This statistic shows the sports big data product distribution among users in China as of September 2018, by product type. During the survey period, almost ** percent of respondents in China used smart bracelets, whereas around ** percent had a smart watch.

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

Popular Attributes:

✔ Detailed product information

✔ High-quality imagery

✔ Extensive attribute coverage

✔ Ideal for UX and SEO optimization

✔ Comprehensive product categorization

Key Information:

Rich dataset with 30+ attributes per product

Pricing: Flexible subscription models

Update Frequency: Daily updates

Coverage: Global and specific markets

Historical Data: 12 Months +

ARC-Lake v2.0 - Global contains data products with global coverage, i.e. data for all (available) lakes are included in each product. These data products contain observations of Lake Surface Water Temperature (LSWT) and Lake Ice Cover (LIC) from the series of (Advanced) Along-Track Scanning Radiometers ((A)ATSRs). ARC-Lake v2.0 data products cover the period from 1st August 1991 to 31st December 2011. A number of different data products are available and are grouped together into eight zip archives, by product type. A summary of the types of data product available is given on http://datashare.is.ed.ac.uk/handle/10283/88 and full details of the file naming convention and file contents are given in the ARC-Lake Data Product Description document (ARCLake_DPD_v1_1_2.pdf). Note that not all types of data product available on a per-lake basis are available as a global product. Details of the methods used and a list of all lakes and their locations are given in the ARC-Lake Algorithm Theoretical Basis Document (ARC-Lake-ATBD-v1.3.pdf). Additional information about the ARC-Lake project and some basic data analysis tools can be found on the project website: http://www.geos.ed.ac.uk/arclake/ Please cite both this dataset and the related publication: * 'MacCallum, Stuart N; Merchant, Christopher J. (2013). ARC-Lake v2.0 - Global, 1991-2011 [Dataset]. University of Edinburgh. School of GeoSciences / European Space Agency. https://doi.org/10.7488/ds/110.' * 'MacCallum, S.N. and Merchant, C.J. (2012) Surface water temperature observations of large lakes by optimal estimation. Canadian Journal of Remote Sensing, 38 (1). pp. 25-45. ISSN 1712-7971 doi: 10.5589/m12-010'

Xverum’s Point of Interest (POI) Data is a comprehensive dataset containing 230M+ verified locations across 5000 business categories. Our dataset delivers structured geographic data, business attributes, location intelligence, and mapping insights, making it an essential tool for GIS applications, market research, urban planning, and competitive analysis.

With regular updates and continuous POI discovery, Xverum ensures accurate, up-to-date information on businesses, landmarks, retail stores, and more. Delivered in bulk to S3 Bucket and cloud storage, our dataset integrates seamlessly into mapping, geographic information systems, and analytics platforms.

🔥 Key Features:

Extensive POI Coverage: ✅ 230M+ Points of Interest worldwide, covering 5000 business categories. ✅ Includes retail stores, restaurants, corporate offices, landmarks, and service providers.

Geographic & Location Intelligence Data: ✅ Latitude & longitude coordinates for mapping and navigation applications. ✅ Geographic classification, including country, state, city, and postal code. ✅ Business status tracking – Open, temporarily closed, or permanently closed.

Continuous Discovery & Regular Updates: ✅ New POIs continuously added through discovery processes. ✅ Regular updates ensure data accuracy, reflecting new openings and closures.

Rich Business Insights: ✅ Detailed business attributes, including company name, category, and subcategories. ✅ Contact details, including phone number and website (if available). ✅ Consumer review insights, including rating distribution and total number of reviews (additional feature). ✅ Operating hours where available.

Ideal for Mapping & Location Analytics: ✅ Supports geospatial analysis & GIS applications. ✅ Enhances mapping & navigation solutions with structured POI data. ✅ Provides location intelligence for site selection & business expansion strategies.

Bulk Data Delivery (NO API): ✅ Delivered in bulk via S3 Bucket or cloud storage. ✅ Available in structured format (.json) for seamless integration.

🏆Primary Use Cases:

Mapping & Geographic Analysis: 🔹 Power GIS platforms & navigation systems with precise POI data. 🔹 Enhance digital maps with accurate business locations & categories.

Retail Expansion & Market Research: 🔹 Identify key business locations & competitors for market analysis. 🔹 Assess brand presence across different industries & geographies.

Business Intelligence & Competitive Analysis: 🔹 Benchmark competitor locations & regional business density. 🔹 Analyze market trends through POI growth & closure tracking.

Smart City & Urban Planning: 🔹 Support public infrastructure projects with accurate POI data. 🔹 Improve accessibility & zoning decisions for government & businesses.

💡 Why Choose Xverum’s POI Data?

• 230M+ Verified POI Records – One of the largest & most detailed location datasets available.
• Global Coverage – POI data from 249+ countries, covering all major business sectors.
• Regular Updates – Ensuring accurate tracking of business openings & closures.
• Comprehensive Geographic & Business Data – Coordinates, addresses, categories, and more.
• Bulk Dataset Delivery – S3 Bucket & cloud storage delivery for full dataset access.
• 100% Compliant – Ethically sourced, privacy-compliant data.

Access Xverum’s 230M+ POI dataset for mapping, geographic analysis, and location intelligence. Request a free sample or contact us to customize your dataset today!

The FDA Device Dataset by Dataplex provides comprehensive access to over 24 million rows of detailed information, covering 9 key data types essential for anyone involved in the medical device industry. Sourced directly from the U.S. Food and Drug Administration (FDA), this dataset is a critical resource for regulatory compliance, market analysis, and product safety assessment regarding.

Dataset Overview:

This dataset includes data on medical device registrations, approvals, recalls, and adverse events, among other crucial aspects. The dataset is meticulously cleaned and structured to ensure that it meets the needs of researchers, regulatory professionals, and market analysts.

24 Million Rows of Data:

With over 24 million rows, this dataset offers an extensive view of the regulatory landscape for medical devices. It includes data types such as classification, event, enforcement, 510k, registration listings, recall, PMA, UDI, and covid19 serology. This wide range of data types allows users to perform granular analysis on a broad spectrum of device-related topics.

Sourced from the FDA:

All data in this dataset is sourced directly from the FDA, ensuring that it is accurate, up-to-date, and reliable. Regular updates ensure that the dataset remains current, reflecting the latest in device approvals, clearances, and safety reports.

Key Features:

• Comprehensive Coverage: Includes 9 key device data types, such as 510(k) clearances, premarket approvals, device classifications, and adverse event reports.

• Regulatory Compliance: Provides detailed information necessary for tracking compliance with FDA regulations, including device recalls and enforcement actions.

• Market Analysis: Analysts can utilize the dataset to assess market trends, monitor competitor activities, and track the introduction of new devices.

• Product Safety Analysis: Researchers can analyze adverse event reports and device recalls to evaluate the safety and performance of medical devices.

Use Cases: - Regulatory Compliance: Ensure your devices meet FDA standards, monitor compliance trends, and stay informed about regulatory changes.

• Market Research: Identify trends in the medical device market, track new device approvals, and analyze competitive landscapes with up-to-date and historical data.

• Product Safety: Assess the safety and performance of medical devices by examining detailed adverse event reports and recall data.

Data Quality and Reliability:

The FDA Device Dataset prioritizes data quality and reliability. Each record is meticulously sourced from the FDA's official databases, ensuring that the information is both accurate and up-to-date. This makes the dataset a trusted resource for critical applications, where data accuracy is vital.

Integration and Usability:

The dataset is provided in CSV format, making it compatible with most data analysis tools and platforms. Users can easily import, analyze, and utilize the data for various applications, from regulatory reporting to market analysis.

User-Friendly Structure and Metadata:

The data is organized for easy navigation, with clear metadata files included to help users identify relevant records. The dataset is structured by device type, approval and clearance processes, and adverse event reports, allowing for efficient data retrieval and analysis.

Ideal For:

• Regulatory Professionals: Monitor FDA compliance, track regulatory changes, and prepare for audits with comprehensive and up-to-date product data.

• Market Analysts: Conduct detailed research on market trends, assess new device entries, and analyze competitive dynamics with extensive FDA data.

• Healthcare Researchers: Evaluate the safety and efficacy of medical devices product data, identify potential risks, and contribute to improved patient outcomes through detailed analysis.

This dataset is an indispensable resource for anyone involved in the medical device industry, providing the data and insights necessary to drive informed decisions and ensure compliance with FDA regulations.

Plant species cover-abundance and presence observed in multi-scale plots. Plant species and associated percent cover in 1m2 subplots and plant species presence in 10m2 and 100m2 subplots are reported from 400m2 plots. Archived plant vouchers and foliar tissue support the data and additional analyses.

IDEA Section 618 Data Products: Static Tables Part B Assessment Table 2 Number and percent of students grades 3 through 8 and high school served under IDEA, Part B, who who received a valid and proficient score on assessments for math, by assessment type, grade level and state

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

Context:

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

Inspiration:

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

Dataset Information:

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

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

Use Cases:

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

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

Collection of zooplankton from water column samples in lakes

ARC-Lake v2.0 - Per-Lake contains data products on a lake-by-lake basis. These data products contain observations of Lake Surface Water Temperature (LSWT) and Lake Ice Cover (LIC) from the series of (Advanced) Along-Track Scanning Radiometers ((A)ATSRs). ARC-Lake v2.0 data products cover the period from 1st August 1991 to 31st December 2011. A number of different data products are available for each lake and are grouped together into a zip archive for each lake. A summary of the types of data product available is given on http://datashare.is.ed.ac.uk/handle/10283/88 and full details of the file naming convention and file contents are given in the ARC-Lake Data Product Description document (ARCLake_DPD_v1_1_2.pdf). Individual lake archives are grouped into larger zip archives by continent (with the exception of the Caspian Sea). Details of the methods used and a list of all lakes and their locations are given in the ARC-Lake Algorithm Theoretical Basis Document (ARC-Lake-ATBD-v1.3.pdf). Additional information about the ARC-Lake project and some basic data analysis tools can be found on the project website: http://www.geos.ed.ac.uk/arclake Please cite both this dataset and the related publication: * 'MacCallum, Stuart N; Merchant, Christopher J. (2013). ARC-Lake v2.0 - Per-Lake, 1991-2011 [Dataset]. University of Edinburgh. School of GeoSciences / European Space Agency. https://doi.org/10.7488/ds/161.' * 'MacCallum, S.N. and Merchant, C.J. (2012) Surface water temperature observations of large lakes by optimal estimation. Canadian Journal of Remote Sensing, 38 (1). pp. 25-45. ISSN 1712-7971 doi: 10.5589/m12-010'

Global Biotic Interactions: Interpreted Data Products

Global Biotic Interactions (GloBI, https://globalbioticinteractions.org, [1]) aims to facilitate access to existing species interaction records (e.g., predator-prey, plant-pollinator, virus-host). This data publication provides interpreted species interaction data products. These products are the result of a process in which versioned, existing species interaction datasets ([2]) are linked to the so-called GloBI Taxon Graph ([3]) and transformed into various aggregate formats (e.g., tsv, csv, neo4j, rdf/nquad, darwin core-ish archives). In addition, the applied name maps are included to make the applied taxonomic linking explicit.

Citation
--------

GloBI is made possible by researchers, collections, projects and institutions openly sharing their datasets. When using this data, please make sure to attribute these *original data contributors*, including citing the specific datasets in derivative work. Each species interaction record indexed by GloBI contains a reference and dataset citation. Also, a full lists of all references can be found in citations.csv/citations.tsv files in this publication. If you have ideas on how to make it easier to cite original datasets, please open/join a discussion via https://globalbioticinteractions.org or related projects.

To credit GloBI for more easily finding interaction data, please use the following citation to reference GloBI:

Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.

Bias and Errors
--------

As with any analysis and processing workflow, care should be taken to understand the bias and error propagation of data sources and related data transformation processes. The datasets indexed by GloBI are biased geospatially, temporally and taxonomically ([5], [6]). Also, mapping of verbatim names from datasets to known name concept may contains errors due to synonym mismatches, outdated names lists, typos or conflicting name authorities. Finally, bugs may introduce bias and errors in the resulting integrated data product.

To help better understand where bias and errors are introduced, only versioned data and code are used as an input: the datasets ([2]), name maps ([3]) and integration software ([6]) are versioned so that the integration processes can be reproduced if needed. This way, steps take to compile an integrated data record can be traced and the sources of bias and errors can be more easily found.

This version was preceded by [7].

Contents
--------

README:
this file

citations.csv.gz:
contains data citations in a in a gzipped comma-separated values format.

citations.tsv.gz:
contains data citations in a gzipped tab-separated values format.

datasets.csv.gz:
contains list of indexed datasets in a gzipped comma-separated values format.

datasets.tsv.gz:
contains list of indexed datasets in a gzipped tab-separated values format.

verbatim-interactions.csv.gz
contains species interactions tabulated as pair-wise interaction in a gzipped comma-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.

verbatim-interactions.tsv.gz
contains species interactions tabulated as pair-wise interaction in a gzipped tab-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.

interactions.csv.gz:
contains species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.

interactions.tsv.gz:
contains species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.

refuted-interactions.csv.gz:
contains refuted species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.

refuted-interactions.tsv.gz:
contains refuted species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.

refuted-verbatim-interactions.csv.gz:
contains refuted species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.

refuted-verbatim-interactions.tsv.gz:
contains refuted species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.

interactions.nq.gz:
contains species interactions expressed in the resource description framework in a gzipped rdf/quads format.

dwca-by-study.zip:
contains species interactions data as a Darwin Core Archive aggregated by study using a custom, occurrence level, association extension.

dwca.zip:
contains species interactions data as a Darwin Core Archive using a custom, occurrence level, association extension.

neo4j-graphdb.zip:
contains a neo4j v3.5.32 graph database snapshot containing a graph representation of the species interaction data.

taxonCache.tsv.gz:
contains hierarchies and identifiers associated with names from naming schemes in a gzipped tab-separated values format.

taxonMap.tsv.gz:
describes how names in existing datasets were mapped into existing naming schemes in a gzipped tab-separated values format.

References
-----

[1] Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. doi: 10.1016/j.ecoinf.2014.08.005.

[2] Poelen, J. H. (2020) Global Biotic Interactions: Elton Dataset Cache. Zenodo. doi: 10.5281/ZENODO.3950557.

[3] Poelen, J. H. (2021). Global Biotic Interactions: Taxon Graph (Version 0.3.28) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4451472

[4] Hortal, J. et al. (2015) Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity. Annual Review of Ecology, Evolution, and Systematics, 46(1), pp.523–549. doi: 10.1146/annurev-ecolsys-112414-054400.

[5] Cains, M. et al. (2017) Ivmooc 2017 - Gap Analysis Of Globi: Identifying Research And Data Sharing Opportunities For Species Interactions. Zenodo. Zenodo. doi: 10.5281/ZENODO.814978.

[6] Poelen, J. et al. (2022) globalbioticinteractions/globalbioticinteractions v0.24.6. Zenodo. doi: 10.5281/ZENODO.7327955.

[7] GloBI Community. (2023). Global Biotic Interactions: Interpreted Data Products hash://md5/89797a5a325ac5c50990581689718edf hash://sha256/946178b36c3ea2f2daa105ad244cf5d6cd236ec8c99956616557cf4e6666545b (0.6) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8284068

Content References
-----

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hash://sha256/77f7e1db20e977287ed6983ce7ea1d8b35bd88fe148372b9886ce62989bc2c22 dwca.zip
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This dataset presents statistics for Finance and Insurance: Types of Credit Financing Products Income for the U.S.

Type measured via CTD in . Part of dataset GEOTRACES Intermediate Data Product 2021 - Sensor

In response to the unprecedented circumstances presented by COVID-19 and the urgent need for data, the U.S. Census launched two new experimental “pulse” surveys to measure temporal social and economic trends in the Nation’s small businesses and households during this crisis. This program expands the Census Bureau’s capability to conduct these types of surveys, to include the Business Trends and Outlook Survey (BTOS), which provides for an ongoing collection of high frequency, timely, and granular information about current economic conditions and trends, as well as the impact of national, subnational, or sector-level shocks on business activity.

IDEA Section 618 Data Products: Static Tables Part B Discipline Table 14 Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal, disability, and state.

Abstract ======== This data set consists of the MESSENGER MASCS UVVS derived data records, also known as DDRs. There are three types of UVVS DDRs: surface, atmosphere, and atmospheric model. There are two surface DDR data products associated with each UVVS observation set: a science header table and a science data table. There are nine geometry-classified atmosphere DDR data products, consisting of three different observation types for each of sodium (Na), calcium (Ca), and magnesium (Mg). There are three orbit-level summary atmosphere DDR data products, one each for Na, Ca, and Mg. There are 3 atmospheric model data products, one each for observations of Na, Ca, and Mg.

Vaisala C51 ceilometer products collected by the EOL/Integrated Sounding System group during Cold Fog Amongst Complex Terrain (CFACT) field campaign in Heber Valley, Utah. The CL51 instrument was operated at the North Pivot site. Two types of files are available: netCDF or raw DAT ascii files. The DAT files are orderable as a single tarred file.

Leosphere Windcube Lidar products collected by the EOL Integrated Sounding System team during the Sundowner Wind EXperiment (SWEX) field campaign in Santa Barbara, CA. All data for a given scan type (i.e. PPI, RHI, VER, and DBS) are available for order as tarred, compressed files (.tar.gz). These include CF-Radial lidar parameters provided in individual netCDF files for each scan. Also available in this dataset are quality controlled, daily netCDF files of Velocity Azimuth Display (VAD) technique derived wind profiles and 30-minute consensus averaged wind profiles. These are also orderable as individual compressed, tar files (.tar.gz) by product type. Please see included Data Report for detailed description of available products as well as quality control information.

Percentage of enterprises that sold only goods, only services or both goods and services, by North American Industry Classification System (NAICS) code and enterprise size, based on a one-year observation period.

SMOS Level 1 data products are designed for scientific and operational users who need to work with calibrated MIRAS instrument measurements, while SMOS Level 2 data products are designed for scientific and operational users who need to work with geo-located soil moisture and sea surface salinity estimation as retrieved from the L1 dataset. Products from the SMOS Data Processing Ground Segment (DPGS) located at the European Space Astronomy Centre (ESAC), belonging to the latest processing baseline, have File Class OPER. Reprocessed SMOS data is tagged as REPR. The Level 1A product is available upon request to members of the SMOS Cal/Val community. The product comprises all calibrated visibilities between receivers (i.e. the interferometric measurements from the sensor including the redundant visibilities), combined per integration time of 1.2 seconds (snapshot). The snapshots are consolidated in a pole-to-pole product file (50 minutes of sensing time) with a maximum size of about 215MB per half orbit (29 half orbits per day). The Level 1B product comprises the result of the image reconstruction algorithm applied to the L1A data. As a result, the reconstructed image at L1B is simply the difference between the sensed scene by the sensor and the artificial scene. The brightness temperature image is available in its Fourier component in the antenna polarisation reference frame top of the atmosphere. Images are combined per integration time of 1.2 seconds (snapshot). The removal of foreign sources (Galactic, Direct Sun, Moon) is also included in the reconstruction. Snapshot consolidation is as per L1A, with a maximum product size of about 115MB per half orbit. ESA provides the Artificial Scene Library (ASL) to add the artificial scene in L1B for any user that wants to start from L1B products and derive the sensed scene. The Level 1C product contains multi-angular brightness temperatures in antenna frame (X-pol, Y-pol, T3 and T4) at the top of the atmosphere, geo-located in an equal-area grid system (ISEA 4H9 - Icosahedral Snyder Equal Area projection). The pixels are consolidated in a pole-to-pole product file (50 minutes of sensing time), with a maximum size of about 350MB per half orbit (29 half orbits per day). Spatial resolution is in the range of 30-50 km. For each L1C product there is also a corresponding Browse product containing brightness temperatures interpolated for an incidence angle of 42.5°. Two L1C products are available: Land for soil moisture retrieval and Sea for sea surface salinity retrieval. The Level 2 Soil Moisture (SM) product comprises soil moisture measurements geo-located in an equal-area grid system ISEA 4H9. The product contains not only the retrieved soil moisture, but also a series of ancillary data derived from the processing (nadir optical thickness, surface temperature, roughness parameter, dielectric constant and brightness temperature retrieved at top of atmosphere and on the surface) with the corresponding uncertainties. The pixels are consolidated in a pole-to-pole product file (50 minutes of sensing time), with a maximum size of about 7MB (25MB uncompressed data) per half orbit (29 half orbits per day). This product is available in both Earth Explorer and NetCDF formats. The Level 2 Ocean Salinity (OS) product comprises sea surface salinity measurements geo-located in an equal-area grid system ISEA 4H9. The product contains one single swath-based sea surface salinity retrieved with and without Land-Sea contamination correction, SSS anomaly based on WOA-2009 referred to Land-Sea corrected sea surface salinity, brightness temperature at the top of the atmosphere and at the sea surface with their corresponding uncertainties. The pixels are consolidated in a pole-to-pole product file (50 minutes of sensing time), with a maximum size of about 10MB (25MB uncompressed data) per half orbit (29 half orbits per day). This product is available in both Earth Explorer and NetCDF formats. For an optimal exploitation of the SMOS L1 and L2 datasets, please refer to the Resources section below in order to access Product Specifications, read-me-first notes, etc.