Data science in data librarianship: Core competencies of a data librarian

Abstract

Currently, data are stored in an always-on condition, and can be globally accessed at any point, by any user. Data librarianship has its origins in the social sciences. In particular, the creation of data services and data archives, in the United Kingdom (Data Archives Services) and in the United States and Canada (Data Library Services), is a key factor for the emergence of data librarianship. The focus of data librarianship nowadays is on the creation of new library services. Data librarians are concerned with the proposition of services for data management and curation in academic libraries and other research organizations. The purpose of this paper is to understand how the complexity of the data can serve as the basis for identifying the technical skills required by data librarians. This essay is systematically divided, first introducing the concepts of data and research data in data librarianship, followed by an overview of data science as a theory, method, and technology to assess data. Next, the identification of the competencies and skills required by data scientists and data librarians are discussed. Our final remarks highlight that data librarians should understand that the complexity and novelty associated with data science praxis. Data science provides new methods and practices for data librarianship. A data librarian need not become a programmer, statistician, or database manager, but should be interested in learning about the languages and programming logic of computers, databases, and information retrieval tools. We believe that numerous kinds of scientific data research provide opportunities for a data librarian to engage with data science.