with the Community Park Audit Tool (CPAT). [Note : title changed 9/4/2020 to reflect study name] Resources in this dataset:Resource Title: Dataset One RALA PPA Data Dictionary. File Name: RALA PPA Data Dictionary.csvResource Description: Data dictionary for dataset one collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA Data Dictionary. File Name: RALA TWA Data Dictionary.csvResource Description: Data dictionary for dataset two collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA Data Dictionary. File Name: RALA SSA Data Dictionary.csvResource Description: Data dictionary for dataset three collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT Data Dictionary. File Name: CPAT Data Dictionary.csvResource Description: Data dictionary for dataset four collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One RALA PPA. File Name: RALA PPA Data.csvResource Description: Data collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA. File Name: RALA TWA Data.csvResource Description: Data collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA. File Name: RALA SSA Data.csvResource Description: Data collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT. File Name: CPAT Data.csvResource Description: Data collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary. File Name: DataDictionary_RALA_PPA_SSA_TWA_CPAT.csvResource Description: This is a combined data dictionary from each of the 4 dataset files in this set.

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Dataset

This dataset was created by Rao Hamza Tariq

Released under Other (specified in description)

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