Publisher's Synopsis
This book is a practical guide to how to get started as a data librarian with little time, no money but great enthusiasm.
Theories and Methods in Data Science Librarianship describes and extends the role of the data science librarian through real-world cases and proposes several methods employed by public and academic libraries to develop and further educate the librarian. It is a collection of the best knowledge from published literature and experts in one place, creating an easy to read overview.
The perspectives offered in the book are critical, in that they are reflexive about the role of the librarian and consider challenges and multidimensionality of the librarians profile. This multidimensionality requires theories that address data services as research objects (data-management, definition of concepts, researchers as individual agents, accessibility, funding, profile and education).
The book covers:
- how to get started as a data librarian
- the history and the motivations of the data science librarianship community
- how data librarians can close the data literacy divide
- library policy and data science
- untangling the jungle of copyright and laws surrounding data use and data protection
- data ethics
- how library leaders can support and develop data science librarianship
- managing data science projects and services on no budget
- inspiring data science projects at the public library
- library carpentry skills and teaching
- how to identify user needs and develop relevant data science services.
The book will be essential reading for librarians and information specialists working working with data related tasks including, research librarians, embedded librarians, metadata librarians, cataloguers, public librarians mediating the possibilities in data for library users and students of library and information science. It will also be of interest to those working in archives, museums and other cultural heritage institutions.
