On 9 December 2016, the second workshop for the Big Data Europe Health, Demographic Change and Wellbeing societal challenge was held in Brussels. The aim of this workshop was to highlight progress from the BigDataEurope project in building the foundations of a generically applicable big data platform which can be applied across all Horizon 2020 societal challenges. This workshop specifically focused on health, and showcased our first pilot’s application to early bioscience research data.
The workshop had 15 participants, from within the health domain and outside it, including many participants from the European Commission. Together we discussed different perspectives on how we may use appropriate H2020 instruments and work programmes to better integrate the ecosystem of linked data repositories, data management services and virtual collaboration environments to increase the pace of knowledge sharing in health.
The workshop featured presentations from BDE’s Simon Scerri and Aad Versteden on the general goals and progress of the BigDataEurope project and the BDE infrastructure respectively. After lunch, Ronald Siebes (BDE / VU Amsterdam) presented the first pilot in this specific domain. More information on that pilot can be found here. An extensive round-table discussion followed, in which possible options for new applications and connections were considered.
One question raised was whether the generic BDE infrastructure can be used by European SMEs. The fact that the BDE infrastructure is completely Open Source, very easy to install and features intuitive interface components makes re-use relatively simple even for smaller institutions and companies.
A significant part of the discussion focussed on possible new use cases for expanding the scope of the pilot. One suggestion was to look at post-hoc integration of clinical data, which represents a typical problem of data ‘variance’. This would require integrating information from different versions of medical questionnaires, which may be recorded or stored in different ways. Data provenance is also a key concern, as keeping a trail of what has happened to clinical data is crucial to tracking patients’ histories. Once integrated, this data could then be mined to identify biases or data patterns.
Finally, the workshop participants discussed potential connections to other European projects. Here many projects were mentioned including the MIDAS project, the Big-O project on childhood obesity, the PULSE projects and IMI / IMI2 projects including EMIF. We will be seeking collaborations with these projects and will continue to develop new and interesting Big Data use cases in this domain in the coming year.