The convergence of computational and social approaches for unveiling meaningful and valuable data

Abstract

The current data paradigm is seeking a more integrated and comprehensive framework to make sense of data and its derived issues. From the perspective of the data life cycle, we argue that computational and social approaches complement each other to confront data challenges. Computational approaches consist of ETL (extract, transform, and load), modeling, and machine learning techniques; social approaches include policy and regulations, data sharing and reuse behavior, reproducibility, ethical and privacy issues. In this panel, we frame these two approaches as data acumen and data stewardship. The merging of these two perspectives allows data not only to become discoverable, accessible, and interoperable, but also to further the value of revealing meaningful patterns and become supportive evidence for important decision making. In this panel, the opening facilitator and three panelists will report on their recent studies in terms of this convergence of both data acumen and stewardship while sharing their recent research insights on case studies in three disciplines: agriculture, biomedicine, and archeology.