Abstract: In computing science, much attention has been paid to generic methods for sharing data in secure infrastructures. These sorts of methods and infrastructures are, of course, necessary for sharing healthcare data. The authors are, however, a long way away from being able to realise the potential of medical and healthcare data to support the sorts of extensive, data-intensive experiments being demanded by precision and stratified medicine. A key architectural problem remaining to be solved is how to maintain control of patient data within the governance of local data jurisdictions, while also allowing these jurisdictions to engage with experiment designs that (because of the need to scale to large population sizes) may require analyses across several jurisdictions. This study provides a snapshot of architectural work underway to provide a clear, effective structure of data safe havens within jurisdictions. It then describes how formally specified experiment designs can be used to enable jurisdictions to work together on experiments that no single jurisdiction could tackle alone. The authors’ current work relates to two jurisdictions (in Scotland and in Italy), but the architecture and methods are general across similar jurisdictions.

Citation: David Robertson, Fausto Giunchiglia, Stephen Pavis, Ettore Turra, Gabor Bella, Elizabeth Elliot, Andrew Morris, Malcolm Atkinson, Gordon McAllister, Areti Manataki, Petros Papapanagiotou, and Mark Parsons (2016). Healthcare data safe havens: towards a logical architecture and experiment automation. The Journal of Engineering, Institution of Engineering and Technology, October, 2016. This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), http://digital-library.theiet.org/content/journals/10.1049/joe.2016.0170.

Download: http://bit.ly/2j3khFT

Executive Summary. This report first situates and outlines the potential of social computation to provide the basis for Social Collective Intelligence (SCI) in future systems. This involves the close interaction of social groups and machines together with systems of incentives and social structures to perform tasks that would otherwise be difficult to achieve either using entirely human or entirely machine solutions. The deliverable considers the challenges both from a technical and from a social science standpoint, identifying the potential for aligning them in order to provide an interdisciplinary perspective on the development of SCI systems. The paper then describes some of the challenges in developing an engineering approach to the development of such systems. Finally the paper outlines some of the “big questions” that arise from the framework for SCI research developed in the white paper.

Citation: Robertson, D., Anderson, S., Carreras, I., Miorandi, D., “D2.1 White Paper on Research Challenges in Social Collective Intelligence WP2 – Research Challenges and Strategic Analysis”, Social-IST (2013).

Download: http://bit.ly/1qzcWOx

Executive Summary. This document includes the outcomes of the activities carried out by the Social-IST Consortium on the identification and analysis of the application areas for which R&D&I initiatives on Social Collective Intelligence (SCI) can have a major impact. This serves as the basis for defining a set of recommendations and a possible roadmap to be taken into consideration when drafting future FET initiatives in the field of Social Collective Intelligence.

In a preliminary phase the Consortium identified, through desktop search, six relevant application areas for SCI, namely the Future of Work, the Future of Learning, Mobility and Transport in Cities of the Future, Healthcare and Well Being, Smart Energy and the Future of Science and Innovation. These areas have been analysed and discussed in details, in particular by means of (i) the two workshops held with the Social-IST Scientific Panel experts (ii) a Web survey open to the research community at large (iii) the final project event held in Oct. 2013. For each area, a number of scenarios were elaborated, leading to the identification of impacts on science, technology and society and of emerging research challenges.

The results of this analysis have been used for defining a roadmap for future EU initiatives in the field of SCI. This included (i) a proposal in terms of research methodology for running SCI projects and initiatives, (ii) a taxonomy of the most relevant research communities (iii) a mapping to the Horizon2020 Work programme and related calls.

This document is expected to provide some key insights on how to potentially exploit a Social Collective Intelligence approach in future calls and EU initiatives.

Citation: Carreras, I., Anderson, S., Robertson, D., Miorandi, D., “D3.1 Roadmap for FET Initiatives in Social Collective Intelligence, WP3 – High Impact Application Areas and Roadmapping”, Social-IST (2013).

Download: http://bit.ly/1p8Tmb1