DIGITALISATION OF SCIENCE, TECHNOLOGY AND INNOVATION KEY DEVELOPMENTS AND POLICIES.

Foreword
Acknowledgements
Acronyms, abbreviations and units of measure
Executive Summary
	Digitalisation and science
	Realising the untapped potential of digital technology in policy
	Digitalisation and innovation in firms
	Developing digital skills
	Committing to public sector research
	Building expertise in government
1 An overview of key developments and policies
	Introduction
	Why does digitalisation matter?
		The broader context in which science, technology and innovation are digitalising
	Measuring the digitalisation of science and innovation
	Digitalisation, science and science policy
		Accessing scientific information
		Enhancing access to research data
		Broadening engagement with science
		Artificial intelligence for science
			Recent drivers of AI in science
			AI can also combine with robot systems to perform scientific research
	Digitalisation and innovation in firms
		Does innovation policy need to be adapted for the digital age?
			Ensuring access to data for innovation
			Providing the right support and incentives for innovation and entrepreneurship
			Ensuring that innovation ecosystems support competition
			Supporting collaboration for innovation
	Digitalisation and the next production revolution
		AI in production
		New materials and nanotechnology
	Developing digital skills
		Education and training systems must draw on information from all social partners
			New courses and curricula may be needed
			Lifelong learning must be an integral part of work
	Facilitating the diffusion of digital technologies and tools
		New digital technologies may make diffusion more difficult
			Institutions for diffusion can be effective, if well designed
			Technology diffusion institutions need realistic goals and time horizons
	Committing to public sector research
		Multidisciplinary research
		Public-private research partnerships
	Developing technology- and sector-specific capabilities in government
	Ensuring access to complementary infrastructures
	Improving digital security
	Examining intellectual property systems in light of digitalisation
	Optimising digital systems to strengthen science and innovation policies
		Ensuring interoperability in DSIP systems
		Using DSIP systems in research assessment
		The roles of the business sector in DSIP
		The outlook for DSIP systems
	Digitalisation in science and innovation: Possible “dark sides”
		Distributional effects and digitalisation of STI
		Complex systems and unmanageable machine ecologies
		Negative impacts on science from digitalisation
		Wider risks linked to digital technology
	The untapped potential of digital technology for STI policy
		Prediction markets for STI policy
			Prediction using human-machine combinations
		Blockchain for science, technology and innovation
		Using social media to spread innovation
	Conclusion
	References
	Notes
2 How are science, technology  and innovation going digital?  The statistical evidence
	Introduction
	Science going digital
		Scientific research on digital technologies
		Scientific research and artificial intelligence
			Scientific production
			Public funding of scientific research on AI
		The science system and its contribution to the development of digital skills
		Scientific research enabled by digital technology
			Research paradigms and digitalisation
			Open science and digitalisation
			Open access to documents
			Open access to data and code
			Digitalisation and the broader impacts of science
		Looking ahead: Scientists’ perspectives on digitalisation and its impacts
	Technology and innovation going digital
		Development of digital technologies
			R&D in ICT industries and ICT-driven R&D
		Use of digital technology in business and the link between digitalisation and innovation
	Conclusion
		Digitalisation is everywhere in STI, but with varying depth and perspective
		Digitalisation is a “game-changer” for STI measurement and analysis
	References
	Notes
3 Digital technology, the changing practice of science and implications for policy
	Introduction
	Accessing scientific information
	Enhanced access to research data
		Business models for data repositories
		Trust and transnational barriers
		Data privacy and ethics
	Broader engagement in science
	Promoting and steering open science systems in the digital world
	Conclusion
	References
	Note
4 Digital innovation: Cross-sectoral dynamics and policy implications
	Introduction
	How is the digital transformation changing the innovation practices of firms?
		Data are a key input for innovation
		Services innovation enabled by digital technologies
		Innovation cycles are accelerating
		Innovation is becoming more collaborative
	The impacts of the digital transformation on innovation across sectors
		How are digital technologies integrating different sectors?
			Agri-food sector
			Automotive industry
			The retail sector
		Why are the implications of digital transformation likely to differ across sectors?
			Opportunities for innovation using digital technology
			Data needs and challenges for innovation
			Digital technology adoption and diffusion trends
	How should innovation policy be adapted to the digital age?
		Data access policies
		Policies to support innovation and entrepreneurship
			Ensure that policies are anticipatory, responsive and agile
			Support service innovation to lever the potential of digital technologies
			Adapt intellectual property systems
			Support development of generic digital technologies to respond to societal challenges
		Public research and education policies
			Promote the digitalisation of public research
			Build digital skills, including in the field of data analytics
		Foster competitive, collaborative and inclusive innovation ecosystems
			Ensure that innovation ecosystems remain competitive
			Support collaboration for innovation
			Support digital technology adoption by all firms, particularly SMEs
			Support social and territorial inclusiveness
		Cross-cutting policy principles
			Set national policies in view of developments in global markets
			Engage with citizens to address technology-related public concerns
			Adopt a sectoral approach to policy making when necessary
	Conclusion
	References
	Note
5 Artificial intelligence,  digital technology and  advanced production
	Introduction
	Digital production technologies: Recent developments and policy implications
		Artificial intelligence in production
			Adopting AI in production: main challenges
		AI: Specific policies
			Governments can take steps to help firms generate value from their data
			Government agencies can co-ordinate and steward DSAs for AI purposes
			Governments can promote open data initiatives
			Technology itself may offer novel solutions to use data better for AI purposes
			Governments can also help resolve hardware constraints for AI applications
		Blockchain in production
			Blockchain: Possible policies
		3D printing
			3D printing: Specific policies
			Government can help develop the knowledge needed for 3D printing at the production frontier
		New materials and nanotechnology
			New materials and nanotechnology: Specific policies
	Selected cross-cutting policy issues
		Technology diffusion
			Diffusion in SMEs involves particular difficulties
			Diffusion requires conditions to support the creation of growth-oriented start-ups  and efficient allocation of economic resources
			Institutions for diffusion can also be effective if well designed
			Technology diffusion institutions need realistic goals and time horizons
		Policies on connectivity and data
			Restricting cross-border data flows should be avoided
			A prospective policy issue: Legal data portability rights for firms?
			A prospective policy issue: Frameworks to protect non-personal sensor data
			Increasing trust in cloud computing
		Developing digital skills
			How learning is delivered matters greatly
			Lifelong learning must be an integral part of work
			Digital technology will itself affect how skills are developed
		Participation in standards-setting processes
		Improving access to high-performance computing
		Intellectual property systems
		Public support for R&D
			An overarching research challenge relates to computation itself
			A need for more – and possibly different – research on AI
			Research and industry can often be linked more effectively
	Conclusion
	References
	Notes
6 Digitalisation in the bioeconomy: Convergence for the bio-based industries
	Introduction
	The great convergence
		Why is convergence necessary?
	Overarching view: Greater integration of biotechnology with the engineering design cycle
		The test phase is the current bottleneck
		An integrated technology platform could unlock the potential
		Reproducibility is a continuing problem
		Reliability, predictability and reproducibility
		Automation can help address test-phase obstacles
	Industrial biotechnology and green chemistry convergence
		Industrial biotechnology converges with chemistry and with information technology/computing
	Data analysis and storage as bottlenecks
		Is DNA storage the answer?
	Blockchain for benefit sharing and protecting sensitive information
	Digital security
	Cloud computing
	Frontiers in bio-production
		Biofoundries
		3D bio-printing
		Cell-free synthetic biology
	Skills and education for the bioeconomy workforce
		Backcasting: Mechatronics revisited to shape the education of the future  engineering biologist
	Digitalisation of the forestry bioeconomy
		Satellite technology in the forest bioeconomy
	Examples of the potential for future bio-based materials
	Policy implications
		Platform technologies to support the delivery of engineering biology materials
		Standardisation, interoperability and intellectual property
		Sustainability
		Digital security
	Conclusion
	References
	Notes
7 The digitalisation of science  and innovation policy
	Introduction
	What is digital science and innovation policy?
	Interoperability
	Using DSIP infrastructures in research assessment
	The roles of non-government actors in DSIP
	Conclusion
	References
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