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Role of machine and organizational structure in science

Thu, Moe Kyaw ; Beppu, Shotaro ; Yarime, Masaru and Shibayama, Sotaro LU (2022) In PLoS ONE 17(8).
Abstract

The progress of science increasingly relies on machine learning (ML) and machines work alongside humans in various domains of science. This study investigates the team structure of ML-related projects and analyzes the contribution of ML to scientific knowledge production under different team structure, drawing on bibliometric analyses of 25,000 scientific publications in various disciplines. Our regression analyses suggest that (1) interdisciplinary collaboration between domain scientists and computer scientists as well as the engagement of interdisciplinary individuals who have expertise in both domain and computer sciences are common in ML-related projects; (2) the engagement of interdisciplinary individuals seem more important in... (More)

The progress of science increasingly relies on machine learning (ML) and machines work alongside humans in various domains of science. This study investigates the team structure of ML-related projects and analyzes the contribution of ML to scientific knowledge production under different team structure, drawing on bibliometric analyses of 25,000 scientific publications in various disciplines. Our regression analyses suggest that (1) interdisciplinary collaboration between domain scientists and computer scientists as well as the engagement of interdisciplinary individuals who have expertise in both domain and computer sciences are common in ML-related projects; (2) the engagement of interdisciplinary individuals seem more important in achieving high impact and novel discoveries, especially when a project employs computational and domain approaches interdependently; and (3) the contribution of ML and its implication to team structure depend on the depth of ML.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS ONE
volume
17
issue
8
article number
e0272280
pages
17 pages
publisher
Public Library of Science (PLoS)
external identifiers
  • pmid:35951620
  • scopus:85135923424
ISSN
1932-6203
DOI
10.1371/journal.pone.0272280
language
English
LU publication?
yes
id
aa6a4bdf-c0ab-413a-804c-67df5bd80454
date added to LUP
2022-08-14 04:36:32
date last changed
2024-06-13 15:49:39
@article{aa6a4bdf-c0ab-413a-804c-67df5bd80454,
  abstract     = {{<p>The progress of science increasingly relies on machine learning (ML) and machines work alongside humans in various domains of science. This study investigates the team structure of ML-related projects and analyzes the contribution of ML to scientific knowledge production under different team structure, drawing on bibliometric analyses of 25,000 scientific publications in various disciplines. Our regression analyses suggest that (1) interdisciplinary collaboration between domain scientists and computer scientists as well as the engagement of interdisciplinary individuals who have expertise in both domain and computer sciences are common in ML-related projects; (2) the engagement of interdisciplinary individuals seem more important in achieving high impact and novel discoveries, especially when a project employs computational and domain approaches interdependently; and (3) the contribution of ML and its implication to team structure depend on the depth of ML.</p>}},
  author       = {{Thu, Moe Kyaw and Beppu, Shotaro and Yarime, Masaru and Shibayama, Sotaro}},
  issn         = {{1932-6203}},
  language     = {{eng}},
  number       = {{8}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS ONE}},
  title        = {{Role of machine and organizational structure in science}},
  url          = {{http://dx.doi.org/10.1371/journal.pone.0272280}},
  doi          = {{10.1371/journal.pone.0272280}},
  volume       = {{17}},
  year         = {{2022}},
}