Building Your Own BioData Club

The following are some lessons learned from building our BioData Club community that may be helpful in building a BioData Club at your institution.

Align your groups together

Find common goals for each person who comes to BioData Club. At BioData Club OHSU, we are a community of co-learners focused on learning data science. However, this goal may be slightly different for your institution.

Make the setting informal

So much of academic learning is very formal didactic lecturing. We try to focus on active learning in an informal context, making it safe to ask questions, and focus on team/pair learning. To this end, we also try to train Teaching Assistants for each workshop we hold so that teams/pairs feel like they can ask questions.

Understand the power of food

We have co-learning events known as “Hacky Hours”, where we chat about various topics. Food and drink is almost always involved, and we hold them at places where we can chat about the current topic. Imbibing food (and to a moderate extent, drinking) together is a good way for people to get to know each other.

Be Inclusive

We believe having a diverse group of learners at BioData Club is important. We do our best to reach out to people who might have never considered data science interesting and accessible. To this end, we try and invite as many departments and people we know to participate. We also have a Code of Conduct that we enforce to encourage a diverse and inclusive audience.

Include the Library

The library at any institution is an underrated resource for bringing groups together, especially data librarians and open science librarians. They know who has been asking questions and seeking help. Ask them to be part of your BioData Club. You’ll be glad you asked them!

Make sure to include students

Students are the powerhouses of BioData Club, brainstorming new activities, learning about new ways to look at data, and new technologies. Be sure to include the beginners in the planning of events.

Build Mentoring into the Community Structure

We aim to empower learners to help teach themselves and others. Even if you’re not an expert, we still want you to teach. From our community, we try to find other members who can help support people with learning. We also ask community members to TA (be teaching assistants) at workshops to make them fun and accessible.


We have a Biodata Club slack and it’s been invaluable for community building. Oftentimes, people will ask data science questions that we can’t answer, but someone in the rest of the community can.

Community Building Guides

Values and Principles of BioData Club

Here is a list of the principles by which we run BioData Club at OHSU. Please feel free to modify as you see fit for your own institution.

  1. Breakdown Hierarchies within the Academic Setting. We are not only multi-disciplinary, but we are also non-hierarchical within BioData Club. We consist of faculty, postdocs, staff, and students. We are united by a common goal of improving our skills and helping others to learn.

  2. Establish a friendly learning environment by emphasizing psychological safety. Our focus is on learning in a safe environment for all, especially beginners. To this end, we attempt to establish norms for co-learning and trust using tools like our code of conduct, team teaching and learning, and a diverse planning committee.

  3. Normalize lifelong learning and teaching. One barrier to learning and teaching is insecurity about one’s own expertise. We emphasize the role of making mistakes when learning, especially by using techniques such as live coding when teaching a technique.

  4. Assess Community Needs and Developing Workshops. BioData Club is nothing without the interests of its members. We assess what our members want to learn and try to pair new learners with experts to present the material. We provide teachers with support and helpful feedback on their material, and try to test our material beforehand.

  5. Provide students and academics useful skills that can be built on. We believe that anyone can code and that gradual and gentle hands-on experience builds confidence in data science skills. Starting with markdown and git, we move beginners into website building and management, and then into more data science skills such as R and the tidyverse and data visualization.

  6. Share materials openly when possible. Where possible, our lessons and materials are shared openly on GitHub for use by others.

  7. Encourage discussion and co-working on topics relevant to a wide audience. We host hacky hours in order to discuss and discover issues in the BioData Club community, talking about topics such as how to be a modern scientist, visualization, and cross-disciplinary collaboration.

  8. Encourage cross discplinary and cross institute collaboration. By building on this community founded on mutual respect and psychological safety, we believe that we can foster a culture of cross-disciplinary collaboration. Our members hail from diverse departments including neuroscience, cancer biology, informatics, systems science and public health.