The specific implementation of global attributes, dimensions, groups, variables, and variable-level attributes in a data product, which is specified via the Product Format Specification (PFS) file.

The Product Generation Algorithm, the version of source code used to produce the data products, need to be carefully preserved and recorded, along with version history, as well as:

• The algorithm's theoretical (scientific and mathematical) basis,
• How the algorithm is numerically implemented, including possible issues with computationally intensive operations,
• A description of the outputted data products at a level of detail to determine if the product met specified product requirements,
• A description of all assumptions that have been made concerning the algorithm performance estimates and any limitations,
• A list of various error estimates and the error budget.

Description of the impact to product quality due to issues with computationally intensive operations (e.g., large matrix inversions, truncation and rounding).

A document that provides the technical specifications relating to the formatting and content of a particular data product.

Names of key science team leads and product team members (development, help desk and operations), roles, performing organization, contact information, sponsoring agencies or organizations and comments about the products.

PRRs evaluate the readiness of system developer(s) to produce the required number of systems within defined project constraints for projects developing multiple similar flight or ground support systems. PRRs also evaluate the degree to which the production plans meet the system's operational support requirements.

The project plan contains the technical approaches and management plans to implement the project requirements. It defines, at a high level, the scope of the project, the implementation approach, the environment within which the project operates, and the baseline commitments of the program and project. (From NPR 7120.5D)

The information that documents the history of the content information. This information tells the origin or source of the content information, any changes that may have taken place since it was originated, the inputs responsible for a product, what versions of algorithms used, who has had custody of it since it was originated etc. Examples of provenance information could be the principal investigator who recorded the data, and the information concerning its storage, handling, and migration.

One or more unique variables within a data file that show what data quality assessments have been performed as well as diagnostics on various aspects of quality. A quality flag can be a byte value with each bit representing a pre-defined quality verification criterion provided as a Boolean expression.

1. A quality indicator shall provide sufficient information to allow all users to readily evaluate the “fitness for purpose” of the data or derived product. A Quality Indicator may be a number, set of numbers, graph, uncertainty budget, or a simple “flag”.
2. One or more unique variables within a data file whose numerical value shows the overall quality of a geophysical measurement. The numerical value should be on a predefined numerical scale. For example, uncertainty per pixel (or measurement), percent cloud cover (in a scene).

Secondary data required to assess the primary data set’s fitness for purpose, e.g. calibration and validation data and quality control results.

A data product, usually related to a Level 1 or higher Standard Data Product, which is generated and distributed in near-real time.

See: Processing Levels

Reprocessing is a part of processing where a complete product collection is systematically generated to obtain a new revision using archived lower level products. Reprocessing is normally initiated after an improved processing algorithm is released. Reformatting could be considered or conducted as part of a re-processing exercise.

Requirements documents are detailed requirements allocated from the project to the next lower level of the project.

SMSRs prepare Agency safety and engineering management to participate in program final readiness reviews preceding flights or launches, including experimental/test launch vehicles or other reviews as determined by the Chief, Safety and Mission Assurance. SMSRs also provide the knowledge, visibility, and understanding necessary for senior safety and engineering management to either concur or nonconcur in program decisions to proceed with a launch or significant flight activity.

Subset of an instrument acquisition data segment, cut by time i.e. across-swath.

The SDMP describes how the program will manage the scientific data generated and captured by operational mission. The SDMP also includes descriptions of how data will be generated, processed, distributed, analyzed, and archived.

Science data product generation software and software documentation. Source code used to generate products at all levels in the science data processing system.

Datasets and documentation. Accuracy of products, as measured by validation testing, and compared to accuracy requirements. Description of validation process, including identification of validation data sets, measurement protocols, data collection, analysis and accuracy reporting.

Identify all ancillary data or other data sets used in generation or calibration of the data or derived product at all levels

Product access (reader) tools. Software source code that would facilitate use of the calibration data, ancillary data and the data products at all levels.

Most of the EOS standard products are produced at facilities under the direct control of the instrument Principal Investigators/Team Leaders (PIs/TLs) or their designees. These facilities are referred to as Science Investigator-led Processing Systems (SIPS). SIPS are geographically distributed across the United States and are generally, but not necessarily, collocated with the PIs/TLs’ Scientific Computing Facilities. 

Products produced at the SIPS using investigator-provided systems and software are sent to appropriate DAACs for archival and distribution. Level 0 Data Products and Ancillary Data that begin the processing sequence are stored at the DAACs and retrieved by the SIPS.

The procedure to search an archive based on specific search criteria (search) and to obtain information on available products (discovery). Data search and discovery are made possible by searchable metadata and browse image catalogues, as well as a catalogue service for making the catalogue accessible. 

During discovery, following a data search, the user finds data based on search criteria and evaluates if the data found are suitable for their application ('fit for purpose'). The user may then decide to retrieve the data.

A file that contains metadata thoroughly describing the characteristics and content of the file.

Device which transmits an output signal as voltage in response to a physical input stimulus. In Earth observation a distinction between passives sensors, such as radiometers, and active sensors, such as radars, is common. Earth observation sensors – or instruments – are operated from different ground-/water-based, airborne, or spaceborne platforms.

A grouping of science data that all come from the same source, such as a modeling group or institution. Series/collections have information that is common across all the datasets/granules they contain.

Metadata elements that describe an entire set of data files. Values of series/collection metadata apply to all of the files in a specific aggregate. Series/collection metadata may represent the same release of any given file, sets of data generated during an experiment, a campaign or an algorithm test.

Method by which location or coverage is designated (e.g., latitude and longitude).

Standard products are agency-certified key products resulting from missions or projects. They are typically acquired systematically and generated by spatially and temporally extensive systematic processing.

The SRB is responsible for conducting independent reviews (life cycle and special) of a program/project and providing objective, expert judgments to the convening authorities. The reviews are conducted in accordance with approved Terms of Reference (ToR) and life-cycle requirements per this document and NPR 7123.1.

The responsibility for planning, management and certification of digital EO data sets throughout the mission phases and to ensure adequate funding. It includes curation and preservation activities. 

Data stewardship covers governance, planning, implementation and management of the sourcing, use and maintenance of space data assets. Data stewardship enables the governance of all the types and forms of space data package and the related repositories or archives.

An organized set of terms, headings or codes representing concepts and their inter-relationships, which can be used to support information retrieval.

The extraction of a multi-dimensional rectangular array of pixels from a single data granule, where consecutive pixels are extracted from each array dimension. For each dimension, the size of the pixel array is characterized by the starting pixel location and the number of pixels to extract.

A swath is defined as the area covered by the spatial samples collected during a scan of a spaceborne instrument defined by an incidence angle.

SARs evaluate whether a specific end item is sufficiently mature to be shipped from the supplier to its designated operational facility or launch site.

SDRs evaluate the credibility and responsiveness of the proposed program requirements/architecture to the Mission Directorate requirements and constraints, including available resources, and allocation of requirements to projects. SDRs also determine whether the maturity of the program's mission/system definition and associated plans are sufficient to begin preliminary design.

SIRs evaluate the readiness of the program, including its projects and supporting infrastructure, to begin system algorithm integration and test (AI&T) with acceptable risk and within cost and schedule constraints.

SRRs evaluate whether the functional and performance requirements defined for the system are responsive to the Mission Directorate requirements on the program and its projects and represent achievable capabilities.

The period during which data was collected, observations were made, or for which a model was run.

Non-negative parameter characterizing the dispersion of the quantity values that are being attributed to a measure based on the information used. 

Where possible, this should be derived from an experimental evaluation but can also be an estimate based on other information, e.g. experience.

One of seven UMM metadata profiles: Collection (UMM-C), Granule (UMM-G), Service (UMM-S), Variable (UMM-Var), Visualization (UMM-Vis), Tools (UMM-T), and Common (UMM-Common).

External person, institution or system that consumes user services provided by the payload data ground segment via data access or a science and service exploitation platform.

User support is a function inside the payload data ground segment to support external users to interact with the segment, to handle user registration, inquiries, complaints. This function is usually provided by a help desk.

The process of assessing, by independent means, the quality of the data products derived from the system outputs.

See: Data Maturity Levels

See: Parameter

Collection means the same thing as version; however, the MODIS science team uses the term Collection such as in MODIS Collection 5, but NSIDC uses Version as in MODIS Version 5. Also, Geoscience Laser Altimeter System (GLAS) refers to a release such as Release 33, which is also the same as version or collection. Thus, all three terms mean the same thing depending on which product you are using.

Visualization—sometimes called scientific visualization—provides a window into reams of observational and simulation data by creating graphical representations that communicate to researchers and public alike. Just as a picture is worth a thousand words, visualizations are worth billions of bytes, both elucidating and simplifying big data.

Object storage accessible through https.