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Toolbox talk: Lights, Camera, Action!

Sun, 03 Oct 2021 14:28:49 +0000

Toolbox talk: Lights, Camera, Action!

It is more important than ever for scientists to be concise, engaging and easy to understand. Most importantly, we need to diversify our means of communication to meet the audience where they live. In the age of TikToks and Reels, it seems our digital lives increasingly revolve around easily digestible visual media. So: time to go video! I realise, as I am writing this, the irony of that statement… But, as part of a recent communication course, I produced my first scientific videos for a general audience - and I think it should be part of every scientist’s toolbox! If you’re keen to see the finished product, head straight to the . Otherwise, if you’d like to hear more about the tools, tips and tricks I picked up along the way, read on! Who knows, maybe I’ll even make a video version.. 😉

The Art of Science Communication: A little learning, a lot of fun!

Last year I tackled my first official professional development course: . Hosted by the ASBMB, this course covers basic communication skills for scientists looking to increase their public and professional impact. Thanks to a well-paced course, dedicated instructors and a diverse cohort of participants, this course was not only hugely informative but also a lot of fun!

The main goal, achieved over seven weeks, was to craft our own individual message which would introduce a general audience to our primary research topic. From the very start, we were thrust into the deep end as the first week required us to record a short video detailing our research for the cohort. This was something entirely new to me, and at first watching yourself on screen is incredibly daunting! But after the initial icky-ness wears off, these practice videos turned out to be a great resource for identifying areas of opportunity in our speaking practice. After establishing this ‘baseline’, the instructors set to work helping us take the bones of that video and craft the message, increase engagement, remove jargon, and improve production. Together, these iterative exercises left each of us with a developed skill set, covering everything from scientific presentations to a non-expert elevator pitch, and a video product showcasing these skills.

Scripting, recording, editing and posting a video was something I had never done, much less for science communication purposes. So, just in case you (like me) missed that whole YouTube vloggers phase, I thought I would cover here some of the hardware, software and general lessons that I learned along the way to creating my final video product.

Lights: Setting up for success means starting with software

While you certainly don’t need any fancy software to shoot videos (just take a look at everyday people shooting Reels) a handful of (free!) software programs can really make a difference for professional content. A few tools that I found useful were a teleprompter-style recording app, and a video editing suite.

When it comes to talking about science, many academics are used to giving presentations where some multimedia slides guide the content you deliver, and there is a conversational meandering that actually enhances talks like this. When you’re recording, however, maintaining eye contact with the camera is much trickier. This is where a good teleprompter app comes in - it allows you to take these cues in the form of text without breaking your connection to the audience through the screen. There are a bunch of free versions depending on your operating system - I used my iPhone for recording, so I found on the App store. Features to look out for include the ability to change the text size and speed, and the option to export without a watermark.

For video editing, again this choice is OS-dependent. iMovie on macOS seems to be fantastic. On PC, I had reasonable success with , but there are quite a few others gaining traction too. Most will have the same functionality - but, importantly, I would suggest looking for something that has a decent online community and plenty of resources for beginners. As novice filmmakers, it can be hard to know where to start otherwise!

Camera: A little investment goes a long way

As with software, you certainly do NOT need any expensive or fancy equipment. I shot all my videos on my phone, and the quality was sufficient. For a small investment though (~$50 AUD) you can get a great quality boost from two simple tools; a lapel microphone that connects to your recording device, and a tripod.

Lapel microphones help improve the sound quality, particularly as captured by a phone - and there are lots of cheap options available. The has a built-in clip for attaching to clothing, allowing it to be positioned low on the chest out of frame during recording. These are particularly great for people living in places of high background noise (hello friendly neighbourhood leaf-blower that seemed to always be running when I wanted to record!), as they allow you to better isolate speech from contaminating noises during editing.

A tripod might seem like a large ask for a simple scicomm video. But hear me out - it has a few benefits! 1) It allows you to easily and stably position your recording device to free your hands entirely, letting you fall into a natural speaking rhythm with optional props, and 2) you can easily use a teleprompter app. Again, another excuse for all you online shopaholics to bust out your best bargain hunting skills!

The only other equipment I used when recording was already lying around the house - a black sheet as a backdrop, and a floor lamp to help with even illumination of my glasses. For any fellow bespectacled scicommers out there, are some great tips on getting rid of the annoying mirror effect light on glasses can have when recording.

Action: Time to talk the talk!

Now for the best (and most important) bit - the doing! All the great equipment in the world does nothing until you have a great science story to tell.

Before thinking about what you want to talk about, start by reflecting a little on the audience that you want to engage. Audiences composed of fellow scientists outside your field will of course require a very different approach to a collection of non-experts. Likewise, a room full of adults requires a different approach to a room full of children. It’s important to hit the right contextual notes, and this rings especially true in video format where people’s attention is so easily lost scrolling to the next video in line.

Once you know your audience, it’s time to draft your story. Unlike normal scientific presentations, I recommend writing a script here. This lets you make sure you have a reproducible story that can be honed, helping you to fit the time limit and minimise the number of recording attempts. Yes - attempts with an s - there will be many. But that’s ok, it’s just part of the process!

Some things to think about when honing your story:

Be critical of how engaging your storywill be for your audience - we all love our science emplicitly, but don’t forget to let your audience know why they should love your science too. What is the important problem or question? What makes your work unique?
Be cautious of spewing facts, figures or findings - instead, take the audience on a journey with you, just like a good book they can’t put down. Have you woven a narrative? What are the challenges this research faces, how have you overcome these?
Be creative in your description of complex topics- ditch the fancy words in favour of metaphors. A good metaphor can take some time to work through, but don’t underestimate their power in helping your audience grasp complicated concepts. Jargon is a huge no-no, even for scientists outside your field.

Ok, you have a story written out. Now, it’s time to set your scene. For example, I used a plain black backdrop, with some soft glasses-friendly lighting, and the tripod, phone and microphone about 1 m away. Then it’s time to record! This is the part where you really just have to feel it. Feel the right pace (conversational, not rushing, with nice pauses), feel your body in space (remember you are a person, not a robot, and if you’re anything like me you talk with your hands, like, a lot), and feel the excitement of your story (even on the fifth or sixth time you read it through). To start, I would suggest reading through the whole story on camera from start to finish - ignoring any missteps in favour of practising the whole story. From there, record a few more tries until you are happy. Depending on the video format (and if you plan to have cut sections along the way or a single-take read) you can even piece together sections from different versions that you like the most - this is where editing comes in!

Movie magic: remix your way to the red carpet.

Once you have recorded a few versions, this is where the movie magic happens. Head over to your editor, where you will be able to add transitions (fade-in, fade-out) music, images, sound effects, remove background noise, crop and combine different videos to get a final product you are proud of. In some cases, you might even want to bring in some - a little out of my league so far but something I am keen to try!

I decided to add a lead-in image from (a fantastic collection of images that are free to use with attribution) and in one case I recorded a PowerPoint slideshow that also had a black background, timed to match my finished video. I then overlayed the PowerPoint recording onto my video content, and viola, animated video! There are lots of neat ways to engage your audience beyond a talking head on the screen, so be sure to be creative and take full advantage of this aspect of the medium.

With the complete version, the last step is sharing it with the world. This is actually quite easy to do via youtube - simply sign up for an account (if you don’t have one already) then head to the “YouTube Studio” section where you will find the “Content” tab.

Uploading a video is as simple as selecting the “Create” icon, and following the prompts to upload your masterpiece. There are a few options, like whether or not your video should be indexed with the searchable youtube content, that you can toggle to suit your needs. Don’t forget a catchy title if you want your content to be found!

And that’s a wrap!

That’s it, you are officially a scicomm superstar! While producing video content can seem a daunting task, once you get past the weirdness of seeing yourself recorded it really is a great way to share your science. I am super proud of the content I produced during the course - with a few simple tools and a couple of hours work, for the first time I feel confident to share my passion for science in a whole new context. So, without further ado, here are the videos I created!

Party tunes: A new phase in the hunt for happy brain cells

Turning brain cells into origami masters

That’s all for now. Hopefully, I’ve inspired you to capture your story on camera and share it with the world! Have you made your own scicomm video? Do you have a favourite editing software? Head over to the , or let me know on !

Image credits: via unsplash

Toolbox Talk: Notion

Sat, 04 Sep 2021 21:23:03 +0000

Toolbox Talk: Notion

Notion is the type of software that, if you know, you know. After hitting the scene only a few years ago, it has amassed a somewhat cult-like following among personal productivity buffs. For me, Notion is a love affair that started during Melbourne’s extended COVID lockdown in 2020. During this profoundly confronting time, where life seemed beyond control, I found solace in taking control of my tools and systems. A whole new consolidated and streamlined workflow emerged with Notion at its centre - and wow, do I have some thoughts! If you came purely for the Notion nitty-gritty, feel free to skip to the Getting Started section. Otherwise, for Notion newbies, here are a few of my feelings about digital tools for science and how Notion fits the bill for me.

Need a notion about Notion?

First and foremost, Notion is a blank canvas that you can make and mould into (almost) anything! In fact, its versatility is often something sceptics point to as a reason to steer clear. But really, this is where the magic happens. Unfortunately, it also makes Notion incredibly hard to describe! So, here goes.

First: think spreadsheets on steroids. At its core, Notion provides an architecture for building relational databases which are like spreadsheets that can be linked. But, **more than that, each entry itself is a ‘page’ that can contain another database. Which brings me to… Second: think lego! The second aspect of Notion’s design strategy is that all content is considered individual Blocks (even databases). This means that you can stack and link different types of content (from normal text, quotes, callouts, bulleted and numbered lists to more complex structures like embedded webpages, code blocks, equations, date reminders, embedded google drive files, and even databases). Layered on top of this is the ability to display databases as tables, boards, galleries or lists, plus filtering and sorting that makes sure you can see the information you need in the context you need it. Last but not least is the aesthetic! With a simple palette for labelling metadata, headings and text, coupled with optional icons and header images it is super simple to make pretty pages

What we arrive at is a highly versatile and customisable ecosystem that you can use to build and personalise any architecture you need, from a to a to a workplace wiki to a … The possibilities are endless! And for people like me, it presents the opportunity to unify all the disparate digital tools that we had cobbled together to take care of these functions in the past, like Evernote and . In this way, Notion for me embodies the principle of removing friction, by letting me focus on the work without context switching.

A simple sneak peek

In honesty, Notion is sometimes a tool you need to see to understand. So, here is a quick toy task management example. I’ve even provided this page as an template in case you want to try it out!

To start, databases can look just like a regular spreadsheet, but can also be displayed as a board ( think: Trello), list, gallery, timeline or calendar. Here is an example of two databases: “My First Database” viewed as a gallery, and “My Second Database” viewed as a table, placed on a single page titled “Example Relational Databases”. Each column of the table view shows some of the metadata for the individual elements. There are lots of types of metadata you can add, including dates, categories from a dropdown, checkboxes, formulas which calculate a property based on other metadata columns, urls, emails, attachments - so many choices!

Most importantly, we can link these two databases using a relational property, such that we assign Task 1 as associated with Project A. This opens up lots of possibilities, including new formula options called ‘rollups’ which let you see how many tasks for a project are complete.

Now, to this point most of these features you could fandangle out of a spreadsheet if you tried hard enough. But here comes the magic. Each of the entries in the database are themselves pages, which can contain any of the rich content blocks I described above, including more databases. It can also include filtered views of a database so, within a single project, you can overview associated elements.

Last but not least, one of my favourite time-saving functions - templates! You can add templates to any database, such that when you add a new entry to the database it can be prefilled with page content. This is a super-efficient way to capture essential and consistent information for each new entry beyond the metadata options. You can even pre-filter included databases, such that the filtered view automatically includes only related sub-entries.

Ok… But what about Notion for science?

In my 10 years as a scientist, I was not once introduced to the idea of “project management”, much less software fit for the task. It was mostly something that we were inherently supposed to do; keeping track of the overarching aim of our individual project, tracking goals and milestones and tasks. It wasn’t until my first post-doc, when suddenly the number of projects I was involved in exploded, that I found myself reaching for help in the form of digital project management tools.

In contrast, Electronic Lab Notebooks (ELNs) are a digital platform that is slowly permeating all levels of science. The available options seem to be multiplying at a steady rate, and institutional uptake is slowly increasing. Many of the front runner ELNs have connectivity covered, being accessible via browser or app across devices, and provide documentation processes, like versioning and witnessing, in line with funding body and research institution requirements. However, all of the many platforms I have tried suffer from similar issues: (i) they have quite a rigid structure for what constitutes an “experiment”, (ii) data storage size is limited, (iii) structured similarly to a paper notebook which treats experiments as a single entity, not just one of a number of specialised outputs researchers manage and produce, (iv) have no mechanism to track what stage in the experimental process that experiment is in (idea, design, data gathering, data analysis, summary and storage) and (v) are somewhat of an isolated island.

Needless to say, Notion has now alleviated most of these pain points. A few of the best bits:

It works where you do - in an app, browser, mobile, PC, Mac… Wherever you need to be, with synced content meaning everything is always accessible when and where you need it
It is less rigid than Windows file structures, taking full advantage of the interlinked nature of what we do as scientists, building a web of research
Allows you to automate some of the ‘boring’ (although I never find them boring!) organisational bits through templating
Functional web clipper to capture resources and info while you browse
Good selection of commenting, collaboration and automatic versioning features
(Almost) infinite customisability enables you to work in a way that works for you

This being said, there are some important caveats to bear in mind:

Funding bodies sometimes mandate data storage conditions, including the global location of servers for digital data. Notion may not always satisfy this criteria.
Similarly, Notion should not be used for confidential information such as clinical data
Due to the unique nature of Notion’s architecture, there is a limited ability to transfer to alternative systems - however there are substantial backup options to get your data out of Notion if you ever need to
Small number of direct integrations with Google products (like calendar) has been limiting, although the fledgling API will hopefully help this to thrive in the near future.
Some functionality has been slow to materialise - like the ability to natively add symbols outside equations, column-wise formatting within other blocks, and native drawing/writing/pdf annotation (I stick with the Notes app on my iPad, where the export-as-image and copy-as-text functions are my fired)

For me, none of these things were deal-breakers. But certainly, Notion is not fit for every purpose! Buyer beware, as they say.

My Notion Nitty Gritty

For my workspace, I started with the broad buckets of Science, Coding, Communication and Personal. Parallel to this are my Projects, Milestones, Tasks and Resources databases ( think: P.A.R.A.), where individual items are linked to their overarching bucket. From here, filtered views of each of the Project, Milestone and Resource databases form the basis of my daily work ecosystem. I can open a Project page and see each of the Milestones on the go for that project (manuscripts, experiments, funding applications), Resources pertaining to that project (like articles, funding, and ideas), and Tasks for that project (of course sorted by priority and due date). I treat all individual research outputs as Milestones; whether that be a single experiment, a manuscript I’m writing, a presentation I’m giving, a funding application I’m preparing, or a seminar I’ve attended. As well as linked databases, you can also ‘mention’ pages or people inline using the ‘@’ function (almost like a hyperlink) that helps to link together pages to create your very own web of knowledge.

To help with easy access and focusing, I also have a Dashboard page, with some of these @ links to my current priority projects, milestones and tasks - plus a filtered and sorted linked database with some friendly reminders to focus my day!

Some of my other favourite Science databases include my Virtual Lab Book (a filtered version of my Milestones database, which has separate properties for Experiments as they move from conception to data gathering to analysis to writeup), my Reading List (captured as a filtered database from my Resources), and my CV database with which I can filter to produce tailored versions of my CV for individual applications. I have templates set up for different types of Milestones, making it easy to create a new Experiment versus a Manuscript. On the personal front, a sneaky favourite of mine is my recipes database - drool worthy organisation if I do say so myself!

Tips for getting started

If you made it this far, I’m sure you’re thinking “Ok - you sold me. Now, where do I start?”. For many people, staring at the blank screen of a fresh Notion workspace will be quite daunting. If you’ve only ever used out-of-the-box systems, the requirement to create your own from scratch can be overwhelming! However, I truly believe this is where the magic happens. A few tips:

Start with stepping back - the first thing I recommend after installing Notion and creating your first workspace is to close the computer. Yup, put it away and find yourself a pen and paper. First, set to thinking about the broad buckets you want to encapsulate within the workspace. For this process, I found an adaptation of the . method of project management was a great place to start.
Remember to be flexible - your initial workspace should provide some guiding principles to start with, but should be fluid over time as you learn what works, what could be better and, most importantly, what you need. This is the true power of Notion - to grow with you.
Prepare for growing pains - changing to a new system (especially if you are planning a whole-life overhaul like I did!) will invariably present some challenges. But, it will be worth it. Stick with it, and pay attention to the pain points at what you need to adapt.
Face the future - with new systems, the temptation to retrofit your past outputs can be seductive. But (!) you should resist this temptation. Focus instead on ingesting only those things that you currently need into Notion, and leave the rest as the BN (before Notion) archive. You will know where to find things (hopefully) by sticking to the BN and AN line!

Once you have decided what your Notion might look like from a top-down point of view, it’s time to set to work creating the databases and entries that will comprise your workspace. If you’re still struggling with the basics, there are many great tutorials (like from the Notion team) and you can find plenty of inspiration, like and and .

Wrapping up

Wow, what a marathon! In my defence, I warned you I had plenty of feelings when it comes to Notion! My hope is that this post serves as a basic run-through of how I use Notion on a daily basis to capture my research process and enhance my productivity by providing a platform designed and curated by me. Beyond the infrastructure that I provide for myself in Notion, there are a host of strategies I use to link outputs across various other platforms, such that Notion serves as my catch-all and launch point, but I can leave more specialised operations like task management and writing code to specific apps that were tailor-made for those purposes.

In short, Notion may be the broadest tool in my research organisation toolbox, but it is still only a tool that helps bring together other essential tools in the box. You could almost think about it as the toolbelt which keeps all my other tools handy!

That’s all for now. As organisation is intensely personal (what works for you will be very different to what I like and need!), I don’t think it is especially useful to go through my workspace in-depth here. If you’re keen to chat details, make sure to drop me a note on or on the page!

Happy organising 📚

Image credits: via unplash

Learning to code: it's as easy and as complex as that.

Sun, 05 Jul 2020 15:12:56 +0000

Learning to code: it’s as easy and as complex as that.

Many life scientists have used their time under pandemic-induced lockdown to learn their first programming language. Yay! Coding has many many advantages (reproducibility, more complex analyses, reusability, time-saving) that I have espoused all too many times before.

Many people start with a tutorial or two, then jump straight in and analyse a dataset. They get a plot, maybe a few statistics and voila! They are now scientists who can code.

Kinda.

See, the thing is there is more (much more) to implementing programmatic workflows in a biological context. Unfortunately, these are the skills that intro-to-programming courses often fail to teach you (or sometimes even mention) - but without them, adding scripts to your workflow can often make your work less reproducible, not more. For the sake of the argument, lets pick one.

Version control.

Imagine a bowl of spaghetti (lots of lines of code), which you admire briefly before throwing at a canvas (your interpreter). You create a beautiful work of art (your plot), and stand back to admire your handiwork. Maybe you show it to a few friends, and they are super impressed. (Awesome!). And then they ask - how did you do it? Not just the throwing motion, or the rough distance between the bowl and the canvas. They want to know the precise location of every single strand of spaghetti in the bowl before you threw it at the canvas. They want to know the recipe you used for the sauce, down to the precise number of grams of oregano.

Now, of course, we aren’t actually talking about a bowl of spaghetti - we are talking about your code. So - of course - you can simply show them the script! Huzzah! The precise instructions that enabled you to make that very specific plot. But here’s the kicker. The code is not static. In fact, chances are that you poured over it for hours (maybe days) adjusting, tweaking, changing, testing and rerunning - all to get the glorious, awe-inspiring plot at the end.

By this stage, you’ve probably made over 100 spaghetti-splattered-spectacles (plots) and your house is starting to smell like an Italian Pizza joint. If I asked you to produce the exact recipe, down to the very position of each strand of spaghetti, for plot number 47 - could you do it?

My guess is probably no.

Conducting experiments is at the heart of science. However, without the essential extracurricular activities like maintaining backups, organising your results files and optimising project management systems, you wouldn’t last very long in academic research. The same is true of coding. Learning to write a functioning script is just the tip of the iceberg when it comes to implementing reproducible computing workflows in biology.

So, with this in mind, what are those all important skills you ask? Well, luckily, far more experienced scholars than I have written on this topic and provided guides (like and ) for those starting out in the computational space. These are highly applicable to biologists learning to code, and I encourage you to check them out before you stray too far down the spaghetti-on-the-wall path (like I did). As with any system, it is easiest to implement new routines before you have become to set in the old ones! At the very least, here are a few things to consider:

, including Github/GitLab/BitBucket
, including efficient folder layouts
and docker containers
practices
, including repositories and citing software you use

For more, make sure you check out the resources below.

Oh, and one last thing.

Unfortunately, this type of organisational work is not typically measured as a key performance outcome for biologists. Your number of git repositories or test coverage for an analysis suite is unlikely to come up in an award nomination or promotion application. And yet, it is entirely crucial as we move toward bigger and more complicated data and analyses - and so I encourage you to take the time to learn anyway. Maybe one day it will be recognised as essential work by the powers that be - but until then, at the very least, it remains essential for anyone wanting to do good reproducible science. And who doesn’t want that?

Final thoughts

So you want to learn to code? Yes - do it, 100%. I honestly couldn’t recommend or encourage it more. But please do so mindfully. If you want to have the best possible chance of integrating this wonderful tool into your scientific ecosystem in the long-term, you have to lay solid foundations and develop sustainable practice/methods.

Have you used any of these techniques as a budding bench-to-bytes biologist? Find me on or head over to the to tell me more!

Resources:

Image credits: Tomwsulcer via & olamishchenko via

Toolbox Talk: VSCode and Conda

Mon, 13 Apr 2020 07:51:36 +0000

There have been many rapid changes to how, and where, we work as the world settles into a pandemic-induced hibernation. After coming to terms with the fact that many labs could be closed for several months, life scientists around the world are increasingly resolving to make the most of their ‘downtime’. For some, this means upskilling in leading data analysis tools and techniques - and of course, I whole-heartedly encourage this initiative!

In my experience, this often takes two forms - those who use a course to learn and tackle generic coding projects, and the learn-by-doing camp who google as they go and tackle their own projects. I was a bit of both: using a few tutorials to understand the basic concepts and the remainder by going to work on real analysis problems. With that in mind, I thought it might be helpful to share a little about my setup and why a combination approach is useful (maybe even important) for self-taught data detectives.

One of the issues I have with generic online python courses is their often-overly simplistic approach to interpreted development environments (IDE’s) and package management. In my case, I had been dabbling in coding for a year or two before I came across the concept of virtual environments, and it took me another year or two to get comfortable in making, using and deploying these environments. Ditto with version control. Don’t get me wrong - I think it is great that scientists are diving headfirst into coding, and jumping into a course with a simple editor helps you hit the ground running. Unfortunately, this only defers the learning curve - if you are serious about integrating these tools into your workflow a grounding in the most basic of programming hygiene habits is essential. Shortcuts were necessary in days past, as installing and running python could be … cough … tricky for beginners. However, these processes have come along in leaps and bounds - meaning that it is now incredibly simple to get going with a basic python install in an editor that is easily expandable to incorporate, even encourage, basic code hygiene.

You may have come across my previous Toolbox talk about - these were the collection of tools I relied on to manage my coding, from writing to running to environments to version control. However, over the last 12 months, I have migrated to Visual Studio Code (VSCode) and Conda. I might be (a little!) biased, but this is a great place to start for anyone new to coding in python!

Conda: easy environment management

Before we dive into an editor, let’s start with creating and managing virtual environments. If you’re still not sure what virtual environments are and why you might need to use them, check out these great tutorials from and . In essence, virtual environments are essential for keeping your base install of python from becoming littered with packages, many of which might have dependencies that break each other.

Conda solves this problem for python and non-python dependencies alike, and it comes bundled with the ability to install various python versions. In fact, Anaconda and Miniconda have become the most popular Python distributions for data science in research laboratories worldwide. For a more in-depth discussion of these features, and whether you should choose the complete (Anaconda) or lite installation (Miniconda) check out the documentation .

Installating MiniConda

For many purposes, Miniconda is ample and is easy to and install by simply following the prompts. Real Python has an extra-detailed walk-through of the installation and starting your first environment in their fantastic , including creating your first environment and installing packages within that environment.

Once you’ve created your first environment using the conda create --name=my_env command, install the ipython and jupyter packages using the conda install ipython jupyter command. These packages will enable VSCode to locate and use this environment as a python kernel (more on that later!)

VSCode: a full-featured, user-friendly editor

Although technically VS Code is not a dedicated IDE, an abundance of out-of-the-box features supplemented with a few extensions gives you an editor that supports debugging, embedded Git, syntax highlighting, intelligent code completion, snippets, and code refactoring, among an abundance of other features. In short, it is incredibly customisable, user-friendly, language-agnostic, and seamlessly integrates with code hygiene tools to give every budding research software scientist the best start in their programming journey. In case I haven’t convinced you yet, here is a quick run-through from zero to coding hero with VSCode.

Basic installation

Download install VSCode for your operating system . Once installed, VSCode will open to the Welcome Screen. This is a great place to get familiar with VSCode functionality,including a number of helpful customisation portals (helloooo colour themes and keybindings!). For first-time users, your first stop should be the interactive playground. This window walks through some of the key features of VSCode and gets you started moving around the editor.

Getting started

The next stop is the Extensions tab, which is your gateway to much of VSCode’s fabulous functionality. You can explore the offerings and pick your favourites (there are extensions for almost anything you could want to do!), but the one must-have for our purposes is the Python extension.

python-extension

After installation, open the command palette using ctrl-shift-p and search for settings. In the settings search box, type “python” then scroll through the settings until you find the Python > Data Science: Send Selection to Interactive Window and make sure this setting is ticked. After this, you’re good to go!

python-setting

Piecing it all together: using your conda environment in VSCode

Now let’s get to work running your first python script in VSCode with your brand new environment! If you don’t have an existing python file to test out, not to worry we can make one!

Open a new file within VSCode, and save with a .py extension.
Inside the file, add the following line:
print(“Hello World”)
Save the file, then place the cursor in the ‘print’ line.

You should see in the bottom left corner of the editor that there is a python version displayed, followed by the name of your current environment. This will likely be the ‘base’ environment to start with. If you click on the environment name, then you should be able to select your new environment from the pop-up list. This will then be used to run the code selection.

To run your code within the built in interactive window, highlight the line and shift+enter

This will send the code to the interactive interpreter, which will open in a new tab within the VSCode window. The kernel may take a little while to start in the first instance, but you should see the Jupyter server details listed in the top right corner - note that it should be the name of your environment. Once it has run, it will look something like this:

hello-world

That’s it! You are all set to run python files for exploratory data analysis in VSCode.

Favourite features

Now we have mastered the basics, you have enough to get going with python in VSCode. A few features that make VSCode my one-stop-shop for all my coding adventures and beyond include:

The python extension itself! It is an evolving masterpiece - huge shout-out to the creators and maintainers - with a multitude of features packed within the interactive window that, frankly, could occupy a post on their own. Highlights include the variable explorer, ability to save the current state of the interactive window directly to a Jupyter notebook, and the ability to render a Jupyter notebook in a standalone tab where you can edit and run code cells without spawning a browser window.

The second important, must-use feature is version control. If you haven’t incorporated version control into your workflow yet, or aren’t familiar with it, you can read more . In short, even if you have never written a single line of code in your life, version control is a must for anyone routinely creating and editing documents of any kind. And the best news is, VSCode makes it only too easy! You can create repositories, then stage and commit changes from right there in the version control tab. More advanced functionality is also available in the extension.

Another handy feature is the ability to launch a command-line terminal within VSCode. This can be done via the “Terminal” menu, or keyboard shortcut ctrl+shift+` . As a bonus, if you have an active conda environment, this command will launch a conda terminal with the conda environment pre-activate on the command line. This makes it easy to install new packages and create new environments right from within the editor.

Last but certainly not least is the dynamic documentation display for functions derived from imported packages. For example, when using a the pandas melt function, typing pd.melt() prompts a popup which contains the function definition and variable explanation. This means no more googling docs to check that you have set all the required arguments!

VSCode also features all the best bits of Atom, like being able to set the colour theme and change the layout of windows, and a built-in debugger window that allows you to investigate the state of variable and monitor breakpoints in your code. The more you use and explore, the more functions you will undoubtedly find that make your life better. However, here are a few more of my favourites for inspiration:

Collaboration: (think googledocs for code)
Text functionality: , ,
Coding cheats: , ,
Prettifying the editor: , ,
Preview functionality: ,

Some final thoughts

I’ve gone through a number of IDE’s since my coding journey began. As a general rule, IDE’s suit my style of work better than command-line activities and the ability to run and interact with code and plots in short succession is essential for this. However, a good programmer needs at least some level of experience with the command line, so try not to avoid it! Luckily, VSCode makes transitioning between the command line and interactive windows so easy that there really is no excuse.

When picking an editor, getting comfortable with a single system setup can make it difficult to leap into something new - especially with the time and effort investment required to get everything customised and functional for how you work best. However, it is worthwhile keeping in mind that where the field moves as a whole is often where the greatest level of innovation will be (extensions, support etc). It is good to keep an eye on what the ’next big thing’ is, but don’t be afraid to wait it out until you are happy with the level of stability and usefulness to make the tradeoff worthwhile.

Last but certainly not least, don’t worry if there were parts of this post that sounded like mumbo-jumbo: everyone is at different stages in their coding journey. One idea I hope I have instilled is that VSCode caters to all skill levels, from the ease of installation and setup to the massive extensibility provided by the extension marketplace. In short, VSCode ticks all of my must-have boxes for an editor and I thoroughly recommend you try it out!

Do you have a favourite IDE for editing python code? Having trouble getting started in VSCode? Or maybe you tried VSCode and found it doesn’t quite suit? Get in touch via the or on - I’d love to hear about your experiences!

Image credits: via unsplash

BioInfoSummer 2019

Sun, 29 Dec 2019 09:28:44 +0000

With half my home state on fire and the lead up to Christmas, this post has been in the works for a few weeks. But here it is - better late than never! My brief recap of my recent adventure to BioInfoSummer 2019.

Conference details

Title:

Date: December 2nd - 6th 2019

Location: University of Sydney, Australia

Overview:

BioInfoSummer brings together advanced undergraduate and postgraduate students, researchers and professionals from the mathematics, statistics, medical sciences and information technology disciplines. Attendees develop bioinformatics skills, national networks and employability. The central themes of the 2019 conference were Epigenetics/genomics, Single cell omics, Mass spec analytics and BioCAsia/precision medicine.

Overall thoughts and impressions

Of all the conference travel I have done this year, BioInfoSummer was the closest to home and the furthest from a normal conference. The wide applicability of omics techniques meant a broad spectrum of research topics presented by a diverse collection of researchers with a common love for data analysis. In addition, being targeted at students and early-career researchers meant the dynamic of the seminar sessions was quite different - an atmosphere in which students were comfortable asking questions and driving the discussion around world-leading best practices in the field of bioinformatics. This was supported by plentiful and generous travel scholarships boosting the participation of interstate early-career researchers.

Even though my research has been edging into bioinformatics for a little while, this was my first formal foray into the community. While I was a little anxious about being immersed in a new group of people, luckily bioinformaticians are a friendly bunch! This meant that the workshops had a welcoming feel, creating a safe space to actively try out the techniques presented during the morning symposium sessions.

Last but not least, the location and timing of BioInfoSummer meant that they were able to share sessions and speakers with , and were followed directly by . This enriched the sessions and provided even greater diversity and networking opportunities. This truly was a fantastic blend representative of the bioinformatics scene in Australia. I cannot recommend enough for new PhD students or post-doctoral researchers new to bioinformatics in it’s many incarnations.

Lessions learnt

1. Bioinformatic techniques for proteomic analyses lags behind the trail blazed by genomics - this was evident in the distribution of the program, but also attendees. However, I also think that…

2. The distinction between proteomics and genomics is sometimes unhelpful - similar underlying data structures make some methods applicable to both, and leveraging the substantial ground that has been covered in genomics will assist in the rapid progression and development of proteomics methods.

3. Single cells are the future, but not the complete story - innovations in single-cell proteomics and ongoing developments in single-cell RNAseq will continue to push the boundaries of our understanding of biology on the smallest scales. However, one of my favourite talks of the conference was on the value of bulk methods in an era of single-cell capabilities. And I agree - these methods should be applied as appropriate and often one and inform the other.

4. Seeing is believing - two-dimensional imaging mass spec and spatial transcriptomics are the next frontier. With these techniques, we are starting to understand the spatial distribution and cooperation of cells in health and disease.

5. The future is now - the big data buzzword - everyone will have heard the term big data. This is becoming more the norm than the exception, even in biological research. This has meant that method development and bioinformaticians are in high demand, and become recognised as an integral part to any research project. This was exemplified by one of the conference keynotes, in which the speaker discussed the essential skills for a bioinformatician in the next five years; many of these included the ability to wrangle, access and store large amounts of data, as well as being able to leverage this data for biological insight.

6. Unexpected acquaintances - I travelled to Sydney expecting to know no-one. In a classic small-science-world moment, within five minutes of arriving I ran into a post-doc from another research group in my building! While we had met and talked a little previously, the chance to spend a week nerding out over data analysis and coding methods was the perfect way to get to know each other better. In fact, this was one of the best parts of the conference. As well as this, I met a handful of people from my alma mata who grew up in a similar corner of the world as I did.

Take home resources

There were too many great resources to share them all, but if you’re looking for tutorial style activities then the Material for each of the is a great place to start. Other than this, below are a few of the packages, tutorials or databases that I am keen to check out after hearing about them during the conference:

’s featured workspaces demonstrate common genomic analysis pipelines deployed using data and tools available on AnVIL.
is a portal to a series of public experiments describing mouse and human stem cells and how they differentiate to become mature cells, tissues and organs.
is a collection of lightweight and handy tools for Python that help to handle various sorts of proteomics data.
: A Python Library for High-Throughput (Phospho)proteomics Data Analysis
The repository contains a list of open source Python tools for Proteomics analysis. The list is very likely incomplete and we are happy to take pull request with new tools.
The workflow management system is a tool to create reproducible and scalable data analyses. Workflows are described via a human readable, Python based language.
workflow: differential discovery in high-throughput high-dimensional cytometry datasets

These will hopefully give you a flavour of the overall themes covered at the conference, but if you are new to the world of bioinformatics I thoroughly encourage you to get involved next year. The conference is slated to be held next December in Canberra, so keep an eye out for the official announcement!

Overall, BioInfoSummer was a great opportunity to connect and build community with like-minded people. I was so lucky to be supported by an AMSI ChooseMaths travel award - without their support, I could not have travelled to attend the conference and I cannot thank them enough for their support of Women in STEM. If you are new to or interested in the world of bioinformatics I whole-heartedly recommend checking it out next year!

Image credits:

Behind the scenes: Investigating the Investigators

Thu, 31 Oct 2019 01:30:27 +0000

The hottest topic on Australian researchers lips of late has been the recent NHMRC Investigator Grant announcement. I recently set about flexing my data-science muscles to see what the outcomes of the scheme were overall, and what a successful application might look like for the next round. If you haven’t come across it yet, you can read more .

Below is a behind-the-scenes look at what went into the data analysis for this post, and what I learnt along the way. I have also released all the code and results as a for those who like the real nitty-gritty details or want to extend the analysis.

Raw data

The raw data used in this analysis came from three main sources.

The first was the , which offers spreadsheet summaries for grants released since 2013. I chose to use only the data from 2015 onwards, for a couple of reasons: (1) the structure of the Fellowship system appears to have changed in 2014 to the ECF, CDF, RF layout which remained in place until 2018. This meant that 2013 data was poorly correlated with the more recent datasets. (2) The 2014 dataset did not have as much detail in the gender, age, state breakdowns that could be easily compared to the following years. (3) Five years seemed like a nice time period to work with!

The second source of data was the Field of Research codes used to classify research. You can find the complete list at the . I struggled to find an easily-downloadable version, and instead copied them from the University of Melbourne intranet. With a little post-processing, I had a fully functional list of each level of classification, which I could then use to understand which types of research were popular for funding.

The last source of data was , which I used to gather the number of research publications and average Field-Weighted-Citation-Impact (FWCI) for each awardee in the ten years previous to their year of award. This was somewhat of a manual process, and I used the ‘best match’ profile for each awardee imported into SciVal. Overall, 88% of the awardees were matched accurately (and this could be increased with a little manual curation). I also did a little digging around in the PubMed Central API using a python package (see the resources list below for more information) to batch-query the author names and collect their publication history, to compare with the matches generated by SciVal.

Processing and analysis

After initial cleaning of the raw data, I then equated the new and old schemes by matching the tiers. Although the correlation is imperfect (due to changes to eligibility between the old and new schemes), this resulted in Early Career Fellowships mapping to Emerging Leader 1, Career Development Fellowships mapping to Emerging Leader 2, and Research Fellowships mapping to Leadership Fellowships.

Lessons learnt

Data is messy. This was evident in all of the raw data I collected - naively, I expected simple-to-use spreadsheets from the NHMRC. At the very least, I was anticipating similar formats for the more recent 5 years. What I was greeted with was a complicated series of tables designed for visual interpretation by human eyes and not easy accessibility via computer scraping. The initial data cleanup took more than half the time it took to complete the analysis.

Through this process, it became clear to me that as a general rule people, despite often working in a science-oriented role, do not handle, label or store data well. With the increasingly data-driven world we live in, we would all benefit from improving our data hygiene.

Lastly, while the trends I saw and commented on are indeed interesting, they should be interpreted cautiously. The data that the NHMRC provides is somewhat fragmented (to protect the privacy of successful applicants). More importantly, the data they do provide is focused on successful applications. There are lots of important details about the makeup of the applicant pool that we don’t see and this is important - albeit missing - context for interpreting the trends I highlighted.

Similarly, publication history searches are a tricky one. Pay-walled publication information is a nightmare to access, and although PubMed searches are OK they are limited. To get standard metrics such as H-indices and field-weighted citation impacts requires access to subscription services. Moreover, people’s names are difficult! Even once you have access to the databases, it can be difficult to know whether/how to split the given names into first/last and there is almost no chance of avoiding manual curation if you want a complete dataset.

Tricks and tools of the trade

As this was my first dedicated data-science style project, I quickly ran across a few questions.

When wanting to plot the per-state distribution of applications and successful awardees, the most obvious visualisation was a map. I’d never plotted a map before, and after a quick google search I found myself asking What on earth is a choropleth and where do I find a shapefile? It turned out a chloropleth is what I wanted to make - a thematic map in which areas are shaded or patterned in proportion to the measurement variable being displayed on the map. And to do this, you need a shapefile - a vector data storage format for storing the location, shape, and attributes of geographic features. Luckily there are a few , and eventually, I found the I was looking for. Amazingly, using meant dealing with this type of data relied on many of the skills I already have and before I knew it Voila! - one map of Australia complete with colour-mapped and labelled data.

While handling the somewhat messy task of linking successful awardees to their publication track record, I came across the problem of slight variations between different naming formats. How do you match text that is fuzzy, and how does Levenshtein help? It turns out that Python has a whole range of functions via the that help to compare strings that are referring to the same thing but are written slightly differently. The most simple version of this makes use of the Levenshtein distance, named after Vladimir Levenshtein who originally considered this phenomenon in 1965. This metric measures how far apart two sequences of words are according to the minimum number of edits needed to change one into the other. These edits can be insertions, deletions or substitutions. One detailed later, and I was on my way matching fellowship awardees to scival authors in no time.

Finally, throughout the whole plotting process, I was conscious of my colour palette. I am a firm believer that good dataviz should be both functional and beautiful. With plenty of gender-based comparisons, you might wonder how I could go past the tried and true colour combination. So why I didn’t choose pink and blue for gender studies? It turns out there are a few reasons why red and blue for gender data is an ‘unawesome choice’ rooted in gender stereotypes (pink = girls = weaky, cute and blue = boys =strong, bold). Luckily there are plenty of good colour-combinations that circumvent these .

With these tools in hand, I had everything necessary to analyse and visualise the datasets at my disposal. To see these tricks and tools in action, don’t forget to check out the or head along to some of the resources listed below.

Resources

Wishlist: I haven’t had a chance to implement this functionality yet but these dataviz tools are high on my to-try list to extend the accessibility of this dataset!

Disclaimer

The original analysis was intended to inform my personal decision of whether to apply for an Investigator Grant in the 2019 round. Any action you take as a result of this information is done at your own peril. If you do decide to act on this information, I wish you the best of luck whichever path you may choose. May the odds be ever in your favour.

This being said, I have of course aimed to be as unbiased and informative as possible. This is also my first foray into data-science-for-public-consumption, so if you do notice any overt errors or bugs feel free to raise an issue on or get in touch via and I will check it out as soon as possible.

Image credits: cadop via

Investigating the Investigator Grants 2019

Mon, 30 Sep 2019 01:26:50 +0000

Introduction

The hottest topic on Australian researchers lips this month has been the recent NHMRC Investigator Grant announcement. After all the commotion around the new scheme and the distribution of research dollars, I set about flexing my data-science muscles to see what the outcomes of the scheme were overall, and what a successful application might look like for the next round.

What are Investigator Grants?

If you are a scientific researcher in Australia, chances are you were aware of the NHMRC announcement a few weeks ago of their latest round of funding. For those who are outside health-focused research, this was a highly-anticipated announcement after a complete re-structure of the NHMRC Funding Schemes over the previous year. The reform included replacing the Fellowship system (funds to support individual researchers to establish their own research programme) under the unified banner of Investigator Grants.

Historically, previous incarnations of these Fellowships (Early Career, Career Development and Research Fellowships) have supported the very best and brightest researchers in Australia with varying levels of lottery-luck in their award. Importantly, having been successful at the first rungs on the Fellowship ladder, researchers were then much more likely to gain the next level of support. Therefore, the award of these Fellowships to emerging researchers is a crucial support system that could make or break a career.

In particular, the lists better support for Early and Mid-Career Researchers (EMCRs) as a key goal of their reform:

Early and mid-career researchers are being discouraged from pursuing a research career.
\[…\]
In response to these issues, NHMRC reviewed the structure of its research funding and has reformed its grant program.

NHMRC, 2019

Why the fuss?

For researchers who spend many (many) weeks (months) working on the application for these schemes, the outcomes are of intense interest. The research funding environment in Australia is extremely competitive, and often the difference between funded and not funded can be little more than pot-luck. Early reports during the peer review process this year suggested that the new scheme had an abundance of issues and was likely to be even more of a lottery than previous years.

After the announcement, scientists flooded Twitter with their thoughts, interpretations and statistical analysis of the overall outcomes. There were many deserving people who were lucky enough to make the grade this time around. However, there were also an abundance of leading researchers who missed out and cursory analyses highlighted an abundance of inequities and unforeseen results of the reform.

_Sheldrick/status/1166908265743015936

_keating/status/1166886599482724352

Many of the numbers you will see online come from the key performance data released by the NHMRC (data on gender, age and state awardee rates has been made available since at least 2013). It is my firm belief that, as scientists, we should use science (including data science) to drive our decision making. The trends reveal biases/underlying issues in the grant scheme, and provide rationale/direction for additional reform. In addition, the profile of previously successful applicants can help researchers like myself understand how we fit within the scheme and, according to whether (or not) we fit the profile, inform our decision to apply in the next round (which is not that far away!).

Crunching the numbers

Most of the raw data used in this analysis came from the . I collected some extra data on the Field of Research codes and individual researchers from additional sources including the and .

After initial cleaning of the raw data, I decided to equate the new and old schemes by ‘matching’ the tiers as best as possible. According to a fact sheet provided by the , equivalent schemes are as follows: Early Career Fellowships map to Emerging Leadership level 1, Career Development Fellowships map to Emerging Leadership level 2, and Research Fellowships map to Leadership levels.

Due to differences in eligibility (according to years post-PhD) as shown above, the correlation is not perfect. But it serves the purpose of being able to compare as the NHMRC intended at least.

For the nitty-gritty details of the number crunching and visualisation techniques that went into the makings of this post, be sure to check out the post soon. If you’re simply here for the pretty pictures and insights, read on!

Key insights

Overall trends

At first glance, it appears as though the new investigator scheme has seen an injection of more money into the Fellowships portion of NHMRC funding. However, this funding includes more money per person at the upper tiers without a substantial increase in the number of applications funded. Moreover, the number of successfully funded applications and dollars was skewed toward the Leadership levels with an overall decline in the number of level 1 and 2 (Emerging Leader) applications funded.

The decline in the number of applications funded was matched by a substantial increase in the number of applications submitted, leading to a nosedive in the success rates particularly in the EL2 and L1 brackets. This increase in applications was thought to reflect the changes in eligibility structure meaning everyone had a go and often at levels far below what was appropriate for their research experience. The NHMRC is reported to be hopeful the number of applicants will decrease in the next round (thus artificially inflating the success rate).

Location, location, location!

With the conglomerate of research institutes, personnel and equipment in Melbourne, it has always been tough to beat in funding success. The same was true of this year, with Victoria the overwhelming leader in the number of awarded applications and second-highest success rate. In fact, Victoria received more than 45% of the awarded Fellowships in 2019.

The large number of applications originating in Victoria, coupled with their high success rate, suggests that not only is there a critical mass of outstanding researchers concentrated around the Melbourne biomedical hubs, but that they benefited immensely from the support processes in place in the lead up to submission. Hopefully, other institutes around the country can find value in their strategies for the following rounds.

Gauging the gender gap

Gender has always been a touchy topic when it comes to Fellowship funding in Australia, especially at the later stages of academia. There were concerted efforts made to target gender equality in the new scheme. However, at first glance, there was an enormous bias in the total number and dollars awarded. In fact, this disparity is the worst that the Fellowships scheme has seen in the last five years.

If we dig a little deeper, it becomes clear that this skew is due mainly to biases in the upper tier of the scheme. In fact, at the first and second tiers (level 1 and 2), there has been progress toward equality and, in some cases, even over-representation of women for the last five years. However, the successful males outnumber females two to one at the highest tier (level 3).

Two to one. The new scheme, if anything, has made this worse with the proportion of awards for women the lowest it has been since 2015. This points toward a systemic issue with how relative to opportunity is assessed and the lack of support for women entering the upper echelons of academia that is still ingrained in the research culture in Australia. Moreover, the extended 5-year term of these awards means that these differences will permeate University faculty for many years to come.

Titles and track records

As a general benchmark, academic titles say something about a researcher’s seniority. Considering the distribution of titles among successful awardees, there is a clear trend toward more and more senior researchers finding success at lower levels of the Fellowship scheme. For example, Associate Professors have taken a share of the level 1 funding for the last two years and for the first time, there was an Emeritus Professor awarded level 3 funding in 2019.

Similar trends can be seen in the track record of successful awardees, especially their publication history. While this is imperfect due to author name mismatches, overall the median number of publications for level 2 awardees has held steady around 50. In contrast, level 3 saw a sharp increase this year for the first time. The median number of publications for level 1 awardees has also steadily increased, meaning that to be competitive ECRs now need on average twice as many publications as they did 5 years ago.

With the revamp of funding levels and removal of specific ‘years-post-PhD’ ranges for level 1 and 2, this was always a concern. While I have (many, many) issues with using the years-post-PhD award as a ruler to measure relative success, at the bare minimum I do believe this distinction helped stratify junior researchers in the eyes of reviewers and assisted their assessment relative to opportunity. Unfortunately, the lack of boundaries this year left many researchers unsure of the appropriate level of funding they should apply for and moreover allowed many to take advantage of the lower levels in the scheme. This placed a large responsibility on reviewers to fairly evaluate an individuals trajectory against others with up to 10 years longer in research. A big ask!

Trendy topics

With every application, researchers include up to five keyword phrases describing the focus of their proposed research. By looking at the most popular keywords, I wanted to understand the research themes attracting the most funding and potentially consider how this has evolved over the last few years.

2015 keywords

2019 keywords

Interestingly, the top five keywords for the previous five years have typically been some iteration of health, disease, biology and cancer. While these seem very general, reading between the lines reveals a transition from ‘disease’-driven research to ‘health’-centric. Interestingly, epidemiology has emerged as a prominent focus this year. Cancer research has also held a steady proportion of funded applications, suggesting an area of high priority either among reviewers or impressive researchers embedded in this field.

What is unclear from these trends is the type of research being funded in these proposals. This is captured in the NHMRC reporting process as ‘broad research themes’. Traditionally, fundamental (Basic) research has held a large share of the total funding. However, the last five years have seen a steady decline in this proportion and a corresponding increase in the more translational themes.

In particular, clinical medicine and science enjoyed a sizeable bump in the proportion of funding awarded such that it has almost reached parody with basic science. This likely stems from the strong emphasis on ‘research impact’ that permeates all aspects of the new scheme, and at face value is not terribly alarming. However, our translational research must be underpinned by quality fundamental understanding and our ability to fund this type of research from the NHMRC scheme appears to be questionable in the future.

What does this mean for science, and ECRs, in Australia?

Overall, there has been a shift toward more senior and established researchers in all three levels of award. This is not merely a consequence of the new scheme, but has definitely been accentuated by it. This is likely reflective of the desire to invest in researchers who can demonstrate their previous impact has a direct and immediate public benefit.

This is also reflected in the share of the pie gobbled up by each of the four broad research areas, where Clinical and Medical Science has seen a steady increase at the expense of Basic Science.

While I made every effort to take a comprehensive snapshot of the available awardee data, these insights were limited in part by fragmentation of the data. The raw data provided by the NHMRC includes summary totals e.g. by gender or by state, but often the per-applicant information, due to obvious privacy reasons, is not available.

Some of the missing data, such as the years-post-PhD for successful applicants, could, in theory, be provided anonymously and would provide tremendous insight for potential applicants. At this stage, the closest proxy for years post-PhD is the mean age of awardees (although this as a measure is complicated by relative to opportunity). This year the mean age for level 1 awardees increased from 35 in 2018 to 37, agreeing with the overall sentiment of increasing seniority among successful applicants. With the restructuring in 2019, the NHMRC has all but done away with the ECR funding dedicated for those entering the postdoctoral workforce and require us instead to relying on the provision of project funding by senior researchers for many years before being deemed worthy and ‘impactful’.

There have been a number of statements released by associations commenting on this and other perceived failures of the new scheme, and suggesting changes to overcome these issues. For example, the released a host of recommendations aimed at alleviating many of the pain points ranging from the application structure to review processes. However, the short turnaround time (given applications for the next round open tomorrow and close in a little under two months) means that these recommendations are extremely unlikely to be implemented until next year. Moreover, this is a general and of research funding schemes world-wide. At this point, like so many other ECRs in my position, it is now time for me to think about whether my time, energy and effort are best placed in the hands of the NHMRC, other Australian funding schemes or - like so many in my position - whether I should instead focus on opportunities abroad. In the spirit of data-driven decision making, hopefully these insights have helped guide your thoughts as they have mine.

Resources

The original data was sourced from the
For more on the initial guidelines provided during the scheme restructure, check out the
Author track record information, including publication number and field-weighted citation impact, were collected from . If you are considering an application in the upcoming round, it’s a great idea to benchmark yourself against previous successful applicants.
For more info on the specific number crunching and data visualisation techniques used here, don’t forget to keep an eye out for my post.

Are you thinking of applying in the next round of Investigator Grants? Did any of these stats surprise you, or were they helpful in your decision of whether or not to apply in the next round? Head over to the , or let me know on .

Image credits: inspecting gears with magnifying glass | @ pluyer via

Toolbox Talk: Scientific posters

Tue, 30 Jul 2019 10:39:57 +0000

Going to a large, often international, conference jam-packed with superstars in your field is one of the privileges of being a scientist. Conferences are essential for building your network, keeping up-to-date with the progress inside your niche and more broadly in your field, and establishing or maintaining collaborations. If you’d like to know more about my conference experiences, check out . I was lucky enough to attend and present short talks at a few conferences in 2017, and it was a wonderful opportunity to show the scientific community what I have been working on.

However, there are only so many oral presenter slots available at such events, and often these are used by the organising committee to highlight the very best and newest science. But what about the rest of us? It can be really difficult to get the most out of a conference, especially as a student, if you don’t have an opportunity to showcase your work. Enter: poster presentations.

Wait, like in elementary school?

If you are thinking of a giant piece of butcher’s paper, covered with scribbles in multi-coloured permanent markers, you aren’t too far wrong. But things have evolved a little since the school days, and we do have slightly more developed tools for displaying our work these days. Unfortunately, making an engaging scientific poster is often not something we are formally prepared for during our scientific training. We are being moulded into the next generation of scientists, after all - what no one tells you is that artist is just one of the many additional hats that you will wear often as your career progresses.

With this in mind, I thought I would share a little about my process and tools for making an engaging poster. It is by no means a one-stop-shop, as every time I make a new poster I learn a little more about the process, but hopefully, you will find something useful as you prepare for that next science-filled conference adventure.

Tools

There are plenty of purpose-built design software packages out there specifically for making commercial posters, and some even targetted at producing scientific visuals. I have included a few links in the additional resources, but to be honest, I have had mixed success with these - if you find any that I have missed or that you’ve found particularly helpful to let me know!

In the meantime, I have settled on a combination of a freely-available vector graphics program (Inkscape) for compiling individual panels into standalone figures and presentation software (e.g. or ) for the overall poster design. If you haven’t heard of vector graphics programs before, or are unsure why you might want to use one, you can read more . In essence, because of the way they are constructed, vector graphics are easy to manipulate and can be scaled indefinitely without losing their quality as they are not composed of pixels, in contrast to the raster-based file types jpeg and png.

For data-laden figure panels, I will usually stick with python or (if you are interested in this, check out some of my other toolbox posts ). For any work dealing with protein structure, is your friend. The learning curve can be quite steep (I am only just now mastering the basics of opening a structure file, editing the colours and changing the display type from ball and stick to ribbon!) but it is relatively easy to produce a striking visual that will aid in any handwavy descriptions of a protein’s structure. Keep an eye out for my beginner’s basic guide to PyMol in a future Toolbox Talk!

Last, but definitely not least, I use a to create a link to this website. Even if the name is not familiar, I am sure that you would have come across in the wild before - they are a type of matrix barcode that links to a website or application when scanned with a smartphone camera. There are plenty of free online platforms for creating your own QR codes, and many that give you the ability to adjust the shape and colour of the individual elements, however be careful to know the difference between static (meaning you have no flexibility to adjust where the link directs after printing) or dynamic (that contain an automatic redirect which you can adjust after printing) to make sure you get one that suits your particular needs. I have found this especially handy for posters - by encoding your contact details or link to your website, it is easy for conference attendees to get in touch without having to hastily scribble down your email on their crumbled napkin.

Techniques and tips

Even the most exciting and life-changing data can find itself on a poor poster. There are many unwritten (and written) rules for making an engaging and eye-catching scientific poster, and many more experienced people than I have written guidelines and tips, some of which you can find in the resources section below. In any case, I thought I would throw in a few of my trusty tips here:

Layouts should be simple and easy to follow. I find that creating boxes to guide the reader through the poster in a logical fashion can be helpful. Remember a poster is about walking someone through your science and stories that jump backwards and forwards can make it hard to stay engaged. As a general rule, try not to require your audience to do too many optical gymnastics to follow the narrative of your science. When creating a new poster from scratch, I like to sketch out the layout, brief text tidbits and figure ideas by hand in A4 format to help get a handle on what data I need to track down and figures I need to produce. I also find drafting this initial layout in A4 size helps to keep my final poster from getting too overcrowded.
Find SVG versions of logos for the institutes that contributed to the work. It is good practice to acknowledge the financial support any project receives, and posters are no exception. In practice, I typically will have anywhere from 1 - 3 logos on my poster and any additional smaller contributions can be acknowledged in the text if you feel it is necessary. I will always find SVG versions of the logos, such that they can be edited and resized without losing any quality. Generally, you will be able to access these from your University brand team, or a quick google search with an advanced filter to find SVGs works well too.

Never underestimate the power of a good colour scheme. There is nothing worse than a poster that looks more like a rainbow kaleidoscope than a scientific document. Try to steer clear of elaborate colour schemes, and away from anything too light or bright. Also, keep in mind that picking a simple colour scheme to use throughout the entire poster (e.g. one type of sample is the same colour in all panels) helps to provide continuity for your reader. For those who are colour-coordinator challenged, a quick google search for colour palettes will provide all the inspiration you need.

Remember the power of size, shape and colour for drawing attention. With the limited time and large amount of information available at poster sessions, conveying the most important points from your work is crucial. Using accent colours, bold fonts or larger sizes can help to draw your reader’s eye to the key aspects of your work, and hopefully prompt them to ask more about the details!
Consistency is key. A poster, aside from displaying scientific results and information, is at the end of the day a piece of art. As with any beautiful artwork, the little things matter! Use the handy aligning and specific dimension setting functions in powerpoint to make sure everything is balanced and consistent. Check that the text sizes, fonts, line widths etc are identical throughout your poster, especially for things like headers and panel outlines. There will always be one or two typos that sneak past you, but it’s also a good idea do several final read-throughs to check for typos - once your masterpiece is printed, there is no turning back!
Print smaller versions for proofing. Printing an A4 version of your poster is a great way to check for readability and those last-minute alignment or spelling errors. As a general rule, anything that is not readable in this format will not become more-so simply by being printed bigger - remember you want to be able to engage your visitors in conversation, not have them glueing their nose to your poster trying to make out the details!
PDF is your friend. Particularly if you include transparent artwork or special characters, it is good practice to export your poster from powerpoint to PDF before submitting for printing. There are also a few tricks with powerpoint to ensure the quality is maximal, which can be found in the ‘Options’ menu of the dialogue box during export.
Be prepared to print early. Some institutions have their own print services, and these can be a cost-effective way to have your poster printed. I have a lot of experience with Officeworks and have generally been thrilled with their service, although I would recommend visiting a store in person to submit and collect your poster. They will generally give you a test print which is handy to make sure your creation survived the file format change. Regardless of which route you choose, plan to have your poster finalised and printed several days before departure - this way you will have a few days up your sleeve to handle the inevitable curve-balls!

Useful resources

from Animate Your Science has some straightforward tips for layout and content. I will admit to disobeying a few of their “rules” but generally great advice!
Toolbox talk on and the

Did I miss your favourite tool or technique? Find me on or head over to the to tell me more!

Image credits: codytdavis, greyritualstudio, alecuffia and arterbury via

FASEB Protein Aggregation Conference 2019

Sun, 30 Jun 2019 04:29:41 +0000

After a science career spanning a decade without having attended an international conference, I have been fortunate enough this year to speak at not one but two! What follows is a not-so-brief rundown of my second international conference, what I learnt along the way and where I am hoping to take these lessons.

The benefits of a niche, narrow-focused conference

From the very first keynote speaker (shout out to Judith Frydman - what a powerhouse for proteostasis research), it was clear to me that this was going to be different to almost any other conference I have attended in the past. This conference (and in fact, the same is true for most FASEB themes) was very targeted around a single area of biology (protein aggregation), meaning that the talks were all very focused and relevant for all attendees. Indeed, the tight-knit and collaborative nature of this community was exemplified by the fact that each speakers acknowledgement slides listed at least one - normally many - other attendees.

It was so refreshing and heartening to see a dedicated community of brilliant researchers converge around this problem with a singular view to solving the puzzle of disease-associated aggregation. As well as this, there was a broad range of techniques on display and I felt that I left with a fantastic cross-section of the breadth of work being done in the field, both for pathogenic and functional amyloid aggregation, ranging from single-molecule through structural, cellular and whole-organism studies right up to clinical trials. Importantly, plenty of researchers presented unpublished data (presumably due to the diminished need for an extensive background in this audience, who are likely to have seen the speakers published works) that I would otherwise not have seen for many more months/years if the paper is held up during publication.

For me personally, this conference represented the intersection of my PhD work (centred on aggregation of a specific protein) and my current postdoctoral work (focusing more broadly on proteostasis and its role in disease-associated protein aggregation). So much has happened in the field since my PhD and it was pure bliss getting to nerd-out surrounded by like-minded people. I was surprised more than once at the new connections my brain was able to find in this setting. I found that by listening to many perspectives/methods targetting similar biology, it was easier to start to integrate those complex ideas rather than reading the papers in isolation. Aside from this, for the junior attendees like myself this conference made for a fantastic job market, with many lab heads in our field advertising positions.

Lessons learnt

A good and engaging speaker is one who is in tune with the audience, including their level of engagement, and who respects the time of the audience by sticking to their allocated slot.
Aim to engage. In a small group such as this, you will inevitably run into many of the high-profile speakers - maybe even at the breakfast table! - so it is essential to have attended and engaged with their talk. Bonus points for devising an intelligent question to follow up on their presented material!
The “comment and a question” audience member will always elicit a collective groan - don’t be this person!
Conference organisation is no small feat - but inevitably the strength of the organisation team will impact on the general experience. In this case, a lack of confirmed program less than a week out from the conference was not a good sign. Missteps in the organisation led to the chaotic herding of humans to food and coffee breaks and program potluck ensued when the speaking slots were revised on day one of the conference.
Location, location, location: Snowmass was an incredible conference location. A small ski village easing into the break between winter ski and summer hiking seasons, there was plenty of sunshine and a brisk breeze all set in front of a stunning snow-capped mountainous backdrop. A huge highlight of the conference was the relative isolation which, while difficult to travel to and from, heightened the sense of community and connectivity between attendees. Spending time immersed in the location will strengthen your connection to that conference. Snowmass made this particularly easy, with plenty of Springtime hikes, walks and sunshine on display.

The who, what, when and where of woeful wifi - an outstandingly woeful conference internet connection elicited plenty of complaints throughout the week. However, I did feel was more engaged with talks that I might not have otherwise prioritised, as it was much more difficult to multitask with attention-sucking admin jobs like email. While sticking almost 100 researchers in a room for large swaths of the day that does not have reliable internet is probably a recipe for disaster, it certainly supports the age-old adage of ‘unplugging’ during seminars/conferences to be emersed in the research at hand.
Chairing is caring - an experienced chair who takes control of a session, including speaker introductions and managing question time, can have a profound impact on the speakers and attendees. It is not a job that should be taken lightly. It is worthwhile paying attention next time you have the chance to observe an adept chairperson, taking note of the often unseen acts that contribute to a flawless, inclusive and positive conference session.
The diversity debate among question askers - it struck me how often the same group of 5 - 10 people in a room of ten times that many were always the ones to ask questions. I have never taken to the microphone at a conference to ask a question of the speaker. Not due to lack of ideas - I will often have a list of question ready and waiting to be addressed - but I always lack courage in a room filled with ’experts’ and my imposter syndrome keeps my butt glued to the seat. Is this maintained as some kind of PI-ready test you have to pass? Throughout this conference, I ruminated on the idea of how to engage people other than those that are brave enough to take to the microphone - there are plenty of free crowdsourcing options out there ( and to name a few), and yet I have not seen a single conference where this is adopted. I am keeping a keen eye open for an opportunity to put this inclusivity initiative into action.
By far the biggest take home from this conference was that everything which seems so significant before leaving for the conference (poster prep, student meetings, experiments, slides, …) will no longer be so important once you arrive, and will be overcome by things like remembering faces of people you meet, feeling comfortable in a room of strangers, testing the confines of your comfort zone and nailing your two-minute “who I am” pitch. I am still working on perfecting these aspects of conference attendance!

Personal perspectives

“Growing a pair” is a lifelong process: it is important to make the most of opportunities at a conference (which can often be once-in-a-lifetime combinations of people, place and perspectives) while understanding and appreciating your limits.
Finding your people and your place: while engaging with diverse people is a key goal of attending any conference, recognising that you need smaller groups and its OK to need more relaxed time (e.g. spending time with small group of colleagues for drinks at a local bar instead of the crowded networking events.

The psychology of ideas: you know that feeling when your idea is discussed during a meeting, and the academic goes on to believe it was theirs? For the first time ever, a senior academic articulated to me their inability to discern where an idea comes from, and it was enlightening. In contrast to my previous reactions, this wisened academic encouraged me (and the other early career researchers present) to hold firm when this happens and to realise that it is most often without malice but simply a product of the multitude of ideas and meetings senior researchers attend every day.
The importance of a mentor in science: it’s no secret. In fact, any time I have spoken to an academic about their career path and trajectory they speak constantly about the different mentors that smoothed their journey. But this conference, I spent a bit of time reflecting on the importance of proactively approaching search for good mentor-mentee relationships. Having mentors that enter your career organically is important, but you should also identify and target strategic additions to your ’team’ of mentors.
Who am I? Every successful and prominent researcher to take the stage at this conference began with “My lab works on…”. It is important to define this early on, and I am very aware of this hole in my current career focus.
The value of contemplation: Conferences have been one of the few times this year that I have taken the chance to sit and think - really think - about science (and life and decisions). While I would like to bring more of this deep contemplation to my every-day science, I am learning to accept and expect these events to be heightened moments of reflection and embrace the soul-searching mood!
Trading hats: with a few days set aside before and after the conference for travel, it was incredible to put on my long-lost tourist hat and enjoy the sights, sounds, smells and sensations of being in a new country. This experience reminded me of how lucky I am to have the opportunity to do this as an academic, and that I should more often take time to reflect on that privilege.

Overall, this conference was a tour-de-force of world-leading experts and cutting-edge research, and included several abstract-selected talks from ECRs (including myself!) that together comprised a diverse and wellbalanced program in a stunning location. Were you in Snowmass this June? Get in touch via and let me know what you thought of the FASEB Protein Aggregation Conference!

Banner image credits:

Toolbox Talk: Storing data using HDF5 files

Sun, 28 Apr 2019 05:43:45 +0000

As an early-career researcher, I am still figuring out the best way to get stuff done. Every day it seems as though my to-do list gets longer and longer, for every item I tick off two more take its place and I have no doubt it will only get worse. I am all about finding the best, most efficient and connected technologies to help alleviate some unnecessary legwork. I have decided to write more on the programs, apps and integrations that I use every day in this Toolbox series. This post focuses on how I store and organise the currency of science: big data!

Data: the invisible bits and bytes of modern life.

Data is an invisible and yet fundamental driver of our digital life in 2019, and the amount of data worldwide is growing every single day. In fact, the International Data Corporation (IDC) estimates the amount of data in the world will grow from an estimated 33 zettabytes (one zettabyte is equivalent to a trillion gigabytes) in 2018 to 175 zettabytes by 2025, an almost unfathomable amount of bits and bytes. Every industry is grappling with the growing mountain of data, and science is no different. Scientists use data as the fuel that powers insight, discovery, and innovation. This demands new infrastructure from the institutes in which we gather data; but it also demands new approaches to the way we as scientists collect, store and analyse data.

During the first five years of my university career, everything I needed for my degree fit safely on a single 16 GB USB stick. Then I went and learnt how to do single-molecule microscopy during the second year of my PhD, and suddenly I was generating 16 GB of data in a single experiment. To cope with this rapid explosion, I invested in the hardware needed to store this data as text or csv files and stumbled my way through establishing a folder-based filing system.

It was during this stint early in my PhD that I first got a taste of programming to deal with this data (for more, check out some of my other programming posts ), however, this still centred on csv/text files for importing data, calculating results, and exporting the final findings. This meant that the data along the way, including input, intermediate and final files, could still be opened by easily-human-readable programs such as Microsoft Excel, and kept supervisors/collaborators without programming experience comfortable.

Recently, this storage pattern was no longer adequate for the size of results I was dealing with. Enter: HDF5 format. If you’ve never heard of this format before, it’s somewhat similar to an Excel document, without the proprietary software tag. Read on for more about what it is, why it’s useful for those dealing with large datasets and how I implemented a storage workflow in HDF5 format using python.

What on earth is HDF5?

Heirarchical Data Format (HDF) is a collection of file formats designed to store large amounts of data in an organised manner. Similar to the way .txt refers to text files or .pdf refers to Portable Document Format files, HDF files are adorned with .hdf, .hdf5, .h5 style extensions. The ongoing development and accessibility of the HDF file format is maintained by the non-profit organisation “The HDF Group”, meaning the tools to store and use HDF files will never rely on proprietary (and often expensive!) software.

More specifically, HDF5 files consist of Datasets that can store arrays of data (think individual sheets in an Excel document), Groups which can store datasets or other groups (think a folder of Excel documents, or folders of folders), and metadata consisting of mapped key-value pairs for attributes of the data (think a detailed description notes page for each sheet/document).

Pros and Cons of the HDF5 format

As with anything, there are good bits and bad bytes about using HDF5 to store data.

Some of the Cons:

Unable to easily open HDF5 with excel or notepad (although there are some being developed that could help overcome this)
No inbuilt calculation or manipulation options
Not readily used by many researchers in the life sciences, including many senior researchers making it difficult to share data with collaborators unfamiliar with the format

A few of the Pros:

Heirarchical format allows for logically storing data in a single file with folder-like architechture
Allows pre-processed data, such as date-time data, to be stored efficiently without loosing the effect of preprocessing (as would otherwise happen in .csv format)
Fast. Compared to other file formats, reading and writing HDF5 files is speedy. For example, writing to HDF5 is 16 times faster than to simple csv file, not to mention the extra overhead that comes from Excel documents. For a comparison to other common formats, check out the
Storing metadata within the file architechture makes sure that these attributes are accessible to anyone wanting/needing to access this data at a later date, and doesn’t rely on the bundling of additional “description” files to make sense of all the parameters and conditions under which the data was collected.
There are advanced options to create datasets that can be edited (rows added etc), and those that are read-only. This is provides an extra level of flexibility that allows, for example, intermediate datasets to be appended to making sure the raw data is not inadvertantly changed.

Sounds great.. But how do I use HDF5 files?

If you have no experience with programming, to get started you can download , which is freeware available from the HDF Group that will allow you to open and edit HDF5 files. In the interest of full disclosure, I have never used this software but it seems to work similarly to graphical user interfaces like Excel (without the price tag!).

If you have a programming language of choice, chances are there are - my tool of choice is of course Python! Even within python, however, there are a few choices for how to interface with the HDF5 machinery. Given that I use Pandas most often for data wrangling, it makes sense to leverage Pandas’ built in support. This includes methods that allows dataframes to be written to directly to HDF5 files using a method provided by the dataframe itself:

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=['a', 'b', 'c'])
df.to_hdf('data_filename.h5', key='Key_1', mode='w')

This method also allows another object to be written to the same file, with a second key ’s':

s = pd.Series([1, 2, 3, 4])
s.to_hdf('data_filename.h5', key='Key_2')

In this case, the heirarchy is quite simple: two datasets are stored under the single file. To get your data back, it is simple to then read back into a dataframe:

new_df = pd.read_hdf('data_filename.h5', 'Key_1')

For storing multiple files in this manner (i.e. using key-dataframe pairs), I have created a utility function using the HDFStore module (another interface provided by Pandas) that collects dataframes that have been loaded into a dictionary, then saves them to a single HDF5 file:

def dict_to_h5(filename, dictionary, **kwargs):
 store = pd.HDFStore(filename)
 for key, df in dictionary.items():
 store.put(key, df)
 store.get_storer(key).attrs.metadata = kwargs
 store.close()

NB: using the HDFStore in Pandas requires PyTables v3.0.0 or higher. To update your existing Tables installation run the ``pip install –upgrade tables``` command in your terminal.

These are both simple examples in which there is a ‘flat’ data storage pattern. This is great for storing a few large dataframes, or sequentially processed intermediate dataframes for a single result. In most cases, however, you will find it useful to introduce an additional level of hierarchical organisation similar to the folder storage system we are familiar with from OS interfaces. To do this, the keys should have a file-path structure which lists the group hierarchy that the dataset is to be added to. For example:

hdf =HDFStore('storage.h5')
hdf.put('tables/t1',DataFrame(np.random.rand(20,5)))
hdf.put('tables/t2',DataFrame(np.random.rand(10,3)))
hdf.put('new_tables/t1',DataFrame(np.random.rand(15,2)))

This will yeild the following data structure within the single HDF5 file:

/new_tables/t1 frame (shape->[15,2])
/tables/t1 frame (shape->[20,5])
/tables/t2 frame (shape->[10,3])

Python also has other packages specifically for HDF5 manipulation, which have a few more specialised tools. If you’d like to know more about these packages, or how to generate more complicated heirarchies, check out the list of resources below.

A few final thoughts.

Data is a part of life as a scientist, and as life scientists we must strive for better, more versatile approaches to storing and handling the ever-growing datasets produced by our experiments. For me, HDF5 has provided a simple and elegant way to interface with my data. It allows me to store raw and computationally-expensive intermediate ‘checkpoints’, and also means that I can maintain a single HDF5 file that combines data and metadata in a single place. The barrier to entry is quite low, and there are plenty of versatile ways to access data stored in HDF format.

Resources

A to HDF5 with Pandas
You can find additional utitility functions that make it simple to create and store HDF5 files for my projects in my package on GitHub.
For more complicated heirarchies, check out
Some other Python HDF utilities include , for which there are some great on how to generate structured heirarchies.

Do you have a favourite way to store, access and manipulate large datasets? Get in touch on and let me know!

Banner image credit: @ gabons via unsplash

#LorneProteins2019 & #FASEB2019

Mon, 11 Feb 2019 06:07:09 +0000

If you’ve landed here after checking out my poster or the , welcome. Thanks for stopping by!

I hope you found something interesting or useful, and enjoyed hearing about the work we are doing to develop new tools for measuring proteostasis. If you have any questions, want to know more about what we do, or simply want to get in touch, you can find me on twitter or head over to the page.

Just can’t get enough of the pretty pretty science? You can find details about the work I presented on my poster here. For anyone who wasn’t at these conferences, or didn’t get a chance to say hi, I have included a brief run-down (and tried to skip over most of the gory - boring, technical - details!).

Now - onto the science!

Setting the scene.

Cells have an extensive quality control network responsible for maintaining their molecular machines, including synthesis, folding, degradation and transport

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. Collectively, this machinery is known as the proteostasis network. Proteostasis imbalance results in protein misfolding and aggregation, the central molecular signature of neurodegenerative diseases such as Alzheimer’s and Parkinson’s.

We lack knowledge of which proteins in the cell become vulnerable to improper folding during proteostasis imbalance. One measure of protein foldedness is the extent to which a protein can be unfolded thermally or with a chemical denaturant such as urea. Until recently, it has been difficult to track foldedness in cells due to sheer complexity.

To overcome this, this work specifically aimed to:

Devise a chemical biology and proteomics approach to monitor the foldedness of the proteome.
Determine the influence of pharmacological agents that unbalance proteostasis on proteome foldedness.

What is tetraphenylethene maelimide, other than a tongue twister?!

Tetraphenylethene maelimide (TPE-MI) is a dye (tetraphenylethene maelimide) that becomes fluorescent when it binds to reactive thiol residues

\[2\]

. Buried, reactive thiols are the most buried residue of all amino acids in globular (properly folded) proteins. Monitoring the exposure of these thiols using TPE-MI gives us a sensitive way to probe foldedness in complex mixtures.

To test this strategy, we used a purified, well-studied protein - β-lactoglobulin. We know both the sequence and 3D structure of this protein, and its behaviour in a range of denaturants is known. β-lactoglobulin has 5 thiol residues, of which two pairs are bonded and the remaining one (Cys121) is buried in the core of the folded protein. As β-lactoglobulin unfolds in increasing concentrations of denaturant, we get a corresponding increase in TPE-MI fluorescence.

But what about the giant protein soup our cells make, I hear you ask…

While TPE-MI can reliably tell us about the unfolding of a single, purified protein, our cells are jam-packed with thousands of copies of thousands of different proteins. How can we possibly know which proteins TPE-MI is binding to?

Enter: . This revolutionary tool is a workflow based on mass spectrometry which can report back on the composition of complex mixtures - both which proteins are present, and the relative amount of that protein. The basics of this technique are beyond the scope of this post, but if you are looking for more details check out the resources section below

\[3-5\]

. I have included a brief explanation of the method we use below, which relies on isotopically labelling cells in culture before lysing, denaturing and labelling with TPE-MI. Proteins are digested (chopped up) into regular, smaller pieces before analysis via mass spectrometry.

Using custom software, we can identify and quantify protein pieces. Unfortunately, we cannot identify the protein pieces specifically labelled with TPE-MI, but instead we look for the loss of those pieces. We can do this for many many proteins and start to understand the concentration at which specific proteins unfold. We can even start to understand intricacies in unfolding for seperate protein domains, such as in the example below where different reactive thiols give information on specific domains of Hsp70.

How does this help us understand disease mechanisms?

This new method now provides a way to monitor how the stability of proteins (measured how much denaturant it takes to unfold them) changes under conditions of proteostasis impairment that are present in disease. For example, we are now investigating the changes in proteome foldedness when we inhibit hubs of the proteostasis network such as molecular chaperones. How do the proteins that rely on specific molecular chaperones to be folded change when those proteins are no longer active? What if we stop the cell from degrading old proteins? Or from decorating their proteins with extra modifications that mediate structure and activity like phosphorylation? Defects in this machinery are common in neurodegenerative protein aggregation diseases and we now have the tools to start tackling these questions.

References and handy links

Chiti F, Dobson CM (2017) Annu Rev Biochem 86: 27–68.
Chen MZ, Moily NS, Bridgford JL, Wood RJ, Radwan M, Smith TA, Song Z, Tang BZ, Tilley L, Xu X, Reid GE, Pouladi MA, Hong Y, Hatters DM (2017) Nat Commun 8: 1–10.
Lottspeich, F. (2009). In Proteomics (pp. 3-10). Humana Press.
Graves, P. R., & Haystead, T. A. (2002). Microbiology and molecular biology reviews, 66(1), 39-63.

That’s all from me for now. Like the pretty pictures? Still can’t get enough of the science? Simply want to get in touch? Find me on twitter or head over the to page - I’d love to hear from you! Now, back to the beach 🏄🌴☀️ or the snow-covered mountains 🏔️⛷️!

Toolbox talk: Atom, Jupyter and Hydrogen

Sun, 23 Sep 2018 11:47:32 +0000

As an early-career researcher, I am still figuring out the best way to get stuff done. Every day it seems like my to-do list gets longer and longer, for every item I tick off two more take its place and I have no doubt it will only get worse. I am all about finding the best, most efficient and connected technologies to help alleviate some unnecessary legwork. I have decided to write more on the programs, apps and integrations that I use every day in this Toolbox series. This post focuses on what is currently the best (in my humble opinion) hackable text editor for writing, programming and data analysis: Atom!

The basics: Atom as a building block.

Billed as the “hackable text editor for the 21st Century”, is a free, open-source, cross-platform desktop application for editing plain text and source code. While the base program was developed by GitHub, many of best functionalities are community built and maintained under free liscences. This combination of core support and extensibility has resulted in an unparalleled level of customisability, and makes Atom my editor of choice for everything from simple text documents, to data analysis to developing python packages. For coders and non-coders alike, there is something for everyone in the Atom ecosystem.

From an aesthetic point of view, Atom has a nice panelled interface which can be extended at will. Below you can see a standard layout I gravitate towards while I am analysing data. The leftmost pane (the file tree) allows you to open an entire folder or folder tree to easily see all of the files associated with the project you are currently working on, complete with coloured file-type icons for easy visual grepping. In addition, I can have multiple panes open side-by-side which allows me to operate on code/data in one building and document the analysis approach in the second. Finally, to the right is the output/Git pane (more on that later).

An example analysis layout in Atom.

Atom also has a vast array of installable themes to customise the window colour and aesthetic. I myself am partial to a dark theme, with a subdued syntax highlighting (check out the OneDark theme). If you prefer a more traditional light theme, there are plenty of those too!

The best bits: Git(Hub), Integrated Development Environment and Real-time Collaboration functionality

Being built by GitHub, it is fair to expect some level of integration with Git. If you’re not yet familiar with Git, or version control in general, you can read more about it . In short, even if you have never written a single line of code in your life, version control is a must for anyone routinely creating and editing documents of any kind. For now, suffice to say that Git has traditionally been a command-line tool and therefore mildly intimidating to those of us who found it with little experience. Recently, GitHub introduced a which alleviates some of the hassles of interacting with Git via the command line. However, Atom takes this functionality and seamlessly integrates it within the environment where the work is being done. No longer do I need to switch windows to another app or terminal - I can immediately see the documents that have detected changes, stage and commit those changes and push to GitHub from within Atom. More than this, Atom allows you to create and change branches, and push/fetch/pull from remote repositories all without ever having to change windows.

One of the reasons I came to it in the first place was the flexibililty to create an Integrated Development Environment (IDE) for any programming language. An IDE typically provides comprehensive facilities to edit, build and debug source code for a given language. Atom has taken on this task to provide support for a long long list of the most popular languages. This is achieved via installation of community-driven packages - more on this in a little bit.

Finally, Atom provides an interface for real-time collaborative coding. While I am yet to test this functionality, Atom’s “Teletype” promises the ability for users to share their workspace with team members and collaborate on code in real time using a shared portal. If you have used this feature, be sure to let me know how you find it!

Easy extensions: Size may not matter, but it’s all about the package(s)

They don’t call Atom the most hackable text editor for nothing! Although the Git interface is packaged as part of the raw Atom install, many of the other features I’ve already described rely on the installation of community developed and maintained packages. The variety and functionality of these packages is seemingly endless, and installing a new package is as simple as opening the settings view and searching for the package. No matter what language you intend to use Atom for (or even just for general editing of plain text or markdown documents), there are bound to be a few packages that will make life easier.

Given Atom is agnotistic to language, there are plenty of packages for common languages like R, Java, C #…. If any of these is your language of choice, I encourage you to poke around online and see what packages people recommend. For the remainder of this post, I will focus on how I set up Atom as a home for my Python development and data analysis.

Accessorising Atom: a Python perspective

When first learning python, I used Spyder as my development environment. After a while, I got itchy feet and investigated Sublime (another text editor) and was intrigued by it’s customisability. After a brief stint, it wasn’t long before I discovered Atom and, as they say, the rest is history! There are plenty of generic “how-to-set-up-Atom-for-Python” articles (you can find examples and ), so I won’t spend too much time rehashing these here. Suffice to say, there are a few basic packages you will want to install to get the most out of Atom for your python projects. A few examples include:

Perhaps my favourite aspect of Atom (and in fact the reason I know recommend it to everyone I help get started with python) is the easy integration with Jupyter. If you haven’t come across Jupyter yet, Jupyter Notebooks provide an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text in a whole host of languages. Notebooks support over 40 programming languages at the time of writing, including Python, R, Julia….This is managed through the installation of different , and if you are keen to run additional languages then check out how to access different kernels . In the case of python, IPython is the central dependency of Jupyter and thus is preinstalled. Jupyter Notebooks are in and of themselves a great environment for data cleaning and transformation, numerical simulation, statistical modelling, data visualization and much more. However, they require the analysis, version control and file management to be completed in different locations (other windows, apps, etc). Enter . This neat package brings all the functionality of Jupyter notebooks (documentation, in-line plotting and results etc) to your atom environment using the installed kernels. In my opinion, this is a must have for someone getting started with Python in Atom. To get started, follow the installation guide . As a bonus, is an extension package providing variable exploration in beta. If this is an IDE feature you cannot live without, check it out!

Better than basics: integrating python virtual environments

Similar to Git, using virtual environments with Python (despite being important best practice!) can be extremely intimidating for a newbie. Again, having to make and activate virtual environments via the terminal, and keep track of potential leftmost to use, set the bar above what I could digest and integrate into my workflow initially. Without this essential tool, my base install of python became littered with packages, many of which had dependencies that broke other packages. And don’t even get me started on the nightmare that was needing a safe install of both Python 2 and Python 3 for different projects!

Eventually, I got my act together and installed both and . If you need a little more advice, check out these great tutorials from and . For a little while, I was content that I could activate different environments if I desperately needed to - but was not overly inclined to use them on a regular basis! That was until I found the Atom package specifically designed to integrate this functionality: . This package lets you activate different virtual environments from within Atom, and combined with Hydrogen’s kernel manageraesthetic can have complete control of the python environment within which you are trying to run code!

Conclusions

Atom is a great multipurpose ecosystem with huge extensibility to tackle common tasks, both for general text documents and coding projects. There is support in the form of community developed packages, and complete customisability. By combining a few of these packages, it is possible to build a friendly development/analysis environment for those new to coding, or seasoned veterans alike, which encourages best practices at every turn.

That’s all from me for now - happy coding!

Toolbox talk: Trello

Thu, 30 Aug 2018 12:34:06 +0000

As an early-career researcher, I am still figuring out the best way to get stuff done. Every day it seems like my to-do list gets longer and longer, for every item I tick off two more take its place and I have no doubt it will only get worse. I am all about finding the best, most efficient and connected technologies to help alleviate some unnecessary legwork. After my first , I have decided to write more on the programs, apps and integrations that I use every day starting with one of the newest additions: Trello!

The basics: What the Tr-hell-o is all the fuss about??

Trello is fundamentally a project management application with a simple but sophisticated hierarchical interface consisting of boards, lists and cards. Trello thrives in settings suited to organisation. In the context of Trello, kanban is most often laid out as three stages (lists) - to-do, doing, done - and each element (or card) of a project (board) is represented within a list according to its state of completion. Elements progress through each list, giving a simple, visual overview of the project’s overall state.

This style works well when you work alone on individual projects - but it is also something you could easily accomplish with a chalkboard and some post-it notes. Where Trello really comes into its own is the ability to share and collaborate, such that individual tasks can be assigned to different people, comments can be added to cards specific for that task, and everyone can know at a glance the current big picture.

Trello is free (for the basic version, which is all I have needed thus far!), and accessible via the web interface, PC and iOS apps. It also integrates with all manner of other productivity apps, including GoogleDrive, GitHub, Dropbox, Slack, Evernote, OneDrive… Together, these features enrich the functionality of boards, providing a kind of dashboard from which to tackle projects and giving unparalleled flexibility for teams with diverse computing and file storage preferences.

Trello in action: managing your research projects

First things first: keeping track of experiments. There are many many electronic lab notebooks currently on the market, and I have tried more than my fair share. If you are not stuck in your ways (like I am) I highly recommend shopping around and picking one that you can integrate into your work as soon as possible. Unfortunately, I am a control freak and I am yet to find one that fits seamlessly with my workflow, and so I have resorted to building my own system using Trello!

The basic elements include lists for Ideas, Planned experiments, Lab work in progress, Analysis in progress, Ready to writeup, and Complete. Each new experiment or idea gets a card, for which I quickly note the purpose, reagents to be used, techniques and instrument details, and any pertinent references in the description. I then link this card (using the GoogleDrive powerup) to the folder containing the full experimental plan, results, analysis and write-up. The card then gets moved across the columns (lists) as each stage of the experiment is completed. For similar types of experiments, I have a checklist that allows me to easily track specific small elements of the experiment (e.g. plating cells, collecting results etc). If I complete a particular action for the experiment, I will also note in the comments any details specifically about that action. These comments are date and time stamped, helping to keep track of my activity on a day-to-day basis.

Another awesome Trello feature is the ability to label cards with a customisable category, which appears as a coloured bar on the front of the card. I use this feature here to label experiments with which one of my (many) projects it is specifically associated with.

This brings me to my second board - my project board. This is a board with which I track all of the ongoing projects I am involved in. There are lists for my individual projects (think: first author work), students projects I am involved with, collaborators projects, and ideas to keep in mind. The cards on each of these lists refer to a specific project, which can then also link to another Trello board or card which contains information, ideas and important references specifically for that project. That’s right baby, it’s Trello-ception!

Along similar lines, I have a board for manuscripts which tracks each manuscript I am involved in (cards) as they progress from the first draft to submission to revision to (if I’m lucky!) acceptance. In this case, labels are assigned to indicate my author position, and whether my contribution is required to move the article to the next stage. Individual cards link to additional Trello boards for each manuscript (normally only those for which I am the first author) where I can store more detailed information and ideas, tasks etc in a kanban layout.

By starring the top-level boards for Projects, Manuscripts and my Virtual Lab Book (starring is an inbuilt Trello function that allows you to favourite boards to be displayed at the top of your home page) I can easily get an overview of current progress, then by clicking a specific project or manuscript card I am automatically linked to all the more specific information I might need when working on that project, article or experiment.

The bigger picture: managing your science

When I first started with Trello, the ability to modularly (and very visually) manage and monitor my projects was a great tool. Since then, I have expanded my use of Trello boards to include the peripherals necessary for managing life as an ECR. Things like how to prioritise your workload, manage your short-term and long-term goals and keep on top of your reading list all become easier using Trello. My most recent addition was a prioritisation board, based on the priorities list shared by Corey J. A. Bradshaw in his amazing book “The Effective Scientist” (stay tuned for a book review - I really cannot recommend this book highly enough!). In short, Bradshaw runs through an exemplary list of how to prioritise the many competing interests vying for the time of an academic to get the maximum bang-for-your-buck in terms of career returns. These include things like writing articles, doing experiments (duh), helping students and colleagues, social media and writing grants.

When I first filled in all of the elements of the board, I realised just how unbalanced I have been in allocating time to each of these aspects. It was a great way to put into perspective where I should be spending the majority of my time and revisiting this structured big-picture priority list once a fortnight has helped refocus my research output.

Resources

That’s enough from me for now - if I have convinced you to give Trello a go, there are a couple of handy links below. I have included template examples for each of the board types that I mentioned above: feel free to use, abuse, copy and manipulate them to fit your own workflow, I honestly believe one of the best assets of Trello is it’s customisability - so don’t be afraid to make it your own. Just be sure to let me know if you find it helpful!

Template boards:

Additional info (from those who’ve said it better than me!):

Happy organising!

Image credit: @sidneyperryphotography via @unsplash

a weekend walk around the Bokeh library

Sun, 13 May 2018 11:13:53 +0000

If you’ve read my , you will know that I (somewhat arrogantly) consider myself “a passionate advocate for the use of programming by life scientists”. This came about following my Cambridge trip during my doctoral degree, in which I began to code analysis programs to derive meaning from the large (tens of gigabyte) datasets that we were collecting on a daily basis. At the time, I first learnt IgorPro - a proprietary language used by the other members of my host lab. While it was a steep learning curve, I was hooked. The power of being able to analyse huge reams of data without a single copy-paste-formula manouver normally completed in spreadsheets? Sign me up! Take out the human error of transferring data from program to program (Excel, GraphPad…) to achieve a result? Tell me more!

After my initial introduction, I quickly went in search of a more versatile language to learn. I disliked the feeling of being locked into the facilities provided within the commercial platform, and was tired of not being able to easily find support for this program online. And so, I met Python. And boy, did we hit it off! Python is easily the most friendly programming language I have come across, it is versatile and open source, the community is amazingly friendly, and I am yet to come across a problem I can’t solve via a quick trip to StackOverflow (seriously, where would I be without you guys?).

Little by little, I have worked on building my skills and I learn a little more each day. For a while now I have been comfortable writing small scripts that assist in my data analysis and plotting needs. However, I was yet to venture into the world of interactive plotting. I have always been intrigued by all of the different ways we can visualise data, and believe interactive plotting is a great way to allow both myself and someone else to explore a dataset. With this in mind, I set out this weekend to explore an interactive plotting library in python - Bokeh.

“Bokeh is an interactive visualization library that targets modern web browsers for presentation… Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.”

Sounds pretty good right? So, I gave it a go. As a test dataset, I decided to recreate the scatter plot from a published by our group for which the supplementary data is readily available. This plot displays the proteins whose cysteine residues significantly changed their reactivity following treatment with the drug Tunicamycin (an inducer of endoplasmic reticulum stress). Below you can find my version of Figure 6b, constructed as it is displayed in the article using Excel and GraphPad.

To recreate this plot in python, I took the raw data and constructed the scatter plot, colouring each datapoint according to the level of significance (as determined by the p-value). I also mapped the gene names associated with each datapoint via the UniProt API (a second learning exercise for this weekend!). This provides a second layer of data which I used to create the interactive feature of the graph - instead of displaying static names for only a few of the genes (which is limited by the need to not overcrowd the plotting area), gene names and ontology information is displayed by hovering over the point of interest. My final product is below - go ahead, give it a try! I found it super simple to embed the output here, plus there is also some handy toolbars provided by Bokeh out of the box that allow the plot to be saved to png or svg from the current view.

Bokeh Plot

I must say, I have been thoroughly impressed so far. Bokeh makes it extremely easy to build simple plots from existing pandas dataframes (think: spreadsheets) and to overlay interactivity without too much extra hassle. It offers the ability to completely customise the plots and layouts with widgets and extra functionality, although I think even the standard plotting elements I used here look pretty stunning in comparison to the original version!

So, here it is - my first passionate post about the benefits of programming for life scientists. Even if you take away the amazing benefits of reproducibilty, shareability, scalability and efficiency - the sheer ability to easily produce stunning visualisations that can be shared is a great asset. If you haven’t jumped on the programming bandwagon yet, what are you waiting for??

Manuscript: The sticky cell savers

Wed, 14 Mar 2018 09:01:25 +0000

All great wines and whiskys are matured, some for many many years. Ageing in this way allows time for complex chemical interactions that improves their quality. Although the maturation process for my latest scientific publication wasn’t quite as long as a quality (traditionally 12 - 21 years, in case you were curious), there were times where it felt as though I was never going to get to crack open the barrel! But, we are - and with that, the remainder of my PhD research is finally published.

I will do my best to not bore you with the details, and instead try to distil 4 years of research, experiments, writing, revisions, more experiments and more revisions into a succinct description of why this paper is important. But first, if you haven’t already found it, I recommend looking here for a little context.

AIM: To examine the ability of the small heat shock molecular chaperone proteins to interact with higher order aggregates (oligomers and fibrils) of alpha-synuclein, whose aggregation is associated with Parkinson’s Disease.

NOTABLE METHODS: Single molecule total internal reflection fluorescence (TIRF) microscopy - allows us to visualise directly the chaperone proteins interacting with fibrillar alpha-synuclein, reactive oxygen cell toxicity assay to monitor the impact of fibrillar alpha-synuclein on live cells in the presence and absence of chaperone.

THE BEST BITS: Directly visualising the interaction between a small heat shock molecular chaperone protein and alpha-synuclein fibrils. While this wasn’t the version that made it into the manuscript, it’s one of my personal favourite science images so far!

TIRF image of fibrils (blue, green, yellow), with bound chaperone (red)

TAKE HOME MESSAGE: Small heat shock proteins have multiple mechanisms of interacting with potentially toxic proteins. These molecular chaperones are well-suited to intervening at various stages in the aggregation pathway to protect cells from toxicity.

If you’d like to read the full article, or maybe check out a few more pretty pictures, you can find it .

Lorne Conference Series 2018

Sun, 11 Feb 2018 05:31:58 +0000

At this point, I think I have written more about conferences than anything else. I am sure you are thinking that is all scientists do - Don’t be fooled! I have been incredibly fortunate to get to so many conferences in the last few months, culminating in two iconic fixtures in the Australian protein science conference circuit - Lorne Proteomics and Lorne Protein Structure and Function. I wont belabour the point about how important or valuable these conferences are (at this point, it gets kind of redundant!), but I will give a quick overview of my experience of the Lorne Meetings 2018.

First, it is important to understand the origins of the Lorne meetings. Starting out in the 70’s as a humble symposia, the Protein Structure and Function Meeting this year celebrated it’s 43rd installment. It is now at the centre of five consecutive meetings that run throughout February in the lovely beach town of Lorne, Victoria, covering Proteomics, Proteins, Genomes, Cancer and Immunology. I have been to a few of the Proteins meetings throughout my PhD, but this is the first time that I have attended two of the Lorne meetings back to back. It gave me a unique perspective on the similarities and differences of each meeting, and I benefited from each in wildly different ways.

A nice feature of the Lorne meetings is that the experience starts during the transit to Lorne. This year I traveled with one of my supervisors, and it was a great opportunity to have some one-on-one time. If you ever get the chance to travel with your academic mentors, I highly recommend grabbing it with both hands! It is through these conversations that you can get to know the person behind the science, and the politics behind the academic. For me, these conversations have been a highlight over the years, and this was no exception.

The first in my lineup was Lorne Proteomics. Given proteomics a very new skill to me, this was a great opportunity to get up-to-date knowledge from leaders in the field and help cement my growing understanding of best proteomic practices. I felt very alone and new to this crowd, with only one or two familiar faces in 240 delegates. However, I was lucky to be a speaker selected from abstract (a fancy way of saying I applied with a description of my research, and they chose me as one of the ~60 presenters). This was a true treat, as it was a great way to introduce myself and my work to a completely new network of scientists. I got some great feedback and a brand new approach to my project. What struck me most about this conference was the diversity of science that was covered in the talks. Being primarily a technique based conference (as opposed to a biological theme as with the Proteins meeting), there were marine biologists, physiologists, ‘method’ologists (mass spectrometry, crystallography, electron microscopy), molecular biologists, statisticians and mathematicians. It was a great overview of the scope of disciplines in which proteomics and associated techniques are both indispensable and actively developed.

An interesting side-note to this conference was the inclusion of the Annual General Meeting for the Australasian Society of Proteomics. This was my first foray into Society politics and management. It was a really great opportunity to observe the inner workings of the society and see where I could potentially get involved in the future. There were rumblings of an ECR-led program for Lorne Proteomics 2020, so stay tuned!

Barely a few hours after the close of the Proteomics meeting, the Proteins delegates descended on Lorne. The faces were more familiar and, for someone who enjoys the sensation of routine, everything felt a little more like ‘home’. The first night featured one of my favourite sessions - the Leach lecture, in which an emminent Australian Protein Scientist is awarded the lecture and intertwines personal experience with scientific detail. This year’s lecture was given by Ray Norton, and he did not disappoint.

Ray Norton on his journey, quoting John Lennon (or more accurately Readers Digest!)

“Life’s what happens when you’re busy making plans”

— Dezerae Cox (@dezeraecox)

Other notable sessions included the Student & Invited Speaker lunch, where I was a ‘facilitator’ (my job was to help stimulate conversation across the table). As well as the great opportunity students had to interact with our international visitors, there were two keynote speakers. First, Liz Miller gave a great summary of her science journey - how she had landed in her first post-doc, managed to coordinate two academic careers across the globe, and how she had taken advantage of the often serendipitous nature of science. Her take home message was planning moves in science takes time, so start looking and thinking about it early. This was echoed by Nobel Laureate Richard Henderson, whose take-home message was to do almost as much research into the paths you can take, and the places you can go, as actual science itself. I think, in our current scientific and political climate, that these messages could not be more timely for students to hear from leaders in our field.

Last but not least, I had the chance to catch up one-on-one with two of the international speakers. Building on my confidence from the Proteostais and Disease Symposium, I took this opportunity to get their input on my current project, their thoughts on my career goals and their advice on moving forward over the next few years. I am truly amazed, and incredibly grateful, for the generosity of experienced researchers in offering their mentorship to me as a young Early-Career Researcher - it turns out that sometimes all you have to do is ask.

It goes without saying, that if you ever have the chance to get to one of the Lorne Conferences you wont be disappointed. Hopefully I’ll see you there next year!

Advanced Scientific Programming in Python Summer School

Thu, 25 Jan 2018 23:10:08 +0000

Another post, another conference!

This time it was the Asia-Pacific “ . A full 6 days of intensive training in the programming language Python, designed to extend my amateur skills and give me more tools to analyse my data.

And boy, did it deliver - kicking off with some free swag!

The formal sessions started with a welcome from the school’s founder, Tiziano, who’s first iteration of the school was over a decade ago. His opening remarks really resonated with me:

In industry there are normally teams tackling a problem. and it is important to be kind to your teammates by writing nice code and documenting well. In science, we often work alone on our own data, and that teammate is your future self. This week is all about being kind to your future self.

Each of the main sessions targeted a specific element of scientific program in python (from GitHub to tidy data in pandas to plotting with MatPlotLib to parallelization to packaging to cython). The tutors would spend a few minutes making sure our basic knowledge was up to speed, before taking those concepts and extending them beyond what I had ever considered possible from my programming life!

A huge part of the success of the school was pair programming. The school provided one computer between two, all formatted identically and containing all the necessary applications for the week, with which we were to work in pairs to solve the lecturer’s exercises. This was a completely new experience for me - not only had I never worked with anyone else collaboratively in a coding environment, I was not accustomed to sharing my code with anyone! I have always been shy of sharing my programs, feeling like they were amateur and ‘half done’. Now, not only did my partner get to see the ‘final’ product, they would get to watch my thoughts take shape on the page! As terrifying as it sounds, I really did learn a lot from observing others and from their input as we worked together.

To consolidate our knowledge, on the final day we were split into teams where we would collaborate using our newly minted GitHub skills. The goal was to program two pacman bots which would be pitted against the other teams in a tournament - not only did I have a great team, and a great time learning to work with the bots, but we WON! This meant the delivery of swag #2: a signed copy of “Elegant Scipy”!

With tutors from Europe, New Zealand, South America and Australia, we were spoilt for expertise. Most surprisingly (and delightfully) of all was that the student profiles were just as diverse! Over the course of the conference, I worked with a particle physicist, a plant biologist, an engineer, a medical radiation physicist and a virologist. Not to mention the age, cultural and gender diversity - the group of students was a beautiful reflection of the diversity of the scientific community.

I can honestly say I felt so privileged to be selected (the conference was free to a limited number of students), and it was hands down the most practical and valuable conference for developing skills I have ever attended. If you ever have the chance to go, I cannot recommend it enough.

Happy programming!

My toolbox: 2018 Edition

Tue, 28 Nov 2017 10:27:50 +0000

After coming back from a wonderful conference, I feel refreshed. I heard a lot about the state of my field of research, the cutting edge ideas and the direction our research is taking. You can read more about it .

Of course this aspect of science is important - the research outcomes, equipment and techniques we use are all critical pieces of information that make up the substance of oral and poster presentations. However, this year’s conference brought with it a stark realisation. I feel there is a large part of the conversation that is missing that is equally as important.

In fact, I think it is the true essence of how it is that we do what we do.

How do we, on a daily basis, manage a varied and conflicting schedule with experimental, administration and personal demands? What are the tools and techniques the help us balance these elements with efficiency and efficacy, ensuring nothing falls through the cracks?

I guess in a sense what I am talking about is a best practice discussion for the practicalities of doing research. Not a one size fits all approach, but at least a discussion of how the tools I use are beneficial and what I think I could use or need more of. While this may not fit a conference panel, here is my current toolbox.

This one is definitely top of the list, one of the more recent additions but easily the most life changing. This is essentially my digital brain, the place where I collect, sort and store information. Everything from web clippings (using my handy integrated chrome extension), papers of interest, post ideas, project ideas, meeting notes to shopping lists - it all lives here. Completely synced across all my devices, and easily searchable. All of the major ’life’ aspects have a notebook folder (e.g. manuscripts I’m coauthoring) with notebooks for each element (e.g. “Paper Name #327”), and individual notes for each idea or aspect of that element (e.g. introduction ideas, responding to reviewers comments etc). I think of this as a virtual brain dump - so that as long as I remember a particular piece of information is in evernote, I know it will always be there when I need it.

My digital assistant. I use Calendar to manage meetings, equipment bookings and deadlines. In addition, the daily tasks feature keeps track of experiment steps or analysis projects. For me, this is one of the most crucial features, for a couple of reasons. First, tasks can be assigned to a specific day and live above that day in the calendar. This makes it easy to see what I need to do for the day, as well as what times I will have to play with. Second, I get to tick off completed tasks! I have found the most effective way to keep momentum is to break everything into small, manageable elements, so 8-10 specific tasks that give me a sense of accomplishment each time I tick off something. Finally (and most importantly) tasks are easily reassigned to other days using drag and drop. This means that it is so easy to be flexible, and doesn’t require tasks to be rewritten as with typical hand written to-do lists.

Drive at the moment is simply my backup storage place that provides some functionality when my hard drive is not plugged in. In some situtations it is really helpful to share documents and easily manage sharing permissions, although I dislike having to download and resync non-google files. I am still figuring out how best to incorporate this into my workflow.

Basically a freeware version of adobe Illustrator, I use Inkscape for all figure preparation. This includes figures destined for manuscripts, posters, or presentations. But also just as a neat way to collate image data from experiments. I am yet to come across something that I have wanted to do with a figure that Inkscape can’t handle. There is a relatively steep learning curve, but for a piece of free software it is remarkably versatile. One thing I will note is that it does not play well with iOS (admittedly this is not from personal experience, but from colleagues who have had a tough time getting started), however windows works well.

& : As a self-taught python enthusiast, I am always looking for the best way to write, edit and run code to analyse my ever-growing data sets. This is my current configuration (although I have been through many others - Spyder, Jupyter, Sublime to name a few) and I am happy with the ease with which I can run scripts within atom, as well as manage my git commits with a few keyboard shortcuts. As someone relatively new to git version control, being able to see the folder layout and files which require an updated commit is incredibly helpful. Also, the ability to test, then tweak my code using attached iPython kernels within the editor is a great time saver.

Until very recently, I was an EndNote purist. I was first given access during my Undergraduate degree, and it did a wonderful job at managing my bibliography compared to the manual method. So I amassed a reference library, despite many issues leaving me disgruntled, and established a pipleline that included downloading the referencing file, downloading and saving the pdf separately, and then linking the two together using EndNote. To find articles, I would search in EndNote then locate the pdf and open it to view my notes and highlighted sections. If I lost you there - apologies - but it goes to illustrate my point: long and tedious. I have recently rediscovered Mendeley, and in terms of accessibility to my articles and notes, and ease of import, Mendeley beats EndNote hands down: click the Mendeley extension in my browser (or drag and drop to import a pdf), add notes/highlight and that’s it. All managed and searchable within a single program, again synced across all my devices. There are still a few kinks to work out (sometimes the imported reference information is a little clunky) but overall much more functional.

I have recently come across the idea of ‘story-boarding’ an article during the initial planning and write-up phases. Coming into a new lab, my writing style was at odds with my new boss and needed a little TLC - I am great at the technical reporting language but struggle to tell a narrative. The basic idea behind story-boarding is to layout the elements of a manuscript as discrete chunks and move them around like puzzle pieces to get the flow right. This also helps to identify holes - more experiments or more detailed analysis etc. I am using Trello as a way to trial this concept - each manuscript gets a ‘board’, the lists correspond to ‘Intro’, ‘Methods’, etc. and cards are tiny chunks - text prompts for the intro, individual figures or panels for the results. While this is the newest addition to my workflow, and firmly still in the trial phases, I am pleased with the outcomes so far. It has encouraged me to think about what papers I am working towards (helping to focus my research), as well as getting me thinking about the story to go with it.

So there you have it - my toolbox. It’s worthwhile noting that many of these I adopted from watching/hearing how someone else achieves a particular goal and making it work for me. I hope you find something new and useful, or maybe you have some ideas for something better? Feel free to let me know in the comments - I’d love to learn from you!

ideas on what helps and hinders symposium speakers

Mon, 27 Nov 2017 20:28:25 +0000

During the recent , a friend was scheduled to give a talk about her work. Sitting in the audience watching a parade of marvellous speakers is a special kind of torture, when you are convinced you could not possibly achieve a similar performance. I should know - until the very last year of my PhD, I was not a very talented communicator either. However, after a venture overseas and a stint in a different lab, I found my voice. I wouldn’t say I’m brilliant (far from it), but I have realised the importance of this aspect of science communication and worked hard at being able to at least stomach the idea.

My friend asked how I’d done it. How do you overcome the paralysing fear most people associate with public speaking? I didn’t really have an answer for her at the time - I encouraged her to be confident, which is of course the main ingredient. However, I have thought about it since then, and while watching the remaining conference talks I came across a few things that as an audience member I think can make or break a presentation.

1. Don’t repeat your talk name and title - people can read the program and your first slide (we are mostly smart cookies after all), and it is the job of a good session to chair (also presumably a smart cookie) to give those details when they introduce you. Instead, open by thanking the organisers for the opportunity to present some exciting new work, and a one line teaser to grab the audience - tell them why they should pay attention RIGHT NOW, and not wait until five minutes into your talk. If you don’t have them at the beginning, there will be no getting them back.

2. Oh, you’re skipping your introduction? Because EVERYONE ELSE has given this intro? At a conference specifically about your topic? NO WAY?! Think about your audience early on in the talk’s preparation stage and don’t be afraid to adjust on Day 1 of a three day event. If available, check out the program/other abstracts early, highlight talks with relevance to yours and gauge the level of any remaining introduction that you will need to give. Being able to refer to someone else’s intro not only shows you have engaged with the symposium but also gives you more time to go through the exciting bits (your results!)

3. Keep it simple, stupid. Most of the time you will have a very short window in which to convey your story, which will inevitably be the result of months and months of your blood, sweat and tears. Trying to cram too much into this time window makes your story confusing and hard to follow (10 tiny bar graphs on one slide, anyone?) and people will loose interest. Focus on a simple message, tell people what they need to understand that message, tell them what the message is, then reiterate why your message is important. Simple. As a bonus, people will chase down details with questions, avoiding the dreaded crickets-and-tumbleweeds at the end of a particularly confusing or dense talk.

4. Following on from this, constantly telling people you don’t have time to present this, that or the other (in an effort to keep the slides simple), sounds arrogant and selfish - figure out a story that you CAN tell in the allocated time and reiterate that you are happy to talk details during the breaks.

5. Fake it till you make it. This one has stood the test of time, and for good reason. So much of our perception of a good presenter is taken from their physical cues - and that stems entirely from confidence. If you are nervous beyond belief and terrified of doing something wrong, that will inevitably come through in your body language. If you own your talk and your work, that will come across instead. I think of this like you are the pilot of a plane - if you sound scared and uncertain, your passengers are going to be unsettled and anxious, leading to this cycle of negative reinforcement. If instead you get on the PA system, calmly detail the weather or flight plan (how would they really know how high you’re going to fly??) and make them believe you are in control of the situation, your passengers (the audience) will be calm and the atmosphere relaxed. Everyone will make it out alive and live happily ever after. Or so we hope.

Got any good tips that I’ve missed? Let me know your secret ingredient!