In the spirit of Love Data Week 2023 (#lovedata23) I asked people to send in questions they have on Open and Reproducible Data Science (AMA). Here are my answers.

Format:

Answer.

Very good questions.

Where to start? I recommend starting with the things that are easy and fun for you. Reproducible research requires continued learning, but you don't have to be perfect right away. Here are some ideas where you could start (order does not matter).

Ways to get good data? I am not 100% sure if I understand the question correctly. I interpret it this way: "How can I ensure my data is of high quality so that my research is of high quality, too?". There are two paths:

1. If you are involved in data collection: make sure to plan well from the beginning (work with a data management plan) and ensure you work with someone (e.g. a statistician) who knows what data needs to be collected in order to answer the question you have.
2. If you are not involved in data collection: ensure that the data was collected in a way that it can actually answer your question. It's often necessary to be able to ask questions about the data collection process and the content expertise of the original data collectors.

That's a big question. Here are a few thoughts:

• Doing good research is a process. You don't have to be perfect, but if you try to do good research, you will get better and better over time.
• Good organisation is more important that using the fanciest tool out there. Version control is the tool that helped me personally the most.
• Learning good coding practices means becoming better at research.
• You will never feel like your code is "good enough for making it openly available". I still don't feel like I write good code, but I got used to being vulnerable with my code 😅 and open source is the way to go.

I think it is the nature of research itself. Research is hard and thus doing research well is hard.

If you want to have your research checked for reproducibility, there are several ways:

• Code reviews from peers (e.g. from your research group)
• Submit to a journal with reproducibility checks (e.g. Journal of Statistical Software or Biometrical Journal) or one that collaborates with CODECHECK.
• Submit your project to ReproHack.

I would already be happy if researchers were at the point that they would share their code and data. That is not the case for most research today. For the others I would already say "congrats and thanks", even if it is not fully or easily reproducible. I agree though, that even when code and data are available that does not automatically make the project reproducible. I used to do reproducibility checks for the Journal of Statistical Software and that really is work that is extremely hard to automate (I tried 😅). So in some way we need to have people who do these check and likely they will have to be paid by the journals (IMHO). Initiative like CODECHECK and ReproHack are in that space as well and that is why I love their work so much. So please check it out and contribute if you can 🙌

The more complex a project becomes, the more we need to think about project management. That includes good organization (good folder organization, file naming, READMEs, ...) as well as good communication among the team. In projects with various programming languages I like using tools like Make, that help with keeping track of the analysis pipeline and work both on the cluster and your local machine.

I agree that with increasing complexity of dependencies things become more and more complicated. So far I've dealt well by doing data processing using code (in my case R) in combination with good folder organization and documentation, literate programming (RMarkdown) and automation (Make). If you need something more advanced than this, I recommend checking out DataLad. I haven't used it yet, but it sounds very promising with regards to your question.

Wow that is both a very kind compliment and a lot of pressure on my tiny shoulders 😅.

My personal goal is to contribute to research quality in the ways that I can. I started out by trying to make my own research as good a possible, then by helping others to do the same and by advocating for a better system. For a while I even tried starting a new University (that project was shut down).

On a broader picture I see myself as a small puzzle piece in the endeavor of improving research quality. No single person can fix academia or fix research. But I do believe that there are enough of us out there who can work together and change a lot. I already see a lot of changes, e.g. funders who require open access, publishers who promote open data, and research institutions who change the way the assess research(er) quality.

So far, I have been really lucky. I rarely get fired on by trolls nor do they post or delete things in e.g. pads or Miro boards that I open for everyone to contribute to anonymously. I am really thankful for this and believe that the Open Science community deserves a good and welcoming reputation (most of the time 😉). I hope this will continue this way.

Side note: I do sometimes get aggravated by replies that I receive on social media. Most of them are due to mansplaining and I try to either ignore them or make it really clear that I am an expert in my field 💪

I am really glad that I do what I do now. It was a wiggly path to where I am now and it was hard. But now I feel good about it.

I do think that being able to focus on Open Science helps me have a bigger impact. Not having to worry about writing papers and being evaluated based on some standardized output, allows me to be creative and focus on the things that matter (to me).

I would have stayed in academia if it had been made easier/possible for me, but it wasn't. Academic career paths are not yet flexible enough for the work I want to do.

Is it a viable career path? Well, for me it is and it seem like it is too for other Open Science Freelancers. But it really depends on what you want. I recommend talking to someone about it who just listens and asks the right questions (instead of giving recommendations). That can be a friend, a mentor, or a coach (e.g. SkillsWeaving). In the end you need to make the right decision for yourself, not the decision that makes others feel good.

🫶🫶🫶

Thank you for asking!

These three things help the mission of improving research quality:

1. Stay curious and stand with your values. If you got into research to improve the world a little bit, do that! Don't let the system change you into a career-optimization-machine.
2. Talk Open Science with your peers. Share useful learnings, posts, readings, .... Also share your burdens and worries.
3. Get involved. Join an Open Science initiative, Open (Source) project, Open Access journal, ...

And if you want to help me personally:

• Share my work. Forward my newsletters, share what you like about my trainings on social media, recommend my YouTube channel to your friends.
• Get in touch. Let's see how we can collaborate and work towards better research together.

Thanks for all your wonderful questions! I hope my answers are useful 🤓

All the best,

Heidi

P.S. If this was useful to you, please consider supporting my work by leaving a tip.

Heidi Seibold, MUCBOOK Clubhouse, Elsenheimerstr. 48, Munich, 81375
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