Road to effective data curation for translational research.

Translational research today is data-intensive and requires multi-stakeholder collaborations to generate and pool data together for integrated analysis. This leads to the challenge of harmonization of data from different sources with different formats and standards, which is often overlooked during project planning and thus becomes a bottleneck of the research progress. We report on our experience and lessons learnt about data curation for translational research garnered over the course of the eTRIKS program (https://www.etriks.org), a unique, 5-year, cross-organizational, cross-cultural collaboration project funded by the Innovative Medicines Initiative of the EU. Here, we discuss the obstacles and suggest what steps are needed for effective data curation in translational research, especially for projects involving multiple organizations from academia and industry.

[1]  Ryan Ramanujam,et al.  Occurrence of Anti-Drug Antibodies against Interferon-Beta and Natalizumab in Multiple Sclerosis: A Collaborative Cohort Analysis , 2016, PloS one.

[2]  Gail Steinhart,et al.  De-Mystifying the Data Management Requirements of Research Funders. , 2012, Issues in Science and Technology Librarianship.

[3]  Martin Hofmann-Apitius,et al.  Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders , 2015, International journal of molecular sciences.

[4]  E. Perakslis,et al.  Effective knowledge management in translational medicine , 2010, Journal of Translational Medicine.

[5]  Martin Romacker,et al.  eTRIKS Standards Starter Pack Release 1.1 April 2016 , 2016 .

[6]  Lucila Ohno-Machado,et al.  The Data Tags Suite (DATS) model for discovering data access and use requirements , 2020, GigaScience.

[7]  Arcadi Navarro,et al.  The European Genome-phenome Archive of human data consented for biomedical research , 2015, Nature Genetics.

[8]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[9]  Lucila Ohno-Machado,et al.  DATS, the data tag suite to enable discoverability of datasets , 2017, Scientific Data.

[10]  Charles Auffray,et al.  Application of ’omics technologies to biomarker discovery in inflammatory lung diseases , 2013, European Respiratory Journal.

[11]  Bernd Rinn,et al.  FAIRDOMHub: a repository and collaboration environment for sharing systems biology research , 2016, Nucleic Acids Res..

[12]  Massimiliano Izzo,et al.  FAIRsharing as a community approach to standards, repositories and policies , 2019, Nature Biotechnology.

[13]  Denny Verbeeck,et al.  The RA-MAP Consortium: a working model for academia–industry collaboration , 2018, Nature Reviews Rheumatology.

[14]  Anthony Rowe,et al.  Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium , 2019, BMC Bioinformatics.