Consolidating data using metadata

There is currently a gap in the informatics literature focusing on the ‘active management’ of data assets during the lifetime of a research project.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.Here we present the development of a lifecycle-based methodology to create a metadata management framework based on community driven standards for standardisation, consolidation and integration of TM research data.

More recently, four foundational principles: Findability, Accessibility, Interoperability, and Re-usability (FAIR) have become the guiding means towards achieving successful reuse of scholarly data.

Translational research (TR) is often described as a data intensive discipline.

An intrinsic complexity in the translational approach is brought by the granularity, scale and diversity of data collected and observed during a study.

The FAIR data principles are guidelines that define the criteria for achieving shareable and reusable data.

However, this still leaves the details of how to actually achieve this in practice.

Focusing on the ‘active management’ of data, the platform provides a set of core functionalities to handle metadata definition, file management and data loading, data storage, retrieval, visualization, export and data sharing.