FAIR data accelerating innovation

Toril Nagelhus Hernes, Vice President of Innovation at NTNU, reflects on how we should boost academia-industry collaboration by ensuring Findability, Accessibility, Interoperability, and Reuse (FAIR) of digital assets.
27th November 2020
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The role of universities in relation to data management was put front and centre by the white paper Advancing Research Data Management in Universities of Science and Technology published earlier this year. In this context, I attended the workshop ‘Trends in academia-industry collaboration based on FAIR data’ during the CESAER Annual Meetings in October 2020 which was jointly hosted by Task Force Innovation and Task Force Open Science. It was a great workshop with tons of vibrant discussion, and here I share my take-away messages in three key areas.

Ensure good framework conditions and infrastructure for FAIR data

Data quality through standardisation and sharing of data

More data does not necessarily provide more value, it depends on the quality of the data and whether the data is FAIR or not. Data quality challenges must be discussed and addressed, which is frequently the reason why data cannot be reused or be of broader benefit, even if it is made available through open platforms. This standardisation work must address collection, storage and sharing, ensuring added value for all actors through broad participation from both large established players, SMEs, service suppliers and relevant public sector actors, as well as academia.

Data across borders to fully unleash potential

Interdisciplinarity, domain knowledge and the involvement of various organisations are crucial to success. To fully benefit from the sharing of data, we need high-quality data sharing across borders, markets, disciplines and organisations, including data from both the private and public sector, as well as research data from academia. Data across borders provide opportunities for exploring new knowledge, data-driven applications, and innovations in novel areas and contexts, including advancing the circular green economy.

Provide ethical guidelines

Regulations must support ethical guidelines related to the collection, storage, access and use of data. It is especially important to take care of matters related to privacy and information security.

Further develop ecosystems to create value from FAIR data

Data and digitalisation affect organisations and societies, and provide the potential for value creation and industrialisation in new areas.

Accelerating value creation in both the public and private domains

It is vital to establish simpler models and funding opportunities to facilitate collaboration between the public sector, academia and private enterprises. The use of data generated within the public and private sector will unleash the potential of innovations and be beneficial for all parties. A data-driven public administration provides opportunities to improve the quality of its own services to citizens and society, as well increase its efficiency and innovative power (e.g. through platforms for business initiatives). Joint solutions for sharing and accessing data across borders would make it easier for supplier and service industries, in particular SMEs, to be able to work effectively towards several different relevant markets and thereby increase the scaling possibilities for new ideas and innovations.

Startups and entrepreneurship

There is a great growth potential for disruptive entrepreneurship, start-ups and industrial digitalisation based on FAIR data. The need for new technologies and routines that provide satisfactory security and privacy, including cyber security, will also open up new innovation pathways.

The role of the universities, and collaboration with industry in relation to FAIR data

Lifelong collaboration with academia

In our rapidly changing world, the private and public sectors share an urgent need to continuously develop their employees’ digital skills and competences. This should be done through close interaction between higher education and lifelong learning initiatives, establishing an agenda for ‘lifelong’ cooperation between the higher education sector and both the public and private sectors. In this context, the development of study plans at academic institutions is a continuous process in close contact with business and the public sector.

New research perspectives

A relevant research area that requires attention is how large amounts of data from different platforms can be used to advance new pedagogical theories. A data-driven approach is important to understand learning mechanisms in wider societal contexts and will have an impact on the learning and teaching approaches of Europe’s universities of tomorrow. In addition to research on technology and infrastructure, the social sciences and humanities are vital to understand the influence of the data-driven economy on ethics and societal development.

The role of the universities of science and technology

In summary, there is a recognised need for expertise in both the public and private sectors in order to address increased digitalisation, data handling, and application of data assets and digital technologies. Due to the increasing pace of innovation, the public and private sector urgently demand that universities have the capacity to deliver the skills and competences needed to translate knowledge into practical application to students and promote lifelong learning. The role of universities of science and technology in the data-driven economy and society will therefore only continue to grow. This also highlights the need for new personnel within academia who are skilled in data management and analysis for all aspects of education, research and innovation.

Toril A Nagelhus Hernes is Vice President of Innovation and Professor at the Norwegian University of Science and Technology. She is on the Board of NTVA (The Norwegian Technology Science Academy), a member of the European University Association's expert group on Innovation Ecosystems and a member of CESAER's Task Force Innovation.

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