The technology I’m referring to is the Online Open Research (OOR) tool, which is currently being developed by Christian Bröer and others at the University of Amsterdam. The aim of OOR is to establish means to collaboratively interpret qualitative data. This means that all the users of the tool, whether they are scientists or not, can determine together the meaning, or meanings, of a certain piece of data. The tool can be compared to massive open online courses (such as Coursera offers) in that it is open and free to join for anyone with access to a computer. Apart from being a promising start for making research more democratic, I argue that this tool might also bring depth to qualitative research.
The tool enables all users to look at other’s data, such as pictures or interview transcripts, and add their interpretation of it to the data. Unlike Atlas.ti, this tool allows for interpretation in full sentences and not just short labels. These interpretations can then be clustered into stacks of similar interpretations (see how this works here). The expectation is that this will lead to neither a single correct interpretation or to as many as there are interpreters. Rather, the expectation is that a couple of recurring interpretations will come to the fore because users may agree with each other’s interpretations or discuss about what interpretations are most congruent with the data. Hence, the tool increases the validity of the interpretations as they are immediately open to reflection by other users.
Now, I bet we’ve all experienced how a group brainstorm on the meaning of certain events, sayings or actions encountered during fieldwork yielded insights we could never have come up with by reviewing these data by ourselves. And some of us might have likened academic debate to one long, protracted brainstorm in which academics exchange views on how something might be interpreted correctly (the best example being theology, a centuries-old discipline dedicated to interpreting the bible). The way I see it, this tool takes both these processes and balls them together by allowing huge amounts of people from all over the world, academics and non-academics, to collaboratively interpret data. In other words, this tool could enable collaborative writing of thick descriptions by identifying the ‘stratified hierarchy of meaningful structures’ involved in social action.
If so, then OOR provides an example of how emerging technologies could make for a qualitative change in ethnographic practices. It not only enables thick description; it can also increase the validity of interpretations. Of course, much remains to be looked into. For one, it would be interesting to see what the backgrounds are of the people that use the tool, how diverse they are and what might influence their interpretations. And two, I wonder what this possible diversity of backgrounds could mean for the compatibility or commensurability of their interpretations. Furthermore, OOR presents users with a rather strict division between data collection and data interpretation. However, interpretation pervades every step of the research including data collection; interpretation is involved in deciding what is data and what is not. So account must be given of the consequences this has for OOR. But in the end, I think that OOR as a platform for collaborative interpretation presents a promising qualitative change to the way qualitative research can be done.
Matthias Teeuwen is student of the Research Master’s Social Sciences at the University of Amsterdam. His research interests include religion, language, and the philosophy of science.
 Geertz, C. (1973). The Interpretation of Cultures: Selected Essays. New York: Basic Books, 7.