Abstract
This chapter contributes to academic library institutions currently engaged in or formulating their strategy for engaging in data science. The chapter provides academic library institutions, and library and information science students, with a context in which to consider how they can collaborate locally and internationally to advance the use of data by scholars (students, researchers, and the public). Presented from the perspective of an association of research libraries, the chapter explores how, together, research libraries work with others to convene, inform, shape, and influence data science and data research policies and practices. The chapter provides examples of data and data science collaborations in teaching, learning, and research so the reader can identify specific skills and knowledge they may need or want to develop in order to collaborate, and they can learn about at least one existing or emerging type of collaboration they would like to explore further.
TopThe Context Starting With 2018
In 2018, the National Academies of Sciences, Engineering, and Medicine issued a report on data science for undergraduates, presented with a description that signaled the urgency for a national emphasis on data science skills:
Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data….It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. (pp. 6–8)
Key Terms in this Chapter
Research Data Management: The activities of data policies, data planning, data element standardization, information management control, data synchronization, data sharing, and database development, including practices and projects that acquire, control, protect, deliver, and enhance the value of data and information (Data management, n.d.).
Data Curation: A managed process, throughout the data life cycle, by which data and data collections are cleansed, documented, standardized, formatted, and interrelated. This includes versioning data, or forming a new collection from several data sources, annotating with metadata, adding codes to raw data (for example, classifying a galaxy image with a galaxy type such as “spiral”) (Data curation, n.d.).
Data-Intensive Research: Research that generates and/or uses vast quantities of data and requires extraordinary computing power to analyze it.
Research Data Policy: Institutional and public policies that set requirements for data ownership, management, archiving, and sharing.
Collaboration: Broad term of engagement including formally documented partnerships such as memoranda of understanding, legal joint ventures, issue-based coalitions, formal appointments to committees or task forces with specific charges and terms, project collaborations such as team science or teaching teams, and support through allyship or more passive forms of informal association or membership.
Research Data Practice: The adoption of data science procedures, skills, and techniques, specifically digital techniques in research.