From Raw Data to FAIR Data: The FAIRification Workflow for Health Research.

Background: FAIR (findability, accessibility, interoperability, and reusability) guiding principles seek the reuse of data and other digital research input, output, and objects (algorithms, tools, and workflows that led to that data) making them findable, accessible, interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governed initiative - defined a seven-step FAIRification process focusing on data, but also indicating the required work for metadata. This FAIRification process aims at addressing the translation of raw datasets into FAIR datasets in a general way, without considering specific requirements and challenges that may arise when dealing with some particular types of data.

Data Provenance Standards and Recommendations for FAIR Data.

This article reviews the main characteristics of five widely used data provenance models and recommendations. We suggest a set of six provenance properties that should be satisfied by any provenance model as a basis for further implementation of provenance mechanisms, supporting the findable, accessible, interoperable and reusable (FAIR) principles for both, research and health data.