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Anissa Tanweer @ CWTS, Leiden

Seminar on "Academic data science: Transdisciplinary and extradisciplinary visions"

Published onMay 28, 2024
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Anissa Tanweer @ CWTS, Leiden

Anissa Tanweer will give a seminar on Wednesday 12th of June 2024, 15-16:00 at CWTS, Leiden

Anissa Tanweer is a Senior Social Scientist at the eScience Institute, an Affiliate Faculty member in the Department of Communication at the University of Washington, Seattle (USA) and a sociotechnical expert for the Scientific Software Engineering Center (SSEC). She conducts ethnographic research on the practice and culture of computationally-mediated science, and applies a sociotechnical lens to the design and implementation of training programs in data-intensive academic research. Dr. Tanweer directs the UW Data Science for Social Good summer internship and ran the Data Science Studies Special Interest Group at UW from 2018-2021.


Seminar of the 12h of June:

Academic data science: Transdisciplinary and extradisciplinary visions

As a nascent field within the academy, the contours, attributes, and bounties of data science are still indeterminate and contested. We studied how participants in an initiative to establish data science at a large American research university defined data science and articulated their relationships to the field. We discuss two contrasting visions for data science among our research participants. One vision is a transdisciplinary view portraying data science as a phenomenon with transcendent, appropriative, and impositional qualities that sits apart from academic domains. Another view of data science—one that was far more prevalent among our research subjects—casts data science as grounded, relational, and adaptive, emerging from crosspollination of numerous academic domains. We argue that this latter formulation represents a more quotidian reality of data science and positions the field as an extradiscipline, defined as a field that exists to facilitate the exchange of knowledge, skills, tools, and methods from an indeterminate and fluctuating set of disciplinary perspectives while conserving the boundaries of those disciplines. We argue that the dueling transdisciplinary and extradisciplinary visions for data science have important implications for how the field will mature, and that the extradiscipline concept opens novel directions for studying academic knowledge production in STS, contributing additional precision to the literature on disciplinarity and its permutations.

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