Romasanta, A.K. (Angelo Kenneth)

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    Systematizing serendipity for big science infrastructures: The ATTRACT project
    (Elsevier, 2021-08-12) García-Tello, P. (Pablo); Wareham-Mathiassen, T. (Thomas); Wareham, J. (Jonathan); Nordberg, M. (Markus); Romasanta, A.K. (Angelo Kenneth); Pujol-Priego, L. (Laia)
    Big Science Research Infrastructures (BSRIs) are tremendous sources of ‘deep-tech’ with the potential to foment alternative commercial applications in diverse industries. Yet, cultivating novel applications of BSRI technologies is not straightforward due to misalignment between their scientific mission, large technological risks, market uncertainties, and long development times. Given these challenges, research is needed to understand if- and how-serendipitous innovations can be purposefully developed from BSRIs. In this study, we analyse ATTRACT, a novel initiative funded by the European Commission's Horizon 2020 program, which funded 170 projects with €100,000 each to develop a proof-of-concept commercial application of BSRI technologies within one year. Our analysis of this dataset identifies three modes employed by researchers to come up with alternate applications: (1) combining different technologies, (2) applying technology into a different field, and (3) using artificial intelligence or machine learning. In a second step, we conducted multinomial logistic regressions using the project data, expert evaluations, and a questionnaire to identify the antecedents associated with the pursuit of each of the three modes. Our findings suggest that scientists and engineers develop many new ideas about novel potential applications of BSRI technologies in their daily work. The main value of ATTRACT is in facilitating project development through financial resources, brokering relationships with industrial partners, and facilitating the applications of technologies in domains outside of the immediate purview of BSRIs.
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    The puzzle of sharing scientific data
    (Taylor & Francis, 2022-02-11) Wareham, J. (Jonathan); Romasanta, A.K. (Angelo Kenneth); Pujol-Priego, L. (Laia)
    Government funding entities have placed data sharing at the centre of scientific policy. While there is widespread consensus that scientific data sharing benefits scientific progress, there are significant barriers to its wider adoption. We seek a deeper understanding of how researchers from different fields share their data and the barriers and facilitators of such sharing. We draw upon the notions of epistemic cultures and collective action theory to consider the enablers and deterrents that scientists encounter when contributing to the collective good of data sharing. Our study employs a mixed-methods design by combining survey data collected in 2016 and 2018 with qualitative data from two case studies sampled within two scientific communities: high-energy physics and molecular biology. We describe how scientific communities with different epistemic cultures can employ modularity, time delay, and boundary organisations to overcome barriers to data sharing.