Objectives In the era of big data, the intensive care unit (ICU) is very likely to benefit from real-time computer analysis and modeling based on close patient monitoring and Electronic Health Record data. MIMIC is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools and experience to be shared. OMOP-CDM is spreading all over the world. The objective was to evaluate the difficulty to transform MIMIC into an OMOP (MIMIC-OMOP) database and the benefits of this transformation for analysts. Material & Method A documented, tested, versioned, exemplified and open repository has been set up to support the transformation and improvement of the MIMIC community’s source code. The resulting data set was evaluated over a 48-hour datathon. Result With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables have been standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon with 160 participants and 15,000 requests executed with a maximum duration of one minute. Conclusion The resulting MIMIC-OMOP dataset is the first MIMIC-OMOP dataset available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.