Sunday, 15:30


Preparing a Workforce to Effectively Re-use Data

Ana Lucic, Catherine Blake
University of Illinois, United States of America


For centuries, library and information science professionals have been responsible for curating and preserving access to information resources. The last few decades have seen an unprecedented change in how new knowledge is created, disseminated and reused both within academe and industry, which provides new opportunities to intervene within the data lifecycle. This paper documents efforts to create a graduate educational program that produces alum who understand both the social and technical aspects of data analytics and who can effectively employ data to address questions in academe and industry. We share perspectives gained from initial interviews with project partners who have data needs, and report on how those needs directly informed curricula development of the Socio-technical Data Analytics (SODA) program at the School of Information Sciences at the University of Illinois. We also provide a formative student evaluation of the program that was conducted to identify aspects of the program that are successful, and those where further work is needed in order to help other schools who are developing similar programs that prepare a workforce who can effectively reuse data.

Toward a Conceptual Framework for Data Sharing Practices in Social Sciences: A Profile Approach

Wei Jeng, Daqing He, Jung Sun Oh
University of Pittsburgh, United States of America


This paper investigates the landscape of data-sharing practices in social sciences via the data sharing profile approach. Guided by two pre-existing conceptual frameworks, Knowledge Infrastructure (KI) and the Theory of Remote Scientific Collaboration (TORSC), we design and test a profile tool that consists of four overarching dimensions for capturing social scientists’ data practices, namely: 1) data characteristics, 2) perceived technical infrastructure, 3) perceived organizational context, and 4) individual characteristics.

To ensure that the instrument can be applied in real and practical terms, we conduct a case study by collecting responses from 93 early-career social scientists at two research universities in the Pittsburgh Area, U.S. The results suggest that there is no significant difference, in general, among scholars who prefer quantitative, mixed method, or qualitative research methods in terms of research activities and data-sharing practices. We also confirm that there is a gap between participants’ attitudes about research openness and their actual sharing behaviors, highlighting the need to study the “barrier” in addition to the “incentive” of research data sharing.

The Durability and Fragility of Knowledge Infrastructures: Lessons Learned from Astronomy

Christine L. Borgman1, Peter T. Darch2, Ashley E. Sands1, Milena S. Golshan1
1University of California, Los Angeles (UCLA), United States of America; 2University of Illinois at Urbana-Champaign (UIUC), United States of America


Infrastructures are not inherently durable or fragile, yet all are fragile over the long term. Durability requires care and maintenance of individual components and the links between them. Astronomy is an ideal domain in which to study knowledge infrastructures, due to its long history, transparency, and accumulation of observational data over a period of centuries. Research reported here draws upon a long-term study of scientific data practices to ask questions about the durability and fragility of infrastructures for data in astronomy. Methods include interviews, ethnography, and document analysis. As astronomy has become a digital science, the community has invested in shared instruments, data standards, digital archives, metadata and discovery services, and other relatively durable infrastructure components. Several features of data practices in astronomy contribute to the fragility of that infrastructure. These include different archiving practices between ground- and space-based missions, between sky surveys and investigator-led projects, and between observational and simulated data. Infrastructure components are tightly coupled, based on international agreements. However, the durability of these infrastructures relies on much invisible work – cataloging, metadata, and other labor conducted by information professionals. Continual investments in care and maintenance of the human and technical components of these infrastructures are necessary for sustainability.