Exemplarise software's role in error discovery #4
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Hi!
Hope this suggestion fits. Not an example of faulty software causing scientific errors, like in the introductions of @npch's talks, but how it helped spot them and become a part of quality-control in a repository's upload process.
I heard that before this, during a rush to crystallise any protein & publish the structure, you couldn't really trust those data. If true, one could learn from this that sometimes, pointing the finger at almost an entire field of research, and sharing the software to prove & correct the errors may guide one throughout the ensuing controversy.