Data‐driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning—typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high‐throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data‐driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data‐driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.

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This paper outlines new perspectives for data-supported foresight by combining participatory expert-based futures dialogues with the power of artificial intelligence (AI) in what we call the hybrid AI-expert-based foresight approach. To this end, we present a framework of five typical steps in a fully fledged foresight process ranging from scoping to strategizing and show how AI can be integrated into each of the steps to enable the hybrid AI-expert foresight approach. Building on this, we present experiences gained from two recent research projects of TNO and Fraunhofer ISI that deal with aspects of the hybrid AI-expert foresight approach and give insights into the opportunities and challenges of the new perspectives for data-supported foresight that this approach enables. Finally, we summarize open questions and challenges for future research.

The successful and fast development and deployment of renewable energy and greenhouse gas reduction technologies is a continuing and structural challenge. The deployment of these technologies is slowed down and sometimes even stalled due to societal challenges like public resistance, lack of appropriate policy and regulations, unsolid business cases and uncertainty concerning the impact on the environment. In this paper we elaborate on societal aspects that influence technology development and deployment and introduce the societal embeddedness level (SEL) framework. Building upon the technology readiness level (TRL), the SEL framework enables the assessment of the current level of societal embeddedness of energy technologies in order to identify the societal aspects which need to be taken into account to accelerate deployment of energy technologies. The SEL framework takes into account four societal dimensions (impact on the environment, stakeholder involvement, policy and regulations, and market and financial resources) and four stages of technology development (exploration, development, demonstration and deployment) that are linked to the TRL. The SEL framework has been elaborated for CCS technologies and is being applied to the monitoring of geological CO2 storage by the ACT II project DigiMon (Digital Monitoring of CO2 storage projects). DigiMon is an ACT second call project, funded by the national funding agencies in the period September 2019–August 2022.