The Director’s View: European Open Science Cloud

‘The title of this first report may have a slightly threatening ring to it and indeed, if we do not act, there might be a looming crisis on the Horizon. The vast majority of all data in the world (in fact up to 90%) has been generated in the last two years. Computers have long surpassed individuals in their ability to perform pattern recognition over large data sets. Scientific data is in dire need of openness, better handling, careful management, machine actionability and sheer re-use. One of the sobering conclusions of our consultations was that research infrastructure and communication appear to be stuck in the 20th century paradigm of data scarcity. We should see this step-change in science as an enormous opportunity and not as a threat. The EOSC is a positive ‘Cloud on the Horizon’ to be realised by 2020. Ultimately, actionable knowledge and translation of its benefits to society will be handled by humans in the ‘machine era’ for decades to come, machines are just made to serve us.

But let’s not ignore the facts: the science system is in landslide transition from data-sparse to data-saturated. Meanwhile, scholarly communication, data management methodologies, reward systems and training curricula do not adapt quickly enough if at all to this revolution. Researchers, funders and publishers (I always thought that meant making things public) keep each other hostage in a deadly embrace by continuing to conduct, publish, fund and judge science in the same way as in the past century.

So far, no-one seems to be able to break this deadlock. Open Access articles are indispensable but solve only a fraction of the problem. Neither ‘open research data’ alone will do. We still try to press petabytes of results in length-restricted narrative, effectively burying them behind firewalls or in supplementary data behind decaying hyperlinks and then trying to mine them back again. Computers hate ambiguous human language and love structured, machine actionable data, while machine readable data are a turnoff for the human mind. As computers have become indispensable research assistants, we better make what we publish understandable to them. We need both in concert to form social machines; in order to do pattern recognition in complex, interlinked data as well as confirmational studies on methodology and rhetorics in plain understandable human language.

We hope that this report will be part of a game-changing effort of all European Member States and our international partners towards true Open Science.’