The LIBER Data Science in Libraries (DSLib) working group explores and promotes library engagement in applying data science and analytical methods in libraries, taking into account all kinds of processes and workflows around library collections and metadata as well as digital infrastructures and service areas.
As Working Groups are the primary units to conduct work on the LIBER strategy, ours will operate under the Steering Committee for the direction, State-of-the-art Services, as defined in LIBER’s 2023-2027 Strategy.
Libraries are data-rich environments and present vast opportunities for applying data science and analytical methods. These activities and initiatives emerge at the overlap between data and information sciences as well as disciplinary domains which build research questions based on digital collections.
We are already seeing investment in analytical capacity and skills, in particular, related to mining and annotating collections, enriching (meta)data, monitoring scholarly communication and publication behaviour and related spendings, providing infrastructure and support for text and data mining of library collections, developing training opportunities for librarians and students, etc.
Areas foreseen to be investigated by the working group may include but are not limited to:
- Identify and analyse key initiatives and projects across Europe and beyond;
- Explore how data science methods and tools can be tailored to library-specific environments and requirements;
- Identify good practices as well as challenges and gaps;
- Evaluate methods and strategies for skills and capacity development.