In this final week of the class I discussed the idea of transforming the process of discovery in our own labs. When I started my undergraduate program in 1992 I learned about the discoveries of others in my courses and textbooks. When I started my graduate program in 1996 I also started to implement what I had learned about the process of discovery with my own research. During this time I often asked myself, and others, “what is typically done by other researchers in this area of research?” I wanted to make sure I was doing what was considered acceptable at the time. For example, using a particular measure of attachment orientations because it seems to be used a lot and thus would not be questioned by reviewers. Or using a particular analytic method because it was frequently used and thus seemingly defensible during the review process. And when it came time to analyze data I learned to run a lot of different models with a number of different combinations of responses across multiple measures to find results that were consistent with our original expectations. Then when it came to write manuscripts, learning what results to not only include but also exclude. It simply felt to me that “this is how science is done” in our field, so I did it that way.

But really it did not have to done in the manner briefly described above. I was searching for what I thought was the best way to be a scientist and in the process also discovered the existing norms for how to be a scientist. Sometimes existing norms overlap with what is best for scientific discovery and dissemination, but sometimes they don’t. I don’t claim to know exactly how scientists should go about making their discoveries, but I do feel strongly that when we feel confident enough to share our results publicly we need to also share to the best of our abilities at the time how exactly we obtained those results. For me that is like a latent construct of open science practices–be open and transparent–that influences what I do throughout the research process. In the early days of the open science movement, I felt that actions could speak just as loud as all the words that were flying around. When others were wondering out loud if sharing details of the research process was worth it, might be costly to the researcher, etc., I felt that I could simply point to our own experiences as examples of how it could be done. Yes, you can preregister hypotheses and *still* conduct analyses not planned in advance. Yes, you can even preregister exploratory research and their is value in doing so. Yes, you can share the measures you used in your study. Yes, you can be open and transparent with longitudinal research designs. Yes, you can share data all the time (sometimes publicly, sometimes via other means). Yes, you can share the syntax you used to produce the results that your presented in your manuscript. Yes, you can preprint your work. And on and on. Debates are fun and all, but when many researchers pondered whether they could/should do these things, we simply did them. One of my hopes was that when new graduate students were learning about the process of discovery, they might stumble across some of our open and transparent research practices and think it was something they could do, something that was becoming normative in the field. Whereas some colleagues saw open science practices as warning flags for the process of discovery, I encouraged my students to see them as challenges for which we have the opportunity to develop solutions to our own process of discovery. With the existence of today’s technologies there are numerous ways to share our research process and make it available for scrutiny, and no solid arguments for keeping this process “available upon request”. That is also the reason why I wanted to teach a course on open and reproducible science. I wanted to do what I could to share these tools with these early career researchers in hopes that they would see value in adopting them in their own research.

My final take home message here: when sharing the results of your research also share how you obtained those results as openly and transparently as possible.

I feel relieved to finally finish this series of posts to accompany the weekly lectures for my course on open and reproducible science. It seemed obvious to me that being open and transparent could also apply to the courses we teach, meaning we could share syllabi and course notes. This series of posts serves as my own personal lecture notes for each class. If you have read them I hope they have been of some value.