Research is more than just publications—you are worth more than your publications

Luke W. Johnston

October 30, 2025

Aim of talk

• Most important: Reminder that publications aren’t your only or biggest metric of success

• Share a bit of my own career path

• Highlight gaps that you can specialise in for your own career

Who am I? 👋 👋

• MSc and PhD in Nutritional Science
• Team Leader, Steno Diabetes Center Aarhus and Aarhus University

Academic path

gantt
    dateFormat YYYY-MM-DD
    axisFormat %Y-%m
    todayMarker off
    MSc (UofT) :msc, 2011-09-01, 2y
    PhD (UofT) :phd, after msc, 4y
    Postdoc (UofT) :postdoc, after phd, 2018-02-01
    Postdoc (AU) :postdoc2, after postdoc, 2019-12-31
    Postdoc (SDCA) :postdoc3, after postdoc2, 2022-06-01
    Team leader (SDCA) :teamleader, after postdoc3, 2025-10-30

MSc and PhD: Non-traditional work

In addition to my research…

• PROMISE data: Organized data into an R package structure.

• Three R packages: mason, prodigenr, carpenter.

• UofTCoders: Graduate student-led group to teach R and Python to other peers.

This isn’t to brag, this is to highlight, that taking the time to learn how to better make use of computers via programming can make you much more effective at doing things more easily and faster.

Learning to effectively organize files and to code = powerful productivity booster

A big lesson I learned during this time is just how powerful it is to organize things effectively and to learn how to code well. Means that those manual things you might do over and over again can be automated or heavily streamlined.

During postdoc: Hard lessons learned

We are woefully behind on basic data engineering, reproducibility, and programming practices.

I started out doing more “traditional” research but have gradually been moving towards more computationally technical work, like software development and data engineering as well as teaching researchers better practices in coding, open science, and reproducibility

As I continued my research career, I got more and more into building software and creating courses to teach researchers about better programming practices. This also meant that I had less and less time to publish papers, because, there’s only so much time in a day.

Postdoc: Data organization, development, and management

• ukbAid: R package and website for UK Biobank data management and analysis.

• DARTER Project: Website of application to and documentation on a Denmark Statistics project.

• registers2parquet: Package to convert Danish health registries into Parquet format for easier use with big data tools.

Postdoc: Created and ran workshops (and still do!)

• Introductory course: r-cubed-intro.rostools.org

• Intermediate course: r-cubed-intermediate.rostools.org

• Advanced course: r-cubed-advanced.rostools.org

Problems arising during postdoc: Published few papers

• After my PhD, no first author publications.

• Too many deep problems that needed fixing.

• Focused on building software, tools, and teaching.

Choice: Ignore all broken things and publish or keep trying to fix things?

My postdoc supervisor said, if you want to stay in academia, you really need to focus down on publishing papers. But that if that wasn’t important to me, I should keep doing what I believe is more important and impactful.

I chose to keep fixing things!

It wasn’t a hard choice, I couldn’t ignore the problems.

I was invited to help apply for a grant because of my work

For Data Infrastructure grant from Novo Nordisk Foundation

Got the funding 🎉 which is my current work

Seedcase Project, a framework for building infrastructure for research data.

Research must learn to value more than just publications

Why? We have big problems that need fixing!

Reproducibility: A major issue we completely ignore

DOI: 10.1093/gigascience/giad113

Science is not about trust, it’s about verification 🤔

Some trust is needed, but shouldn’t be dependent on it.

The whole point of science is verification. Otherwise we are no different from pseudoscience. If we can’t verify that the results are correct, by looking at the code and reviewing it and running it, how can we know and trust it?

Desperately need more deep programming expertise!

As in, their main work is programming, not publishing papers.

It takes a lot of work and learning to deeply understanding how to program correctly and effectively. Writing papers takes time away from that. And no one can be a master at everything.

“Science as amateur software development”

“But the code runs!”

From this academic presentation.

The state of programming and software development in research is abysmal to say the least.

The problem that researchers aren’t even really aware of this problem No funding goes to it, there are few courses on it, it isn’t required, we cannot hire skilled personnel for this either because it isn’t funded nor valued. What ends up happening? You get numbers from running the analysis… But now do you even know it’s right? Programming is hard, way harder than doing research.

Analyzing large data (and reproducing the results) requires programming skills

• With massive data, you really need to know how to code and programming.

• But, researchers are not trained for this kind of skill.

Hoping your skills and knowledge are enough to do the analysis, without actually knowing if they are good enough.

Desperately need research-specific software!

So many research-specific problems that could be fixed with software.

Need more high-quality data

Data is hard to build, manage, and maintain. It takes time and effort.

Take home pieces of advice?

There are so many opportunities outside of academia

And they clear even less about publications.

Your skills, knowledge, and experiences are worth more than your number of publications

So: Find problems you enjoy fixing and dive deep!

Even if that means less publications.