PlatformTM, a standards-based data custodianship platform for translational medicine research

Ibrahim Emam,Philippe Rocca-Serra,Alex Matthews,Denny Verbeeck,Yike Guo,Giuseppe Del Giudice,Lucinda Grainger,Florian Guitton,Paul Houston,Erica Borgogni,Stelios Pavlidis,Vahid Elyasigomari,Mansoor Saqi

doi:10.1038/s41597-019-0156-9

Abstract

Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies; focusing primarily on the challenges in data integration and analysis. However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing. 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. 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. Based on this framework, we also present the development of a new platform (PlatformTM) focused on managing the lifecycle for translational research data assets.

Highlights

Translational research (TR) is often described as a data intensive discipline
We believe that establishing a data custodianship environment is essential to promote the importance of managing data as assets independent of specific analytical needs, improving the analysis and reproducibility of research results
We present the design, development, and application of PlatformTM: a standards-compliant data custodianship environment for all user roles involved in managing the data lifecycle between data collection and data analysis

Summary

Introduction

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. Recent reviews of non-commercial TBI solutions[3,4] demonstrate their success in enabling TR studies to conduct integrative analysis, generation and validation of complex hypotheses, data exploration and cohort discovery[5]. These platforms focus on supporting the analytical requirements of a research project ensuring its scientific goals are met during its short-term life span. Other platforms such as dbGap[6] and ImmPort[7] offer a data archive to preserve data after the termination of a project, but they do not play a role during its active phase

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Conclusion