They say that to a man with a hammer, the solution to every problem looks like a nail. And so perhaps it its unsurprising that I see a role for EPrints in helping students and researchers to gather, as well as curate and preserve, their research data. Especially when the data gathering requires a managed, long-term process that results in a large dataset.
|EPrints Twitter Dataset,|
Rendered in HTML
(1) simply collecting the raw data, uninterpreted and requiring the user to analyse the material with their own programs in their own environments
(2) partially interpreting the results and providing some added value for the user by offering intelligent searches, analyses and visualisations to help the researchers get a feel for the data.
We experimented with both approaches. The first sounds simple and more appropriate (don't make the repository get in the way!), but in the end the job of handling, storing and providing a usable interface to the collection of temporal data means that some interpretation of the data is inevitable.
So instead of just constantly appending a stream of structured data objects (tweets, emails, whatever) to an external storage object (a file, database or cloud bucket) we ingest each object into an internal eprints dataset with appropriate schema. There is a tweet dataset for individual tweets, and a timeline data set for collections of tweets - in theory multiple timeline datasets will refer to the same objects in the tweet dataset. These datasets can be manipulated by the normal EPrints API and managed by the normal EPrints repository tools: you can search, export and render tweets in the same way that you can for eprints, documents, projects and users.
EPrints collects Twitter data by regular calls to the Twitter API, using the search parameters given by the user. The figure on the left shows the results of a data collection (on the hashtag "drwho") resulting in a single twitter timeline that is rendered as HTML for the Manage Records page. In this rendering, the timeline of tweets is shown as normal on the left of the window, with lists of top tweeters, top mentions, top hashtags and top links together with a histogram of tweet frequency on the right. These simple additions serve to give an overview of the data to the researcher - not to try to take the place of their bespoke data analysis software, but simply to help understand some of the major features of the data as it is being collected. The data can be exported in various formats (JSON, XML, HTML and CSV) for subsequent processing and analysis. The results of this analysis can themselves be ingested into EPrints for preservation and dissemination, along with the eventual research papers that describe the activity.
All this functionality will soon be released as an EPrints Bazaar package; as of the time of writing we are about to release it for testing by our graduate students. The infrastructure that we have created will then be adapted for other Web temporal data capture sources as mentioned above (Flickr, YouTube, etc).