Scaling Up: How Data Curation Can Help Address Key Issues in Qualitative Data Reuse and Big Social Research : Synthesis Lectures on Information Concepts, Retrieval, and Services

Sara Mannheimer

ISBN 10: 3031492242 / ISBN 13: 9783031492242

Editore: Springer Verlag Gmbh, 2025

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Titolo: Scaling Up: How Data Curation Can Help ...

Casa editrice: Springer Verlag Gmbh

Data di pubblicazione: 2025

Legatura: Taschenbuch

Condizione: Neu

Descrizione articolo

Riassunto:

This book explores the connections between qualitative data reuse, big social research, and data curation. A review of existing literature identifies the key issues of context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. Through interviews of qualitative researchers, big social researchers, and data curators, the author further examines each key issue and produces new insights about how domain differences affect each community of practice’s viewpoints, different strategies that researchers and curators use to ensure responsible practice, and different perspectives on data curation. The book suggests that encouraging connections between qualitative researchers, big social researchers, and data curators can support responsible scaling up of social research, thus enhancing discoveries in social and behavioral science.

Informazioni sull?autore:

Sara Mannheimer, Ph.D., is an Associate Professor and Data Librarian at Montana State University. She earned her Bachelor’s degree in Literature from Bard College, her MS in Information Science from University of North Carolina at Chapel Hill, and her Ph.D. in Library and Information Science from Humboldt University of Berlin. In her work as a librarian, she helps shape practices and theories for curation, sharing, discovery, and preservation of data. Her research examines the social, ethical, and technical issues of a data-driven world. She is currently the project director of the IMLS-funded Responsible AI project, which produces ethical decision-making resources for librarians and archivists who engage with artificial intelligence.