Text and data mining literacy for librarians / Whitney Kramer, Iliana Burgos, Evan Muzzall.
Idioma: Inglés Editor: Chicago, Illinois : Association of College and Research Libraries, 2025Descripción: 415 pages cmTipo de contenido:- text
- unmediated
- volume
- 9798892555951
- 23 027.7
| Imagen de cubierta | Tipo de ítem | Biblioteca actual | Biblioteca de origen | Colección | Ubicación en estantería | Signatura topográfica | Materiales especificados | Info Vol | URL | Copia número | Estado | Notas | Fecha de vencimiento | Código de barras | Reserva de ítems | Prioridad de la cola de reserva de ejemplar | Reservas para cursos | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Libro | Biblioteca Encarnación Valdés Colección General bev | 025.0425 T3557 2025 (Navegar estantería(Abre debajo)) | Disponible | 82000002671422 |
"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: 1. Essentials of Text and Data Mining (TDM) Literacy, 2. Data Literacy, Licensing, and Management Challenges with TDM, 3. TDM Research in Action: Practical Applications and Case Studies, 4. Generating Insights from Library Reference Data, and 5. 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"--