We asked data scientists about their best practices for publishing usable, high-quality data — here’s what they said.
What those instructions should say varies from experiment to experiment — microscopy data require different metadata than do gene sequences, say. But according to Sarah Supp, an ecologist at Denison University in Granville, Ohio, they can generally be put into a simple ‘README’ text file that lists when, where and how the data were collected, and by whom; the licence under which they are released; whether data collection is complete; and their status — raw or processed, for instance.
For Acevedo, good metadata practice has made his lizard malaria project maintainable. “It’s like learning from trauma,” he says.What with raw numbers, exploratory dead ends and the final processed data set, “at the end of the project, there’s actually thousands of versions of the data”, says Ciera Martinez, a research data scientist at the Eric and Wendy Schmidt Center for Data Science and Environment in Berkeley, California.
That said, raw data sets can be unwieldy and expensive to store. In that case, says Martinez, a “good rule of thumb” is to publish the data that were used to generate your figures. Never post data to personal websites, says Tracy Chen, a scientific analyst at the NASA Exoplanet Science Institute in Pasadena, California, who co-authored a best-practices document for astrophysics dataData should be in an open, non-proprietary file format, says Ellen Bledsoe, who teaches ecological data science at the University of Arizona in Tucson; otherwise, they could become unreadable.
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