Unfortunately, organization wide project boards (that aggregate issues from several GitHub repositories) are currently only visible to the members of that organization. It’s unclear “if” and “when” GitHub will allow this feature to be public, which is crucial for Open Source projects.
So to overcome this, me and Ismail Moghul created a Node.JS script that pulls this data from GitHub’s API to generate an html page that can be hosted on GitHub pages and rebuilt daily with a TravisCI Cron Job.
But then I saw @billdoesphysics (from TRIUMF and CERN), and Angelina Fabbro (she worked at Mozilla!) give a talk about bad code in science! They mentioned how physics had been increasingly generating more data (e.g. 200 TB/week), but the field hadn’t kept up with progress coming from the open source and industry…
One pillar of the scientific method is reproducibility, that is, being able to redo an experiment and get the same result. Sometimes this is very hard to accomplish. For example, if you’re doing field work, you might end up collecting some very rare samples or observe a rare natural event. If you work in the lab, tracking all the variables that go into an experiment and might influence it (e.g. the way a person pipettes or mixes reagents) is similar to following your mom’s recipes… most of the times it doesn’t give you the same amazing dish.
However, when analyzing…
I got invited to the TGAC — AllBio: Open Science & Reproducibility Best Practice workshop. On their form they asked me three question. I got a bit carried away with the answers and that’s how this post was born.
Please name three topics you believe are a priority towards a roadmap for open science and reproducibility of Bioinformatics?
Possible subtopics for discussion: