{"product_id":"text-and-data-mining-literacy-for-librarians-paperback-softback-9798892555951","title":"Text and Data Mining Literacy for Librarians (Paperback \/ softback) 9798892555951","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eText and Data Mining Literacy for Librarians\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eWhitney Kramer (Edited by), Iliana Burgos (Edited by), Evan Muzzall (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9798892555951\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 27 October 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e434 pages\u003cbr\u003e25.4 x 17.8 x 2.5 cm, 0.794 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eText 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. \u003cbr\u003e \u003cbr\u003e \u003ci\u003eText and Data Mining Literacy for Librarians \u003c\/i\u003ecollects 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. \u003cul\u003e \t\u003cli\u003e \t\tEssentials of Text and Data Mining (TDM) Literacy \t\u003c\/li\u003e \t\u003cli\u003e \t\tData Literacy, Licensing, and Management Challenges with TDM \t\u003c\/li\u003e \t\u003cli\u003e \t\tTDM Research in Action: Practical Applications and Case Studies \t\u003c\/li\u003e \t\u003cli\u003e \t\tGenerating Insights from Library Reference Data \t\u003c\/li\u003e \t\u003cli\u003e \t\tProprietary TDM Software: Examples and Implementations \t\u003c\/li\u003e \u003c\/ul\u003e Chapters cover a range of disciplines and subject areas from a variety of institution sizes and types. \u003ci\u003eText and Data Mining Literacy for Librarians\u003c\/i\u003e 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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eIntroduction \u003cbr\u003e Whitney Kramer, Iliana Burgos, and Evan Muzzall \u003cbr\u003e \u003cbr\u003e Part I: Essentials of Text and Data Mining (TDM) Literacy \u003cbr\u003e Chapter 1. Language, Variation, and Change \u003cbr\u003e Heather Froehlich \u003cbr\u003e \u003cbr\u003e Chapter 2. Reference Interview Recommendations for Text and Data Mining \u003cbr\u003e Laura Egan \u003cbr\u003e \u003cbr\u003e Chapter 3. You Are Here: Mapping TDM Consults Across Disciplines and Infrastructures \u003cbr\u003e Jessica C. Hagman and Mary Borgo Ton \u003cbr\u003e \u003cbr\u003e Chapter 4. Navigating Text Data Mining Training for Humanities Librarians: A Microcredential Case Study from Baylor University \u003cbr\u003e Ellen Hampton Filgo, Laura Semrau, and Ezra Choe \u003cbr\u003e Chapter 5. Exploring the Power of Text Data Mining: Syllabi Analysis for Information Literacy Instructional Outreach \u003cbr\u003e Amy James and Joshua Been \u003cbr\u003e \u003cbr\u003e Chapter 6. Mining Expertise to Maximize Support: Leveraging Campus Partnerships for Text and Data Mining \u003cbr\u003e Cody Hennesy and Michael Beckstrand \u003cbr\u003e \u003cbr\u003e Chapter 7. Envisioning Librarians’\u003cbr\u003e New Roles in the Age of Text and Data Mining \u003cbr\u003e Douglas MembreÑ\u003cbr\u003eo \u003cbr\u003e \u003cbr\u003e Part II: Data Literacy, Licensing, and Management Challenges with TDM \u003cbr\u003e Chapter 8. Framing Large Language Models: Teaching Foundational Concepts of Generative AI and Information Literacy for Critical Student Engagement \u003cbr\u003e Isaac Wink and Jennifer Hootman \u003cbr\u003e \u003cbr\u003e Chapter 9. Teaching Algorithmic Literacy for Text and Data Mining in Libraries: A Case Study at a Canadian Academic Institution \u003cbr\u003e Christina Dinh Nguyen \u003cbr\u003e \u003cbr\u003e Chapter 10. Humanities Computing, Legal Informatics, and Text Analysis Pedagogy in Italy: A Brief History of the Practice \u003cbr\u003e Deborah Grbac \u003cbr\u003e \u003cbr\u003e Chapter 11. Legal and Ethical Considerations for Curating Copyrighted Literary Collections as Data \u003cbr\u003e Sarah Potvin and Alex Wermer-Colan \u003cbr\u003e \u003cbr\u003e Chapter 12. Protecting Academic Research Opportunities: Key License Terms and Policies in Dataset Licensing \u003cbr\u003e Erik Limpitlaw and Sarah Forzetting \u003cbr\u003e \u003cbr\u003e Chapter 13. A Comparative Study of Non-Commercial Text Data Mining Policies in German Libraries \u003cbr\u003e Andrea Quinn \u003cbr\u003e \u003cbr\u003e Part III: TDM Research in Action: Practical Applications and Case Studies \u003cbr\u003e Chapter 14. Library-Researcher Partnerships in Computational Social Science: Text Data Selection and Management \u003cbr\u003e Amy L. Johnson \u003cbr\u003e \u003cbr\u003e Chapter 15. Using Text Data Mining to Assess Historical Trends in Archival Description \u003cbr\u003e Lia Warner \u003cbr\u003e \u003cbr\u003e Chapter 16. Text Mining in the Archives: Preparing Materials for Using in Text Mining \u003cbr\u003e Paula S. Kiser \u003cbr\u003e \u003cbr\u003e Chapter 17. Enriching the Past: Maximizing the Value of a Congressional Hearings Corpus Using LLM Coding Tools \u003cbr\u003e Jeremy Darrington \u003cbr\u003e \u003cbr\u003e Chapter 18. TDM Reimagined: A Case Study of Leveraging Generative AI to Mine Japanese Diet Proceeding Records \u003cbr\u003e Keyao Pan \u003cbr\u003e \u003cbr\u003e Chapter 19. Text and Data Mining in Science and Engineering: Exploring Use Cases and Support Services \u003cbr\u003e Ye Li \u003cbr\u003e \u003cbr\u003e Part IV: Generating Insights from Library Reference Data \u003cbr\u003e Chapter 20. Data Mining and Textual Analysis: An Approach to Efficient and Customizable Library Assessment \u003cbr\u003e Crissandra George \u003cbr\u003e \u003cbr\u003e Chapter 21. Utilizing Prodigy: Collaborative Library Assessment Projects with Advanced Natural Language Processing in Python \u003cbr\u003e Jiebei Luo and Alyssa Brissett \u003cbr\u003e \u003cbr\u003e Chapter 22. Sentiment Analysis of Online Library Reference Chat: A Cross-Site Longitudinal Comparison \u003cbr\u003e Jingjing Wu, Jianqiang Wang, and Amy Jiang \u003cbr\u003e \u003cbr\u003e Chapter 23. Emoji in Context: How to Analyze Communication, Relationships, and Behavioral Performance Through Mining Emojis in Chat Reference Transcripts \u003cbr\u003e Jen-chien Yu and Lindsay Taylor \u003cbr\u003e \u003cbr\u003e Part V: Proprietary TDM Software: Examples and Implementations \u003cbr\u003e Chapter 24. Open TDM: How the Open Movement is Transforming the Way Academic Libraries Support Text and Data Mining Research \u003cbr\u003e John Knox and Kate Boyd \u003cbr\u003e \u003cbr\u003e Chapter 25. From Investigation to Implementation: Workflows for Supporting TDM Tools within the Library \u003cbr\u003e Kara Handren and Sean Forbes \u003cbr\u003e \u003cbr\u003e Chapter 26. Beyond the Tool Demonstration: An Internal Workshop to Enhance TDM Literacy Among Librarians \u003cbr\u003e Brianne Dosch and Joshua Ortiz Baco \u003cbr\u003e Chapter 27. Building a Text Mining Service at a University Library from the Ground Up: A Case Study ​\u003cbr\u003eUsing the LexisNexis Webservices API 2018-2024 \u003cbr\u003e Andrew Dudash and Jeffrey A. Knapp \u003cbr\u003e \u003cbr\u003e Chapter 28. Embracing Bookness: Introducing Library Staff and Library Students to Text and Data Mining with HathiTrust Research Center \u003cbr\u003e Rachel N. Hogan and Patrick Williams \u003cbr\u003e \u003cbr\u003e Chapter 29. Multilingual Text Mining using TDM Platforms: A Librarian’\u003cbr\u003es Guide to Constellate \u003cbr\u003e Jajwalya Karajgikar \u003cbr\u003e \u003cbr\u003e Chapter 30. Evaluating Python and R Scripts from Proprietary Text Data Mining Products \u003cbr\u003e Katharine Teykl \u003cbr\u003e \u003cbr\u003e Chapter 31. Reproducible TDM examples in R and Python: A Teaching Appendix \u003cbr\u003e Evan Muzzall and Anthony Weng \u003cbr\u003e \u003cbr\u003e About the Editors and Authors\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003c\/font\u003e","brand":"ACRL","offers":[{"title":"Brand New","offer_id":52653066486040,"sku":"9798892555951","price":95.99,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/freshlyprintedbooks.co.uk\/products\/text-and-data-mining-literacy-for-librarians-paperback-softback-9798892555951","provider":"Freshly Printed Books","version":"1.0","type":"link"}