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This is part of an open-ended series of marginalia to Donoho’s 50 Years of Data Science 2015 paper.

In many scientific labs, the skills and knowledge required for the research (e.g. linguistics fieldwork, sociological interview practices, wet-lab biological analysis) are not the same skills involved in software engineering or in data curation and maintenance.

Some scientists thus find themselves as the “accidental techie” in their local lab — maybe not even as the “accidental data scientist“, but doing specific software engineering tasks — you’re the poor schmuck stuck making sure that everybody’s spreadsheets validate, that nobody sorts the data on the wrong field, that you removed the “track changes” history the proposal before you sent it off to the grant agencies, etc.

Scientific labs of any scale (including academic labs, though they probably don’t have the budgets or the incentives) can really benefit from data science, but especially software engineering expertise, even — or perhaps especially — when the engineer isn’t an inside-baseball expert in the research domain.  I list below a number of places an experienced software engineer (or data scientist) can make a difference to a field she (or he) doesn’t know well.

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