Ever wondered what the ideal (scientific) workflow would look like? And what kind of tools you need for it? It's maybe an impossible question to anser, but many will say the workflow should be efficient, transparent, and reproducible. I don't know the answer as well, but I fully support these principles. Over the past years, I've used my GitHub profile to share and collaborate on projects aimed at developing the ideal academic workflow. The following projects are my top interest at the moment:
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Data access: If you're looking for an easy way to download scientific data, be sure to check out Datahugger 👐 - the easiest way to download scientific data! I'm also involved in projects like pyalex (new!), cbsodata, and rispy.
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Superfast reading: Can we make systematic reviews fun to work on by using AI for the boring 💤 parts? With ASReview and asreview.ai, we speed up systematic reviewing. I'm lead of ASReview's development team.
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Transparent workflows: I'm experimenting with projects like scitree and scisort, which help and promote to use repoducible project folder structures.
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Data linkage: I work on projects like recordlinkage and List of data matching software. Although my attention may sometimes waver from these projects, but they are still close to my heart ❤️.
In addtion to this, you can also find me at Utrecht University (in the Netherlands) as the project lead for the Open and FAIR Data and Software movement.
- How to reach me? Send me an email (see bio) 📫
- Sponsoring https://github.com/sponsors/J535D165 or https://asreview.ai/donate 💰
- Wondering what my username J535D165 is about? Learn about the Soundex algorithm 💬
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