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Editorial
. 2021 May 13;17(5):e1008879.
doi: 10.1371/journal.pcbi.1008879. eCollection 2021 May.

Ten simple rules to cultivate transdisciplinary collaboration in data science

Faryad Sahneh  1   2 , Meghan A Balk  3   4 , Marina Kisley  2 , Chi-Kwan Chan  1   5 , Mercury Fox  1   6   7   8   9 , Brian Nord  10   11   12 , Eric Lyons  3   13   14 , Tyson Swetnam  3 , Daniela Huppenkothen  15   16 , Will Sutherland  17 , Ramona L Walls  3 , Daven P Quinn  18 , Tonantzin Tarin  19 , David LeBauer  20 , David Ribes  17 , Dunbar P Birnie 3rd  21 , Carol Lushbough  22   23 , Eric Carr  24 , Grey Nearing  25 , Jeremy Fischer  26   27 , Kevin Tyle  28 , Luis Carrasco  24 , Meagan Lang  29 , Peter W Rose  30 , Richard R Rushforth  31 , Samapriya Roy  32 , Thomas Matheson  33 , Tina Lee  14 , C Titus Brown  34 , Tracy K Teal  35 , Monica Papeș  24   36 , Stephen Kobourov  2 , Nirav Merchant  1   14
Affiliations
Editorial

Ten simple rules to cultivate transdisciplinary collaboration in data science

Faryad Sahneh et al. PLoS Comput Biol. .
No abstract available

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. How the rules work together and intersect.
There are multiple components in collaborations: person–person interactions, person–technology interactions, person–data interactions, and data–technology interactions. Synergy between these components results in a successful collaboration.
See this image and copyright information in PMC

References

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