by Dom Mason

Before I arrived as an intern in the I-DEEL lab, Shinichi purchased four completely automated experimental boxes from Zantiks Ltd – a behavioural research equipment company. Sent all the way from Cambridge, the boxes arrived soon after I began working at Garvan Institute of Medical Research.

These new ‘Skinner boxes’ were to be utilised in a variety of zebrafish experiments, providing experimental standardisation and requiring minimal set up. The boxes can perform behavioural assays involving tracking, feeding and light stimuli. They are equipped to record video and have plastic inserts to modify the zebrafish arena. Fish are tracked and recorded by an infrared camera to ensure light (or lack thereof) does not impact data collection. Tracking data can then be exported as .csv files for analysis.

My initial goal was to implement two behavioural conditioning assays using the boxes – appetitive learning (food reward) and aversive learning (undesirable stimuli). As scripts are required to run the automation, the first challenge was to understand the code that drives the boxes. I wasn’t much of a programmer before tinkering with the scripts; fast-forward 6 months, and I’m still not much of a programmer. In saying that, I did get some practice modifying the base scripts which Zantiks provided.

Eventually, the scripts were suitable to begin pilot experiments. While it would be i-deel (😉) to implement both aversive and appetitive learning trials, we decided to focus solely on aversive learning due to time constraints involved in getting the fish to learn.

They say you should never work with children or animals. I don’t know who they are, but they are not wrong.

Many mornings and afternoons have been spent toiling over which settings would yield the best results; and whether these results can be replicated. A key lesson learned was to give heed to all aspects of the experimental process provided by the literature. Once we included an acclimation period at the beginning of the assays, our confidence in the boxes reached an all-time high.

The next set of assays will set out to determine the variance in preference with different colour combinations – if there is any preference at all. And once we produce a consistent output of ‘significant’ results, other lab members can include Zantiks assays (quite easily) into their own work!

by Susi Zajitschek

Sharing our knowledge with the world is important, not only in scientific articles and specialized conferences. Why always wait for the media to cover scientific advancements, if we’re the experts? 

The Public Engagement Team at the Garvan Insitutue of Medical Research has recently organised a series of seminars and workshops, in which they were addressing how to effectively communicate science. I am here sharing my main insights for both visual presentations (i.e. talks) as well as written science communications (blogs, outreach articles).
 
1. Know your audience!
This has been brought up as a key point multiple times. Different expectations and levels of background knowledge demand that every talk is tailored for its specific audience. For example, a talk on dietary supplements would look very different if delivered to a group of diabetic adults, compared to a group of school kids. Similarly, a blog entry aimed at a wide audience may be kept simpler than an outreach article that is published for a well-educated (but non-expert) readership. 

2. Keep it simple!
We work in an environment where those around us understand perfectly well what we’re working on, and why this is so important. However, when prompted try to tell our grandmother in under two minutes what it is that we do, we tend to struggle!

It is impossible to convey all of “your science” within a single talk, or one article. As a rule of thumb, narrow it down – decide on ONE key message, which you can summarize in a single sentence, before you start drafting your talk or article. Only present the information that is most relevant to your audience.

Related to this: de-jargonize. Big or complicated words don’t make you look smart, but it’ll annoy your audience if they can’t follow what you’re saying. Keep in mind that even commonplace words such as significance or theory may have very different meanings in the scientific versus the public domain. 

While some technical terminology may be necessary – explain it well, and do it multiple times (differently). Avoid acronyms.

Use examples, analogies, metaphors, anecdotes to make your research relevant and accessible. Relate to people using stories. Be authentic and honest, telling a bit about yourself might help the audience to connect. 

3. Be confident & enthusiastic
Passion spreads – so if you want the audience to be excited about your research, use your body language, engage with the audience, and make eye contact. Speak clearly and slowly (despite the passion). Never fear pausing. Apparently the use of planned pauses conveys greater competence in a presenter, and it gives your audience some time to process and understand some of your more complex concepts. 

To keep your readers engaged, bear in mind that today’s reading audience does not have much time, and is easily bored and distracted. Therefore, start with the exciting bits! Bring up your main message first, and follow journalism’s rule of the 5W’s: Who – What – When – Where  – Why.  Only then present important details, and background follows last, opposite to what you’re used to from your standard scientific publication.

Practical hints
- Design your 1 sentence key message before doing anything else
- Check out how complicated your language is: use the De-jargonizer at http://scienceandpublic.com/  to identify where you could do better
- Practise! In from of the mirror, with adult family members, with 5year olds.  

by Alfredo Sánchez-Tójar and Nick a P. Moran

This January, we (Alfredo and Nick) escaped Germany’s cold dark winter to visit Sydney and the I-DEEL. Our plan was to learn more about systematic reviews and meta-analyses. Did we accomplish our goal? No doubt about it.
Let us first introduce ourselves. We are both postdoctoral researchers at Bielefeld University, conducting meta-analyses to understand why individuals differ. Both of our projects are part of a large collaborative research centre named the NC3; and it was thanks to their support that we could go to the I-DEEL and learn from world-leading meta-analysts in ecology and evolution. Our plan was simple, get there, learn, learn, learn and, well, enjoy the amazing Sydney coffee and the weather.

It became clear from the start that the following equation would be key, and we had better understand it well:
yij = b0 + si + uii + eij

With Shinichi there, that was an easy task. We met Shinichi regularly, carefully planning which analyses we needed to understand and do, and how to get there step by step. The final outcome: we learned and understood how to run bivariate multilevel meta-analyses in a Bayesian framework, which we are now using for several ongoing projects.

Parallel to our stats discussions with Shinichi, we had the opportunity to meet many of the members of the  I-DEEL team to learn more about the strategies and techniques they use to conduct thorough, reproducible and efficient systematic reviews and meta-analyses. Thanks particularly to:

• Losia Lagisz, for showing us how to efficiently build phylogenetic trees in R for our meta-analyses, and giving us lots of tips on how to conduct systematic reviews,
• Rose O’Dea, for showing us the gold standard for keeping meta-analysis databases well organized, reproducible and easy to work with,
• Yong Zhi Foo, for showing us his strategy for efficient full-text screenings using Google Forms,
• Dan Noble, for sharing some Rmarkdown tricks to make neat and beautiful supplementary materials,
• And everyone else for making it a wonderful collaborative and engaging lab.

We would like to say thank you to all the people from the I-DEEL for their time, help, and support (also for an amazing farewell cake – ping Rose O’Dea). We hope to visit you again soon!

For updates about our projects, follow us in twitter: @ASanchez_Tojar, @NC3Niche

We all heard about Open Science, and particularly Plan S, which has been announced in Europe last year (read more here). On 14th February 2019, I had an opportunity to be a panellist during discussion on what it all could mean for Australia. The panel discussion was organised by Springer Nature as a part of the ALIA conference, which is the main meeting for the librarians and information specialists in Australia and New Zealand (I realised these are mostly lovely middle-aged ladies, although they said more men are starting to join this profession with the new technologies, closing the “gender gap”…)
 
The discussion panel itself was made of different stakeholders and actors in scholarly communication, including: Director of Policy and Integrity ARC, Institutional Engagement Manager and Head of Data Publishing from Springer Nature from Springer Nature, Associate Librarian from Scholarly Information Services & Campus Libraries VU, and me as “the centrally important view of the researcher” (that’s at least how my presence was justified…)
 
After a short introduction we had to answer three pre-determined questions:

1. “Is Plan S the right plan for ANZ in the short term? And long term?”
2. “What is the role of institutional repositories in a scholarly publishing system that is moving towards gold open access as the preferred model for funders and authors?”
3. “Outline an example of an open scholarship or open data initiative and why this underlines the benefits of open research?”

 
We did not reach a strong conclusion on any of these questions, but there were a few emerging insights (at least for me):

• “Open access” is a general term encompassing publications that are freely available and permanently archived in a public repository or from more traditional publishers and licensed in way that allows broad use and reuse. While Plan S requires all publicly funded research outcomes to be immediately made available to the public and for the benefit of the public, it also places restrictions where and how these outcomes have to be published.
• Depositing in institutional repositories is currently mandatory in most of the Australian research institutions and is also the preferred option for freely sharing the research outputs. Plan S acknowledges the role of repositories for archiving research, but does not see it as the main publishing venue. Importantly, institutional repositories and many other free non-profit repositories (like preprint servers) are not likely to be compliant with Plan S requirements.
• Another concern is related to academic freedom – the right of academics to decide where to publish their findings and the impacts on the researchers themselves. Especially in the early stage of Plan S, there will be not many journals fully compliant with the requirements, and thus reduced choice of publishing venues. This may mean having to publish in less reputable or less impactful journals than the researchers would otherwise submit their work to. Such restrictions may strain international collaborations and also affect career prospects of researchers.
• Plan S would also negatively affect research societies and their role in fostering good quality research and research careers. Many societies earn the bulk of their income from the subscription-based journals they publish. Flipping to publishing in a full open access model requires significant financial resources and many societies will not be able to afford this. If they don’t flip, they may lose on submissions, reputation and income (for more details read this opinion).
• It is not clear whether under Plan S there will be savings in the overall research costs. If not-for-profit publishers and repositories get marginalised, the overall bill might be actually higher, with the costs shifting from the reading/access fees to publishing fees (and it is not quite clear how the later will be covered).
• Finally, change to Open Access and Open Science should not be rushed. Taking time will allow figuring out the safest path for transition for the publishers and researchers. Changing the mindsets of academics via education, not enforcement, will be an important factor. It will also be easier with new generation of young scientists joining academia, more ready to embrace open science.

The time has come for me to move on to the next adventure. I’ll be leaving the I-DEEL group to start up my own group at the Australian National University! My time at UNSW was nothing short of amazing, and I must admit, I am very sad to be leaving all the wonderful people that are part of the I-DEEL group, and UNSW more generally.
 
I recall back to when I first started; it was just Shinichi, Losia, Rose and I. How much it has changed since 2015. Us four kicked off all the fishy work together, in collaboration with Dan Hesselson at Garvan. Much of the initial work really formed the foundation for Rose’s amazing set of empirical projects…I think Rose is one of the most efficient people I know and probably has collected more data than just about any PhD student on the planet (although, I think Fonti is a pretty close second). I’ve always been so impressed with how she can juggle huge experiments and then two weeks later come with a wonderfully written manuscript (I need to learn how to do that). In any case, I’m excited to see what Rose gets up to in the future, no doubt it will be very cool stuff.

In addition to all of us, the group has now expanded to include Susi, Dom, Hamza, Maddy, Nathan, Yong and Donny. It’s actually getting so large now that I think we need to start holding lab meetings in a bigger room, and probably cater in sweets every week so Rose and Fonti don’t kill themselves baking so much. Nonetheless, it has been very exciting having them all as part of the group and I’d like to thank them for being such great people and teaching me a bunch.

Of course, I can’t thank Shinichi and Losia enough for all that they have taught me. It has been one of the most pleasant, productive and enriching periods of my academic career and I really could not have landed the job at ANU without their support and encouragement. I’m really looking forward to continuing working with them up until they retire. Although Shinichi says this will happen, I may actually retire before him ;)

While I am leaving, I’m not that far away, and I am really excited that I can continue working with all the wonderful people in the I-DEEL group for some time to come; probably forever given how many papers we need to write! Shinichi and I should probably tally this number up at some point; thankfully his “sticky note” wall will remind us of all the fun times we still have ahead!

by Yong Zhi Foo 

Travelling is an essential part of academia. Academics travel for many reasons, ranging from short-term conferences and research visits to long-term resettlements for new jobs. But managing the logistics of moving to a new place can be daunting. Having recently moved to Sydney to join the I-DEEL lab for a research fellowship, I thought I might share some useful tips on moving to Sydney.

Housing rental 

Finding a place to stay is probably one of the biggest issues for someone moving to Sydney, where rental demand is always high (although apparently the housing and rental market is cooling down at the moment). So, this is one of the main things that I will be talking about in this post. There are several things to take note when finding a place to stay:
 
Sources of rental: There are two main options when renting: finding your own place or sharing with others. Some of the popular websites for finding your own rental place include domain.com.au and realestate.com.au. Most housing agents advertise on these two websites. One important thing to note about the ads on these websites is that the photos are not always representative of the actual property. If the ad does not explicitly say that the property is furnished, you should assume that it is not, even if the photos show a fully-furnished place.
 
If you are keen to share with someone else, flatmates.com.au is a good option. I found my current place from there. For shorter-term visits, there is Airbnb.
 
Costs: Just as a ballpark, at the time of this blog, a decent (i.e. not breaking apart, does not have mouldy walls/carpets etc.) 1-bedroom apartment in suburbs surrounding UNSW costs at least AUD450 per week (yes PER WEEK). At this price, the place would most probably be unfurnished. For sharing, depending on the number of people that you are sharing with, the price would be north of AUD200 per week.
 
Besides the weekly rent, there are other potential start-up and ongoing costs, such as rental bond (usually 4 weeks’ rent), purchasing furniture and white goods, home internet, and utilities.
 
Where to stay: Many people working/studying at UNSW stay in the nearby suburbs, such as Randwick or Kensington, for the convenience. If you enjoy going to the beach, Clovelly and Coogee are pretty nice and still relatively close. If you are keen to stay closer to the City, places like Surry Hills or Paddington might suit you. I chose my place based on proximity to the food places that I like (see the photos below for evidence :D).

When to start looking: Rental in Sydney moves really quickly. Plus potential tenants are required to inspect a place before they can submit a rental application. Therefore, I would not advise people start looking too early. Instead, plan your move so that you have some time to go around and inspect places personally at the start. You can probably begin looking on websites a few days ahead to create a potential shortlist of places.
 
Getting around
 
Sydney is pretty well connected in terms of public transport. If you are taking public transport, get an Opal Card and load funds into it for travelling on trains, buses, and ferries.
 
Tip: There are several options for traveling to and from Sydney Airport, including train, bus, taxis, etc. I find that Uber offers the best value for money. Once you make a request, your Uber ride will be directed to a special pick-up spot. Just go to where it is indicated on the app to wait for your ride.
 
Mobile/Internet
 
Some of the major mobile/internet network providers include Vodafone, Telstra, and Optus. Some of my friends have used companies such as TPG and reported good things about them. A typical mobile plan is around AUD50 and home internet is around AUD60 and above. Pre-paid is another option for mobile plans.
 
Banking
 
Any of the major banks, like Westpac, ANZ, or Commonwealth should fit your needs. Just go in and speak to the banking staff and they will sort you out. With any banking or mobile applications, international people will need to bring their passport for identification purposes.
 
Hopefully this post will be of use to people moving to Sydney! 

by Shinichi

It has been nearly 4 years after starting I-DEEL with only 4 initial members (Losia, Rose, Dan and me in 2015). We now have a bigger group with over 10 people. Great (yet sad) news is that Dan got a job at the Australian National University (ANU) and is leaving us soon. I often tell Losia that I want to clone myself because I chronically have too much to do. Dan is probably the closest it can be to my clone. Dan, however, is even better, as he is more patient and codes much better than I could. It will be hard without his capable hands. At the same time, I am curious about what a cool research group Dan will establish at ANU (farewell!).

It has actually been a little shy of a decade since I got my first job as a lecturer at the University of Otago. And I have just achieved my long-term goal of becoming a full professor. At this special occasion, I think of two things. First, I have been extremely lucky with people I have worked with. Among these people, I have my 3 wise men – Jarrod Hadfield, Wolfgang Forstmeier and Tim Parker. It is especially memorable that open science movement brought all of us together to work on the same publication. Second, working smart is really important although some tasks just take a lot of time. Around 2 years ago, I decided to get up early and work from 6 am. This gives me 2 hours of an uninterrupted working time before breakfast (especially great for writing).

For now, I seem to have lost an audacious goal to pursuit (I need to find one soon). But, for the time being, I like the sound of this: “Japanese working ethics and Scandinavian efficiency” (i.e. working hard and smart). Actually, Susi told me about this the other day because, she thought, I was unusual in asking both from my people not just one (i.e. asking for a Darwinian demon = no trade-off). I believe striving for this principle will bring something amazing over the next 10 years or so not only to me, but to all. I will make this as an ideal for I-DEEL.

by Losia Lagisz

Managing research projects is not easy. It gets even more complicated if there is more than one person involved. Not only many project participants need to know what needs to be done and by whom, but also what others just finished and what they are currently working on. This applies especially to meta-analyses.

Robust meta-analytic process requires at least 2 people involved in screening and extracting data. The other steps of the process (planning, searching, analysing and writing up) also greatly benefit from having more than one person involved. Large teams are not uncommon and the whole process can drag for months and even years. Both large and small teams need efficient communication and job management tools to get a good quality output in reasonable time.

There a few communication and job management software tools that worked for us, so far. For general, lab-level, across the projects and teams, task management we use a Todoist (basically an online “to-do list”), and for communication we use Slack. Last year, we started using Workstreams add-on for Slack, which appeared to be a good tool for managing systematic review and meta-analytic projects.

Workstreams have at least three big advantages:

1) Workstreams are integrated with Slack – so, no new sign-ups, setting and all the hassle of new system. Slack channels can be used to display alerts on what is happening with the tasks and you can also use shortcuts directly from Slack to crate tasks and change their status.

2) Workstreams are visual – the system it is based on a concept of a Kanban board (see the screenshot of one of our taskboards, below). Rather than just displaying the list of tasks that need to be done, it has three columns (you can add more and customise). The default columns are: planned, in progress and finished – you can drag the tasks between the columns, as needed. The tasks can also be assigned to people, labelled, priority reordered, commented on, have attached files, etc. So, Workstreams have quite a lot of functionality, but they are also easy and intuitive to use.

3) Workstreams are free - at least the basic version. The PRO version is quite pricey (user/month $9.99) and it comes with a few more functions, of which only one is really useful – setting deadlines and reminders (however, we can go around this problem by using /remind bot function from Slack).

You can learn more about Workstreams here. From my experience, if used consistently by the team members, Workstreams (with Slack) is a good tool for managing research projects such as systematic reviews and meta-analyses (and other projects), especially if you are not a big fan of traditional (even digital) to-do lists.

Last week (14-15 Nov), I went to Melbourne for a workshop (“From Replication Crisis to Credibility Revolution”). The workshop was hosted by my collaborator and “credibility revolutionary” Fiona Fidler.

I suspect many workshops and mini-conferences of this nature are popping out all over the world as many researchers are very much aware of “reproducibility crisis”. But what was unique about this one is its interdisciplinary nature; we had philosophers, psychologists, computer scientists, lawyers, pharmacologists, oncologists, statisticians, ecologists and evolutionary biologists (like myself).

I really like the idea of calling “reproducibility crisis” “credibility revolution” (hence the title). A speaker at the workshop, Glenn Begley, wants to call it “innovation opportunity” (he wrote this seminal comment for Nature). What a cool idea! And these re-namings make things a lot more positive than a bit of doom-and-gloom feel of “replicability crisis”. Indeed, there are a lot of positive movements toward Open Science and Transparency, happening to remedy the current ‘questionable’ practice.

Although I live in Sydney, I was also in Melbourne early last month (4-5 Oct) for a small conference. This is because Tom Stanly invited me over, as an evolutionary biologist, to give a talk on meta-analysis to a bunch of economists who love meta-analyses. To my surprise, I had a lot of fun chatting with meta-enthusiastic economists.

Tom is not only an economist but also a credibility revolutionary, like Fiona. He has asked me to invite ecologists and evolutionary biologists to comment on his blog about a credibility revolution. It is an excellent read. And if you can make comments to join the conversation, Tom will appreciate it a lot, and get conversations going. Disciplines need to unite together to make this revolution successful or make the most of this innovation opportunity. So, join the credibility revolution!

by Rose O'Dea

Measuring fish. In my short research career this is the activity I’ve done the most. ​Whether measuring mosquitofish or zebrafish, the method has been:

1. Photograph fish next to a scale
2. Rename photo with the ID of the fish and the date
3. Measure photo using ImageJ

Here I share my tricks to renaming photos using R.
​

Step 3: Generate a file with the name of each fishTo link the marking of the fish to their records, we save a file that contains three pieces of information: the tank ID, the fish ID, and the marking of each fish. The file is exported from our master records, and requires a bit of manipulation so that we only have the records of the marked fish in our particular tanks. In this example we are only renaming photos from the tank called “DTG03”.

# note that working directory is already set to our project folder (this is automatic if you use an R Project)
# importing excel document
library(readxl)
marking <- read_excel("Fish Records.xlsx", sheet = 1)
# renaming columns to remove "::" (which filemaker uses this to distinguish related table fields)
names(marking) # before

## [1] "fish_group_ID"
## [2] "individual_fish main 3::fish_indiv_ID"
## [3] "individual_fish main 3::marking"

names(marking) <- gsub(".*:", "", names(marking)) names(marking) # after
## [1] "fish_group_ID" "fish_indiv_ID" "marking"

# finding fish in the tank "DTG03", which we are renaming
# the problem is Filemaker doesn't export the repeating records for fish_group_ID, so we need to
# take the first instance of DTG03, and every subsequent NA until the next tank record
marking <- marking[which((marking$fish_group_ID == "DTG03") == T):nrow(marking),] # first record of DTG03
marking <- marking[1:(which(!is.na(marking$fish_group_ID)==T)[2]-1),] # removing everything not in DTG03

Step 4: Import the old file names, and create a corresponding vector of new file names.

old <- list.files("./original_DTG03_15_03_17") head(old, 5)

## character(0)

# Taking the markings of these fish so that they can be matched to their unique IDs
photomark <- gsub(".JPG", "", old) head(photomark, 5)

## character(0)

# Using indexing to check we're using `match` correctly
marking$marking[match(photomark, marking$marking)] == photomark # all match

## logical(0)

# saving JPEG names of new photos
new <- paste0("15_03_17_", marking$fish_indiv_ID[match(photomark, marking$marking)], ".JPG")
head(new, 5)

## [1] "15_03_17_.JPG"

Now we should have two vectors of equal length for the old and new names of photos:

length(old) == length(new)

## [1] FALSE

If this isn’t TRUE, then we need to go back and work out why. Usually one of the photos has been mislabeled, but this can also catch bigger mistakes such as missing or misplaced fish.


Step 5: Rename photos.First we will create an empty destination folder where we want the photos to go: in this case it’s called photos_to_measure. We then add the file path to the old and new file names.

oldfile <- paste0("./original_DTG03_15_03_17/", old) head(oldfile, 5)

## [1] "./original_DTG03_15_03_17/"

newfile <- paste0("./photos_to_measure/", new) head(newfile, 5)

## [1] "./photos_to_measure/15_03_17_.JPG"

file.rename(from = oldfile, to = newfile)

​## [1] TRUE

​Note that this function file.rename will transfer the photos from the old folder into the new folder, leaving nothing behind (i.e., something’s gone wrong if the old folder isn’t empty):

​Not just for fish photos!

Whenever you want to rename a bunch of files, file.rename could save you lots of time.


R is found in tRial and eRRoR

file.rename is a base function. Whenever I discover that something I’ve been doing in R for ages can be more easily done using a simple base function, I’m alternately amazed and infuriated. Before I found file.rename I renamed thousands - thousands! - of photos using a looping method that is more complicated and slower.

library(jpeg)
oldfile <- paste0("./original_DTG03_15_03_17 copy/", old)
newfile <- paste0("./photos_to_measure/", new)
for(i in 1:length(newfile)){
    writeJPEG(image = readJPEG(oldfile[i]), target = newfile[i], quality = 1)
}

I don’t regret the time it took me to discover file.rename, for a couple of reasons. The loop allows you to specify the file size of the renamed image by specifying quality on a scale form 0 to 1 (see ?writeJPEG). In our database we sometimes store photos of important fish, and to keep the file size lower it’s useful to export these photos with quality = 0.3. More importantly, R is about the journey as well as the destination. That small piece of code I wrote in 2016 - which took at least an hour of trial and error! - was the first loop I ever wrote and used regularly. My inelegant code taught me a lot, and watching it rename photos was immensely satisfying. Still, we need to pick our R battles, and I hope file.rename will leave you more time for your own.