Hoeppe2014

From emcawiki
Revision as of 08:46, 11 December 2019 by AndreiKorbut (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search
Hoeppe2014
BibType ARTICLE
Key Hoeppe2014
Author(s) Götz Hoeppe
Title Working data together: The accountability and reflexivity of digital astronomical practice
Editor(s)
Tag(s) EMCA, astronomy, data reuse, digital data, ethnomethodology, scientific observation, scientific training
Publisher
Year 2014
Language
City
Month
Journal Social Studies of Science
Volume 44
Number 2
Pages 243–270
URL Link
DOI 10.1177/0306312713509705
ISBN
Organization
Institution
School
Type
Edition
Series
Howpublished
Book title
Chapter

Download BibTex

Abstract

Drawing on ethnomethodology, this article considers the sequential work of astronomers who combine observations from telescopes at two observatories in making a data set for scientific analyses. By witnessing the induction of a graduate student into this work, it aims at revealing the backgrounded assumptions that enter it. I find that these researchers achieved a consistent data set by engaging diverse evidential contexts as contexts of accountability. Employing graphs that visualize data in conventional representational formats of observational astronomy, experienced practitioners held each other accountable by using an ‘implicit cosmology’, a shared (but sometimes negotiable) characterization of ‘what the universe looks like’ through these formats. They oriented to data as malleable, that is, as containing artifacts of the observing situation which are unspecified initially but can be defined and subsequently removed. Alternating between reducing data and deducing astronomical phenomena, they ascribed artifacts to local observing conditions or computational procedures, thus maintaining previously stabilized phenomena reflexively. As researchers in data-intensive sciences are often removed from the instruments that generated the data they use, this example demonstrates how scientists can achieve agreement by engaging stable ‘global’ data sets and diverse contexts of accountability, allowing them to bypass troubling features and limitations of data generators.

Notes