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Data-Centric BiologyA Philosophical Study$
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Sabina Leonelli

Print publication date: 2016

Print ISBN-13: 9780226416335

Published to Chicago Scholarship Online: May 2017

DOI: 10.7208/chicago/9780226416502.001.0001

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PRINTED FROM CHICAGO SCHOLARSHIP ONLINE (www.chicago.universitypressscholarship.com). (c) Copyright University of Chicago Press, 2019. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in CHSO for personal use.date: 15 October 2019

What Counts as Experiment?

What Counts as Experiment?

Chapter:
(p.93) 4 What Counts as Experiment?
Source:
Data-Centric Biology
Author(s):

Sabina Leonelli

Publisher:
University of Chicago Press
DOI:10.7208/chicago/9780226416502.003.0005

Chapter 4 examines the relationship between experimental practices at the bench, particularly ones that involve the physical manipulation of organic materials, and the processes through which data on organisms are packaged for dissemination. Capturing the embodied knowledge involved in experimental work is a crucial task for database curators. This type of knowledge is a key component of data analysis; and codifying it in ways that can be accessed and understood by a wide variety of users constitutes one of the fundamental challenges confronted by database curators. A close investigation of how databases are set up reveals how information about experimental practices of data production (‘meta-data’) is essential to the successful adoption and re-use of data in a new research context, and yet cannot be accurately captured without relying on regular feedback and active participation by database users. Tracing the ways in which embodied knowledge is involved in data journeys sheds light on the high number and varied expertise of individuals involved in making data travel. This leads me to portray data-centric biology as a remarkably effective form of distributed cognition; and reject the possibility that experimental research could be fully automated, and thus replaced by data analysis in silico.

Keywords:   experimentation, metadata, data generation, data analysis, scientific knowledge

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