Chemical data management: An open way forward epfl_en NatureChemistry
, and data publication—all with minimal cost to researchers. The guiding principle is that data should be FAIR: easily findable, accessible, interoperable, and re-usable."At the moment of data collection, the data will be automatically converted into a standard FAIR format, making it possible to automatically publish all 'failed' and partially successful experiments together with the most successful experiment," says Smit.
But the authors go a step further, proposing that data should also be machine-actionable."We are seeing more and more data-science studies in chemistry," says Jablonka."Indeed, recent results in machine learning try to tackle some of the problems chemists believe are unsolvable. For instance, our group has made enormous progress in predicting optimal reaction conditions using machine-learning models.
Finally, the authors propose five concrete steps that the field must take to create a FAIR data-management plan:Journals need to make deposition of reusable raw data, where community standards exist, mandatory.Electronic Lab Notebooks that do not allow exporting all data into an open machine-actionable form should be avoided."We think there is no need to invent new file formats or technologies," says Patiny.
The authors also point out that just storing data in any electronic lab notebook—the current trend—does not necessarily mean that humans and machines can reuse the data. Rather, the data must be structured and published in a standardized format, and they also must contain enough context to enable data-driven actions.
"Our perspective offers a vision of what we think are the key components to bridge the gap between data and machine learning for core problems in chemistry," says Smit."We also provide an open science solution in which EPFL can take the lead."
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