Role of machine and organizational structure in science
(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.
(Less)
- author
- Thu, Moe Kyaw ; Beppu, Shotaro ; Yarime, Masaru and Shibayama, Sotaro LU
- organization
- publishing date
- 2022
- 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
- 2024-06-28 19:57:21
@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}},
}