Information Quality as a Foundation for User Trustworthiness of Earth Science Data.

Information quality is multidimensional. Four different aspects of information quality can be defined based on the lifecycle stages of Earth Science data products: science, product, stewardship and services. With increasing requirements on ensuring and improving information quality coming from multiple government agencies and throughout industry, there have been considerable efforts toward improving information quality during the last decade, much of which has not been well vetted in a collective sense until recently. Given this rich background of prior work, the Information Quality Cluster (IQC), established within the Federation of Earth Science Information Partners (ESIP) in 2011, and reactivated in the summer of 2014, has been active with membership from multiple organizations. The IQC's objectives and activities, aimed at ensuring and improving information quality for Earth science data and products, are also considered vital toward improving the trustworthiness of Earth science data to a vast and interdisciplinary community of data users. During 2016, several members of the IQC have led the development and assessment of four use cases. This was followed up in 2017 with multiple panel sessions at the 2017 Winter and Summer ESIP Meetings to survey the challenges posed in the various aspects of information quality. What was discovered to be most lacking is the transparency of data lineage (i.e., provenance and maturity), uniform methods for uncertainty characterization, and uniform quality assurance data and metadata. While solutions to these types of issues exist, most data producers have little time to investigate and collaborate to arrive at and conform to a consensus approach. The IQC has positioned itself as a community platform to bring together all relevant stakeholders from data producers, repositories, program managers, and the end users. A combination of both well-vetted and "trailblazing" solutions are presented to address how data trustworthiness can be elevated and maintained through optimized extraction, curation, and dissemination of information quality artifacts.