Consolidating data using metadata
We believe that establishing a data custodianship environment is essential to promote the importance of managing data as assets independent of specific analytical needs, nevertheless improving the analysis and reproducibility of research results.
It will also ensure that data conforms to the FAIR data principles, which are established and reinstated by decisions and actions taken at each stage of the data lifecycle governed within this environment.
This paper begins by describing a data lifecycle management approach to develop a standard-compliant metadata management framework for translational medicine research.
This framework is designed to incorporate different types of metadata models, i.e.
In translational research studies data tend to be produced and administered “in the wild,” meaning that researchers typically devote very little consideration to how the data could be used beyond its initial purpose.
There is currently a lack of dedicated infrastructure focused on the ‘manageability’ of the data lifecycle in TM research between data collection and analysis.
Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results.
Challenges in integration and analysis of such diverse and voluminous data led to the emergence of Translational Bioinformatics (TBI).
Essentially, these platforms focus on supporting the analytical requirements of a research project ensuring its scientific goals are met during its short-term life span.With this recognition comes increasing pressure for researchers to do more with their data to ensure its availability and utility for purposes outside of the context in which they were originally generated.