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Data Standards are Needed to Move Translational Medicine Forward

Ann Marie Martin, Nathalie Seigneuret, Ferran Sanz and Michel Goldman

Data standards and data management procedures help to overcome the data and information management challenges in translational medicine for the purpose of deriving new results and knowledge. In regulated medicine, the reason for using data standards is to facilitate the review, verification and replication of the research results as part of a drug or device approval process. Additionally, data standards are indispensable in enabling ongoing safety and quality surveillance of marketed products. Data management procedures must be adhered to and documented to ensure the integrity and verifiability of the research results. Lack of data standards and appropriate data management procedures and absence of good documentation make it hard and labour intensive to combine data in order to answer new research questions. Fortunately, many data standards are available which are well-recognized and certified and which can be readily applied. We argue that it does not matter which data standards are chosen as long as well-recognized data standards are used and the data management procedures are documented and referenced in a citable way in the write-up of the study results and publications. Data could be used in far more powerful and efficient ways when one observes these principles. For this reason, the Innovative Medicines Initiative (IMI) is actively encouraging its projects to both use and contribute to data standards.