Frontiers | Editorial: Data science and artificial intelligence for (better) science

"Meaningful and explainable AI in research can only be fulfilled when as much data as possible is made FAIR (Findable, Accessible, Interoperable, and Reusable). How meaning is communicated in science “as precisely as possible” to machines when we formulate scientific concepts is a key question. Machine readability and interpretability is needed in order to make data and information “Fully AI-Ready” and support data-intensive research (Schultes et al.). The future of science is where there is only “one computer” and FAIR services see all FAIR data and effectively access a global FAIR database.... Finally, the question is how to enable (better) open science. Increasingly relevant today than ever before is the greater reliance on access to data, artificial intelligence (AI) and machine learning (ML). Data access increasingly determines scientific discoveries and advancements. Data reuse is at the forefront of an emerging “third wave of open data” (Verhulst et al., 2020). But despite progress in implementing open data and FAIR principles, science data asymmetries (as in disparities in access to science data) are a growing problem and can undermine scientific progress. Comparative research is needed to document (Verhulst and Young) for instance, investigating the creation of new types of data asymmetries by, e.g., new private-sector investments in data platforms and knowledge repositories, how data portability and interoperability impact the practice of data collaboration, the relationship and interplay between existing asymmetries and technological and societal drivers. Finally, new methods for achieving a social license for data use and reuse toward the public good are needed, capturing multiple stakeholders' acceptance of standard practices and procedures."