Ashton Dickerson, Biosciences PhD researcher and member of the Urban Light Lab. Photo: Eric Jong

Working smarter with Ashton Dickerson
Using High Performance Computing for increasing efficiency of research.

For the last year Ashton has been using Spartan with a PhD project that examine the effect of light on the nocturnal songrate of Willie Wagtails.

By using a automatic song detection package through R to extract data from the over 2000 hours of audio recordings she has gathered in her field work, Ashton has been able to automate the otherwise labour intensive handling of this data.

Then by working with Research Platform Services, Ashton has been able to complete these processes on a HPC system where large numbers of these tasks can be run simultaneously, saving her time that she can use of other aspects of her research.

Research Community Coordinator Eric Jong sat down with Ashton to talk about her project, and how she is integrating high performance computing into her workflow.

A Willie Wagtail.  Photo: Timon van Asten.

Can we start with the question that I’m sure you’ve answered a million times now as a graduate researcher,  what are you doing your PhD on?

Well, I research a quite unusual behaviour of birds that not only sing during the day but at night time as well.

Some diurnal (active during the daytime) bird species, also sing during the night time. This is an unusual because you would instead expect these birds to be sleeping during the night.

For my PhD I aim to understand why diurnal species are singing during the night.

To answer this question I have been examining this behaviour in an iconic Australia species, the willie wagtail (Rhipidura leucophrys), who has a reputation for its prolific nocturnal song.

So it sounds like a big part of your PhD is listening to the song of the Willie Wagtail, how have you been gathering this data so far?

To measure nocturnal song, I use bioacoustics recorders from Frontier Labs that allow me to record audio for prolonged periods. I target the roosting spots of willie wagtails to record their nocturnal song.

Thus far I have gathered over 2000 hours of audio.

A researcher checking a bioacoustic recorder.  Photo: Justine E. Hausheer / TNC.

That is a huge amount of data, can you talk a bit about how you have been handling that volume of information for your research?

To be able to handle such large data sets I am utilizing an R package, monitoR, which automatically detects bird song.

I import templates of willie wagtail songs into this package, which then is run along my recordings and it detects when the template matches a song. From this data I can extract the song rates (how often the willie wagtails are singing) and then I can examine the data to look for patterns.  

A spectrogram showing an example of the automatic song detections from an hour-long recording. Blue line indicates where the R package, monitoR, has detected a willie wagtail.  Image courtesy Ashton Dickerson.

A spectrogram showing an example of the automatic song detections from an eight minute long recording. Blue boxes indicate where the R package, monitoR, has detected a willie wagtail. Image courtesy Ashton Dickerson.

I am using the Spartan service through HPC at the University of Melbourne to be able to handle such large data loads. Lev Lafayette has assisted me by uploading my audio recordings to the UniMelb cloud, which is much more efficient that uploading this data via my personal computer.

The HPC is significantly faster than running these scripts on my personal computer. It would take me about 7 mins to process 1 hour of audio this way. Now using HPC it is about 3 to 4 times faster.

And in addition I am able to run this script over multiple recording sets at one time thanks to the multiple nodes. Not only does this save me immense amounts of time, this also means my personal computer is free for me to use while this data is being processed.

One of our mottos at Research Platform Services is ‘work smarter not harder’, which I think you are most definitely doing by automating these processes. Do you think there are things that you are able to spend more time with now in your research because of this?

Most definitely so, it frees me up to read papers and continue researching. To form thoughts and ideas around what this data actually means.

Ashton using HPC to give her more time to do MAXIMUM SCIENCE.  Photo: Eric Jong.

Using HPC has allowed me to take away the manual processing and gives me time to think about what this data actually means, to analyse it and put together ideas from it.

Thus far from the data I have extracted using the HPC services, I have discovered that willie wagtails’ nocturnal song significantly increases with lunar illumination, showing that this behaviour has a relationship with light, and therefore may be related to a visual cue.

This is an interesting finding and gives insight into the possible function of nocturnal song, furthering our understanding of the evolution and function of bird song in general. I am now preparing a manuscript for this finding.

Furthermore, given that I have discovered that nocturnal song has a relationship with light, I will also examine if this behaviour also responds to artificial light at night (e.g. streetlighting), which could highlight a possible stressor for urban bird species with nocturnal song. I will again utilize the HPC services at UniMelb for data extraction.

Thanks for your time today Ashton, do you have any advice to share with other researchers?

The first step for me using HPC was just hearing whispers that something like this was possible, and then from there I looked for and found people who could help me with it and point me in the right direction, and also gave me different options to choose from.

So I guess I would say it’s all about building and engaging others in the research community.

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