Speaker
Description
A consensus has been reached that research data must be FAIR. Most researchers understand it as making their data findable, accessible, interoperable, and reusable during or after the publication process. However, by this stage, it often turns out that some key information about experiments is missing or the experimental design is irreparably incomplete or flawed, questioning the validity of the corresponding datasets. One solution is producing FAIR data from the very inception of the study—rather than trying to make them FAIR post hoc. This requires a FAIR lab, a kind of lab organisation where all ins and outs of the research process can be traced back to their origins, starting with unambiguous identification of samples and reagents, faithful registration of protocols and metadata, and ending by the quality control of final data, their sharing and linking to publications. Acceding to this higher level of responsibility for own research data takes a significant change in lab culture.
