Christie Bahlai and Elise Zipkin have received an award from the Mozilla Science Foundation to develop materials on open data policy and management for the Foundation's open data training program. The project will create an easy-to-follow set of resources to get people started in open data across research fields.
"This project is important because it gives everyone a common framework, and language when talking about open data," said Christie Bahlai, a research associate in the Zipkin Lab and 2015 Mozilla Fellow for Science award winner. "The idea is to foster more collaboration with high-quality science and transparency standards. This work has the potential to change how we do science, for the better!"
In collaboration with the Foundation's personnel and Dr. Danielle Robinson at the Oregon Health and Sciences University, the team will create resources for three audiences interested in open science and open research:
- Learner Primers: A series of short fact sheets addressing important topics in open science (e.g., why open data, how to share data, ethics and best practices, etc.). The primers will be a great starting point for beginners.
- Teacher Modules: Lesson plans and materials to teach a one hour class about managing, archiving, sharing, and publishing data including issues of data privacy and ethics. The intent of these materials is to teach these topics to larger groups.
- Advocate Documents: Materials to help open data advocates reach out to academic administrators and early career researchers about why open data and web-enabled reproducible research is important and should be incorporated into science.
Once the open data policy and management teaching materials are finished, they will be made available under a non-restrictive open license on the Mozilla Foundation website.
Open science and open data is important to the Zipkin Lab. As a quantitative ecology lab, most of the work done by the Zipkin Lab is inherently dependent on open science and open data. "We create code and models that integrate data over large spatial and temporal scales, from a variety of different sources," Christie explained. "We want people to be able to use and extend the models we develop, which makes computational reproducibility critically important to us. Helping other scientists become more open lets us all work together more efficiently, and ultimately ask bigger questions."