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Short abstract:

I suggest that open data and open source practitioners at CERN propagate varying anticipations of openness. While open data experts connect openness to a more equitable knowledge generation process, open source practitioners envision their work in terms of efficiency and community growth.

Long abstract:

In academic literature and policy discourses, the area of open science is typically categorized into subdomains such as open access, open data, open source software and open source hardware. This categorization suggests that open science activities can be differentiated in terms of the research objects to be opened up such as publications, data, software or hardware designs.

Based on semi structured interviews with open science practitioners at CERN, I suggest that this separation based on research objects does not hold in practice. For instance, open data practitioners are concerned with more than just releasing research data. They additionally focus on access to various types of analysis software. Similarly, open source activities not only focus on software or hardware designs, but are also concerned with various types of (meta)data. However, what distinguishes these domains are underlying anticipations of openness. While open data projects connect openness to reproducibility and equitable access to knowledge generation, open source practitioners tend to envision their work in terms of usability, efficiency and community growth.

On the level of practice, open science activities are thus not only delineated based on the ontology of the research objects, but based on diverging anticipations of openness. Acknowledging these anticipations could allow for collaboration across open source and open data domains. For instance, community centered visions in open source projects could serve as inspiration for open data endeavors. On the policy level, a discussion of these visions could lead to a more tailored incentivization of open science practices.