We do rigorous quantitative science to support decision-making for living marine resource management. We conduct our work using Open Data Science principles, emphasizing scientific excellence (not perfection) that is transparent, reproducible, collaborative, and ethical. We aim to make our methods and results available and support ongoing learning.
Our quantitative work is based on statistical thinking, using evidence-based approaches to compare among alternatives for models and management options.
See the next chapter for more detail on our lab culture and philosophy. We are motivated heavily by the following two papers - which provide a blueprint for how we think about the way we do our work:
How we meet
Currently, we meet biweekly for a hybrid branch meeting on Tuesdays at 9:30am ET. For those who are at the lab in person, we typically meet in the Albatross Room on the second floor. All branch members are encouraged to attend whenever possible.
We use Google docs to set up our agenda, take meeting notes, and outline action items during meetings. Team members are welcome to contribute to the meeting agenda, add their own notes and updates, and make other changes as needed. One-on-one meetings and smaller team meetings are also encouraged whenever they are useful. Some team members hold regular (weekly or biweekly) check-ins with Dani; others do so more sporadically. We are flexible!
How we give feedback
Feedback, both giving and receiving it, is an important aspect of how we work. …
How we share things
We think it is useful to have standard ways of sharing things. These don’t always have to be followed but are a useful guide and will make things easier. When sending material to someone, always make sure to describe what you are sending and try to make it as easy as possible for them to help you.
Taking a project-based approach to organizing your work makes it easier to share and solicit feedback from others, as things tend to be self-contained. Try to keep only 1 working instance of material, and use some form of version control to facilitate this (see recommendations in Wilson et. al paper linked above).
Project management tools in Github are a good way to record and document questions on analyses. Use ‘Issues’ on github repositories for project-related tasks and problems. Alternatively, make use of a GoogleDoc for each project to record this history, much like you would a lab notebook.
Code: can be shared via Github repositories, or via a dedicated Google Drive folder. For specific questions on problems, please try to create a minimal reproducible example. Ensure that others can run and interact with the material being shared.
Writing: Preferably via GoogleDoc, R Markdown, or Overleaf, but MS Word documents can also be shared. Google docs are particularly useful for collaborative writing, and have the advantage of there only ever being one version (as opposed to files that are sent around via email). Word documents should always have your last name as the first part of the file name (e.g. please no “mythesis.doc”).
We also share our institutional knowledge through our lab-chat github ‘issues’ repository. This is a community-driven troubleshooting resource, please contribute!
We maintain a lab Google Drive folder for lab publications, presentations, photos, CVs, etc. Please make use of these so that others in the lab can make fair use of our work.
Shared lab resources
Where to find shared resources: - Google Drive - You will be given access to the shared drive during onboarding, take a look at the Lab Meetings folder to find Lab Meeting Notes for each semester (previously called the Supercharge Plan) - GitHub - thefaylab is the shared GitHub account for the lab - lab-manual - This repository contains the lab manual, see section 13 for other useful resources and take a look at the Wiki to see the lab meeting schedule - lab-chat - This repository contains shared institutional knowledge for the Fay lab, take a look at Issue #22 for Zoom meeting links - Website - Take a look at the lab website, most of its information is duplicated in one of the above resources: http://www.smast.umassd.edu/lab_fay/
References
Lowndes, Julia S. Stewart, Benjamin D. Best, Courtney Scarborough, Jamie C. Afflerbach, Melanie R. Frazier, Casey C. O’Hara, Ning Jiang, and Benjamin S. Halpern. 2017.
“Our Path to Better Science in Less Time Using Open Data Science Tools.” Nature Ecology & Evolution 1 (6).
https://doi.org/10.1038/s41559-017-0160.
Wilson, Greg, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, and Tracy K. Teal. 2017.
“Good Enough Practices in Scientific Computing.” Edited by Francis Ouellette.
PLOS Computational Biology 13 (6): e1005510.
https://doi.org/10.1371/journal.pcbi.1005510.