The OECD’s Committee for Scientific and Technological Policy (CSTP) brings together representatives from OECD countries, and a number of partner economies, to examine major aspects of public policy relevant to science, technology and innovation (STI). By guiding the OECD’s empirical research and data gathering, and promoting peer-based learning, the Committee works to improve understanding of these policies and, ultimately, to advance policymaking itself.

This report examines digitalisation’s effects on science, technology and innovation and the associated consequences for policy. Digitalisation today is the most significant vector of innovation in firms, science and governments. If properly harnessed, digital technologies could advance science, raise living standards, help protect the natural environment and improve policymaking itself.

Chapter 1 summarises the main themes and policy lessons examined in the rest of the report. It provides background to the broader policy concerns facing OECD countries. It also introduces topics not considered elsewhere in the report, particularly in connection with artificial intelligence in science; using digital technology to deliver skills in science, technology, engineering and mathematics; possible targets for public research; and blockchain in science. The chapter also discusses potential uses of digital technology for policy making and implementation, mainly linked to various forms of collective intelligence. These essentially untapped opportunities – such as self-organised systems for funding allocation, and prediction markets – might have significant benefits for science, technology and innovation. They invite further study and, possibly, pilot testing.

Chapter 2 examines the digitalisation of science and innovation drawing on statistical measurement and analysis by the OECD’s Working Party of National Experts on Science and Technology Indicators, including material featured in the OECD report Measuring the Digital Transformation. This chapter maps the ICT specialisation of research and the growth of scientific production and government funding of research related to artificial intelligence. It examines the multidimensional nature of the digital transformation of science. This chapter also shows how innovation in firms can be linked to the adoption of digital technologies and business practices. It concludes by summarising possible next steps for OECD’s own measurement agenda.

This chapter considers how digital technologies that have arisen out of publicly funded scientific research are now rapidly transforming the practice of research and enabling open science. This transformation is apparent across all of the three main pillars of open science: dissemination of scientific information, access to research data and engagement with stakeholders from outside of research. Recent developments and analysis are presented for each of these areas. This is followed by a discussion of what these developments mean for the governance of science as a whole, including for international co‑ordination and co‑operation. The chapter builds on earlier work by the OECD’s Working Party on Innovation and Technology Policy and the report “Making open science a reality” and synthesises findings from recent work by the OECD Global Science Forum.

With a focus on the agri-food, automotive and transportation, and retail sectors, Chapter 4 explores the impacts of digital transformation on innovation and identifies sector-specific dynamics. In view of such impacts, the chapter evaluates how innovation policies should adapt to promote vibrant and inclusive innovation ecosystems effectively. Examples of novel innovation policy approaches implemented in various countries are provided. The chapter also synthesises key findings from the OECD’s Working Party on Innovation and Technology Policy and, specifically its Digital and Open Innovation project. It explores in detail the changes needed in innovation policy in the digital age, considering the impacts of the digital transformation on innovation across sectors.

Chapter 5 examines a selection of policies to enable the use of digital technology in advanced production. The first part looks at individual technologies, their uses and specific policy implications, namely artificial intelligence (AI), blockchain and 3D printing, as well as new materials and nanotechnology (development of which involves complex digital processes). The second part addresses cross-cutting policy issues relevant to digital technology and production. These are technology diffusion, connectivity and data, standards-setting processes, digital skills, access to and awareness of high-performance computing, intellectual property systems and public support for research and development. With respect to public research, particular attention is given to research on computing and AI, as well as the institutional mechanisms needed to enhance the impact of public research.

Chapter 6 focuses on digitalisation and the bio-based industries that are starting to make impacts in the chemicals and materials sectors. As a result of next-generation genome sequencing, biology and biotechnology have become data-rich. Developing bioprocesses has often been hampered at the biological stage – the efficiency of the production strain or biocatalyst. The new discipline of synthetic biology or engineering biology is ushering in an era of more precise control of construction of DNA parts, genes, and all the way to production strains. Engineering biology needs digitalisation and vice versa. The bioeconomy is wider than biotechnology, however. There are many other ways that converging technologies and digitalisation can be applicable to the bioeconomy.

This chapter is based on the OECD Committee for Scientific and Technological Policy project and its exploration of digital science and innovation policy (DSIP) and the challenges it faces. DSIP initiatives refer to the adoption or implementation by public administrations of new or reused procedures and infrastructures relying on an intensive use of digital technologies and data resources to support the formulation and delivery of science and innovation policy. The chapter focuses on three issues in particular. First, it examines the need to ensure interoperability through which diverse data sets can be linked and analysed to aid policy making. Second, it looks at preventing potential misuses of DSIP systems in research assessment practices. Third, it explores management of the roles of non-government actors, particularly the private sector, in developing and operating DSIP infrastructure components and services.