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How Data Need People

Autor Götz Hoeppe
en Limba Engleză Paperback – 18 iun 2026
From genome sequencing to large sky surveys, digital technologies produce massive datasets that promise unprecedented scientific insights. But data, for being good to use and reuse, need people - scientists, technicians, and administrators - as embodied, evaluative, social humans. In this book, anthropologist Götz Hoeppe draws on an ethnography of astronomical research to examine the media and practices that scientists and technicians use to instruct graduate students, make diagrams for data calibration and discovery, organize collaborative work, negotiate the ethics of open access, encode their knowledge in datasets - and undertake social inquiries along the way. This book offers a reflection on the sociality of data-rich research that will benefit attempts to integrate human and machine learning. It will be of interest for students and scholars in data science and science and technology studies, as well as in anthropology, sociology, history, and the philosophy of science. This book is also available Open Access on Cambridge Core.
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Specificații

ISBN-13: 9781009686730
ISBN-10: 1009686739
Pagini: 300
Dimensiuni: 152 x 229 x 16 mm
Greutate: 0.4 kg
Editura: Cambridge University Press

Notă biografică

Götz Hoeppe is Associate Professor of Sociology at the University of Waterloo, Canada. He is the author of Conversations on the Beach: Fishermen's Knowledge, Metaphor and Environmental Change in South India (2007) and Why the Sky is Blue: Discovering the Color of Life (2007), which received the American Meteorological Society's Louis J. Battan Author's Award.

Cuprins

Preface; Acknowledgements; Introduction: making sense of data-centric socialities; 1. Medium: digital affordances; 2. Evaluations: the ethical life of data production; 3. Membership: learning to become a competent data user; 4. Diagrams: spaces for cultivating data and making discoveries; 5. World: mundane reason and the relief from trust in data makers; 6. Organizing: social, medial, and epistemic orders in data-centric collaboration; 7. Normativity: inhabiting statuses in 'Open Science'; 8. Encoding knowledge: how to make data speak for themselves; Outlook: scientific data, artificial intelligence, and people; Appendix: Transcription conventions; References; Index.