A Contrast in Use of Metrics in Earth Science Data Systems

In recent years there has been a surge in the number of systems for processing, archiving and distributing remotely sensed data. Such systems, working independently as well as in collaboration, have been contributing greatly to the advances in the scientific understanding of the Earth system, as well as utilization of the data for nationally and internationally important applications. Among such systems, we consider those that are developed by or under the sponsorship of NASA to fulfill one of its strategic objectives: "Study Earth from space to advance scientific understanding and meet societal needs." NASA's Earth science data systems are of varying size and complexity depending on the requirements they are intended to meet. Some data systems are regarded as NASA's "Core Capabilities" that provide the basic infrastructure for processing, archiving and distributing a set of data products to a large and diverse user community in a robust and reliable manner. Other data systems constitute "Community Capabilities". These provide specialized and innovative services to data users and/or research products offering new scientific insight. Such data systems are generally supported by NASA through peer reviewed competition. Examples of Core Capabilities are 1. Earth Observing Data and Information System (EOSDIS) with its Distributed Active Archive Centers (DAACs), Science Investigator-led Processing Systems (SIPSs), and the EOS Clearing House (ECHO); 2. Tropical Rainfall Measurement Mission (TRMM) Science Data and Information System (TSDIS); 3. Ocean Data Processing System (ODPS); and 4. CloudSat Data Processing Center. Examples of Community Capabilities are projects under the Research, Education and Applications Solutions Network (REASON), and Advancing Collaborative Connections for Earth System Science (ACCESS) Programs. In managing these data system capabilities, it is necessary to have well-established goals and to measure progress relative to them. Progress is measured through "metrics", which can be a combination of quantitative as well as qualitative assessments. The specific metrics of interest depend on the user of the metrics as well as the type of data system. The users of metrics can be data system managers, program managers, funding agency or the public. Data system managers need metrics for assessing and improving the performance of the system and for future planning. Program managers need metrics to assess progress and the value of the data systems sponsored by them. Also, there is a difference in the metrics needed for core capabilities that tend to be more complex, larger and longer-term compared to community capabilities and the community capabilities that tend to be simpler, smaller and shorter-term. Even among community capabilities there are differences; hence the same set of metrics does not apply to all. Some provide data products to users, some provide services that enable better utilization of data or interoperability among other systems, and some are a part of a larger project where provision of data or services is only a minor activity. There is also a contrast between metrics used for internal and external purposes. Examples of internal purposes are: ensuring that the system meets its requirements, and planning for evolution and growth. Examples of external purposes are: providing to sponsors indicators of success of the systems, demonstrating the contributions of the system to overall program success, etc. This paper will consider EOSDIS, REASON and ACCESS programs to show the various types of metrics needed and how they need to be tailored to the types of data systems while maintaining the overall management goals of measuring progress and contributions made by the data systems.