For scientists, data is the lifeblood of research. Collecting, organizing and sharing data both within and across fields drives pivotal discoveries that make us better off and more secure.
Making data open and available, however, only answers part of the question about how different scientists — often with very different training — can draw useful conclusions from the same dataset. In order to promote and guide the cultivation and exchange of data, researchers have developed a set of principles that could make the data more findable, accessible, interoperable and reusable, or FAIR, for both people and machines.
Although these FAIR principles were first published in 2016, researchers are still figuring out how they apply to particular datasets. In a new study, researchers from the U.S. Department of Energy’s (DOE) Argonne National Laboratory, Massachusetts Institute of Technology, University of California San Diego, University of Minnesota, and University of Illinois at Urbana-Champaign have laid out a set of new practices to guide the curation of high energy physics datasets that makes them more FAIR.