Taking Culture Seriously: The challenges of data sharing
I promised some thinking out loud and likely naive and uninformed opinion in my plans for the new year. Here’s a start on that with apologies to Science and Technology Studies and Cultural Studies people who’ve probably thought all of this through. Yes, I am trying to get people to do my due diligence literature review for me.
It’s a common strand when we talk about improving data sharing or data management, or access to research, or public engagement…or… “Cultural Change, its hard”. The capitals are deliberate. Cultural Change is seen as a thing, perhaps a process that we need to wait for, because we have internalised the Planck narrative that “science progresses one funeral at a time”1. Yet we are also still waiting for the Google generation with their “culture of sharing” to change the world for us.
Some of us get bored waiting and look for hammers. We decide that policies, particularly funder policies, but also journal or institutional policies requiring data sharing, are the best way to drive changes in practice. And these work, to some extent, in some places, but then we (and I use the “we” seriously) are disappointed when changes in practice turn out to be the absolute minimum that has to be done; that the cultural change that we assumed would follow does not, even in some cases is hampered by an association of the policy with all those other administrative requirements that we (again, I use the word advisedly) researchers complain are burying us, preventing us from getting on with what we are supposed to be doing.
We (that word again) point at the successes. Look at the public genome project and the enormous return on investment! Look at particle physics! Mathematics and the ArXiv. The UK ESRC and the UK Data Archive, the ICPSR! Why is it that the other disciplines can’t be like them? It must be a difference in culture. In history. In context. And we make up stories about why bioinformatics is different to chemistry, why history isn’t more like economics. But the benefits are clear. Data sharing increases impact, promotes engagement, improves reproducibility, leads to more citations, enhances career prospects and improves skin tone. Some of these even have evidence to back them up. Good data management and sharing practice has real benefits that have been shown in specific contexts and specific places. So its good that we can blame issues in other places, and other contexts, on “differences of culture”. That makes the problem something that is separate from what we believe to be objectively best practice that will drive real measurable outcomes.
Then there are the cultural clashes. When the New England Journal of Medicine talked about the risk of communities of “research parasites” arising to steal people’s research productivity the social media storm was immense. Again, observers trying not to take sides might point to the gulf in cultures between the two camps. One side committed to the idea that data availability and critique is the bedrock of science, although not often thinking deeply about what is available to who, or for what or when. Just lots, for everyone, sooner! On the other a concern for the structure of the existing community, of the quality of engagement, albeit perhaps too concerned with the stability of one specific group of stakeholders. Should the research productivity of a tenured professor be valued above the productivity of those critiquing whether the drugs actually work?
In each of these cases we blame “culture” for things being hard to change. And in doing so we externalise it, make it something that we don’t really engage with. It probably doesn’t help that the study of culture, and the way it is approached is alien to most with a sciences background. But even those disciplines we might to tackle these issues seem reticent to engage. Science and Technology studies appears to be focussed on questions of why we might want change, who is it for, what does the technology want? But seems to see cultures as a fixed background, focussing rather on power relations around research. Weirdly (and I can’t claim to have got far into this so I may be wrong) Cultural Studies doesn’t seem to deal with culture per se so much as seek to generalise stories that arise from the study of individuals and individual cases. Ethnography looks at individuals, sociology focusses on interactions, as does actor-network theory, rarely treating cultures or communities as more than categories to put things into or backgrounds to the real action.
It seems to me that if we’re serious about wanting to change cultures and not merely to wait for Cultural Change (with its distancing capitals) to magically happen then we need to take culture – and cultures – seriously. How do they interact and how do they change? What are the ways in which we might use all of the levers at our disposal: policy yes, but also evidence and analysis, new (and old) technology, and the shaping of the stories we tell ourselves and each other about why we do what we do. How can we use this to build positive change and coherent practice, while not damaging the differences in culture, and the consequent differences in approach, that enrich scholarship and its interaction withe communities it is carried out within?
The question of who “we” is in any given context, what cultures are we a part of, what communities is a challenging one. But I increasingly think if we don’t have frameworks for understanding the differing cultures amongst research disciplines and researchers and other stakeholders, how they interact, compete, clash and change, then we will only make limited progress. Working with an understanding of our own cultures is the lever we haven’t pulled, in large part because thinking about culture, let alone examining our own is so alien. We need to take culture seriously.
1. Of course as always with popular quotes that’s not quite what he said.