msleckman
commented
2 years ago Key takeaways:
- Reproducible: with same data and same code, can we get the same results) different than Replicable: (redo the entire experiment with different data and get same results, or results that are alike.)
- Organizing data and code clearly from the start, avoid chaos
- "If you do some manual manipulation, you are going to do it all the time." I like this point. Even the opening of excel sheet should be scripted.
github-learning-lab[bot]
We're asking everyone to invest in the concepts of reproducibility and efficiency of reproducibility, both of which are enabled via dependency management systems such as
remake,scipiper,drake, andtargets.Background
We hope that the case for reproducibility is clear - we work for a science agency, and science that can't be reproduced does little to advance knowledge or trust.
But, the investment in efficiency of reproducibility is harder to boil down into a zingy one-liner. Many of us have embraced this need because we have been bitten by issues in our real-world collaborations, and found that data science practices and a reproducibility culture offer great solutions. Karl Broman is an advocate for reproducibility in science and is faculty at UW Madison. He has given many talks on the subject and we're going to ask you to watch part of one of them so you can be exposed to some of Karl's science challenges and solutions. Karl will be talking about GNU make, which is the inspiration for almost every modern dependency tool that we can think of. Click on the image to kick off the video.
:computer: Activity: Watch the above video on make and reproducible workflows up until the 11 minute mark (you are welcome to watch more)
Use a GitHub comment on this issue to let us know what you thought was interesting about these pipeline concepts using no more than 300 words.
I'll respond once I spot your comment (refresh if you don't hear from me right away).