(Originally posted at Digital Curation Centre)
Data re-use is an elixir for those involved in research data.
Make the data available, add rich metadata, and then users will download the spreadsheets, databases, and images. The archive will be visited, making librarians happy. Datasets will be cited, making researchers happy. Datasets may be even re-used by the private sector, making university deans even happier.
But it seems to me that data re-use, or at least a particular conceptualisation of re-use as is established in most data repositories, is not the definitive way of conceiving of data in the 21st century.
Two great examples from the International Data Curation Conference illustrated this.
Barend Mons declared that the real scientific value in scholarly communication is not abstracts, articles or supplementary information. Rather the data that sits behinds these outputs is the real oil to be exploited, featuring millions of assertions about all kinds of biological entities.
Describing the sum of these assertions as the explicitome, it enables cross fertilisation between distinct scientific work. With all experimental data made available in the explicitome, researchers taking an aerial view can suddenly see all kinds of new connections and patterns between entities cited in wholly different research projects.
Secondly, Eric Kansa’s talk on the Open Context framework for publishing archaeological data. Following the same principle as Barend Mons, OpenContext breaks data down into individual items. Instead of downloading a whole spreadsheet relating to a single excavation, you can access individual bits of data. From an excavation, you can see the data related to a particular trench, and then items discovered in that trench.
(A screenshot from Open Context)
In both cases, data re-use is promoted, but in an entirely different way to datasets being uploaded to an archive and then downloaded by a re-user.
In the model proposed by Mons and Kansa, data is atomised, and then published. Each individual item, or each individual assertion, gets it own identity. And that piece of data can then easily be linked to other relevant pieces of data.
This hugely increases the chance of data re-use; not whole datasets of course, but tiny fractions of datasets. An archaeologist examining remains of jars on French archaeological sites might not even think to look at a dataset from a Turkish excavation. But if the latter dataset is atomised in a way that it allows it identify the presence of jars as well, then suddenly that element of the Turkish dataset will become useful.
This approach to data is the big challenge for those charged with archiving such data. Many data repositories, particularly institutional ones, store individual files but not individual pieces of data. How research data managers begin to cope with the explicitome – enabling it, nourishing and sustaining it – may well be a topic of interest for IDCC17.