In biotechnology and biomedical research, a sometimes too rigid separation between Wet (experimental laboratories) and Dry (computational analysis of generated data) environments slows down innovation.
However, knowing the context in which data are generated is essential for a detailed understanding of the biological or medical questions posed. This context is necessary to ensure scientific interoperability in the case of data aggregation, but also to define relevant and adapted analytical models.
Silos between players, materialized by the lack of common collaborative tools and by non-integrated tools, significantly slow down scientific production and innovation in healthcare.
Connectivity between the laboratory (wet lab) and analytical environments responsible for processing the data generated (dry lab) remains one of the main challenges facing the life sciences industry.
Over and above the importance of ensuring linear connectivity between skills, scientific discoveries are made possible by the countless iterations between scientific players throughout the projects. Hypotheses, mistakes and unexpected results are all part of the discovery process. Today, however, there are too few environments in which this can be put into practice, let alone structured to accelerate science.
Our aim is to transform the connected laboratory into an integrated ecosystem, where every piece of data generates immediate and lasting value.
