{"id":103748,"date":"2025-04-30T12:27:10","date_gmt":"2025-04-30T10:27:10","guid":{"rendered":"https:\/\/www.pcb.ub.edu\/irb-barcelona-is-coordinating-a-study-involving-more-than-a-hundred-experts-to-create-a-fair-database-of-molecular-simulations\/"},"modified":"2025-04-30T17:10:35","modified_gmt":"2025-04-30T15:10:35","slug":"irb-barcelona-is-coordinating-a-study-involving-more-than-a-hundred-experts-to-create-a-fair-database-of-molecular-simulations","status":"publish","type":"post","link":"https:\/\/www.pcb.ub.edu\/en\/irb-barcelona-is-coordinating-a-study-involving-more-than-a-hundred-experts-to-create-a-fair-database-of-molecular-simulations\/","title":{"rendered":"IRB Barcelona is coordinating a study involving more than a hundred experts to create a FAIR database of molecular simulations"},"content":{"rendered":"

IRB Barcelona<\/a>, based at the Barcelona Science Park, has coordinated the publication of a study with more than a hundred experts in molecular simulation, advocating for a paradigm shift in the management of molecular dynamics data. The article, published in the journal Nature Methods, supports the implementation of FAIR principles (\u201cFindable, Accessible, Interoperable, Reusable\u201d) to enhance the reproducibility of calculations and facilitate their subsequent use as a source of information on the flexibility of biomacromolecules. The work, led by Dr. Modesto Orozco and Dr. Adam Hospital, proposes the creation of a common infrastructure to store and reuse data in the context of the AI revolution.<\/strong><\/p>\n

Unlike structural biology or genomics\u2014where data storage and sharing follow established common standards\u2014molecular simulation data remain fragmented and are often left forgotten on personal computers. This hinders the reproducibility of calculations and prevents their future reuse.<\/p>\n

The result is a formidable obstacle to integrating such data into the workflows of structural biology and biophysics, while also slowing the development of artificial intelligence (AI) methods, whose training relies heavily on access to vast amounts of dynamic data.<\/p>\n

In the article published in the journal Nature Methods<\/em>, more than one hundred researchers raise awareness of this situation and advocate for a change of model: applying the FAIR principles\u2014which guarantee that the data are findable, accessible, interoperable, and reusable\u2014to simulation results. The article has been signed by renowned international experts, including two Nobel laureates and leading figures from the best research centres in the world. The proposed goal is to build an open and sustainable ecosystem that amplifies the impact of these data and avoids unnecessary duplication.<\/p>\n