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Technological readiness in Europe
Published in Lisa De Propris, David Bailey, Industry 4.0 and Regional Transformations, 2020
While the third production revolution brought its waves of innovations through a wider penetration of information and communications technology (ICT) and automation, I4.0 is expected to extend, accelerate, connect and scale up these disruptions and transformations, and to trigger a wider integration across domains and discoveries. It will enable this through the multiplication of interactions across the physical, digital and biological spheres (Schwab, 2016)3 allowed by the convergence of new and emerging technologies and materials and the related technology-enhanced processes and systems, including 3D printing, the Internet of Things (IoT), big data and cloud computing, artificial intelligence (AI), advanced robotics, smart factories, precision farming and agriculture, fintech, neurotechnology, micro-engineering, predictive medicine, synthetic biology and predictive gene-based healthcare. The transformational and disruptive nature of the ongoing and upcoming technology-enabled or -pushed changes are already altering our learning, education, consumption, distribution, productive, financial, legal and governance systems (see e.g. Smit et al., 2016; Ulmann 2017; Craglia et al., 2018). They modify our established conceptions of privacy and ownership, work organisation, industries and competitive markets, and prompt the adoption of new business and governance models, as well as new collaborative and sharing practices.
Applying Data Science Solutions in Healthcare Industry
Published in Durgesh Kumar Mishra, Nilanjan Dey, Bharat Singh Deora, Amit Joshi, ICT for Competitive Strategies, 2020
Vinay Kumar, Reema Thareja, Rashi Thareja, Priti R. Jain
Predictive medicine- Data science techniques can be used to predictive analytics methods learn from historical data to make predictions about the possibility of presence of a disease. For this, data science solutions analyzes patient’s data, clinical notes to computer the correlations, associations of symptoms, familiar cases in the past, habits, diseases to predict the disease.
When nature goes digital: routes for responsible innovation
Published in Journal of Responsible Innovation, 2020
Digitalization of natural resources aims at knowledge generation and at deriving innovations with market value and/or societal value. Often large-scale data generating endeavours aim at deeper scientific understanding of the targeted biological systems. Digitalized natural resources therefore have been characterized as the subject of research commons. The research community dealing with the exchange of information and samples on microbial strains has been for instance described as a microbial commons (Dedeurwaerdere, Melindi-Ghidi, and Broggiato 2016). Next to research, many large-scale initiatives also explicitly seek to foster a flourishing innovation landscape around the generated scientific data. For example, the genomic data gathered in the Genomics England initiative is intended to be used to develop predictive medicine practices (Marx 2015). The creation of a silicon-valley like cluster around Amazonian biodiversity has been proposed as a route to a new biodiversity based economy (Nobre et al. 2016). Such initiatives therefore aim at the fostering of high levels of data-driven innovation. Data-driven research differs from settings where the data are generated in order to investigate dedicated research hypotheses. This type of research focuses on exploratory rather than theory-driven experimentation (Pietsch 2015).