Text and Data Mining Literacy for Librarians
Association of College & Research Libraries (Verlag)
9798892555951 (ISBN)
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.
Whitney Kramer is the research and data librarian at Catherwood Library at Cornell University. In this role, she supports students and faculty in the School of Industrial and Labor Relations, and the Economics, and Statistics and Data Science departments who work with both structured and unstructured data. Her research interests include data literacy and the usage of text data in social science research. Her work has been published in the Data Literacy Cookbook, CHOICE, the Journal of New Librarianship, and ResearchDataQ. Previously, she was the entrepreneurship and public services librarian at the Roanoke Public Libraries in Roanoke, Virginia, and the business research librarian at Lippincott Library at the University of Pennsylvania. Iliana Burgos is the emerging data practices librarian at Digital Scholarship Services at Cornell University Library. She supports researchers in exploring digital and computational approaches to humanities scholarship. Burgos specializes in text data management and computational text analysis methods guided by open scholarship principles. A Ronald E. McNair Scholar and American Library Association Spectrum Scholar, her research engages text data and algorithmic literacies, critical platform studies, and community-based data justice movements. She previously worked in community outreach roles as a Carolina Academic Library associate at the University of North Carolina at Chapel Hill and the Wilmington Institute Free Library of Delaware. Evan Muzzall, PhD, is a formally trained forensic anthropologist and bioarchaeologist. He was a leader in various data science, anthropology, and humanities teaching and consulting settings for a decade at San Francisco State University, UC Berkeley, and Stanford University. He has also published machine learning and statistical modeling applications in top scientific journals and presently consults for the high-voltage electrical grid construction industry.
Introduction
Whitney Kramer, Iliana Burgos, and Evan Muzzall
Part I: Essentials of Text and Data Mining (TDM) Literacy
Chapter 1. Language, Variation, and Change
Heather Froehlich
Chapter 2. Reference Interview Recommendations for Text and Data Mining
Laura Egan
Chapter 3. You Are Here: Mapping TDM Consults Across Disciplines and Infrastructures
Jessica C. Hagman and Mary Borgo Ton
Chapter 4. Navigating Text Data Mining Training for Humanities Librarians: A Microcredential Case Study from Baylor University
Ellen Hampton Filgo, Laura Semrau, and Ezra Choe
Chapter 5. Exploring the Power of Text Data Mining: Syllabi Analysis for Information Literacy Instructional Outreach
Amy James and Joshua Been
Chapter 6. Mining Expertise to Maximize Support: Leveraging Campus Partnerships for Text and Data Mining
Cody Hennesy and Michael Beckstrand
Chapter 7. Envisioning Librarians’ New Roles in the Age of Text and Data Mining
Douglas MembreÑo
Part II: Data Literacy, Licensing, and Management Challenges with TDM
Chapter 8. Framing Large Language Models: Teaching Foundational Concepts of Generative AI and Information Literacy for Critical Student Engagement
Isaac Wink and Jennifer Hootman
Chapter 9. Teaching Algorithmic Literacy for Text and Data Mining in Libraries: A Case Study at a Canadian Academic Institution
Christina Dinh Nguyen
Chapter 10. Humanities Computing, Legal Informatics, and Text Analysis Pedagogy in Italy: A Brief History of the Practice
Deborah Grbac
Chapter 11. Legal and Ethical Considerations for Curating Copyrighted Literary Collections as Data
Sarah Potvin and Alex Wermer-Colan
Chapter 12. Protecting Academic Research Opportunities: Key License Terms and Policies in Dataset Licensing
Erik Limpitlaw and Sarah Forzetting
Chapter 13. A Comparative Study of Non-Commercial Text Data Mining Policies in German Libraries
Andrea Quinn
Part III: TDM Research in Action: Practical Applications and Case Studies
Chapter 14. Library-Researcher Partnerships in Computational Social Science: Text Data Selection and Management
Amy L. Johnson
Chapter 15. Using Text Data Mining to Assess Historical Trends in Archival Description
Lia Warner
Chapter 16. Text Mining in the Archives: Preparing Materials for Using in Text Mining
Paula S. Kiser
Chapter 17. Enriching the Past: Maximizing the Value of a Congressional Hearings Corpus Using LLM Coding Tools
Jeremy Darrington
Chapter 18. TDM Reimagined: A Case Study of Leveraging Generative AI to Mine Japanese Diet Proceeding Records
Keyao Pan
Chapter 19. Text and Data Mining in Science and Engineering: Exploring Use Cases and Support Services
Ye Li
Part IV: Generating Insights from Library Reference Data
Chapter 20. Data Mining and Textual Analysis: An Approach to Efficient and Customizable Library Assessment
Crissandra George
Chapter 21. Utilizing Prodigy: Collaborative Library Assessment Projects with Advanced Natural Language Processing in Python
Jiebei Luo and Alyssa Brissett
Chapter 22. Sentiment Analysis of Online Library Reference Chat: A Cross-Site Longitudinal Comparison
Jingjing Wu, Jianqiang Wang, and Amy Jiang
Chapter 23. Emoji in Context: How to Analyze Communication, Relationships, and Behavioral Performance Through Mining Emojis in Chat Reference Transcripts
Jen-chien Yu and Lindsay Taylor
Part V: Proprietary TDM Software: Examples and Implementations
Chapter 24. Open TDM: How the Open Movement is Transforming the Way Academic Libraries Support Text and Data Mining Research
John Knox and Kate Boyd
Chapter 25. From Investigation to Implementation: Workflows for Supporting TDM Tools within the Library
Kara Handren and Sean Forbes
Chapter 26. Beyond the Tool Demonstration: An Internal Workshop to Enhance TDM Literacy Among Librarians
Brianne Dosch and Joshua Ortiz Baco
Chapter 27. Building a Text Mining Service at a University Library from the Ground Up: A Case Study Using the LexisNexis Webservices API 2018-2024
Andrew Dudash and Jeffrey A. Knapp
Chapter 28. Embracing Bookness: Introducing Library Staff and Library Students to Text and Data Mining with HathiTrust Research Center
Rachel N. Hogan and Patrick Williams
Chapter 29. Multilingual Text Mining using TDM Platforms: A Librarian’s Guide to Constellate
Jajwalya Karajgikar
Chapter 30. Evaluating Python and R Scripts from Proprietary Text Data Mining Products
Katharine Teykl
Chapter 31. Reproducible TDM examples in R and Python: A Teaching Appendix
Evan Muzzall and Anthony Weng
About the Editors and Authors
| Erscheinungsdatum | 29.10.2025 |
|---|---|
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 794 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Sozialwissenschaften ► Kommunikation / Medien ► Buchhandel / Bibliothekswesen | |
| ISBN-13 | 9798892555951 / 9798892555951 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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