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Massive amounts of data are collected and stored on a routine basis in virtually all domains of human activities. Such data are potentially useful to biomedicine. Yet, access to data for research purposes is hindered by the fact that different kinds of individual-patient data reside in disparate, unlinked silos. We propose that data cooperatives can promote much needed data aggregation and consequently accelerate research and its clinical translation. Data cooperatives enable direct control over personal data, as well as more democratic governance of data pools. This model can realize a specific kind of data economy whereby citizens and communities are empowered to steer data use according to their motivations, preferences, and concerns. Policy makers can promote this model by recognizing citizens' rights to access and to obtain a copy of their own data, and by funding distributed data infrastructures piloting new data aggregation models. ; ISSN:2210-5433 ; ISSN:2210-5441

<b><i>Aims:</i></b> This study examined the attitudes of 1,146 Swiss University students to direct-to-consumer (DTC) genomic testing and to genomic research participation. <b><i>Methods:</i></b> Data were collected through a self-completion online questionnaire by students from 2 higher education institutions in Zurich, Switzerland. The survey aimed to capture motivation for undergoing or refraining from genomic testing, reactions to mock genetic risk results, and views about contributing data to scientific research. Descriptive and inferential statistics were used for the analysis. <b><i>Results:</i></b> A total of 1.5% of the students had undergone testing. Most respondents were studying natural sciences and were interested in undergoing DTC genomic testing. The main motive was to contribute their data to scientific research, followed closely by their interest to find out disease risks and personal traits. Overall, 41% of the respondents were not interested in DTC tests. The primary reasons were concerns about receiving potentially worrying results. There was a significant correlation between studying natural sciences, as opposed to the humanities, and interest in undergoing testing. Male respondents were more interested in testing compared to females. There was a strong interest in genetic research participation and notably limited privacy concerns. <b><i>Conclusion:</i></b> Although 59% of the respondents were interested in DTC genomic testing, they were not likely to be affected by them or act upon them. This raises questions about concerns relating to potential risks of DTC genomics users and users' understanding of genetic information including their awareness of privacy risks. Furthermore, the strong interest in genetic research participation signals an underexplored personal utility of genomic testing which needs to be both better understood and better harnessed.

We are in the middle of a technological upheaval that will transform the way society is organized. We must make the right decisions now.

We are in the middle of a technological upheaval that will transform the way society is organized. We must make the right decisions now

Background While the European Union is striving to become the 'Innovation Union', there remains a lack of quantifiable indicators to compare and benchmark regional innovation clusters. To address this issue, a HealthTIES (Healthcare, Technology and Innovation for Economic Success) consortium was funded by the European Union's Regions of Knowledge initiative, research and innovation funding programme FP7. HealthTIES examined whether the health technology innovation cycle was functioning differently in five European regional innovation clusters and proposed regional and joint actions to improve their performance. The clusters included BioCat (Barcelona, Catalonia, Spain), Medical Delta (Leiden, Rotterdam and Delft, South Holland, Netherlands), Oxford and Thames Valley (United Kingdom), Life Science Zürich (Switzerland), and Innova Észak-Alföld (Debrecen, Hungary). Methods Appreciation of the 'triple helix' of university–industry–government innovation provided the impetus for the development of two quantifiable innovation indexes and related indicators. The HealthTIES H-index is calculated for disease and technology platforms based on the h-index proposed by Hirsch. The HealthTIES Innovation Index is calculated for regions based on 32 relevant quantitative and discriminative indicators grouped into 12 categories and 3 innovation phases, namely 'Input' (n = 12), 'Innovation System' (n = 9) and 'Output' (n = 11). Results The HealthTIES regions had developed relatively similar disease and technology platform profiles, yet with distinctive strengths and weaknesses. The regional profiles of the innovation cycle in each of the three phases were surprisingly divergent. Comparative assessments based on the indicators and indexes helped identify and share best practice and inform regional and joint action plans to strengthen the competitiveness of the HealthTIES regions. Conclusion The HealthTIES indicators and indexes provide useful practical tools for the measurement and benchmarking of university–industry–government innovation in European medical and life science clusters. They are validated internally within the HealthTIES consortium and appear to have a degree of external prima facie validity. Potentially, the tools and accompanying analyses can be used beyond the HealthTIES consortium to inform other regional governments, researchers and, possibly, large companies searching for their next location, analyse and benchmark 'triple helix' dynamics within their own networks over time, and to develop integrated public–private and cross-regional research and innovation strategies in Europe and beyond. ; ISSN:1478-4505

Background: While the European Union is striving to become the 'Innovation Union', there remains a lack of quantifiable indicators to compare and benchmark regional innovation clusters. To address this issue, a HealthTIES (Healthcare, Technology and Innovation for Economic Success) consortium was funded by the European Union's Regions of Knowledge initiative, research and innovation funding programme FP7. HealthTIES examined whether the health technology innovation cycle was functioning differently in five European regional innovation clusters and proposed regional and joint actions to improve their performance. The clusters included BioCat (Barcelona, Catalonia, Spain), Medical Delta (Leiden, Rotterdam and Delft, South Holland, Netherlands), Oxford and Thames Valley (United Kingdom), Life Science Zürich (Switzerland), and Innova Észak-Alföld (Debrecen, Hungary).Methods: Appreciation of the 'triple helix' of university–industry–government innovation provided the impetus for the development of two quantifiable innovation indexes and related indicators. The HealthTIES H-index is calculated for disease and technology platforms based on the h-index proposed by Hirsch. The HealthTIES Innovation Index is calculated for regions based on 32 relevant quantitative and discriminative indicators grouped into 12 categories and 3 innovation phases, namely 'Input' (n = 12), 'Innovation System' (n = 9) and 'Output' (n = 11).Results: The HealthTIES regions had developed relatively similar disease and technology platform profiles, yet with distinctive strengths and weaknesses. The regional profiles of the innovation cycle in each of the three phases were surprisingly divergent. Comparative assessments based on the indicators and indexes helped identify and share best practice and inform regional and joint action plans to strengthen the competitiveness of the HealthTIES regions.Conclusion: The HealthTIES indicators and indexes provide useful practical tools for the measurement and benchmarking of university–industry–government ...