Data at work

TLDR

Data are fundamental to science and engineering, and sharing them is essential for validation and progress, yet data sharing and reuse remain difficult in many fields. The paper argues that designing effective CSCW systems for data sharing in work groups requires a deeper understanding of how data are used in practice. They propose that this can be achieved by supporting social interaction around data abstractions, extending beyond current metadata models, and reinforcing the social roles of data. Their work with three scientific disciplines shows that data serve two general roles: as evidence to support scientific inquiry and as a social contribution that establishes and maintains communities of practice.

Abstract

Data are a fundamental component of science and engineering work, and the ability to share data is critical to the validation and progress of science. Data sharing and reuse in some fields, however, has proven to be a difficult problem. This paper argues that the development of effective CSCW systems to support data sharing in work groups requires a better understanding of the use of data in practice. Drawing on our work with three scientific disciplines, we show that data play two general roles in scientific communities: 1) they serve as evidence to support scientific inquiry, and 2) they make a social contribution to the establishment and maintenance of communities of practice. A clearer consideration and understanding of these roles can contribute to the design of more effective data sharing systems. We suggest that this can be achieved through supporting social interaction around data abstractions, reaching beyond current metadata models, and supporting the social roles of data.