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

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

Versions:

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

Reach out to contact@crawlfeeds.com

Use Cases:

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

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

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

With Google Shopping API you can get:

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

With Amazon API you can get:

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

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

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

The dataset refers to the measurement of axes of Ti-6Al-4V cylindrical surfaces obtained by lathe turning. The machined surfaces were measured using a Coordinate Measuring Machine (CMM) and the axis of each cylinder was derived from the CMM measures.

The dataset consists of a MAT-file including the CMM measurements and a Matlab function “LoadData.m” to extract and convert the data into Cartesian coordinates.

All the details about the dataset can be found in:

Colosimo, B.M., Pacella, M. Analyzing the effect of process parameters on the shape of 3D profiles (2011) Journal of Quality Technology, 43 (3), pp. 169-195.DOI: 10.1080/00224065.2011.11917856 Pacella, M., Colosimo, B.M. Multilinear principal component analysis for statistical modeling of cylindrical surfaces: a case study (2018) Quality Technology and Quantitative Management, 15 (4), pp. 507-525.DOI: 10.1080/16843703.2016.1226710

subject to appropriate attribution.

TL3O3M_4 is the Tropospheric Emission Spectrometer (TES)/Aura Level 3 Ozone (O3) Monthly Gridded Version 4 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This product consisted of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, ozone, which were provided at 2 degree latitude X 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolated the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products were composed of L3 HDF-EOS grid data. A separate product file was produced for each different atmospheric species. TES obtained data in two basic observation modes: Limb or Nadir. The product may have contained, in separate folders, limb data, nadir data, or both folders could have been present. Specific to L3 processing were the terms Daily and Monthly, representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process were complete Global Surveys; in other words a Global Survey was not split in relation to time when they were input to the L3 processes even if they exceed the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represented Global Surveys that were initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS).

ESG DATA PRODUCT DESCRIPTION

This ESG dataset offers comprehensive coverage of corporate energy management across thousands of global companies. Our data captures detailed patterns of energy consumption, production, and distribution, providing granular insights into various energy types—including electricity and heat—and the technologies (e.g. solar PV, hydropower...) and sources (e.g. biofuels, coal, natural gas...) utilized. With thorough information on renewability and rigorous standardization of every energy metrics, this dataset enables precise benchmarking, cross-sector comparisons, and strategic decision-making for sustainable energy practices.

Built on precision and transparency, the energy dataset adheres to the highest standards of ESG data quality. Every data point is fully traceable to its original source, ensuring unmatched reliability and accuracy. The dataset is continuously updated to capture the most current and complete information, including revisions, new disclosures, and regulatory updates.

ESG DATA PRODUCT CHARACTERISTICS

• Company Coverage:              5,000+ companies • Geographical Coverage:       Global • Sectorial Coverage:                All sectors • Data Historical Range:           2014 - 2024 • Median Data History:             5 years • Data Traceability Rate:           100% • Data Frequency:                     Annual • Average Reporting Lag:         3 months • Data Format:                            Most Recent/Point-in-Time

UNIQUE DATA VALUE PROPOSITION

Uncompromised Standardization

When company energy data do not align with standard energy reporting frameworks, our team of environmental engineers meticulously maps the reported figures to the correct energy types and flow categories. This guarantees uniformity and comparability across our dataset, bridging the gap created by diverse reporting formats.

Precision in Every Figure

Our advanced cross-source data precision matching algorithm ensures that the most accurate energy metrics are always delivered. For instance, an exact figure like 12,510,545 Joules is prioritized over a rounded figure like 12mio, reflecting our dedication to precision and detail.

Unbiased Data Integrity

Our approach is grounded in delivering energy data exactly as reported by companies, without making inferences or estimates for undisclosed data. This strict adherence to factual reporting ensures the integrity of the data you receive, providing an unaltered and accurate view of corporate emissions.

End-to-End Data Traceability

Every energy data point is directly traceable to its original source, complete with page references and calculation methodologies. This level of detail ensures the reliability and verifiability of our data, giving you complete confidence in our energy dataset.

Full-Scope Boundary Verification

We tag energy figures that do not cover a company's entire operational boundaries with an 'Incomplete Boundaries' attribute. This transparency ensures that any potential limitations are clearly communicated, enhancing the comparability of our energy data.

USE CASES

Asset Management

Asset Management firms use energy data to benchmark portfolio companies against industry standards, ensuring alignment with net-zero goals and regulatory frameworks like SFDR and TCFD. They assess energy transition risks, track renewable energy adoption, and develop sustainable investment products focused on energy efficiency and climate-conscious innovation.

Financial Institutions & Banking

Financial Institutions & Banking integrate energy data into credit risk assessments and sustainability-linked loans, ensuring borrowers meet renewable energy targets. They also enhance due diligence processes, comply with climate disclosure regulations, and validate green bond frameworks with precise renewable energy metrics.

FinTech

FinTech companies leverage energy data to automate regulatory reporting, power energy management analytics, and develop APIs that assess corporate climate risk. They also build sustainable investment tools that enable investors to prioritize companies excelling in energy efficiency and renewability.

GreenTech & ClimateTech

GreenTech & ClimateTech firms use predictive energy analytics to model energy transition risks and renewable adoption trends. They optimize supply chains, facilitate renewable energy procurement, and assess the environmental and financial impacts of energy investments, supporting PPAs and carbon credit markets.

Corporates

Corporates rely on energy data for performance benchmarking, renewable energy procurement, and transition planning. By analyzing detailed energy consumption and sourcing metrics, they optimize sustainability strategies and improve energy efficiency.

Professional Services & Consulting

Professional Services & Consulting firms use energy data to advise on energy transitions, regulatory complia...

Open Prices What is Open Prices? Open Prices is a project to collect and share prices of products around the world. It's a publicly available dataset that can be used for research, analysis, and more. Open Prices is developed and maintained by Open Food Facts. There are currently few companies that own large databases of product prices at the barcode level. These prices are not freely available, but sold at a high price to private actors, researchers and other organizations that can afford them. Open Prices aims to democratize access to price data by collecting and sharing product prices under an open licence. The data is available under the Open Database License (ODbL), which means that it can be used for any purpose, as long as you credit Open Prices and share any modifications you make to the dataset. Images submitted as proof are licensed under the Creative Commons Attribution-ShareAlike 4.0 International. Dataset description This dataset contains in Parquet format all price information contained in the Open Prices database. The dataset is updated daily. Here is a description of the most important columns: id: The ID of the price in DB product_code: The barcode of the product, null if the product is a "raw" product (fruit, vegetable, etc.) category_tag: The category of the product, only present for "raw" products. We follow Open Food Facts category taxonomy for category IDs. labels_tags: The labels of the product, only present for "raw" products. We follow Open Food Facts label taxonomy for label IDs. origins_tags: The origins of the product, only present for "raw" products. We follow Open Food Facts origin taxonomy for origin IDs. price: The price of the product, with the discount if any. price_is_discounted: Whether the price is discounted or not. price_without_discount: The price of the product without discount, null if the price is not discounted. price_per: The unit for which the price is given (e.g. "KILOGRAM", "UNIT") currency: The currency of the price location_osm_id: The OpenStreetMap ID of the location where the price was recorded. We use OpenStreetMap to identify uniquely the store where the price was recorded. location_osm_type: The type of the OpenStreetMap location (e.g. "NODE", "WAY") location_id: The ID of the location in the Open Prices database date: The date when the price was recorded proof_id: The ID of the proof of the price in the Open Prices DB owner: a hash of the owner of the price, for privacy. created: The date when the price was created in the Open Prices DB updated: The date when the price was last updated in the Open Prices DB proof_file_path: The path to the proof file in the Open Prices DB proof_type: The type of the proof. Possible values are RECEIPT, PRICE_TAG, GDPR_REQUEST, SHOP_IMPORT proof_date: The date of the proof proof_currency: The currency of the proof, should be the same as the price currency proof_created: The datetime when the proof was created in the Open Prices DB proof_updated: The datetime when the proof was last updated in the Open Prices DB location_osm_display_name: The display name of the OpenStreetMap location location_osm_address_city: The city of the OpenStreetMap location location_osm_address_postcode: The postcode of the OpenStreetMap location How can I download images? All images can be accessed under the https://prices.openfoodfacts.org/img/ base URL. You just have to concatenate the proof_file_path column to this base URL to get the full URL of the image (ex: https://prices.openfoodfacts.org/img/0010/lqGHf3ZcVR.webp). Can I contribute to Open Prices? Of course! You can contribute by adding prices, trough the Open Prices website or through Open Food Facts mobile app. To participate in the technical development, you can check the Open Prices GitHub repository.

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.

Lamini Product Catalog QA Dataset

  Description

This dataset contains questions about products and their corresonding product information like product id, product name, product description, etc. This questions catalog has been built on top of open-source product catalog from kaggle.

  Format

The questions and product information are in the form of jsonlines file.

  Data Pipeline Code

The entire data pipeline used to create this dataset is… See the full description on the dataset page: https://huggingface.co/datasets/lamini/product-catalog-questions.

TL3O3D_5 is the Tropospheric Emission Spectrometer (TES)/Aura L3 Ozone Daily Gridded Version 5 data product. TES was an instrument aboard NASA's Aura satellite and was launched from California on July 15, 2004. Data collection for TES is complete. This data product consists of daily atmospheric temperature and volume mixing ratio (VMR) for the atmospheric species, which were provided at 2 degree latitude by 4 degree longitude spatial grids and at a subset of TES standard pressure levels. The TES Science Data Processing L3 subsystem interpolated the L2 atmospheric profiles collected in a Global Survey onto a global grid uniform in latitude and longitude to provide a 3-D representation of the distribution of atmospheric gasses. Daily and monthly averages of L2 profiles and browse images are available. The L3 standard data products were composed of L3 HDF-EOS grid data. A separate product file is produced for each different atmospheric species. TES obtains data in two basic observation modes: Limb or Nadir. The product file may have contained, in separate folders, limb data, nadir data, or both folders may be present. Specific to L3 processing are the terms Daily and Monthly representing the approximate time coverage of the L3 products. However, the input data granules to the L3 process are completed Global Surveys; in other words a Global Survey was not split in relation to time when input to the L3 processes even if they exceeded the usual understood meanings of a day or month. More specifically, Daily L3 products represented a single Global Survey (approximately 26 hours) and Monthly L3 products represented Global Surveys that were initiated within that calendar month. The data granules defined for L3 standard products were daily and monthly. Details of the format of this product can be found in the TES Data Products Specifications (DPS).

CryoSat's primary payload is the SAR/Interferometric Radar Altimeter (SIRAL) which has extended capabilities to meet the measurement requirements for ice-sheet elevation and sea-ice freeboard. CryoSat also carries three star trackers for measuring the orientation of the baseline. In addition, a radio receiver called Doppler Orbit and Radio Positioning Integration by Satellite (DORIS) and a small laser retroreflector ensures that CryoSat's position will be accurately tracked. More detailed information on CryoSat instruments is available on the CryoSat mission page. The following CryoSat datasets are available and distributed to users: Level 1B and L2 Ice products: NRT, LRM, SAR and SARIn Consolidated Level 2 (GDR): (LRM+SAR+SARIN) consolidated ice products over an orbit Intermediate Level 2 Ice products: LRM, SAR and SARIn L1b and L2 Ocean Products: NOP, GOP and IOP Cryo-TEMPO Land Ice Cryo-TEMPO Winter Sea Ice Cryo-TEMPO Summer Sea Ice Cryo-TEMPO Coastal Ocean Cryo-TEMPO Polar Ocean Cryo-TEMPO Inland Waters Cryo-TEMPO EOLIS Point Products Cryo-TEMPO EOLIS Gridded Products CryoSat Quaternions Product. Detailed information concerning each of the above datasets is available in the CryoSat Products Overview. CryoSat Ice and Ocean products CryoSat Level 1B altimetric products contain time and geo-location information as well as SIRAL measurements in engineering units. Calibration corrections are included and have been applied to the window delay computations. In Offline products, geophysical corrections are computed from Analysis Auxiliary Data Files (ADFs), whereas in FDM products corrections are computed for Forecast ADFs. All corrections are included in the data products and therefore the range can be calculated by taking into account the surface type. The Offline Level 2 LRM, SAR and SARIn ice altimetric products are generated 30 days after data acquisition and are principally dedicated to glaciologists working on sea-ice and land-ice areas. The Level 2 FDM products are near-real time ocean products, generated 2-3 hours after data acquisition, and fulfill the needs of some ocean operational services. Level 2 products contain the time of measurement, the geo-location and the height of the surface. IOP and GOP are outputs of the CryoSat Ocean Processor. These products are dedicated to the study of ocean surfaces, and provided specifically for the needs of the oceanographic community. IOP are generated 2-3 days after data sensing acquisition and use the DORIS Preliminary Orbit. GOP are typically generated 30 days after data sensing acquisition and use the DORIS Precise Orbit. Geophysical corrections are computed from the Analysis ADFs, however following the oceanographic convention the corrections are available but not directly applied to the range (as for FDM). CryoSat Ice and Ocean products can be accessed through ftp://science-pds.cryosat.esa.int/ via an FTP client and HTTPS under the folders named “SIR_” followed by the data product type and the processing level (e.g., SIR_SAR_L2 for Level 2 SAR data). Additionally, data can be downloaded from all the other services listed in the How to Access Data section. Cryo-TEMPO Products The CryoSat ThEMatic PrOducts (Cryo-TEMPO) projects aim to deliver a new paradigm of simplified, harmonized, and agile CryoSat-2 products, that are easily accessible to new communities of non-altimeter experts and end users. The Cryo-TEMPO datasets include dedicated products over five thematic areas, covering Winter Sea Ice, Summer Sea Ice, Land Ice, Polar Ocean, Coastal Ocean and Inland Water. The standard Cryo-TEMPO products include fully-traceable uncertainties and use rapidly evolving, state-of-the-art processing dedicated to each thematic area. Throughout the project, the products will be constantly evolved, and validated by a group of Thematic Users, thus ensuring optimal relevance and impact for the intended target communities. More information on the Cryo-TEMPO products can be found in the Product Handbook and on the Project Website. The products can be accessed through ftp://science-pds.cryosat.esa.int/ via an FTP client and HTTPS under the folders named “TEMPO_POCA_(SI/LI/PO/CO/IW)', where the last two letters are the initials of the thematic area (e.g., SI stands for Sea Ice). Cryo-TEMPO EOLIS The CryoTEMPO-EOLIS swath product exploits CryoSat's SARIn mode and the novel Swath processing technique to deliver increased spatial and temporal coverage of time-dependent elevation over land ice, a critical metric for tracking ice mass trends in support to a wide variety of end-users. The dataset consists of systematic reprocessing of the entire CryoSat archive to generate new L2-Swath products, increasing data sampling by 1 to 2 orders of magnitude compared with the operational L2 ESA product. In addition, the EOLIS dataset is joined with the ESA L2 Point-Of-Closest-Approach to generate monthly DEM (Digital Elevation Model) products. This dataset will further the ability of the community to analyse and understand trends across the Greenland Ice Sheet margin, Antarctica and several mountain glaciers and ice caps around the world. More information on the Cryo-TEMPO products can be found on the Project Website and the products can be accessed through ftp://science-pds.cryosat.esa.int/ via an FTP client and HTTPS under the folders named “TEMPO_SWATH_(POINT/GRID)”, where the last word is used to choose between the available “Point” and “Gridded” datasets. Additionally, Cryo-TEMPO EOLIS products can be visualised and downloaded from the CS2EO Platform. CryoSat Quaternions Product This product contains the attitude quaternions for the CryoSat-2 mission. It is obtained starting from the corrected mispointing angles measured by the platform star trackers. More information on the product can be found in the Algorithm Description and Product Format Specification documents. The quaternions product can be accessed from the CryoSat CalVal FTPS server via an FTPS client, and are located in the “AUX_PROQUA” folder. Users wishing to access the quaternions products should request a personal account to be created by emailing the CryoSat Mission Geophysicist, Dr. Alessandro Di Bella (alessandro.di.bella@ext.esa.int). The ESA CryoSat ThEMatic PrOducts - SWATH Cryo-TEMPO products (CryoTEMPO-EOLIS) exploit CryoSat's SARIn mode and the novel Swath processing technique to deliver increased spatial and temporal coverage of time-dependent elevation over land ice, a critical metric for tracking ice mass trends in support to a wide variety of end-users working in the areas of sea ice, polar oceans, land ice, coastal areas and hydrology. The dataset consists of systematic reprocessing of the entire CryoSat archive to generate new L2-Swath products, increasing data sampling by 1 to 2 orders of magnitude compared with the operational L2 ESA product. In addition, the EOLIS dataset is joined with the ESA L2 Point-Of-Closest-Approach to generate monthly DEM (Digital Elevation Model) products. This dataset will further the ability of the community to analyse and understand trends across the Greenland Ice Sheet margin. -->

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

This service describes the ATKIS base DLM. The underlying data was modeled according to the AdV product specification ATKIS base DLM shape in version 1.1.:This topic covers only one layer without geometry. Here, all technical data connections to objects of the basic DLM (relation showsexternal) are recorded tabularly, which were modeled in the URN variant, as described in the main document in the currently valid version of the GeoInfoDoc.A link between objects that are involved in a relation showsexternal is possible via the field OBJID.In this level, technical data connections to both REOs and ZUSOs are mapped. The link between technical data and ZUSOs must be made with the field OBJID_Z in the corresponding level.This level is automatically part of each data provision and is restricted in content to the delivery-relevant objects.

S-111 is a data and metadata encoding specification that is part of the S-100 Universal Hydrographic Data Model, an international standard for hydrographic data. This collection of data contains surface water currents forecast guidance from NOAA/NOS Operational Forecast Systems, a set of operational hydrodynamic nowcast and forecast modeling systems, for various U.S. coastal waters and the great lakes. The collection also contains surface current forecast guidance output from the NCEP Global Real-Time Ocean Forecast System (GRTOFS) for some offshore areas. These datasets are encoded as HDF-5 files conforming to the S-111 specification, and are geospatially subset into individual tiles conforming to the NOAA/OCS Nautical Product Tiling Scheme, with filenames indicating the corresponding NOAA Electronic Navigational Chart (ENC) Cell Identifier. A full set of S-111 tiles is created for each new model run cycle, which occurs four times per day for all models except for RTOFS, which updates only once per day. Files are organized using a path naming convention that includes the OFS identifier (e.g. 'cbofs' corresponding with output from the Chesapeake Bay Operational Forecast System) as well as the year, month, day, and hour corresponding with each model run initialization time. Each individual S-111 (HDF-5) file contains all forecast projections from a single model run for that geographic area. In other words, a single S-111 file will contain multiple gridded arrays each containing a forecast valid at a distinct time in the future, out to the forecast horizon of the underlying modeling system. All surface currents forecasts in this collection are computed at a depth of 4.5 meters below water surface, or half the water column depth, whichever is shallower.

The data set for the INSPIRE topic Annex 1 Water Network Hydro — Physical Waters has been derived from the ALKIS according to the INSPIRE product specification of the AdV.

World Ocean Database 2001 (WOD01) is comprised of 8 CD-ROMs and contains in situ profile data such as temperature, salinity, nutrients, oxygen, chlorophyll, plankton biomass data and more. It is an update to the World Ocean Database 1998, plus it contains some new parameters.

Some changes since WOD98: - addition and specification of data from undulating oceanographic recorders, profiling floats, drifting buoys, moored buoys, surface only data, and autonomous pinniped bathythermograph data; - addition of carbon variables to the database; - submitter-defined data quality flags received as part of data submissions are now kept in the database; - modifications to the data format.

Copies of these discs are no longer available via the NODC Online Store.

Citation: If using this dataset please cite the following in your work: @misc{VotDasNemSri2010 , author = "Petr Votava and Kamalika Das and Rama Nemani and Ashok N. Srivastava", year = "2010", title = "MODIS surface reflectance data repository", url = "https://c3.ndc.nasa.gov/dashlink/resources/331/", institution = "NASA Ames Research Center" } Petr Votava, Kamalika Das, Rama Nemani, Ashok N. Srivastava. (2010). MODIS surface reflectance data repository. NASA Ames Research Center. Data Description: The California satellite dataset using the MODerate-resolution Imaging Spectroradiometer (MODIS) product MCD43A4 provides reflectance data adjusted using a bidirectional reflectance distribution function (BRDF) to model the values as if they were taken from nadir view. Both Terra and Aqua data are used in the generation of this product, providing the highest probability for quality input data. More information at: https://lpdaac.usgs.gov/lpdaac/products/modis_products_table/nadir_brdf_adjusted_reflectance/16_day_l3_global_500m/v5/combined Data Organization: The nine data folders correspond to three years of data.Under this top level directory structure are separate files for each band (1 - 7) and each 8-day period of the particular year. Within the period the best observations were selected for each location. File Naming Conventions: Each of the files represent a 2D dataset with the naming conventions as follows: MCD43A4.CA_1KM.005.. .flt32 where is the beginning year-day of the period that where YYYY = year and DDD = day of year (001 - 366) represents the observations in particular (spectral) band (band 1 - band 7) - since the indexing is 0-based, the range of indexes on the files is from 0 - 6 (where 0 = band 1, and 6 = band 7) The spectral band frequencies for the MODIS acquisitions are as follows: BAND1 620 - 670 nm BAND2 841 - 876 nm BAND3 459 - 479 nm BAND4 545 - 565 nm BAND5 1230 - 1250 nm BAND6 1628 - 1652 nm BAND7 2105 - 2155 nm File Specifications: Each file is a single 2D dataset. DATA TYPE: 32-bit floating point (IEEE754) with little-Endian byte ordering NUMBER OF ROWS: 1203 NUMBER OF COLUMNS: 738 FILL VALUES (observations that are either not valid or not on land, such as ocean etc.): -999.0 Overview: DATASET: MODIS 8-day Surface Reflectance BRDF-adjusted from Terra and Aqua COLLECTION: 5 DATA TYPE: IEEE754 float (32-bit float) BYTE ORDER: LITTLE ENDIAN (Intel) DIMS: 1203 rows x 738 columns FILL VALUE: -999.0 SPATIAL RESOLUTION: 1km PROJECTION: Lambert Azimuthal Equal Area

Citation and Main Description:

This product is described in Chapter 5 of the 2018 DWR Delta Modeling Section annual report, produced jointly with USGS. https://data.cnra.ca.gov/dataset/methodology-for-flow-and-salinity-estimates-in-the-sacramento-san-joaquin-delta-and-suisun-marsh/resource/84d4fd29-c839-4efa-82be-b58f7ed176db

as well as in ESA. Delta Bathymetry Digital Elevation Model Update Project: Technical Report. California Department of Water Resources, 2024 (Draft) Available upon request

Domain and Product

This product is a mutually compatible suite of DEMs covering most of the aquatic and terrestrial areas of the Bay-Delta. The product was derived from original point data collections, lidar and other DEMs. Also included in the resources are images and shapefiles describing the source data.

Changes between 4.2 and 4.3 are documented in the change log below. Changes before that are recorded on the 4.2 web page.

Changes in version 4 relative to prior products are limited to the region east of the Carquinez Strait (starting around Carquinez Bridge). To facilitate compatibility between products released by DWR and USGS/NOAA partners, DWR distributes the region west of the active work at 10m resolution but does not actively work in this region. The San Pablo Bay boundary of active revision in the present product in a place where its source data matches that of other Bay elevation models, e.g., the 2m seamless high-resolution bathymetric and topographic DEM of San Francisco Bay by USGS Earth Resources Observation and Science Center (EROS) (https://topotools.cr.usgs.gov/coned/sanfrancisco.php ), the 2010 San Francisco Bay DEM by National Oceanic and Atmospheric Administration (https://www.ngdc.noaa.gov/metaview/page?xml=NOAA/NESDIS/NGDC/MGG/DEM/iso/xml/741.xml&view=getDataView&header=none ) or the prior (version 3) 10m digital elevation model (https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-v3 ).The 10m DEM for the Bay-Delta is based on the first on the list, i.e. EROS’ 2m DEM for the Bay

Version: 4.3

• Time Completed: March 2025
• Horizontal Datum: NAD83
• Spheroid:GRS1980
• Projection:UTM_Zone_10N (meters)
• Vertical Datum:NAVD88 (meters)

Changes since 4.2

• Develop 2m DEM for the entirety of the South Delta based on new bathymetric surveys collected between 2021-2022 (DWR, NCRO and Cinquini & Passarino Inc) and 2017 Lidar (DWR)
• Develop 2m DEM of the entirety of the South Delta, including all available terrestrial data that covers the islands into a seamless tile
• Develop 2m DEM of Woodward Cut based on the 2017 Bathymetry Survey (DWR, NCRO) and 2017 LiDAR (DWR)
• Develop 2m DEM of Railroad Cut based on the 2017 Bathymetry Survey (DWR, NCRO) and 2017 LiDAR (DWR)
• Minor modification to existing dem_montezuma_sl_2m_20200909.tif : fix alignment problem.
• clip dem_ccfb_south_delta_san_joaquin_rvr_2m_20200625.tif to remove areas that are superseded by the dem_south_delta_2m_20231103.tif. Now called dem_omr_sjr_2m_20200625.tif

Geographic Extent: Garrett County, MD, covering approximately 739 square miles. Dataset Description: Garrett County, MD 2015 LiDAR project called for the Planning, Acquisition, processing and derivative products of LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1. The data was developed based on a horizontal projection/datum of UTM Zone 17, NAD83 (2011), meters and vertical datum of NAVD1988 (GEOID12A), meters. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 6 individual 1829 meter X 1219 meter tiles for the pilot (948 individual 1829 meter X 1219 meter tiles for the entire project area), and corresponding Intensity Images and Bare Earth DEMs tiled to the same 1829 meter X 1219 meter tile schema, and Breaklines in Esri geodatabase format. Ground Conditions: LiDAR was collected in spring of 2015, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 87 Land Cover control points that were used to calibrate the LiDAR to known ground locations established throughout the Garrett County, MD project area (20 calibration control points).This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://lidar.geodata.md.gov/imap/rest/services/Garrett/MD_garrett_slope_m/ImageServer

This guide is intended to explain the new approach to submission of Finished Product Specifications for Product Licence Applications.