The release of ChatGPT has triggered global attention on artificial intelligence (AI), and AI for science is thus becoming a hot topic in the scientific community. When we think about unleashing the power of AI to accelerate scientific research, the question coming to our mind first is whether there is a continuous supply of highly available data at a sufficiently large scale.

We present an extension to the Brain Imaging Data Structure (BIDS) for motion data. Motion data is frequently recorded alongside human brain imaging and electrophysiological data. The goal of Motion-BIDS is to make motion data interoperable across different laboratories and with other data modalities in human brain and behavioral research. To this end, Motion-BIDS standardizes the data format and metadata structure. It describes how to document experimental details, considering the diversity of hardware and software systems for motion data. This promotes findable, accessible, interoperable, and reusable data sharing and Open Science in human motion research.

Developing Earth science data products that meet the needs of diverse users is a challenging task for both data producers and service providers, as user requirements can vary significantly and evolve over time. In this comment, we discuss several strategies to improve Earth science data products that everyone can use.

Curated resources that support scientific research often go out of date or become inaccessible. This can happen for several reasons including lack of continuing funding, the departure of key personnel, or changes in institutional priorities. We introduce the Open Data, Open Code, Open Infrastructure (O3) Guidelines as an actionable road map to creating and maintaining resources that are less susceptible to such external factors and can continue to be used and maintained by the community that they serve.

The solution of the longstanding “protein folding problem” in 2021 showcased the transformative capabilities of AI in advancing the biomedical sciences. AI was characterized as successfully learning from protein structure data, which then spurred a more general call for AI-ready datasets to drive forward medical research. Here, we argue that it is the broad availability of knowledge, not just data, that is required to fuel further advances in AI in the scientific domain. This represents a quantum leap in a trend toward knowledge democratization that had already been developing in the biomedical sciences: knowledge is no longer primarily applied by specialists in a sub-field of biomedicine, but rather multidisciplinary teams, diverse biomedical research programs, and now machine learning. The development and application of explicit knowledge representations underpinning democratization is becoming a core scientific activity, and more investment in this activity is required if we are to achieve the promise of AI.