Ten simple rules for a successful cross-disciplinary collaboration

TY - JOUR

T1 - Ten simple rules for a successful cross-disciplinary collaboration

AU - Knapp, Bernhartd

AU - Bardenet, Rémi

AU - Bernabeu, Miguel O

AU - Bordas, Rafael

AU - Bruna, Marina

AU - Calderhead, Ben

AU - Cooper, Jonathan

AU - Fletcher, Alexander G

AU - Groen, Derek

AU - Kuijper, Bram

AU - Lewis, Joanna

AU - McInemy, Gregg

AU - Minssen, Timo

AU - Osborne, James

AU - Paulitschke, Verena

AU - Pitt-Francis, Joe

AU - Todoric, Jelena

AU - Yates, Christian A

AU - Gavaghan, David

AU - Deane, Charlotte M

N1 - Knapp B, Minssen T., Bardenet R, Bernabeu MO, Bordas R, Bruna M, et al. (2015) Ten Simple Rules for a Successful Cross-Disciplinary Collaboration. PLoS Comput Biol 11(4): e1004214. doi:10.1371/journal.pcbi.1004214

PY - 2015/4/1

Y1 - 2015/4/1

N2 - Cross-disciplinary collaborations have become an increasingly important part of science. They are seen as a key factor for finding solutions to pressing societal challenges on a global scale including green technologies, sustainable food production and drug development. This has also been realized by regulators and policy-makers, as it is reflected in the 80 billion Euro "Horizon 2020" EU Framework Programme for Research and Innovation. This programme puts special emphasis at breaking down barriers between fields to create a path breaking environment for knowledge, research and innovation.However, igniting and successfully maintaining cross-disciplinary collaborations can be a delicate task. In this article we focus on the specific challenges associated with cross-disciplinary research in particular from the perspective of the theoretician. As research fellows of the 2020 Science project (http://www.2020science.net) and collaboration partners, we bring broad experience of developing interdisciplinary collaborations [2–12]. We intend this guide for early career computational researchers as well as more senior scientists who are entering a cross disciplinary setting for the first time. We describe the key benefits, as well as some possible pitfalls, arising from collaborations between scientists with backgrounds in very different fields.This paper has inter alia been cited by Times Higher education: http://www.timeshighereducation.co.uk/news/people/the-secrets-to-successful-interdisciplinary-work/2020267.article .

AB - Cross-disciplinary collaborations have become an increasingly important part of science. They are seen as a key factor for finding solutions to pressing societal challenges on a global scale including green technologies, sustainable food production and drug development. This has also been realized by regulators and policy-makers, as it is reflected in the 80 billion Euro "Horizon 2020" EU Framework Programme for Research and Innovation. This programme puts special emphasis at breaking down barriers between fields to create a path breaking environment for knowledge, research and innovation.However, igniting and successfully maintaining cross-disciplinary collaborations can be a delicate task. In this article we focus on the specific challenges associated with cross-disciplinary research in particular from the perspective of the theoretician. As research fellows of the 2020 Science project (http://www.2020science.net) and collaboration partners, we bring broad experience of developing interdisciplinary collaborations [2–12]. We intend this guide for early career computational researchers as well as more senior scientists who are entering a cross disciplinary setting for the first time. We describe the key benefits, as well as some possible pitfalls, arising from collaborations between scientists with backgrounds in very different fields.This paper has inter alia been cited by Times Higher education: http://www.timeshighereducation.co.uk/news/people/the-secrets-to-successful-interdisciplinary-work/2020267.article .

KW - Faculty of Law

KW - Interdisciplinary collaboration, computational biology, law, Horizon 2020

U2 - 10.1371/journal.pcbi.1004214

DO - 10.1371/journal.pcbi.1004214

M3 - Journal article

C2 - 25928184

SN - 1553-734X

VL - 11

JO - P L o S Computational Biology (Online)

JF - P L o S Computational Biology (Online)

IS - 4

ER -