What next? Many steps to take! Small reminder though…

… There are still very strong barriers to progress…

Not going to lie, there are major barriers.

Institutional barriers

You will encounter a lot of resistance, a lot of barriers and hardship.

Lack of adequate awareness, support, infrastructure, training

At the institutional level, there is no real awareness of this, no support or infrastructure. You’re basically doing this on your own. Which probably isn’t that uncommon anyway.

Research culture values publications over all else

What would you spend your time on if we didn’t have this publication-obsession?

Research culture and incentives pretty much only care about publishing journal articles. Creating software tools or datasets to be shared, meh. Making teaching materials to help other researchers, meh. Communicating your science to the public and doing outreach, meh. Doing actual science that might take years and not lead to any “hard papers”, meh.

Imagine if the number of publications and where you published didn’t matter for getting funding or getting a research job. What would you spend your time on? What would you do differently compared to now?

Legal and privacy concerns about sharing data, intellectual property protection, patents

Legal and privacy concerns are big topics that institutions in particular focus on a lot, about ownership and so on, since research can lead to commercialization and the potential for profit. For individual researchers, we often worry about these concerns too much and sometimes stops us from doing work because we’re afraid we’re doing something wrong

More traditional academics don’t understand or resist change

We have a large portion of traditional academics who have benefited from and succeeded in this system and are invested in continuing it. They often say there’s nothing wrong with the system. This is what we in epidemiology call “survivorship bias”. There’s nothing wrong, for them, because they succeeded. But all the others that didn’t succeed? We don’t count that data.

‘Business as usual’ is easier

We have a system that favours each individual person repeating the same mistakes that others make because the system doesn’t allow for us to take the time to create tools and infrastructure that helps ourselves and others out.

Because business as usual is the easiest way in the short term. Our current scientific culture is just not prepared for this, for the rising modern analytic and computational era.

There are also personal barriers

Fear of …

Fear of being scooped or ideas being stolen
Not being credited for ideas
Errors and public humiliation
Risk to reputation

And there aren’t just institutional barriers. We as researchers have fears of being scooped, of embarrassment and humiliation for your methods being gasp wrong. Which is actually just part of science.

Mention Black Hole imaging issue for reputation/not being credited.

Overwhelmed with everything that needs to be done

It is also really overwhelming, having so many things to think about to make sure you’re doing solid science. No researcher in the past had to consider and think and know as much as we have to know and to do. Another reason why we need more team science, to distribute the tasks and skills.

Need to constantly stay updated

You also have to constantly stay updated, and that can be tiring.

Finding better opportunities outside of academia

And the last barrier, which may actually be a benefit, is that one reason you don’t see a lot of researchers sharing their code or being more reproducible is.. they end up getting picked up by industry and paid really well or decide to leave academia for the reasons I mentioned.

Just as an example, I found a Norwegian group who had a really inefficient workflow and decided to re-build their workflow to make use of programming, to be reproducible, to have a pipeline. I looked up the lead author as well as several other of the co-authors and guess what… many of them now work in really great companies as data scientists or software engineers, probably making a lot of money and having potentially a less stressful life.

So… what can you do right now?

Remember: Reproducibility is a spectrum, not a binary state

Easiest thing right now? Start sharing your code

No matter how ugly. It also doesn’t mean it’ll be reproducible, but at least it will be inspectable.

If you do nothing else: share your code.

If its ugly, that’s fine! The point is you start and that you get more comfortable doing it until it becomes second nature to share and in the process, your code gets better because you know someone might look at your code.

And even if your code isn’t reproducible, even if others can’t run it own their own, sharing is the first step to becoming better. At the least, others can inspect your code for its overall logic.

We as researchers try to find our niche, make our own space in the research world. Sometimes its a real struggle to find that niche.. but guys! No one is doing this, no one is sharing their code! You start doing the simplest thing of sharing your code and you will be one of very very few people who do. And this isn’t a niche, this is a gaping hole in our modern scientific process. A huge hole.

What next? Many steps to take! Small reminder though…

Institutional barriers

Lack of adequate awareness, support, infrastructure, training

Research culture values publications over all else

Legal and privacy concerns about sharing data, intellectual property protection, patents

More traditional academics don’t understand or resist change

‘Business as usual’ is easier

There are also personal barriers

Fear of …

Overwhelmed with everything that needs to be done

Need to constantly stay updated

Finding better opportunities outside of academia

So… what can you do right now?

Remember: Reproducibility is a spectrum, not a binary state

Easiest thing right now? Start sharing your code

… Or teach! 😉😉