Data librarians and data scientists: how do we see them today and what do they have to do with data literacy?

Abstract

Data science is an interdisciplinary field that draws on the ideas and tools of many disciplines and varied professions. Data scientists may work as data analysts, or data engineers. We also can speak about vertical and horizontal data scientists. Data science education requires a richly layered system of relationships that are not just useful mechanisms, but also tools for navigating data as social text. Library and information science (LIS) overlaps significantly with data science, but data science is (at least for the time being) not standardised, because it often demands personalised, exploratory solutions. At the same time, data science offers new methods and practices for data librarianship that draw on the core values, ethics, skills and professional knowledge of general librarianship. Computational thinking, a common way of human problem solving, is one of the increasingly prominent competencies of data literacy that is an important part of both disciplines. The ideas that characterise our thinking about data science are linked not only to the cultivation of science, or commercial goals, but also to active and informed citizenship and action, which is also one of the reasons for the emergence of critical data
literacy.