“Societal impact is investing in creative ways to engage the future generation in science.”


On Expedition in Franeker: Social SYNC lab

On the 6th of May, the SYNC lab presented the Social SYNC Lab at Expedition NEXT; the national science festival for children, organized by the NWO-NWA. The festival was sold out, which means that 6000 children and their caregivers came to the former university city Franeker to learn more about and experience science. The goal of the NWO-NWA was to get children and their parents excited about science and spark their curiosity. In festival-style, children could really experience and engage in all fields of science. Expedition NEXT fits in the broader ambition of NWO-NWA to show the value and importance of science to society. SYNC shares this ambition and therefore contributed by developing a special Social SYNC Lab; designed to represent important parts of our research on social development in children and youth. Our Social Lab consisted of three main parts, reflecting different research lines within SYNC: self image, the voice of children, and brain synchronization. In addition, children could ask Eveline Crone everything about friendship and feeling good in a mini lecture. The overall question and topic of the Social SYNC Lab for children and their parents to engage in was: ‘Why do you need others to feel good?’


Self image: Discover your superpower!

Based on our research line on the development of self image, we wanted children and their caregivers to experience the association between giving and receiving compliments – superpowers – and feeling good. We asked children to pick already written compliments or write compliments themselves on stickers and give those to someone else, such as their caregiver, a friend or a brother or sister. Examples of compliments were ‘you are a good friend’, ‘you are a kind person’ and ‘you are good at helping others’. The researchers asked children how they felt when they gave a compliment compared with when they received one. They showed children that both can make you feel happy. A cool side effect of the stickers was that children and caregivers walked away with the compliments on their shirt, feeling good about it the rest of the day and showing others their compliments!

After the giving and receiving of compliments, children and their caregivers could also take a picture in the compliment photobooth in which three pictures were taken. When waiting in line, children were encouraged to select a compliment on a stick about each other. The first picture was taken without props. For the second picture, children pointed the compliment on a stick to the other on the picture; sometimes creating a nice surprise! For the last picture, children could either pick another compliment or make a crazy picture, using other props such as a moustache or party glasses. We printed the children’s series of three photos instantly, asking them whether they saw differences between the first and second picture. Did they feel happier? Did they also see this in the picture? Why did they think this was the case? Could this be linked to giving and receiving compliments? It was nice to see and hear that caregivers engaged in conversation with the children afterwards; continuing on the question how often and when they would give and receive compliments and how it made them feel. Some children said they were already giving a lot of compliments and now they understood why this made them happy. The instantly printed photos also gave the children and their caregivers a nice memory of the Social SYNC Lab at Expedition NEXT to stick on the fridge door!


Make your voice heard!

Within SYNC, we value the voice of (unheard) youth and study how and why they want to contribute to society. We also work with novel methods with which we engage youth in our studies, such as using a living lab approach or citizen science. In our Social SYNC Lab, we asked children to express their voice as well. One way of doing this was by asking children to choose one of the two statements framed as difficult dilemmas. These dilemmas included topics important to children, such as ‘rather living without friends or living without internet’ or ‘rather being the boss of the Netherlands or being a famous celebrity’. We distinguished different age groups based on the color of the stickers they used to make their choice. It was nice to hear and see the conversations the dilemmas started between the children and their caregivers or between brothers and sisters. It was interesting to see that older children typically chose to be the boss of the Netherlands over being a famous celebrity and children picked the middle line between rather giving or rather receiving compliments. It was also striking to see that some older children preferred to live without friends rather than living without internet. They said that they had friends on the internet as well. We discussed the choices with caregivers and children and linked what we saw to our research.

In addition to the dilemmas, children were encouraged to envision their ideal world and draw elements of this world on a blank canvas. They were prompted by questions such as ‘Imagine, you’re the boss of the Netherlands.. what would you do to free the world of bullying?’ Or ‘what would you do to help other children to feel happy?’ Children came up with lots of interesting and out of box ideas. It was nice to see that a lot of them were about their (social) environment, such as placing more fountains. Some of them were also very idealistic, such as giving poor children more money, but it was refreshing to see how children think and dream about their future. In the afternoon, the minister of Primary and Secondary Education, Dennis Wiersma, visited the Social SYNC Lab. He was interested to see the outcomes of the dilemmas and of course also what the children find important. Just like he has been inspired for education policy, we as researchers will use the input of the children as inspiration in our studies on social development and the role of the social environment.







Dance your brain in SYNC!

The third research line we translated into a festival element was on the musical brain and the role of (movement) synchronization in stimulating interaction and joy between people. In order to make children (and their caregivers) experience this synchronization, we collaborated with dance teacher Kimberley from Crea bij Kim. She and her dance group prepared a simple and joyful dance for children to spontaneously join. Children were encouraged to join and move together. The more children who joined the dance, the more the ‘brain’ would be filled with water of joy. We also showed the brain parts that are active when moving together, linking their movement to their brain. The dance was very popular and well-attended and the large musical brain attracted also other groups who made music in Franeker, such as the local marching band!

Expedition NEXT contributes directly to societal impact

This reflection on Expedition NEXT in Franeker started with the ambition we as a lab share with NWO-NWA, bridging science and society by motivating children to become curious about science. I loved to see how inclusive this festival-style science event was, as it attracted a variety of children and caregivers. Although the festival was focused on children between 6 and 13, it also attracted younger children and grandparents. There were groups of children from the Asylum Seekers Centre who could still join the activities as they could engage without speaking the Dutch language. This also applied to children with disabilities, who could still join a lot of activities. Therefore, the festival was a great way to engage all children in science activities. To me, societal impact is investing in creative ways to engage the future generation in science, sparking their curiosity, feeding their critical mind, and really listening to them. Expedition NEXT was a perfect example of this!

Read and watch more!

Here you can find the Twitter thread we made on the day itself. We also made a (Dutch) vlog about the day! Watch it here! On the website of Expedition NEXT, you can find more impressions of the day and follow Expedition NEXT on tour.

I was asked to write about what I do as a research software and data engineer in my work with the SYNC Lab. There are quite a number of resources out there explaining what research software engineers (or RSEs, for short) are and do (see for example 1, 2 and 3). I thought I’d add to that by focusing less on general descriptions and more on some specific tasks and tools that I keep myself busy with. I’ll link to a few of my talks and resources along the way if you’re interested in knowing more about specific topics.

First, a bit of background to give you context. I was trained as a mechatronic engineer (a mix of mechanical and electrical engineering) and I set out to work in the field of biomedical engineering on robotic prostheses. Not being able to find a job in that field, I moved on to industrial automation for a few years, then joined an exciting, small software startup for a few more, and then switched gears to do my PhD in real-time functional MRI signal processing at Eindhoven University of Technology. After that, a year ago, I started my current job as an RSE at Research Center Jülich in Germany, and my part-time position with the SYNC lab in The Netherlands.

From this short history, perhaps it’s clear to you that I was never explicitly trained as a research software engineer (if not, don’t worry, it is very clear to me whenever I struggle with some technical aspects that well trained colleagues can do in their sleep). Perhaps it’s also obvious that I did not follow the expected path of a career-researcher. It’s not like I did my bachelors, masters and PhD all in the same field, finally becoming disillusioned with academia and then applying my true passion of software development in an academia-adjacent position. (A single PhD project, however, is more than enough food for disillusionment :P).

But research software engineering is the job I’ve loved doing the most, and it’s because of the fact that I can use my existing range of skills, hone them on new challenges in the broad field of science that I adore, and solve interesting problems every day, all while working towards improvement that others can benefit from. Not all RSEs have the same makeup. Some are trained and work as software engineers for years, or some happen into the position by chance. Some focus on a specific software language or implementation, for others it might be all about data management or high performance computing. But all of us solve problems in support of our colleagues and the scientific field in general. And, in doing so, we often draw from a set of common skills and focus on similar tasks. For me, these include:

  1. Research data management
  2. Developing tools and software for reproducible research
  3. Visualisation
  4. Learning and educating


Research data management

Three words to describe something that is best executed by a team instead of by a single person, based on the sheer amount of work this can require. Doing research data management basically means that you build and support systems and practices for managing the full lifecycle of research data: from design to acquisition to processing to publishing to archiving. Where should the data be saved, and in which formats? Are there community standards for structuring the data that we can use? How should we transport the data between storage and computing servers? And when it comes to format or structure conversion, data cleaning, (pre)processing, and scientific analysis: how do we track how the data changes? Can we set up a workflow for decentralised collaboration and contribution? Who did what with the data when and where, and how can we change or revert that if we want to?

The tools I use to do this are easier for me to talk about, since that is the main focus of my job at Research Center Jülich. We develop the free and open source software tool called DataLad, which is a distributed and collaborative data management system supporting arbitrarily large datasets, on-demand content retrieval, dataset nesting, data version control, provenance capture, and metadata management. That’s a lot of jargon that is perhaps better explained by this comic:

Heunis, Stephan. (2022). F.A.I.R Data Management with DataLad – a comic strip. Zenodo. https://doi.org/10.5281/zenodo.6400523

When I do data management, I mostly use:

  • Command line tools (including DataLad) to operate on data in its various formats
  • Data storage servers and compute clusters when accessing and processing data, which are almost always on Unix-based operating systems
  • Cloud environments for accessing/storing data such as Amazon S3 and GIN

Most of these steps are done via the command line, which, if you weren’t trained in computer science or don’t have years of experience, could make you feel both immensely powerful and dreadfully incompetent at the same time. Luckily, I’ve gotten used to it. I find opportunities to make a fool of myself, and learn, every day…


Developing tools and software for reproducible research

The border between this set of skills and tasks, and the one described above, is very vague. Therefore, I’ve arbitrarily decided to describe the next set of tools in this section and not as part of Research Data Management. However, you’ll often find a very close link between reproducibility and data management, and research engineers are often big proponents of using tools and practices to conduct both efficiently. That’s why many aspects that fall under the umbrella of “open science” often play an important role in our jobs.

In my work with the SYNC lab, especially when working with the multi-modal and longitudinal LCID dataset, I use many open source tools to accomplish my goals for efficient data management and reproducible research outputs. My daily workload consist of a mixture of these tasks and tools:

  • Organise neuroimaging data in the BIDS format
  • Write and run Python scripts to clean/extract/convert raw data
  • Organise, track and collaboratively develop scripts/software with git and GitHub
  • Manage datasets, dataset linkage, data transport, and provenance tracking with DataLad
  • Use bash scripting for file handling/copying/renaming and general data organisation on a storage server or compute cluster
  • Develop and maintain software packages for the analysis of functional MRI data (e.g. fMRwhy)
  • Use job schedulers or workload managers (such as SLURM) to run data analysis jobs on compute servers
  • Consider data privacy and the GDPR at every step of the data management and analysis workflows
  • Decide how to split derived data into those that can be shared and those that cannot
  • Host derived statistical images of fMRI data on NeuroVault
  • Create publication packages for project material and data, using services like GitHub, Figshare and Zenodo.
  • Running computing examples on cloud infrastructure using free and open source tools such as Binder and JupyterHub.


For some more contextual detail on the above processes, you can have a look at this talk where I present and discuss several tools as they apply to code and data sharing in the field of functional MRI:

Hands-on fMRI data and code sharing: practices and tools - Stephan Heunis - Food for Psychologists
Hands-on fMRI data and code sharing: practices and tools - Stephan Heunis - Food for Psychologists
Heunis, J.S. 2020. Hands-on fMRI code and data sharing. Invited workshop at the Food for Psychologists. The Hague, The Netherlands (online). Slides. Code.


As you can see from the bullet list above, almost all of these tasks involve some form of software programming or scripting. I despise manual and repetitive tasks, and if there’s even a slight chance that someone might benefit from automation, I’ll probably do it. Luckily, collaborative coding, automation, and data sharing all have loads of other (more important) benefits including improved global access and collaboration, easier error detection, and drawing on the skills and strengths of many (which also address other things that I despise: competition and exclusivity).



An important part of the scientific process is conveying results in an understandable and interesting way, and scientific visualisation has a large role to play here. I am often frustrated by how little we, as scientists and researchers, employ the powerful tools at our disposal. The technology already exists for us to create interactive and engaging visualisations, to turn our results into veritable works of art, yet we often stick to boring graphs published in static formats. Of course, it is not reasonable to expect a single researcher to master all of these skills in addition to conducting actual research and writing journal articles and grant applications. But I guess that’s a good reason why research institutions and labs should hire research software engineers!

When working on scientific data and results, I often create both static and interactive figures to convey things like data quality, statistical measures, and data distributions, mostly as they relate to brain images. For this, I use many tools:

  • For visualisations, I mostly work with Python, Matplotlib and Plotly, which is an open source graphing library for Python, R, and JavaScript that provides great forms of interactivity. Have a look at all the pretty plots you can create!
  • Plotly’s Dash adds dashboard-like functionality that allows you to design browser-based applications for your scientific data. Here’s an example of a data visualisation application I created for a brain imaging study last year.
  • Even without using these tools (and with the benefit of native browser support, i.e. not having to rely on server infrastructure for running code that your application relies on) pure JavaScript and CSS can be very powerful.
  • JavaScript frameworks like VueJS or React are also very useful for extending interactivity in the browser.
  • When it comes to static images and maintaining high image quality, I mostly work with the SVG format (scalable vector graphics) and I edit images in the free and open source tool Inkscape.


For a demo on using Python, Plotly and Dash to create interactive visualisations and applications, you can check out this video:

Stephan Heunis - Interactive data visualization with Python, Plotly and Dash | OpenMR Virtual 2021
Stephan Heunis - Interactive data visualization with Python, Plotly and Dash | OpenMR Virtual 2021
Heunis, J.S. 2021. Interactive data visualization with Python, Plotly and Dash. Demo talk at the OpenMR Virtual 2021 event. Slides. Code.


There are of course loads more tools and frameworks to use when creating visualisations, for example R, ggplot, and Shiny apps. The ones I mentioned are mainly preference- and experience-based.


Learning and educating

Last but far from least. I think these are the two aspects that I love the most about my job (apart from the fact that being able to create things with code sometimes makes me feel like a magician). I am (1) easily bored with and distracted from something that has the slightest taint of monotony, and (2) easily demotivated from doing something if I don’t understand why I’m doing it (and once I understand why, I have to deem it worthwhile). This is why I love and seek out opportunities for learning new skills and tools, and why I enjoy turning what I learned into an educational resource that can ideally help others.

It is often seen as the boring part of software engineering, but documentation is immensely important to help users to:

  • Understand how to use the software
  • Find practical examples on which to base their own software workflows
  • Find specific technical details about design, usage, outputs and integration
  • Know where/how to ask for more help
  • Know how they might contribute to the software


That is why much of what I do consists of explaining concepts to others. For documenting software or scripts, I depend largely on the following tools:

  • A README file!
  • The Markdown markup language, which is a very common language to use when writing a README file for software projects
  • The Sphinx tool for auto-generating documentation for a software project (very commonly used with Python packages), along with the reStructured Text markup language used by Sphinx
  • Jupyter Notebooks, for interactively presenting code, narrative, and visualisations in the same live document, are very useful when sharing tutorials or educational content.
  • Jupyter notebooks and Markdown can be used together to create publish-ready and interactive electronic books with the Jupyter Book framework.

Creating output that is not only interesting, visually pleasing, and engaging, but also educational to the extent that it simplifies the lives of others is reason enough for me to keep doing it.

So that was it. A day in the life of one research software engineer. Hopefully this adds some transparency to what might be opaque to some. And don’t be misled by what is seemingly an impressive list of skills and tools. If I remembered to add an item to a bullet list above, that memory is there because of the associated pain and struggle that it took to achieve it. Struggling is part of our daily lives, and not everything always works out seamlessly with the use of a single tool or trick. It often takes a team of colleagues to solve complicated tasks, just have a look at the issue log of any open source software project. And it takes an immense amount of Googling #noshame.

I didn’t cover all RSE aspects in equal detail, and I probably missed something that could be the very air that some RSEs breathe. But take that as an opportunity to go to your nearest (virtual or physical) research software engineer, buy them a coffee (the fuel for good code), and ask them a bit about what they do 🙂