As an open scientist I strive to use open source software and open tools whenever possible. But sometimes I choose a more efficient tool over the open tool. When and why? Let's break it down!
It is no secret that I am an open source enthusiast. I've been working with R since 2009 and the wonderful R community has shown me the path to Open Science. But at the same time I am using commercial tools like this newsletter service. I use Miro for teaching and Google Docs for collaborating on documents.
Since working on my self-employment and the Digital Research Academy, the decisions between open tools and other tools have become more and more difficult. Sometimes there are no good open source options available, but sometimes I just want to use a closed tool, because it is just so good at what I want to do.
So I've decided that I need to somehow at least be clear with myself and the team, what I think is a good strategy. Let's see what they say 😉😅.
Heidi's rules for deciding on tools (WIP):
Ok, so this list is a work in progress. I am very much looking forward to your thoughts! How do you make those kinds of decisions?
In other news
Open Science Retreat 2024
I am so excited to announce that there will be a second edition of the Open Science Retreat. Edition 2024 is co-organised by OSC-NL: the bottom up, community-led, social infrastructure of Open Science Communities in the Netherlands, and will take place on the sea side, again amidst nature in a secluded setting. Hope to see you in the Dutch dunes next March!
Updates on the Digital Research Academy
Our Train-the-Trainer (TTT) pilots were successful and we are currently working on the plans for the next TTTs. You can find the materials from both TTT pilots on the OSF:
Want to get involved, too? There's a mailing list, where we will let you know about next meetups, TTTs, and more:
That's all for today. Hope you enjoyed it! If you did, consider forwarding this post to your friends and colleagues.
All the best,
P.S. If you're enjoying this newsletter, please consider supporting my work by leaving a tip.
All things open and reproducible data science.
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