Biomedical researchers are facing data management challenges brought by a new generation of data driven by the advent of translational medicine research. These challenges are further complicated by the recent calls for data re-use and long-term stewardship spearheaded by the FAIR principles initiative. As a result, there is an increasingly wide-spread recognition that advancing biomedical science is becoming dependent on the application of data science to manage and utilize highly diverse and complex data in ways that give it context, meaning, and longevity beyond its initial purpose. However, current methods and practices in biomedical informatics remain to adopt a traditional linear view of the informatics process (collect, store and analyse); focusing primarily on the challenges in data integration and analysis, which are challenges only pertaining
to a part of the overall life cycle of research data. The aim of this research is to facilitate the adoption and integration of data management practices into the research life cycle of biomedical projects, thus improving their capabilities into solving data management-related challenges that they face throughout the
course of their research work. To achieve this aim, this thesis takes a data life cycle approach to define and develop a systematic methodology and framework towards the systematization of FAIR data management in biomedical research projects. The overarching contribution of this research is the provision of a data-state life cycle model for research data management in Biomedical Translational Research Projects. This model provides insight into the dynamics between 1) the purpose of a research-driven data use case, 2) the data requirements that renders data in a state fit for purpose, 3) the data management functions that prepare and act upon data and 4) the resulting state of data that is _t to serve the use case. This insight led to the development of a FAIR data management framework, which is another contribution of this thesis. This framework provides data managers the groundwork, including the data models, resources and capabilities, needed to build a FAIR data management environment to manage data during the operational stages of a biomedical research project. An exemplary implementation of this architecture (PlatformTM) was developed and validated by real-world research datasets produced by collaborative research programs funded by the Innovative Medicine Initiative (IMI) BioVacSafe 1 , eTRIKS 2 and FAIRplus 3.Open Acces
