Text and Data Mining Literacy for Librarians

Academic libraries are embracing automated techniques to unlock insights from vast digital content. The work explains how to integrate TDM into research workflows while overcoming licensing and management challenges, and it offers practical case studies that empower library professionals in diverse academic settings. Text and data mining (TDM) is the process of using automated techniques to derive information from large sets of digital content. Librarians who liaise with a wide range of academic disciplines need TDM skills to support research at their institutions. Text and Data Mining Literacy for Librarians collects ways that academic libraries are supporting TDM literacy through services, workflows, and professional development. In five parts, it offers a variety of perspectives, insights, and experiences that can help you address the challenges of supporting TDM research, fit it into your existing reference and instruction work, and conduct your own. Essentials of Text and Data Mining (TDM) Literacy Data Literacy, Licensing, and Management Challenges with TDM TDM Research in Action: Practical Applications and Case Studies Generating Insights from Library Reference Data Proprietary TDM Software: Examples and Implementations Chapters cover a range of disciplines and subject areas from a variety of institution sizes and types. Text and Data Mining Literacy for Librarians is intended to empower library workers, inform decision-makers, and support our research communities as working with textual data becomes further embedded into the research landscape.