On March 11 and 12, UC Libraries will sponsor the Center of Open Science for a workshop on reproducible research practices.
The Center for Open Science (COS), http://centerforopenscience.org, is a non-for-profit technology organization whose mission is to increase openness, integrity, and reproducibility of scientific research. This workshop would benefit all data generators, but especially graduate students and junior faculty.
This 3-hour workshop will focus on factors that contribute to low levels of reproducibility, and simple, practical steps researchers can easily take to increase the reproducibility of their work. Participants will be introduced to open source tools such as the Center for Open Science’s Open Science Framework and the analysis software R. In addition, participants will learn about proper research documentation, data sharing resources and the choices involved in tracking data.
The format will mix lecture and hands-on activities, so that participants will engage in creating a reproducible project from beginning to end. Putting these tools in to practice will ensure that UC researchers are prepared for changing incentives and mandates from funders and journals for transparent and open research.
why is reproducibility important?
what are the current barriers?
easy, practical steps that researchers can take to make the workflow more reproducible
by the end of the workshop, participants will have:
created a reproducible project from start to finish using an example
learned how to apply these general principles to projects
Working through an example problem as a group
Participants, in conjunction with the instructor, will create a reproducible, well documented workflow for an example study. While creating the project, participants will learn
why a central location for all research inputs and outputs is important and easy ways to implement this
participants create project on the OSF to document example study
why project documentation is important
participants create a README for the project
why power analyses are important for reproducibility
participants perform power analysis with R
what is pre-registration and why is it important?
participants create and document a pre-registered analysis plan
participants created syntax using R
why and how to comment analysis code
participants create well commented code in R
how to keep track of changes in project related documents over time and why this is important
discussion of version control and participants implement version control using OSF
proper data documentation
what files are important to keep and in what formats
good documentation of data files
participants create codebooks
why sharing research inputs and outputs publically can be useful
how to do this with OSF
How to generalize these concept to own research?
extensive Q&A about how they might use these concepts in their own work, what obstacles they imagine running into and how to work around them, etc.
Courtney Soderberg : Center for Open Science
Statisical Consultant | Community