This post originally appeared on the Talis Consulting Blog
Well actually, not just one letter, but over a thousand letters from the middle ages.
Last weekend, the National Archives held a Hackathon in the reading room at Kew. Around 40 developers and interested people took data from the National Archives and played with it. There were new mobile interfaces for the NRA discovery API; collections of tweets mined for the data and PDFs they contained; stats on historical participation in the olympics pulled from the archives and shown on interactive maps. In all it was a fun weekend with lots of smart people in the room and very quiet but rapid typing on keyboards to get something finished by the 4pm Sunday deadline.
Prizes were:
- 1st – Jonathan Tweed and Kai En Ong (ably assisted by Michael Smethurst, Faith Mowbray and Paul Rissen). A hack that pulls out data surrounding people & places in documents tweeted by @ukwarcabinet (and which – for a hack – is beautifully presented!).
- 2nd – Jamie Mahoney – Debtors & creditors dataset hack maps the most popular lenders & shows who’s borrowing from where – Show me the money.
- 2nd – Tim Hodson – A hack showing who wrote to whom in the middle ages.
- 3rd – Crystal and Steven Hirschorn – A hack showing participation in the Olympics on an interactive map.
You can read more about these entries on the National Archives blog.
I hope you’ll forgive my showing off of my joint second prize winning contribution to the pizza and jelly baby fuelled hack fest.
I took a suggestion from Paul Risson as a personal challenge, and started puling the data that I wanted into a new CSV file. I then converted that CSV file to a rudimentary RDF based model of the letters and people that the data described. I now had a graph dataset which captured – in the way only a graph can – the network of relationships between people who are corresponding. It was then a case of finding a suitable javascript library to render my graph as a visual and to allow people to find out about who wrote to whom without cluttering up the graph diagram.