An improbable informatician

By Brendan Ansell

A black screen with a tiny blinking cursor. Silence except for the quiet whir of a room full of computers. No Microsoft Windows here. What have I got myself into?

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Where are the LOLcatz??

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This was the world I found myself in when, in total ignorance, I mentioned to the Undergrad Research Opportunities team that I was ‘interested in bioinformatics’ (sounds cool and technical huh?). At this point my computing prowess amounted to some vague notions about Excel, and familiarity with the word ‘bioinformatics’ from first year biology lectures.

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The job involved processing brain MRI scans to test for differences related to prescription drugs.


At my side was a coffee stained Introduction to Unix.  

“Create a file called list1 containing the following fruit: orange, plum, mango, grapefruit…”

How this was meant to help me get the job done was less than clear.

A long 18 months followed, but thanks to my excellent (read ‘patient’) supervisors, I eventually got my head around Unix.

The next year it was back to good old biochemistry. The stuff you can see down a microscope, knock a gene out of, or get a horrific infection from:

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But as I recovered from my honours project, in my fever dreams the command line was calling, even commanding me back.

The efficiency of a loop… the power of combining lots of different data to answer an interesting question… the joy of heading to the pub while it does your work for you… I was hooked!!

…but still fairly useless at coding.

I spent about a year mashing Unix and Excel together to work through a genomics project. All the while, I was reading papers in journals like PLoS Computational Biology, and wondering how they made those awesome figures?

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from Goncalves et al., 2017 DOI:10.1371/journal.pcbi.1005297

The answer, I eventually discovered, was R. The free, powerful, widely used and completely hackable salve to any scientist’s Excel-induced rage.

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This coding language really comes into its own with Rstudio - where you can write code and see the graphical results instantly on your desktop.

I enrolled in a PhD in infectious disease biology, and decided to get SRS about R.

I scoured the user guides for the software I wanted to use.

I chopped up code and put it back together to try to isolate the important parts.

I was a Guinea pig for the DataSociety R course and learned a lot through their materials.

I replaced online examples with my own data, searched Google relentlessly, patched up and saved anything that worked, until my figures started to look presentable.

The more I practiced, the more I found (overly-nerdy) uses for R.

Go-karting lap times….that would work as a box plot! Snow depth this winter? Line chart. Journal publishing cost by impact factor? Scatter plot. The interactive plot functions are especially cool:

https://plot.ly/~plot_ranger/13.embed

Alright, I was addicted to #chartlyf. You don’t have to go that far to see the point in learning R, though.

In all, this is the tool to help you find the stories in your data (and make them look great), with maximal reproducibility, and progressively less effort.

If you’re sick of scouring through Excel sheets, Prism is getting too fiddly, and VLOOKUP is losing its charm, come to ResBaz and try R. You won’t Rgret it.

To discuss any R problems, contact me at rp.brendan@gmail.com, or through twitter @ansellbr3  

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My story: From music to medical imaging

Post by: Rosa Shishegar

 Hi All, I’m Rosa!

What was my journey?

I am a final year PhD candidate in the University of Melbourne’s Department of Electrical and Electronic Engineering, working in the field of medical imaging. I have recently joined Research Platform Services as a Research Community Coordinator to build a community around medical imaging. Two years ago, the Research Bazaar (ResBaz) community was introduced to me as “a group of computer programming geeks”. Not surprisingly, I found them to be one of the nicest groups of geeks in the university and they helped immensely in solving one of my software issues. So, after more than a month of being stuck in a problem, thanks to them, I could move on with my research.

Let me take you a few years back to the beginning of my journey in medical imaging research. I studied Electrical Engineering (Electronics) in my undergrad. I was intrigued by the idea of studying ‘the Effects of music on brain signals’ to combine my major, Electronics, with my old love, music. I then started a masters in Bioelectronics. In my mind, it was the perfect marriage of my interests and passions. However, at the beginning of my masters and before this relationship could fully bloom, I was introduced to ‘Medical Image Processing’. Ever since I have adhered to medical imaging. I am passionate about the gorgeous, mysterious structures of the brain and all those meaningful colors.

What do I do with medical imaging?

My PhD research is on brain development. In particular, my research is focused on methods to better understand the process of cortical folding in the fetal sheep brain.  As you know the human brain cortex is folded (like a walnut). Understanding the patterns of cortical folding and its mechanism during development is important for early diagnosis of Neurodevelopmental disorders and making improvements in treatments. Why study sheep brains? Because it has easy folding patterns that are similar to the human brain.

In the last few years of my PhD I have developed new methods for processing and the analysis of cerebral cortex structures and morphology using structural MRI and diffusion MRI. The image below is the profile picture of our new Facebook group ‘Medical Imaging @ Unimelb’ that showcases the diverse range of medical imaging applications and analysis tools. I am showing the results from my very own method of assessing the morphology of human brain surfaces (the first three colorful human brains in the top row). Other medical imaging applications highlighted here are visualizing neural tracts (or fancily speaking: tractography), segmentation, 3D modeling, 3D printing and statistical analysis. The tools used to implement these applications include but are not limited to:
  • MATLAB
  • FreeSurfer
  • FSL
  • MRtrix
  • 3D Slicer
  • 3D printers
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Our awesome team the Cadventurers are offering heaps of workshops and training sessions throughout the year to upskill aspiring researchers in 3D modelling, 3D printing and Medical Imaging. My favorite tools to teach are FSL, FreeSurfer, MATLAB for MRI and MRtrix. These are comprehensive tools for 1) analysis of MRI, fMRI and Diffusion MRI data, and 2) 3D modeling of brain morphometry, and functional and structural connectivity

What I hope to accomplish in this job?

Before being a Research Platforms Community Coordinator, I am a sociable researcher and I enjoy meeting people and growing in a team. I used to complain that there are not many people working in my field in the University of Melbourne. The funny part is that whenever I travel continents for a scientific conference, I meet people who are working in my field from beautiful city of Melbourne or even from another department in my own university. I hope with the help of my Research Community friends Louise, Jas and Warda, we can help the Medical Imaging community in Melbourne connect more and grow stronger together.

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A preview of my recent conference trips!

If you feel you would like to help the growth of this community, or if you are interested in our workshops (all free!), please feel free to join our Facebook group or to contact me rosita.shishegar@unimelb.edu.au, or tweet me at @RShishegar.

How I discovered MATLAB!

Hello lovely people! 

It is time to meet your new MATLAB Research Community Coordinator (Rescom) – tada!

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My name is Warda and I am a data wrangler and a neuroimagineer- yes, this is a thing. In case you are wondering what it means, here is a hint:

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A Walt Disney employee card. 

Originally posted here:  http://tinyurl.com/jsqnygz

I first started coding when I was just over 10 years old. One magical morning in a galaxy far far away, I stumbled upon the deceptively simple looking black hole of GW-BASIC programming environment. I innocently stepped into the world of programming and I have never returned since - I have no regrets :D.  Soon after, I found myself fathoming the secrets of algorithm development, syntax, logic and design.

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 Humble beginnings… 

Image courtesy: https://en.wikipedia.org/wiki/GW-BASIC

My experience as a programmer in GW-BASIC led me to the world of C/C++ and very soon I found myself indulging deeply into coding. I became that proverbial coder who is always looking for challenging programming problems and spent hours glued to the computer typing away one line of code after another.

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Fire…? What fire…?

 Gif credit: http://giphy.com/gifs/13HgwGsXF0aiGY

After completing high school, I started an undergrad degree in Electrical Engineering. In my heart, I knew that C/C++ is the solution to all problems that engineers face and I was confident that I am set to conquer the technical world with the help of my faithful C/C++. However, right after reading the manual of my first ever signals and systems workshop all my confidence came crashing down.

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Originally posted by gifsboom

 ^^ Yes, that is my confidence crashing down!

The thought of implementing convolution of signals and systems in C/C++ sent shivers down my spine and I gaped at the task ahead of me in horror. However, before I lost all hope, I noticed that the manual is instructing me to open ‘MATLAB’! Now here comes a funny bit. ‘Matlab’ is also a word in Urdu, my mother tongue, and it literally translates to ‘meaning’. For a moment, I just sat there, wondering what in the world does this mean and then the awareness dawned when my instructor told me to go to the start menu and open MATLAB! Hallelujah! I must admit that it was love at first sight and I have stayed faithful to MATLAB ever since. Here is why I love MATLAB

  • Elegant data structures
  • Vectorized operations
  • Powerful toolboxes
  • Great graphics
  • A thriving online community

Now that I have convinced you the MATLAB was my knight in shining who rescued me from having to write intricate code by presenting me with easily implementable data structures and a treasure chest of built-in functions, let me tell you how MATLAB is still helping me in my pursuit of knowledge.

I am a PhD candidate at Electrical and Electronic Engineering Department of The University of Melbourne. I am part of the Neuroimaging research group in Melbourne Brain Centre. My research is focused on studying movement of water molecules in biological tissue under the influence of extremely high magnetic field for the purpose of characterising tissue morphology. Technically, this area of research is known as ‘Diffusion Magnetic Resonance Imaging (dMRI)’. I spend most of my time analysing and visualising rat brain MRI data in MATLAB, though I am inclined to switch to studying bananas after seeing the MRI image below.

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MRI image of a banana - hypnotic!  

I found this beauty here: http://giphy.com/gifs/interesting-banana-mri-To3eb1ZkKe7de

 I am a data-wrangler at ResBaz and not surprisingly, my favourite tool to teach is MATLAB. Throughout the year, we offer ‘Beginners for MATLAB’ workshops to introduce basic MATLAB concepts to researchers. I am also an admin of our fabulous Facebook group - MATLAB @ Unimelb. This group is a vibrant community of MATLABers for exploring, sharing and connecting online.

Currently, we are in the process of developing extension courses to provide specialised training to researchers with diverse backgrounds. Recently, our champion Rescom, Pippa, beta-tested a fantastic extension course called ‘MATLAB Statistics Toolbox Course’. Rescoms Yamni and Ewan are developing advanced courses for Image Processing and Parallel Computing in MATLAB. As your new Rescom, I am currently developing a ‘MATLAB for MRI’ course with help from amazing cadventurer and fellow Rescom Rosa. This course will focus on importing, visualizing, analysing, 3D modelling and exporting MRI images in MATLAB. If you are an MRI buff and have some cool suggestions for this course, please feel free to ping me (@WardaTaqdees).

Rosa and I have also started a new facebook group ‘Medical Imaging @ Unimelb’ with the aim of providing a platform for sharing, collaborating and discussing various medical imaging modalities. If you are facing a teething coding problem, feel free to come to Colab, hacky hour or PhTea and I will be happy to help. A schedule for my regular consultation hours and our trainings can be found here.

http://melbourne.resbaz.edu.au/calendar

Happy Coding!

R is for R

Hi everyone! My name is Tim, and I’m one of the brand new RStudio trainers (along with @nikkirubinstein) at ResPlat. I thought I’d take a minute to introduce myself, tell you what I do and how I fell in love with R.

My introduction to the R universe came when I started work as a statistical modeller for an insurance consulting company after graduating from uni in 2013. I was fortunate enough to observe a field-wide transition from using a bunch of old and outdated tools, to using R, in the few years I was there.

As a noob in the company, I was often given the job of learning cool new applications available in R and demonstrating how we could use them to make our jobs easier.

One thing that stuck in my mind was the effect this had on our product sales, which would typically hinge on the sales team selling a series of un-tested and even un-created statistical tools! Before using R to develop product demos, the poor sales team would be like:

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R, with its plethora of open-source packages, enabled us to whip up last minute demos which actually worked, like the glorious coding ninjas we were:

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And like a swashbuckling pirate, it didn’t take long before my answer to everything started with “R!”.

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Needless to say, by this point I was infatuated by R, leading to the second phase of my spiral into R addiction. Let me explain why that is not an exaggeration.

For the past few years, I have spent a disturbing amount of my free time competing in data science competitions hosted via a website called Kaggle. For those of you who haven’t heard of it, Kaggle is the hard drug of the R/python/data science world. It’s purpose is to host competitions for research institutions and big companies, who have data-centric problems that need to be solved, offering very alluring cash prizes for winners. 

Nowadays, I probably spend WAY too much time slaving away on the latest competition which, like a true gambling addict, I will truly believe I will win, despite the complete lack of historical evidence for that belief!

That about sums me up, R-coder and Kaggle addict. If you want to know more about R, RStudio, data science (and Kaggle) and why they are all awesome, come find me (@TimEsler). And if you manage to catch me without my computer, I’ll be this guy:

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My journey to R

Hi, my name is Nikki and I have joined Research Platforms as an R Research Community Coordinator. My journey into learning programming did not follow a well-trodden path. In fact, if you’d asked me about R a few years ago, I would have asked you if you meant the letter in the alphabet between Q and S. So gather round and let me tell you my story.

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https://commons.wikimedia.org/wiki/File:USMC-090910-M-4913M-026.jpg

In Year 12, I had no idea what I wanted to do with my life. I spent many a weekend trolling university open days, looking for inspiration. Inspiration struck at the Melbourne Uni open day, where I did a tour of the Australian College of Optometry and was shown a camera that could take a picture of the back of a person’s eye. That seemed pretty cool to me, so I enrolled in a Bachelor of Optometry at Melbourne Uni.

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https://en.wikipedia.org/wiki/Fundus_photography

As I progressed through the degree I began to realise that perhaps clinical optometry wasn’t the right profession for me. I really enjoyed the optics lectures, but with the profession mainly focussed on primary healthcare, optics doesn’t get much air time.

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https://commons.wikimedia.org/wiki/File:US_Navy_090707-N-9689V-010_Lt._Jackie_Anderson,_optometrist,_conducts_an_eye_exam_with_a_local_man_at_a_Pacific_Partnership_2009_Medical_Civic_Action_Project.jpg#filelinks

During the final year of my degree I did a research project looking at divided attention. Participants sat in front of a screen with constantly moving stimuli, performing various tasks, while I measured their eye movements. I really enjoyed building a headrest for the chair we were using (which turned out to be extremely uncomfortable for my participants – my bad!) and designing stimuli for the experiment. With my creative side engaged, I decided to enrol in a research degree.

Wanting to venture a little from optometry, but not too far, I did an MPhil looking at the relationship between visual perception and pointing behaviour. The experimental code was written in Matlab by an academic in Adelaide. Having had no prior programming experience, I spent many days trying to work out what each line of code meant, so that I could make small edits of my own.

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After finishing the MPhil, I travelled around Europe and the USA for 6 months, trying to decide what to do next. The resounding response I received from fellow travellers was that I sounded like I wanted to go back to study.

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Having developed an intrigue into the (what seemed to me then) mystical world of programming, I enrolled in a PhD focused on developing new algorithms for visual field machines. Visual field machines are used to measure vision in glaucoma, mapping out visual sensitivity across space.

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With no real programming experience, I spent the first 6 months of my PhD learning basic programming concepts using the language C. During this time, I spent many hours with Alistair Moffat’s text book: Programming, Problem Solving and Abstraction with C.

Once I got my head around C, I needed a way to analyse and visualise my data. Enter R. I would love to say that my ardour for R was immediate, but in reality I had some reservations. Coming from a low-level programming language, it wasn’t immediately apparent to me why R would be useful. I have seriously revised my opinion since then.

Here are some of the reasons why:

  • Open source – when you leave the university you won’t need to buy a licence
  • Huge online community – if you have a programming question, someone else on StackOverflow has probably already found a solution
  • Can integrate with other languages – C, Java, Python
  • Functional programming – R provides many tools for the creation and manipulation of functions
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Graphic created in R

My route to learning programming has been unconventional, which has given me a unique perspective on programming concepts. I would love to share my R knowledge with you. Watch this space for details of future training sessions. 

If you would like to discuss your R problems with me, you can contact me at nikkiR@student.unimelb.edu.au or on twitter @nikkiRubinstein. I look forward to hearing from you!

You’re a Vizard Errol

By Errol Lloyd

Hi all!

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Yes, I’m catching up on my Dr Who.

I’m Errol and I’ve just joined as the ResCom in the new Data Vizard stream, which is dedicated to getting researchers sharing, communicating and collaborating through visualisations on the web.  

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Worth a dance I reckon.

During the day I’m a neuroscientist.  I work in systems neuroscience, which broadly tries to work out how the brain and mind work.  In this introspective era, it’s an undoubtedly big scientific question, with the added curiosity of potentially connecting, through a cohesive understanding of the mind, the mechanistic sciences with politics and art (see Susan Langer for more).

Specifically, I use the visual system as a means to study the brain.  This is because vision involves nearly half our brain.  We densely sample the world along a number of dimensions—wavelength, energy, space, time — simultaneously.  And over many inter-weaving iterations, we integrate and contrast multiple streams of information to finally develop an understanding of our world.

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Dali, Head Exploding

Soooo, why data visualisation then?  Well …  "vision involves nearly half our brain.  We densely sample …etc … an understanding of our world”.  Vision is kinda our brains’ thing and probably instrumental to understanding, discovery and science.

Even Plato is surprisingly on point on this, calling vision the cause of the greatest benefit to us, having ’created the art of number and … given us not only the notion of Time but also means of research into the nature of the Universe’. (Plato, Timaeus, 47a)

I see our aim as being to go further, to break the ‘collaborative ice’ by having researchers tell the story of their research to the world.  As Alice asked “what is the use of a book … without pictures or conversations’’, so we ask what’s the use of research without visualisations and open data?

Us researchers are bad at the communication thing, and it hurts not only ourselves and our standing in society but science and research generally.  By tapping into the brilliant little abstract visual computer in our brains, visualisations can not only summarise and make navigable and comprehensible otherwise boring or impenetrable data, but can also capture the essence of a topic or question and close the understanding gap so many of us find hard to bridge.  Visualisations can tell our research stories.  

And what if we all put them on the web?  That might even be a little magical.

Also, eye candy is eye candy:

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2001: A Space Odyssey

Glad to have joined the gang!

Errol Lloyd.

Meet Rosie ResPlaty

by Pip Karoly

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Hi, I’m Rosie and I’m a platypus. I’m an all-round-awesome research babe who can out-code you any day of the week, in any given language. I’m also the official mascot for the Women of ResBaz. I could talk about myself all day, but instead I’m going to tell you a couple of stories about other amazing coders.

Fun Facts

  1. The first mechanical computer was programmed by a woman
  2. The first electrical computer was programmed by a team of six women
  3. The first programming compiler was invented by a woman

 Ok, story time.

1. Ada Lovelace

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The first mechanical computer was called The Analytical Engine. It was designed (not built) in 1837 by a mathematician, Charles Babbage. You might know him as “the father of the computer”. His design was a true computer because it could do anything you told it to, if you told it in a sufficiently logical way. All well and good, but pure logic doesn’t come naturally to humans, which can make computer communication frustrating (don’t tell me you’ve never threatened or committed an act of violence towards a computer).

Anyway the point is, it was a woman who programmed the first ever computer. Ada Lovelace (also a mathematician) wrote a detailed piece of code that could run on the Analytical Engine. It was probably the first ever computer algorithm. So I reckon if Babbage gets to be father of the computer, we should call Ada the “countess of code”. (In the interest of full disclosure – it’s been said that Ada didn’t really write the code, and that Babbage just credited her with it, because poor thing she was manic depressive. Funny how so many smart/strong women are posthumously diagnosed with mental disorders.)

2. The ENIAC Programmers

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The Electrical Numerical Integrator and Computer was the first ever electrical computer to be built. But it was operational during WWII so unfortunately there weren’t that many men around to make it work. Instead a team of women - Fran Bilas, Betty Jennings, Ruth Lichterman, Kay McNulty, Betty Snyder, and Marlyn Wescoff - took on the job.  They got right in there and learned to program the mighty ENIAC. I mean they really did get right in there – debugging in those days involved crawling inside your computer and fiddling with switches and cables. These six women were not only learning to code, they were inventing the concept of “coding”. Their story may never have come to light if it weren’t for a committed programming student who documented their achievements in the ENIAC Programmers Project. I highly recommend watching the film (’The Computers’) if you want to learn more.

3. Grace Hopper

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Grace Hopper’s name is legendary in the programming world. She was heavily involved in the programming of early, pre-ENIAC computers. She’s best known for leading the team who developed the first compiler program. A compiler program translates between languages. It converts all your lovely code into computer-speak so it actually works. This concept revolutionized programming. Check out this short, aptly named video The Queen of Code for more. Grace also invented the word debugging, when she fixed a programming error by removing an insect from the computer (unfortunately for dad-jokers everywhere, it was not a Grass Hopper but a moth).

So next time someone tells you coding is not for women, kindly remind them that we invented it.

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Hello world, this is me

by Pip Karoly

If you picked up on both the programming and Saddle Club references in the above title, you may consider yourself truly multi-disciplinary.

I’m Pippa, the newbie MATLAB research community coordinator for ResBaz. I’m stoked to become part of the ResBaz data-wranglers team, especially since I just read everyone else’s intro posts for some inspiration and these guys are legends!

So what is there to know about me? I love MATLAB but I didn’t always. When I first had to use MATLAB as an undergrad engineer I found it awful. But I got there in the end and now I love programming. Too much actually - it’s gone from scary to enjoyable to addictive. 

MATLAB then

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Originally posted by yourreactiongifs

MATLAB now

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Originally posted by maisyroselove

Apart from working for ResBaz, I’m a PhD student at the University of Melbourne, in the NeuroEngineering Research Lab. I have an office at St Vincent’s hospital, where I work with a lovely team of engineers, neurologists, statisticians, and mathematicians in epilepsy research. My research is in computational and statistical models of the brain to help understand (maybe even predict) seizure dynamics, and I have LOTS of data to wrangle.

Confession time - I just realised (after being a data-wrangler for over a month) that I don’t know the definition of wrangle. Google informs me that to wrangle (verb) is to:

1) round up, herd, or take charge of
2) have a long, complicated dispute or argument. 

Or, in other words

1) How I think I handle my data
2) How I actually handle my data

Just kidding, I love data. And I’m a big advocate for cool and creative data visualization, to communicate research to the public. For instance this is one of my fave (and okay, not very scientific) visualizations of cocktail recipes.

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SO much info packed in one beautiful, fridge-pinnable graphic (see it full size here). Oh and it was made with AutoCAD - nice!

What else do you need to know about me? I can always be bribed with chocolate. I dislike inspirational quotes and instant coffee. I’m actually quite terrible at social media. I mean, I really am – don’t make meme jokes to me

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Did I mention I’m pretty great at making dad-jokes?

I’m also planning some new MATLAB courses later this year and will be attempting to gamify the learning process with codathon challenges held at Tsubu – so keep an eye on our events calendar. And I generally enjoy meeting other researchers so if you ever feel like a coffee/chat/coding-sesh you can find me at Hacky Hour or hit me up on Twitter @pjkaroly.

Goodbye (and hello!)

Pippa

Renaissance Dan

The Slow road to Enlightenment

Hi everyone, I’m Dan Sandiford, a PhD aspirant in geoscience at the University of Melbourne. My work involves the modelling of convective flows in the solid earth; the surface part of which we call Plate Tectonics.

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Using the Underworld code, we are able to explore millions of years of Earth history, through the breakup and collision of continents, the birth of new oceans and the formation of plate boundaries; in short, where we came from, how we got here, and what physical processes underlie it all.

Though I now use a computers in all aspects of my research (from a Raspberry Pi to Raijin), the fact is I had hardly touched one until after I’d finished my undergraduate degree. Using GUI-based software, I often find myself looking for a function that I thought came off a drop down on a toolbar that no longer seems to exist. Just as had I reconciled with the fact that my technological proficiency would hover around the level of your average Pennsylvanian Anabaptist (#Plainpeople), I began tooling around with programming languages like Python to help doing basic data analysis and visualisation. 

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To my surprise, I found that programming languages were a much better fit to the way my mind learns.  So, while I still often forget the name of a particular function in Python, I find 99 % of the time all I need to remember is what I did last time, not precisely how I did it. This is partly a testament to the Stackoverflow forum, but also to the kind of universality that you get in a programming language. A great example of this is a python data structure called the Numpy Array. Whether I’m looking at output from a computer simulation, analysing a timeseries, or inspecting an image file or raster, I’m dealing with the same fundamental object:

fabio = misc.imread('/Users/Fabio/100_greatest_portraits/fabio.jpeg')

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Over the last few years, and despite all efforts to resist, my interest in data science, computer programming and the open source movement has grown. A typical morning in my research consists of a cup of black coffee and a quick dive into github to look for code updates that may have materialised overnight. In this way I’m constantly expanding the toolbox, and learning about the approach others are taking. There is a very strong global movement afoot to make research more open, more collaborative and more enjoyable; Research Platform services, here at the University of Melbourne, are supporting and investing in these ideals.

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I’m taking over from the inimitable Dr. Climate as the Research Community Coordinator for the physical sciences. Through Research Bazaar, I’ll be teaching basic skills in Python as well as developing new courseware for spatial geospatial analysis. My background is in geoscience, but I’m happy to talk about tinkering with data in any field: from urban planning, to law, to economics. You can find out more about our trainings here: http://melbourne.resbaz.edu.au/. 

See you in the Bazaar.  

Dan

There are some who call me … Tim

Hey everyone,

My name is Tim Rice. As one of the new Research Community Coordinators in ResBaz the time has come for me to introduce myself.

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An image of Tim

In 2008 I graduated from a Bachelor of Science with Honours in Mathematics & Statistics focusing on applied maths and the theory of random processes. My thesis with Kostya Borovkov involved developing a probabilistic model to look at how population density affects virus deadliness.

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Used under licence CC-BY-NC-SA from DeviantArt/Tess-27

When I finished my degree I wasn’t sure what type of work I wanted to get into. Naive notions came to mind about applying mathematics in some industrial or commercial management career. I didn’t know at the time how much I would end up falling in love with academia and developing a passionate obsession for all things computational! Working as a sessional tutor in Maths & Stats and as a programmer in MASCOS, then having material from my thesis published in the Journal of Mathematical Biology, helped provide an initial exposure to the types of work that later came to seem ever more fun and important to me.

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Another image of Tim

I spent 15 months as an Operations Analyst with Defence Science & Technology Organisation. It was interesting to work with people deployed to Afghanistan and Timor Leste, but most day-to-day tasks weren’t super top secret. My main niche was to use Visual Basic for Applications (VBA) to develop administrative tools. This was necessary because deployed defence computers are heavily locked down; any new software always needs to go through an exhaustive (and exhausting) security audit. One thing that could be counted on was that these computers always had Microsoft Office installed. This meant requests for new functionality could often be addressed by writing an app in VBA.

After a while I began to miss the freer academic environment; furthermore the ubiquitous reliance on clumsy, heavy-duty proprietary software was at odds with my preference for the lean, mean Unix approach to scientific computing. So, I returned to Melbourne to commence a PhD in computational genomics. Unfortunately my PhD experience was beset by turmoil from various quarters. After it began heading in a non-computational direction, away from what I wanted to be doing, it became difficult to progress and I eventually decided to withdraw.

However, that doesn’t mean I’m ready to give up on research! My plan is to see whether I can pursue a Masters in the area of Computer Science. If successful this will put me in a better position to focus on a PhD in a heavily computational area.

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An image of COMBINE

My first exposure to Research Platforms and Research Bazaar came via COMBINE, which I’ve been involved in for a couple of years. COMBINE and ResBaz have often helped each other run Software Carpentry workshops for grad students and early career researchers in the life sciences. ResBaz had lots of interesting people and I began making appearances at HackyHour to chat about computational topics and help people with any problems they’re having in their research. I also bumped into a few more ResBaz people at HealthHack 2014.

I guess I did something right because recently for MozSprint 2015 ResBaz asked me to begin putting together material for a LaTeX workshop. I thought it would be a breeze but it turns out building a workshop from scratch takes a lot of time! Hopefully it will begin looking fairly complete over the next month or so and after that we can do a pilot run. The intended audience will be LaTeX novices who need to use it for their theses and journal submissions. It might also be of interest to people who want a deeper understanding of how tools like Authorea work under the hood.

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Hacky Hour

So far I’m really enjoying working with the ResBaz team. The culture is very warm and dynamic, and everyone is always working on something fascinating. It remains to be seen which particular niche will end up becoming my special superpower. I have some crazy ideas such as teaching a bit of C programming, first of all for its high performance aspects, but also because C incentivises small modular programs and removes some of the “black box” feeling of higher level languages. I’d also like to see whether we can help people make the most of powerful text editors like Vim or Emacs.

Whether or not such left-field ideas attract interest, I still look forward to helping everyone discover better ways to do digital research and scientific computing. It is a real privilege to be able to assist anyone doing research, and very exciting to contribute to the development of intellectually rich and computationally empowered research communities.

If you want to keep track of what I’m working on or thinking about, follow me on GitHub as cryptarch or on Twitter as @0x7472. I also write about a miscellany of computational, mathematical and statistical topics on my Department of Maths & Stats website.

If you want to discuss ways I can help your research community, you can either email me at t.rice@ms.unimelb.edu.au ― or better yet come along to Hacky Hour and have a chat in person!

I hope to see you soon!

ResCom Introduction : Survey Tools & Spreadsheet with Yuandra

By Yuandra Ismiraldi

Hello readers ! My name is Yuandra and I’ve just joined the Research Bazaar team as a Research Community Coordinator within Research Platform Services.

Finishing up my first week in as ResCom in @ResPlat ! Have so much things lined up to help researchers in their research :)

— Yuandra Ismiraldi (@iniandra)
July 31, 2015

After getting a Bachelor and Master degree in Informatics Engineering with Software Engineering focus, I’ve worked in ICT industries and startups for several years focusing on mobile technologies. Following my work in the ICT field, I got introduced to the field of ICT for Development and Open Data, fell in love with it, and ended up collaborating with various international organisations, NGOs, and government institutions in helping them to use technology and open data to better engage the people, enhance service delivery, and raise transparency.

This year I’ve decided to go back to university and currently pursuing a Master in Development Studies degree in the University of Melbourne. I’m focusing on how government and NGOs can leverage technology and data to help development in a country - it can be as simple as using your phone to help save bananas 

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credit : memecrunch.com

At ResBaz I’m going to be running training on survey tools and spreadsheet, or in more literal terms: helping you get your data, clean it up, put it to a nice spreadsheet, and analyse it.

To do that, I will use tools. While other trainings related to this might focus on going to the deep (and interesting!) world of programming, I will focus on using various tools that enable researchers (that’s you!) to do data collection and spreadsheet wrangling without any (or maybe a little) programming. Some tools include: Google Forms - allowing you to deploy web based forms to almost the entire internet community and easily hook up the data for analysis and visualisation, Kobo Toolbox - enabling you to collect data in remote places where there’s no internet, and Open Refine - helping you clean all those 10K+ rows of mistyped data.

Best of all? The tools are all free to use ! This means you can allocate your precious research budget out of survey and spreadsheet tools to other important things :)

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credit : memecrunch.com

The training is still in the early stage of development but there is a lot of opportunity for you to engage with the community and join the discussion! If you have any feedback and suggestions regarding the training curriculum or any tools you want to be included feel free to give me a shout on twitter @iniandra or send me an email on yismiraldi [ at ] student.unimelb.edu.au

Looking forward to hearing from you all :)

Vincent talks #SharksDen: Day 1

by Vincent Khau

Hey everyone!

My name is Vincent Khau, the newest recruit to join the ranks of Research Bazaar! I will be coming on board with the rest of the team as a Research Community Coordinator, working primarily with TinkerCAD and 3D printing, although some of you may recognise me during your #CADventures as one of Aliza’s minions for the Inventor 3D modelling course. I’ve completed my Bachelor of Science just last year, and am currently studying a Master of Engineering (Mechanical) at the University of Melbourne (so I’ll definitely be seen around campus a great deal!). Feelings about joining the community? EXCITED!

Recently I participated in the exciting Shark’s Den Challenge hosted by Research Platforms. If you are, like I was prior to the event, slightly afraid that this might be a gambling den populated by card sharks, let me reassure you by explaining what this event actually is. The Shark’s Den Challenge is an opportunity for technical enthusiasts, entrepreneurs-in-the-making, and innovators to meet new people, form teams, and get creative by designing a product (over consecutive Wednesdays) that they will be pitch to a panel on the 29th of July. Successful teams may have the opportunity to pursue their design with the Melbourne Accelerated Program (MAP)!

My aforementioned premonitions quickly dissipated with David and Paul’s warm hospitality, and with so many interesting and affable people to meet and talk to, the first event on the night was a quick 2 minute ‘speed dating’ of the 20+ crowd where we discussed our strengths and whether we saw ourselves fitting into CEO, CTO, CFO, or CMO roles. The participants who gave the best first impressions were then nominated as CEOs and the teams were picked for the upcoming weeks.

In these teams, we were confronted with the challenge of innovating on an invention which has been around since… well since forever really; the door. After coming up with prototype designs (a foam door with key-less entry and a 'quarantine zone’ that cleans you? What’s not to like!), we immediately started working on a pitch to present our design to the other teams (with CFOs taking frivolous notes as to who were our main competitors).

With an event that was so enjoyable, with so many fascinating ideas being shared, some teams even stayed well beyond the end of the event to continue discussing the next steps to their designs! It was truly an amazing experience to see the power of idea sharing at work, and I am really looking forward to next session on Wednesday 8th July. Can’t wait!

MATLAB – A Love Story

By Kerry Halupka

Hey there,

My name is Kerry Halupka, I’m a PhD student in Biomedical Engineering, and I’m the newest member of the Research Platforms team. Since joining the team I’ve been involved with teaching free MATLAB workshops to researchers, and tweaking the course materials.

I had a ball teaching the @MATLAB for Women workshop with @Isa_Kiko last week at @unimelb. Looking forward to more in the future! @ResPlat

I started my PhD at the University of Melbourne in March 2013 after completing a Mechatronic Engineering degree at The University of Adelaide. My current work focuses on the bionic eye that is being developed by Bionic Vision Australia. By recording activity in the brain while activating the retina with our retinal prosthesis, I’ve been developing a computation model built in MATLAB to predict the brain’s responses to stimulation. My aim is to use the model to optimise the way that we electrically activate the retina, hopefully increasing the resolution of the images our patients can currently see.

I’ve been using MATLAB since the first year of my bachelor’s degree. Way back when I thought that university couldn’t possibly be any more difficult than year 12 in high school. I realised how very wrong I was at about the same time I discovered that a steady diet of vending-machine items and the occasional all-nighter does not a healthy person make.

MATLAB was introduced briefly as a side aspect to one of my courses. At the time I was taking another course in which the programming language C was the main focus, and I distinctly remember wondering why anyone would bother with the intricacies of C (pointers were the bane of my existence at the time) when they could instead languish in the supporting arms of MATLAB.

Since that first course MATLAB and I have had a lot of time to bond. I’ve spent many a late night bathed in the light of my computer screen as I type “just one more line”.  MATLAB has continued to enchant me with it’s beautiful development environment; the helpful, yet not pushy error messages (even going so far as to quietly hint at errors before I even execute my code); and the huge and incredibly helpful community of people around the world sharing their own functions (I often say that for anything you want to do in MATLAB, there is very likely someone who has already done it and shared their code, or there is a blog/discussion thread surrounding the issue). I’ve lost count of the number of times I’ve stumbled upon a new, exciting function and just think “I can’t wait till I have a chance to use that!” (this is my current obsession). 

Through the years MATLAB and I have had a complicated relationship. I spiced things up by introducing MATLAB’s graphical programming environment Simulink as a third party during my honours year. Together we made some lasting memories – I used Simulink to build a biomimetic detection and pursuit algorithm modelled on specific neurons in the visual pathways of certain insects, and tested the model in a virtual arena.

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(a) Virtual target pursuit arena built in Simulink (b) Camera viewpoint 1 (Cam. 1) records the chase from above. © Image information from the pursuer viewpoint (Cam. 2) undergoes analysis through algorithm in Simulink.

I had a brief fling with Python during the first year of my PhD while undertaking a computational neuroscience course in Okinawa, Japan. Suffice it to say that strained our relationship. However, I maintain that it was nothing but a meaningless holiday romance. Between you and me though, I’m still a little bit intrigued by Python, there is beauty in imperfection after all,  I sometimes consider taking one of the Python courses taught by Research Platforms.

In my other life (you know, the one that doesn’t involve a thesis) I enjoy martial arts, cooking, eating and music. Sometimes my love of eating and MATLAB even cross over!

 I’ve always loved learning new things - I’m half way through about 3 different courses on Codecademy right now, and I’m also learning German from another of the Research Community Coordinators, Isabell. So I’m a big proponent for learning. Since becoming a Research Community Coordinator I’ve realised that teaching is just as enjoyable and rewarding, if not more so. The two sides of this educational coin are also complimentary - every time I’ve taught MATLAB so far I’ve learnt something new myself.  So if you think you might want to learn along with me, come to one of our MATLAB courses, or email me at kerry.halupka@gmail.com  or tweet at me (@EngKerry) for more information (I promise I won’t wax lyrical at you about my love for MATLAB).

Meet Louise - 3D Modelling Medical Scans!

Hello all,

My name is Louise and I am the newest member of the Research Bazaar team, having just started as a Research Community Coordinator with the team at Research Platform.

After completing a Bachelor of Science and a Bachelor of Engineering (Materials) at Monash University in 2008, I went on to do a PhD in collaboration with Monash University, CSIRO, and Smith & Nephew. During my PhD I developed novel multi-component composite fibres with thermochromic behaviour. In other words, they changed colour with temperature changes (think mood rings in fibre form). Tuned to work in the physiological temperature range, these fibres were created as a potential method for thermal mapping of wounds beds, via their incorporation into bandages.

Later down the track, I was involved in a Researcher in Business program within CSIRO, helping Australian medical devices company Anatomics develop their porous polyethylene surgical implant material PoreStar. Outside of academia I have pursued my own developing interest in 3D printing by purchasing a Makerbot Replicator 2X, combining my knowledge and interest in polymer processing with my passion for DIYing and craft. With this new and exciting piece of hardware I taught myself some rudimentary 3D modelling skills and combined 3D printing with silicone moulding, epoxy resin casting, and general crafty inspiration to come up with some cool new products I now sell online and at markets under the brand name Wordosaurus Text.

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a. my Makerbot replicator 2X, b. a treble clef 3D model in MakerWare c. a silicone mould of the 3D printed master shapes d. A final epoxy resin cast.

I have joined the ResBaz team at Research Platforms to develop awareness and run training sessions relating to medical image processing, in particular the generation of 3D models from medical scan data in the DICOM file format. DICOM image files are produced by a range of medical imaging equipment, (MRI, CT, PET, XRay etc), and consist of a series or stack of cross-sectional image slices across a region of interest in the body. By combining these slices a 3 dimensional representation of the body can be generated.

Generating 3D models of anatomical features from DICOM image files can be beneficial in multiple scenarios. Physical 3D prints of anatomical features such as bones may be used for teaching or training purposes, for the planning of a complex surgery to reduce overall theatre time and risk, and for the creation of implants custom designed and fit to a particular patient. The “3D Printing for Medical Applications” seminar (#3DMed), held on the 14th of April this year, highlighted many applications in which this functionality has been harnessed, and how it could be harnessed in the future.

The software around which the training will be designed is 3D Slicer, an open source community platform with a wide range of capabilities. It’s modular nature provides flexibility and the ability to add on new functionalities as required.  It’s interactive visualisation capabilities includes the ability to display arbitrarily oriented image slices; segment, label features and generate colourmaps, and render 3D models of desired anatomical structures.

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Screenshot taken within 3D Slicer from inbuilt example dataset ‘CTChest’

As this training course is in the very early stages of development, there is ample opportunity for community input and feedback in regards to the content of the material covered. This course is intended to provide researchers and medical staff with valuable and relevant image processing and 3D modelling skills. If you feel you would benefit from this course, or if you know somebody who might, please feel free to contact me to discuss your requirements, at louisevanderwerff@gmail.com, or tweet me at @LouWerff.

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