In recent years, researchers have sought more effective ways of making data “open,” for purposes of accountability, engagement, and reuse. Often, such efforts focus on making existing data sets available to broad audiences. The expression data set itself suggests something discrete, complete, and easily transferable. But data are none of those things. In this paper, we argue that open data projects could benefit from a more contextual understanding of what open means. Instead of focusing on open data sets, researchers can seek to create and understand open data settings: contexts in which things of public significance can be presented as evidence. We share our experiences creating and analyzing open data settings for the Map Room Project, a research through design initiative to establish local spaces for collaborative data exploration and mapping. Our contribution is to offer a conceptual framework through which researchers, as well as designers, might think about the openness of data settings. This framework comes out of a situational analysis of comparative empirical case studies. In data settings, we find that open can mean accessible, inclusive, or indeterminate. Practices of contextualization, such as configuring, convening, and claim-making, shape these dimensions of openness by defining all of the following: where data can work, who is empowered to use them, and what can count as data.
https://doi.org/10.1145/3479501
The 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing