Search for: All records

Advances in data infrastructure are often led by disciplinary initiatives aimed at innovation in federation and sharing of data and related research materials. In library and information science (LIS), the data services area has focused on data curation and stewardship to support description and deposit of data for access, reuse, and preservation. At the same time, solutions to societal grand challenges are thought to lie in convergence research, characterized by a problem-focused orientation and deep cross-disciplinary integration, requiring access to highly varied data sources with differing resolutions or scales. We argue that data curation and stewardship work in LIS should expand to foster convergence research based on a robust understanding of the dynamics of disciplinary and interdisciplinary research methods and practices. Highlighting unique contributions by Dr. Linda C. Smith to the field of LIS, we outline how her work illuminates problems that are core to current directions in convergence research. Drawing on advances in data infrastructure in the earth and geosciences and trends in qualitative domains, we emphasize the importance of metastructures and the necessary influence of disciplinary practice on principles, standards, and provisions for ethical use across the evolving data ecosystem. 

A report summarizing the “Keeping Data Science Broad” series including data science challenges, visions for the future, and community asks. The goal of the Keeping Data Science Broad series was to garner community input into pathways for keeping data science education broadly inclusive across sectors, institutions, and populations. Input was collected from a community input survey, three webinars (Data Science in the Traditional Context, Alternative Avenues for Development of Data Science Education Capacity, and Big Picture for a Big Data Science Education Network available to view through the South Big Data Hub YouTube channel) and an interactive workshop (Negotiating the Digital and Data Divide). Through these venues, we explore the future of data science education and workforce at institutions of higher learning that are primarily teaching-focused. The workshop included representatives from sixty data science programs across the nation, either traditional or alternative, and from a range of institution types including community colleges, Historically Black Colleges and Universities (HBCU’s), Hispanic-Serving Institutions (HSI’s), other minority-led and minority-serving institutions, liberal arts colleges, tribal colleges, universities, and industry partners.