3 ms·

Yeah there is no excuse for comp bio work really, but I don't think you should cast aspersions of laziness. By far the least lazy people I know are scientists.

by Gatsky 8y ago

Yeah there is no excuse for comp bio work really, but I don't think you should cast aspersions of laziness. By far the least lazy people I know are scientists. Publishing a paper is an incredible amount of work often under less than ideal conditions. The authors are usually exhausted. That isn't an excuse, but it is a factor nonetheless. There are some incentives to make work reproducible. If you publish a method implemented in software, then making that software easy to use, well documented, with example data and respond to questions/issues etc it greatly enhances the chance other people will use it and cite it. Things have been getting much better in the last 5 years. Preprints also help - I saw a preprint the other day where people complained on twitter there was no methods section and no code. The authors responded. They realise that potential reviewers may be the ones making these comments or at least see the comments. This talks to your point about not treating a paper as a single point in time, but an ongoing process. I think things are improving. I am now seeing papers publish Jupyter notebooks in python or R scripts to reproduce all the diagrams and analyses, along with curated data that just plugs in. In general this actually works (although sometimes crucial bits of data are missing, and unlike an error in the paper, these are not necessarily fixed).

rspeer 8y ago
I'd probably encounter the bad kind of comp bio if I actually worked in that field, but a tool made by computational biologists for reproducible research has also been very useful in my work: the build tool Snakemake [1]. Snakemake is a parallel build tool designed for data instead of software as its main use case. [1] http://snakemake.readthedocs.io/en/stable/ http://snakemake.readthedocs.io/en/stable/
- Q6T46nT668w6i3m 8y ago
  It should also be said major parts of the Python scientific ecosystem was written by computational biologists!
Q6T46nT668w6i3m 8y ago
That would be awesome and I think everyone would like to do this but funding for that barely exists. If you’re in the United States, complain to NIH, NSF, and especially your rep.
buchanae 8y ago
> I don't think you should cast aspersions of laziness You're right. I use laziness out of frustration, but in reality I know it's hard for me to make my own work fully documented and reproducible. A large part of the situation is cultural. Software engineers are born into a world of version control, unit tests, documentation, managing complexity, reproducibility, debugability, etc (and we still struggle with it). That sort of culture and associated tools are missing from data science.

by Gatsky 8y ago

rspeer 8y ago

I'd probably encounter the bad kind of comp bio if I actually worked in that field, but a tool made by computational biologists for reproducible research has also been very useful in my work: the build tool Snakemake [1]. Snakemake is a parallel build tool designed for data instead of software as its main use case. [1] http://snakemake.readthedocs.io/en/stable/ http://snakemake.readthedocs.io/en/stable/

Q6T46nT668w6i3m 8y ago
It should also be said major parts of the Python scientific ecosystem was written by computational biologists!

Q6T46nT668w6i3m 8y ago

That would be awesome and I think everyone would like to do this but funding for that barely exists. If you’re in the United States, complain to NIH, NSF, and especially your rep.

buchanae 8y ago

> I don't think you should cast aspersions of laziness You're right. I use laziness out of frustration, but in reality I know it's hard for me to make my own work fully documented and reproducible. A large part of the situation is cultural. Software engineers are born into a world of version control, unit tests, documentation, managing complexity, reproducibility, debugability, etc (and we still struggle with it). That sort of culture and associated tools are missing from data science.