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The good and the bad sides of developing open source tools for neuroscience

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The good and the bad sides of developing open source tools for neuroscience
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490
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CC Attribution 2.0 Belgium:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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The reproducibility crisis has shocked the scientific community. Different papers describe this issue and the scientific community has taken steps to improve on it. For example, several initiatives have been founded to foster openness and standardisation in different scientific communities (e.g. the INCF[1] for the neurosciences). Journals encourage sharing of the data underlying the presented results, some even make it a requirement. What is the role of open source solutions in this respect? Where are the problems with open source projects in (neuro-)sciences? In this presentation I will address these questions at the example of the entirely open-source based workflow in our laboratory[2] and our efforts in developing generic solutions for storing metadata[3] as well as unifying data and metadata storage[4] that we take together with the German Neuroinformatics Node (G-Node[5]). [1] https://incf.org [2] https://github.com/bendalab [3] https://github.com/g-node/python-odml [4] https://github.com/g-node/nix [5] https://g-node.org