A Reusability Assessment of Recommended Software Solutions for Improving the Quality of Earth Science Data Products and Services

Software can contribute to the quality of Earth science data products and services. Software solutions can play an important role in capturing, describing, enabling discovery of and facilitating use of information about data quality. Such software solutions can be adopted by science teams and data centers to improve their current capabilities and their future efforts if the software is in a state that fosters reuse. When the reusability of software has not been facilitated by the team that originally developed and published the software, potential adopters may need to invest substantial resources to bring a particular software solution to a state where it can be reused by others. Software developers prepare software solutions for adoption by enabling reuse of the software. For example, adoptable software solutions should be available as free and open source software (FOSS), with license information that is clearly described and accessible by potential adopters so that they can make a determination about the reusability of the software and whether there are any restrictions on such use. Likewise, potential adopters of software will need to access any documentation for the software to be able to determine the potential reusability of the software. Software solutions that have been recommended for improving the quality of Earth science data products and services are assessed in terms of their potential for reuse. The methodology used for assessment involves assignment of one of nine levels in each of nine distinct criteria such as documentation, modularity, portability and support. The assessment reported here focused on software that supports data quality. It was carried out by the Data Quality Working group, one of NASA's Earth Science Data System Working Groups. The impetus, assessment instrument, methodology, and results of the assessment of recommended software solutions are described, along with lessons learned and recommendations for identifying, assessing, and selecting software solutions for improving the quality of Earth science data products and services.