A measurable or derived variable occurring in the physical or digital world. Variable and Parameter are used interchangeably. GCMD uses the term variable and NSIDC DAAC uses the term parameter.

PFARs evaluate how well mission objectives were met during a human space flight mission. PFARs also evaluate the status of the flight and ground systems, including the identification of any anomalies and their resolution.

PLARs evaluate the in-flight performance of the program and its projects. PLARs also determine the program's readiness to begin the operations phase of the life cycle and transfer responsibility to the operations organization.

PDRs evaluate the completeness/consistency of the program's preliminary design, including its projects, in meeting all requirements with appropriate margins, acceptable risk, and within cost and schedule constraints, and to determine the program's readiness to proceed with the detailed design phase of the program.

Preservation covers all processes and operations on individual or multi-mission data sets for ensuring the technical and intellectual survival of space data sets and their metadata through time. It grants dataset integrity, its discoverability and accessibility, and facilitates its use and reuse in the long term. 

Preservation is one of the tasks of data curation. Examples are data record improvement and consolidation.

The processing function generates higher-level products from lower level products and auxiliary products. The processing is performed by core algorithms supplemented by administrative functions (e.g. formatting). The algorithms are version controlled. Processing is able to produce the desired products systematically or on request.

Raw Data 
The physical telemetry payload data as received from the satellite, i.e. a serial data stream without de-multiplexing. 

Level 0 
Reconstructed unprocessed data at full space-time resolution with all available supplemental information to be used in subsequent processing (e.g. ephemeris, health and safety) appended. 

Level 1A 
Reconstructed unprocessed data at full resolution, time-referenced, and annotated with ancillary information, including radiometric and geometric calibration coefficients and geo-referencing parameters (e.g. ephemeris) computed and appended but not applied to the Level 0 data. 

Level 1B 
Radiometrically corrected and calibrated data in physical units at full instrument resolution as acquired. Level 1C L1B data orthorectified, re-sampled to a specified grid 

Level 2 
Derived geophysical parameters (e.g. sea surface temperature, leaf area index) at the same resolution and location as Level 1 source data. 

Level 3 
Data or retrieved geophysical parameters which have been spatially and/or temporally re-sampled (i.e. derived from Level 1 or 2 products), usually with some completeness and consistency. Such re-sampling may include averaging and compositing. 

Level 4 
Model output or results from analyses of lower level data (i.e., variables that are not directly measured by the instruments, but are derived from these measurements; could be derived from multiple instrument measurements).

1.
Electronic data package distributable to users; content is derived from instrument data via processing involving ancillary and auxiliary data. Products may comprise metadata and browse images. 

2.
A product may be part of a collection – a distinction useful for archiving and cataloging purposes. 

3.
The term product may be used to denote a product type, such as e.g. ENVISAT_ASAR_L1B_PRI data. 

4.
End users may distinguish between (input, "raw") data and products, i.e. the derived geophysical parameters.

Useful references to published articles about the use of the data and user feedback received by the science and instrument teams about the products. Includes reports of any peculiarities or notable features observed in the products.

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.