TweetCoding is a tool for simple coding of tweets. It displays 5 tweets per page using Twitter’s tweet embedding to display each tweet as it looks on the Twitter website on a local web page. It needs internet access to get the tweets from Twitter, but all coding data is stored on your computer, not the internet. Tweet Coding Screen The page has form elements for coding each tweet as yes/no/not coded and a comment field for notes.

It is my pleasure to advise you that on 18 April 2016 the relevant Delegated Authority of the ANU Colleges of Science confirmed the approval of the award of your degree of Doctor of Philosophy. Congratulations on this significant achievement.

My thesis is available in the ANU Open access thesis collection:

I’ve asked this question on StackOverflow (, but couldn’t include images because I haven’t posted on stackOverflow before. So here it is, with the images. I want to be able to set the major and minor xticks and their labels for a time series graph plotted from a Pandas time series object. The Pandas 0.9 “what’s new” page says: “you can either use to_pydatetime or register a converter for the Timestamp type” but I can’t work out how to do that so that I can use the matplotlib ax.

I have released the Java programs that I’ve been using to collect data from Twitter for my PhD since November 2009. It is suite of programs that use the Twitter Search API and Twitter Stream API to get tweets and then store them in a mySQL database. They have been tested on Mac OS X and Debian Linux. The Java source is split into 9 Eclipse projects using Maven to bring in the external library dependencies.

On June 18th I was awarded the 2012 ANU Award for Excellence in Tutoring or Demonstrating. The award was presented by DVC (Academic) Professor Marnie Hughes-Warrington at a celebration during ResearchFest. I was one of three joint winners to receive the award, from a pool of 38 nominees and hundreds of ANU tutors and demonstrators. Photo by Erin Walsh: DVC (Academic) Professor Marnie Hughes-Warrington presents Brenda with her award certificate.

Hearing the Unseen has evolved into an iphone game and generative music machine called Ear Trading.  It is now ready for beta testing after which it will be released as on the iTunes store as a free application.

This is based on a Poster/Demo presented at the “Humanities + Digital Visual Interpretations Conference” hosted by HyperStudio - Digital Humanities at MIT 20-22 May 2010. Ear diagram Barry Moon Arizona State University Brenda Moon The Australian National University This project started as a exploration of data sonification techniques. The abstract published in the conference program is a testament to this. As it progressed, the idea of using real-time data to create a game for mobile devices became more alluring.

I’ve been trying out the MSAFluid library by Memo Akten for Processing. Starting from the example program that comes with the library, I’m making a wind tunnel. I’ve seperated adding force to the fluid, adding particles and adding dye so that I can add each where I want to. Processing is fun because the sketching approach means that you see the results at each step, and this often provides weird / interesting visual results.