Pressures from funders and the nature of many of today’s most crucial challenges are pushing more and more researchers towards interdisciplinary work. So what are the essential tools for those venturing beyond the comfort zones of their own disciplines?
Some of the answers are set out in an article titled “Ten simple rules for a successful cross-disciplinary collaboration”, published in the online open access journal PLOS Computational Biology last month.
This was written by Bernhard Knapp, research fellow in structural bioinformatics at the University of Oxford, and a team of 19 other authors. Although most are theoretical computational biologists drawing on more or less happy experiences of working with experimental scientists, Dr Knapp strongly suspects that the lessons apply much more widely.
Many of the problems that arise in interdisciplinary work, Dr Knapp explains, come down to “expectations – people are used to what happens in their own fields and assume they happen elsewhere. The earlier and more explicitly you address these issues, the better. If you just wait and see what happens, that is generally not good for the project.”
Two key areas in which such troubles can arise are flagged up in the article. The first is the danger of failing to recognise “the different pace of different fields”. Remember that “journals in different disciplines might have different periods of time from submission to publication”. Even more important is to avoid “mak[ing] assumptions about how hard fellow collaborators are working based on how long they take to get back to you with results”.
Equally crucial, according to the authors of the PLOS paper, is understanding that “different fields have different reward models”. This applies to “impact factor scales”, “the preferred ordering of authors”, even what counts as “a ‘significant’ contribution to a manuscript”. Anyone who assumes that their discipline’s way is the only way is likely to get very confused and cause friction with their partners.
Different fields often have different definitions of what counts as “data” and may use the same word in very different ways (“model” is a notorious example of a term that has as many meanings as there are disciplines). To get around this, Dr Knapp and his co-authors urge research workers to remember that “a good relationship is based on mutually understandable communication” and to “agree on a joint nomenclature”.
The “Ten Rules” also incorporate guidance on managing “structural bonds” (“keep the number of meetings at a reasonable level and set clear agendas”); valuing the unglamorous “service work” that some partners contribute to a project; and recognising when things are going wrong. They suggest that researchers should make an effort to try to “enjoy entering a completely new field of research”. And they conclude with what Dr Knapp sees as the most crucial message of all: “be synergistic”, so that even highly successful teams can build on each other’s strengths to produce something more than “just the sum of the single parts”. The essence of achieving this is “making sure to always give enough credit to partners, and caring for their interests as you would for your own”.
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