History bloggers have had a lot to say recently about social networking and what the various applications of this particular aspect of the Web 2.0 world will mean for the history business. I’ve been fairly optimistic about it all, but without a clear vision of how, exactly, social networking might really revolutionize some aspect of what we do as historians.
Well, I now have a candidate for your consideration: Swivel.com.
Still in its preview version, Swivel has already seen a lot of traffic. The core of the Swivel model is making it possible for users to upload, share, comment on, and manipulate data sets. As of this morning, Swivel already contains more than 3,200 datasets uploaded by users. The site also makes the lofty claim of more than 1.6 million graphs, but this is just because once you upload a dataset, the system generates one of almost every possible graph from your data. Users then have to go back into their account and edit those graphs and in my poking around over the past few days, only a few graphs per data set actually get edited. So more accurately, Swivel contains something like 15,000 or so graphs. The rest is just automatically generated noise.
That quibble aside, Swivel could become a major phenomenon in the sciences (social and hard). As more and more researchers upload more and more data, the sharing of that data will become easier and easier. And, more importantly to my way of thinking, the more eyes there are on the data, the more interesting and useful insights are like to emerge from those data.
For quantitative historians (I claim partial membership in that club), being able to share out data sets with one another will be a tremendous boon. Not only will we be able to share our data more easily, but transparency will be improved significantly. No longer will the readers of our work have to rely on the data tables we include in the finished products–if we upload the data to a site like Swivel, then our audience will be able to examine the data in all its excruciating detail. If we’ve made an error in our calculations we can find out quickly. If someone wants to quarrel with our analysis of those data, that conversation can happen in real time and in public. Either way, the project will advance and we are likely to get new ideas about our data and what they mean.
Just to give you one example of how simple this system is to use, I uploaded one small piece of the vast database of socioeconomic data I developed for my book on Czech nationalism in the late nineteenth century. This particular slice of my database is the table that displays data on death rates in those districts of Bohemia with a significant population of Czech speakers from 1909-1911. This data took me about 20 minutes to upload and get formatted the way I wanted it. Then I spent another 20 minutes or so noodling around with the graphs generated automatically. I don’t really like any of the graphs, so I’ll probably end up playing with these some more, but right now I don’t have time. The important thing is not the graphs–it’s the data. Anyone who is interested in these data now have access to them without having to go find a copy of the Austrian census for 1910, and best of all, they can download the data directly to their computer, rather than creating their own database for the data. I did all the heavy lifting for them.
I don’t expect a lot of social networking around this particular data set–after all, these are pretty obscure data and so not likely to see a lot of traffic. But being able to post them online means there is the possibility that someone else with an interest in the issues I worked on in the book might find them useful–giving my work on the database life beyond the book–and who knows, I might even gain some new insights from those who do peruse what I’ve posted.