Why academics under-share research data - A social relational theory

As an academic, I have cheered for and welcomed the open access (OA) mandates that, slowly but steadily, have been accepted in one way or another throughout academia. It is now often accepted that public funds means public research. Many of our universities or funding bodies will demand that, with varying intensities–sometimes they demand research to be published in an OA venue, sometimes a mandate will only “prefer” it. Lately, some journals and funder bodies have expanded this mandate toward open science, requiring not only research outputs (that is, articles and books) to be published openly but for the data backing the results to be made public as well. As a person who has been involved with free software promotion since the mid 1990s, it was natural for me to join the OA movement and to celebrate when various universities adopt such mandates.

Now, what happens after a university or funder body adopts such a mandate? Many individual academics cheer, as it is the “right thing to do.” However, the authors observe that this is not really followed thoroughly by academics. What can be observed, rather, is the slow pace or “feet dragging” of academics when they are compelled to comply with OA mandates, or even an outright refusal to do so. If OA and open science are close to the ethos of academia, why aren’t more academics enthusiastically sharing the data used for their research? This paper finds a subversive practice embodied in the refusal to comply with such mandates, and explores an hypothesis based on Karl Marx’s productive worker theory and Pierre Bourdieu’s ideas of symbolic capital.

The paper explains that academics, as productive workers, become targets for exploitation: given that it’s not only the academics’ sharing ethos, but private industry’s push for data collection and industry-aligned research, they adapt to technological changes and jump through all kinds of hurdles to create more products, in a result that can be understood as a neoliberal productivity measurement strategy. Neoliberalism assumes that mechanisms that produce more profit for academic institutions will result in better research; it also leads to the disempowerment of academics as a class, although they are rewarded as individuals due to the specific value they produce.

The authors continue by explaining how open science mandates seem to ignore the historical ways of collaboration in different scientific fields, and exploring different angles of how and why data can be seen as “under-shared,” failing to comply with different aspects of said mandates. This paper, built on the social sciences tradition, is clearly a controversial work that can spark interesting discussions. While it does not specifically touch on computing, it is relevant to Computing Reviews readers due to the relatively high percentage of academics among us.