A draft bit of text on open science communities

This is early draft text that Anita and I put together from a bunch of brainstorming done at the Imagining Tomorrow's University workshop. Comments welcome!

Communities are the fabric of open research, and serve as the basis for development and sharing of best practices, building effective open source tools, and engaging with researchers newly interested in practicing open research. Effective communities often emerge from bottom up interactions, and can serve as a support network for individual open researchers. A few points:

  • These communities can consist of virtual clusters of likeminded individuals; they can include scholars, librarians, developers and tech staff or open research advocates at all levels of experience and with different backgrounds; the communities themselves can be short-lived and focused on a specific issue, tool, or approach, or they can have more long-term goals and aspirations.
  • A key defining feature of these groups is that the principles of open science permeate their practice, meaning they are inherently inclusive, and aim to open up the process of scholarly exploration to the widest possible audience.
  • We recommend that all stakeholders take steps to create an ecosystem that encourages these communities to develop. This means supporting common standards, funding "connective tissue" between different efforts, and sharing practices, tools, and people between communities

After collecting a series of narratives on effective and intentional approaches to creating, growing, and nurturing such communities, we recommended the following actions for different stakeholders to support the formation of adaptive and organic, bottom-up, distributed and open research communities:


  • Provide physical space and/or admin support for community interactions.
  • Recognize the need for explicit training in principles and practice of open research.
  • Explore what "design by a community" looks like in areas where it’s not traditional, e.g. (mechanical) engineering, to change views of what constitutes excellence in a discipline.
  • Reward incremental steps: provide incentives for aspects of open science (e.g. only share code, not data, or vv) then make it really easy to continue down a "sharing trajectory".


  • Recognize how "disciplinary shackles" can hinder adoption of Open Science practices (e.g. development of common software/workflows and other community resources may not be respected as part of disciplinary work).
  • Award interdisciplinary and team efforts next to or instead of individual competition. Inclusivity is a defining feature of Open Science, as well as extensibility, reproducibility - goal is not solely to further individual rewards but to facilitate involvement of others: not lock-in economics, explore other reward methodologies.
  • Reward incremental steps: provide incentives for aspects of open science (e.g. only share code, not data, or vv) then make it really easy to continue down a "sharing trajectory"

Platforms and publishers:

  • Integrate training materials into platforms.
  • Support development of platform specialists inside institutions.
  • Start "pop-up" open science communities around e.g. datatype manipulation.
  • Build support for openness into tools.
  • Create communities around specific tools and practices; build norms and codes of conduct into these platforms endemically
  • Lower barriers of entry to sharing practices, tools can support the "automatic" creation of communities (cf social media tools, platforms help define communities (e.g. "My Facebook friends", "My Jupyter Friends")

Community organizers:

  • Build openness into governance
  • Recognize the value of simple narratives for roping people into community participation.
  • Funding culture changers: hiring people who are tasked with changing e.g. data dissemination processes/practices
  • Praise incremental steps towards openness by community members.
  • Establish a code of conduct and community interaction expectations.

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