Last week, Matej Kurian published a message on the okfn-labs mailing list, describing the various sources he had discovered for machine-readable excerpts of the EU’s joint procurement system, TED. What struck me about this message was that, apparently, this polite and brilliant policy wonk had turned into something strange: into a data catalogue.
While not quite a Kafka-grade transformation, it’s an odd turn to take for a researcher. But Matej is not the only one: the team of FarmSubsidies.org has experienced a similar re-definition, as did the ERDF researchers at the Bureau of Investigative Journalists.
The best data catalogues today are well-informed people.
When I talk to journalists about data acquisition, they seem to know this already: its often not just about where to look; it’s even more important to know who to talk to. But why does this observation from a telephone-and-filofax world hold true even in digital space, where every bit of knowledge is supposed to be only a click away?
I believe that some blame goes to the simplistic model underlying our efforts to catalogue data: the question of where to find a dataset is certainly important, but for those actually working with the data it’s just not enough. Once you dig into data, other questions rise to the foreground:
How do the different available datasets interact and integrate? Does the data I am looking for even make sense on its own - or do I need to combine several sources? Take, for example, the UKs Whole of Government Accounts: while data.gov.uk lists a few gigabytes worth of downloads for this dataset, it is completely impossible to interpret the data without also fetching Excel files (and PDF guidance) off the Treasury web site, the Department of Communities and Local Government site and - bonus points - emailing the Treasury for their internal toolkit.
How complete and up-to-date is the data? What technical and political constraints apply to the publication? Again, FarmSubsidies provide a nice example, as a 2010 European Court of Justice verdict has severely limited the availablity of the data - leading to an oddly limited dataset today.
Who else is working with this data and what are they doing? Are there derivative datasets that I should use instead of the source material? It may be worth knowing, for example, that as well as browsing the 6000-odd departmental spending spreadsheets, journalists can also search across a consolidated version of this data on OpenSpending.org
But why are current data portals so bad at capturing such information? Certainly, adding a few comment boxes and an app gallery can do a good job glossing over the problem, but the real problems seem to lie deeper in the technology:
Datasets are a useless unit. A while ago, Richard Cyganiak defined a dataset as “a set of data” - which I assume is a computer scientists way of telling you to get lost. And while I’m not normally a big fan of LOD-clouds, they got this right: all the interesting stuff is happening in between datasets. Whether it’s about reconstructing a process across several datasets or finding out about geographical and temporal coverage - datasets are at best building blocks, more often they are just arbitrary. So maybe its time to think about other mechanisms to represent data sources: what about policy maps and government wiring plans?
Even worse, the metadata we keep about datasets is mostly based on a bureaucratic mindset: they’re library-inspired, static index cards that hope to represent datasets, while data are really subject to complex processes both within and outside the institutions that produce them. For anyone using the data, activity metadata is the interesting part. We’ve already figured this out for software, where libraries like FreshMeat and SourceForge have been replaced by activity-driven platforms like GitHub. The key aspect here is that GitHub doesn’t require me to explictly make metadata - the relevant narrative is simply summarized from my working pattern.
Of course, all of this is just a long way of saying that the best metadata is in the data itself. So unless you’re working on the LHC stuff there really isn’t much of a reason to separate the two any longer: let’s make public, audit-trailed databases that report on themselves. This, of course, is easier said then done as it implies that all data will fit into one storage mechanism. In the real world (i.e. outside Linked Data land), this is unlikely to be true of structured data any time soon.
Still, even after fixing our model of how we talk about datasets on the web, I think we would still find that the best way to ensure that people collaborate around data is community-building: creating networks that garden the commons. Perhaps we should start cataloguing those.