Open Access science needs Open Science Sensemaking (OSSm): open infrastructure for sharing scientific sensemaking data

While open access publishing effectively broadens access to scientific research products, the problem of \emph{making sense} of the volumes of new information is becoming increasingly acute. Traditional curation methods like peer-reviewed journals and recommendation services are failing to keep pace, resulting in unprecedented information overload and knowledge fragmentation. We contend that making sense of science requires open access to diverse sources of scientific sensemaking data, and that current frictions and failures of scientific sensemaking arise from deficiencies in reckoning with these kinds of data. Sensemaking data are the digital traces of sensemaking processes, in which individuals and groups organize and structure new information to improve subsequent decision-making and actions. Sensemaking data include explicit annotations (tags, votes, ratings, marginal comments) and commentaries made by researchers, as well implicit behavioral data generated through app usage (reference managers, website metrics, etc). Crucially, sensemaking data is currently scattered and siloed across a multitude of apps and formats, and also increasingly enclosed by publishers for profit. We provide an outline for Open Science Sensemaking (OSSm), an interoperable and decentralized annotation network. Such a system would enable researchers to record, own, and share their sensemaking data, thus contributing to the network while remaining resilient to platform capture. Shared annotation data will greatly benefit individual and collective sensemaking by enabling development of diverse content discovery services, from simple aggregation of reviews and ratings (e.g., ``Goodreads for scientific research'') to more advanced AI-augmented scientific intelligence systems.