IN14A-01
Improving Information Quality for Earth Science Data and Products – An Overview
Monday, 14 December 2015: 16:00
2020 (Moscone West)
Hampapuram Ramapriyan, Science Systems and Applications, Inc., Lanham, MD, United States, David F Moroni, NASA Jet Propulsion Laboratory, Pasadena, CA, United States and Ge Peng, NC State University, Asheville, NC, United States
Abstract:
In recent years, the quality of Earth science data has received much attention from several points of view. First, the scientific quality, defined in terms of accuracy, precision, uncertainty, validity and suitability for use (fitness for purpose) in various applications is considered paramount. In addition, the product quality is important as well. Product quality addresses how well the scientific quality is assessed and documented, how complete the metadata and documentation are, etc. Stewardship quality addresses questions such as how well data are being managed and preserved by an archive or repository, how easy it is for users to find, get, understand, trust, and use data, and whether the archive has people who understand the data available to help users. In general, we can refer to all these aspects of quality together as Information Quality. The purpose of this paper is to discuss the context of and ideas for further work on Earth science information quality. Several related prior activities will be discussed such as: QA4EO, ISO Metadata Quality Standards, NOAA CDR Maturity Matrix, NOAA Data Stewardship Maturity Matrix, NCAR Community Contribution Pages, NASA MEaSUREs Product Quality Checklists, and NASA Earth Science Data System Working Groups (ESDSWG) Data Quality Working Group recommendations. The ESIP Information Quality Cluster is proceeding in this context to: identify additional needs for consistently capturing, describing, and conveying quality information through use case studies with broad and diverse applications; establish and provide community-wide guidance on roles and responsibilities of key players and stakeholders including users and management; prototype conveying quality information to users in a consistent and easily understandable manner; establish a baseline of standards and best practices for data quality; and engage data providers, data managers, and data user communities as resources to improve our standards and best practices.