Open Science Post Doc

This post is a response to the query posted by Titus Brown on advice for doing an Open Science Post doc.

Open Science is a broad tent, and I believe there are many ways to engage in open science without crossing boundaries of collaborators or PIs. Some of the most valuable open science practices are those least likely to cross boundaries: in particular, those practices associated with post-publication of academic papers. Many widely regarded journals, including Nature and Science, have relatively strong data publication requirements, increasingly strong code publication expectations, and are compatible with the use of pre-print archives. Yes, their are those who would hesitate even to comply with the most minimal interpretation of these expectations; but there are many more who, given the expectation to do this at all, would appreciate your knowledge and effort on doing these things well and consistent with best practices. Using data repositories instead of supplemental material; exhibiting good management of data and code; providing good metadata in consistent formats; citing software and data appropriately.

Many of the practices promoted by open scientists work almost as well in a setting that is closed until you ‘flip the switch’ to make them public; even if that is long after publication. Good data management, a private Github repository, or a private electronic lab notebook are all ways to leverage best practices tools and approaches in a setting that can either be shared securely or made open later. Attitudes to post-publication sharing are rarely black-and-white, and having everything curated and ready to go ahead of time can help nudge collaborators in the direction of best practices.

I’ve been a post-doc for just over 2 years while enjoying a relatively open-science approach to my research: I’ve kept an open lab notebook, posted my papers ahead of publication on pre-print archives, released code, data, and knitr versions of my papers, discuss my work on social media, sign reviews, shared grant applications, contributed open source software and been active in open science communities.

I was fortunate to have independent funding for most but not all of my post-doc, and to work in a field where researchers are often given substantial independence and in which open practices are common. Nonetheless these were not practices shared by either of my two excellent co-advisers, who gave only a neutral or vaguely positive response to these ideas.

Nevertheless, I learned more about old-school open science from them than I had ever imagined. When I sent Marc the first draft of our paper, I had buried almost all of the equations in a curt appendix with minimal and jargon-laden supporting text. No sir, those equations not only had to appear in the main text, but I must endeavor to explain the meaning and relevance of each one in a language clear and concise enough for any ecologist to follow. I won’t claim to have succeeded, but boy did continuous integration on my unit tests feel like a low bar for openness by comparison.

Meanwhile, I also felt I had the support and mentorship of an online community of open scientists even without going to work for one of them. I am thankful that my mentors have always been tolerant of my open science experimentation, but I would have enjoyed working with them even if it had been otherwise. There was much to learn from them, much to learn from the open science community, and after all, a post-doc position doesn’t last forever.