I am not the typical early career researcher. I am not particularly young; nor is this my first time attending an RDA plenary. I am, however, at an early stage in what I consider to be my third career - working as a PhD researcher at the Data Archiving and Networked Services (DANS) in the Netherlands. Thanks to the RDA Europe Early Career Researcher grant program, I was able to attend the 13th RDA Plenary in Philadelphia, where I connected with the work of RDA and its members at a deeper level than I have before.

My PhD research investigates practices of data search - how researchers discover and make sense of data for reuse. Given this focus, it was natural for me to attend the meeting of the Data Discovery Interest Group. In this session, we discussed the possibility of creating a working group exploring the use of the schema.org description vocabulary to increase data findability. This possibility also came up at a later BoF titled “Using Schema.org and Enriched Metadata to Enable/Boost FAIRness on Research Resources.” Much of the talk in both sessions centered around the technical advantages and limitations of schema.org, as well as on its use by the new Google Dataset search engine.

I found myself wondering which data would become less findable, rather than more, if schema.org and Google Dataset Search become go-to tools. What would happen to data not marked-up and described with schema.org? Which data would be buried by the ranking algorithms of Google Dataset Search? I was reminded of work that I had seen (but not yet read) by Matthew McCarthy from the University of Wisconsin examining the use of schema.org from a sociological standpoint. I hunted down these references and started a reading list.

I also attended the first meeting of the working group for developing a FAIR data maturity model, whose aim is to create a core set of assessment criteria for FAIRness. The group set out to present the significant amount of work that they have already accomplished, but the session became sidetracked by a discussion of whether manual or automated assessment approaches would work best.

The lively discussion made me think of the challenges involved in “human interoperability,” an idea that was touched on at the earlier plenary panel session with Dr. Steve MacFeely, Dr. Devika Madalli, and Tom Orrell. One of the panelists mentioned this idea while talking about the Data Commons Framework, a model stating that data interoperability is contingent upon human and institutional interoperability. I decided this was an idea worth following up on, and added this and a few other related references to my quickly growing reading list.

Still thinking about interoperability, I felt impelled to attend a new BoF group on the final day of the conference: “Social Dynamics of Interoperability.” The purpose of the session was to gauge interest in forming an interest group on the topic. The room was full, and the discussion active, as we thought about the organizational, economic and social dynamics that need to be considered when making data (and infrastructures) interoperable. An unexpected and welcome output of the session was a list of suggested references,  some of which (naturally) I added to my own list.

So what were my main takeaways from my experience at the 13th RDA plenary? I came away with new ideas about how my research could connect both to work in data description and interoperability. I made connections with other people who are interested in exploring data from socio-technical viewpoints. And, I left with something that every PhD student needs - more literature to read.

Follow-up Reading List

McCarthy, M. (2017). Enacting the Semantic Web: Ontological Orderings, Negotiated Standards, and Human-machine Translations. (Doctoral dissertation). University of Wisconsin, Milwaukee.  https://dc.uwm.edu/etd/1666

Mccarthy, M. T. (2017). The Semantic Web and Its Entanglements. Science, Technology and Society, 22(1), 21-37. https://doi.org/10.1177/0971721816682796

Goldstein, E. Gasser,U. & Budish, R. (2018). Data Commons Version 1.0: A Framework to Build Toward AI for Good. https://link.medium.com/TsL732CePV

Palfrey, J., & Gasser, U. (2012). Interop: The promise and perils of highly interconnected systems. Basic Books.

Positive Deviance: https://positivedeviance.org/

Tsing, A. L. (2011). Friction: An ethnography of global connection. Princeton University Press.