Experimentalists often need help to analyse data. Here’s how to ensure your collaboration is productive.
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Data science is increasingly an integral part of research. But data scientists can wear many hats: interdisciplinary translator, software engineer, project manager and more. Fulfilling all these roles is challenging enough; but this difficulty can be exacerbated by differing expectations and, frankly, an undervaluing of data scientists’ contributions.
For example, although our primary role is data analysis, researchers often approach data scientists for help with data acquisition and wrangling as well as software development. Although in one sense this is ‘technical’ work, which perhaps only a data scientist can do, thinking of it as such overlooks its deep connection with reproducible research. The work also involves elements of data management, project documentation and adherence to best practices.
As data scientists with a collective 17 years of experience across dozens of interdisciplinary projects, we have seen at first hand what does and doesn’t work in collaborations. Here, we offer tips for how to make working relationships more productive and rewarding. To our fellow data scientists: this is how we strike the balance. To the general audience: these are the parts of data science with which everyone on the team should engage.Set boundaries and norms for how communication will happen.
Interdisciplinary collaborations that integrate data science can be challenging, but we have found these guidelines to be effective. Many involve skills that you can develop and refine over time. Thoughtful communication, careful project organization and equitable working relationships transform projects into genuine collaborations, yielding research that would not otherwise be possible.
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