Good Enough Practices in Scientific Computing
1–10 of 21 posts
Re: Good Enough Practices in Scientific Computing
#2Seems reasonable. An interesting bit for me was the CITATION file. The lack of citability for software is a recurring annoyance when writing up work that uses that software.
Re: Good Enough Practices in Scientific Computing
#3I'd like to see ongoing discussion of the extent that HN-posted research conforms to being "Good Enough."
Re: Good Enough Practices in Scientific Computing
#4I'd like to see ongoing discussion of the extent that HN-posted research conforms to being "Good Enough."
It's important to differentiate the general concept of "Good Enough" as in "that's good enough" from the software engineering approach that, while coined from the same term, is surprisingly more formalized around balancing risk/effort vs. achieving perfection
Re: Good Enough Practices in Scientific Computing
#5What fields of scientific computing does this cover? E.g., biology and physics would probably have some overlap, but also some differences.
Re: Good Enough Practices in Scientific Computing
#6I'd like to see ongoing discussion of the extent that HN-posted research conforms to being "Good Enough."
It's important to differentiate the general concept of "Good Enough" as in "that's good enough" from the software engineering approach that, while coined from the same term, is surprisingly more formalized around balancing risk/effort vs. achieving perfection
Agreed. I think scientists and software engineers can really teach each other quite a bit. So I'd love to see a day whereby the phrase is more often used in science to mean making defensible trade-offs rather than achieving basic competency.
Re: Good Enough Practices in Scientific Computing
#7What fields of scientific computing does this cover? E.g., biology and physics would probably have some overlap, but also some differences.
It's fairly general. The document is discussing management of scientific software, which I would think is fairly robust across disciplines. They separate the discussion into the following topics:
* data management
* software
* collaboration
* project organization
* tracking changes
* [writing] manscripts
Re: Good Enough Practices in Scientific Computing
#8One thing they (intentionally) left out was including a Makefile. This is one of the "Good Enough" practices that I also include. It doesn't matter if it is a Makefile or another master script or tool. What is important is that you have a manifest for how each derived data file was generated. And it should be able to be executed w/o arguments to have the entire analysis run from the raw/primary data.
This has saved me many times in the past.
Re: Good Enough Practices in Scientific Computing
#9One thing they (intentionally) left out was including a Makefile. This is one of the "Good Enough" practices that I also include. It doesn't matter if it is a Makefile or another master script or tool. What is important is that you have a manifest for how each derived data file was generated. And it should be able to be executed w/o arguments to have the entire analysis run from the raw/primary data. This has saved m…
They do mention this, though it's somewhat buried in #4 on page 8 and in their paragraph saying they left out "Build Tools". They do suggest having a "controller script" (or set of shell scripts) to run all the complete analysis.
Re: Good Enough Practices in Scientific Computing
#10One thing they (intentionally) left out was including a Makefile. This is one of the "Good Enough" practices that I also include. It doesn't matter if it is a Makefile or another master script or tool. What is important is that you have a manifest for how each derived data file was generated. And it should be able to be executed w/o arguments to have the entire analysis run from the raw/primary data. This has saved m…
Unless you use R markdown, in which case there is no need for a separate script or makefile.