Skip to main content

A Science Collective: A decentralized organization built for researchers, by researchers

Interested in getting involved or contributing in some way?

Check out our Contributing Documentation for more details about how to get involved.

On GitHub open an issue or submit a pull request by clicking the “Edit this page” link at the side of this page.

This website is still in development. There’s lots we need to fix and clarify. So if something doesn’t make sense, let us know by contributing!

4 ‘a Science Collective’ for Better Science

We believe that science can make faster progress, use resources more efficiently, and can be a more rewarding experience for scientists if they could follow their curiosity in a constructive, collaborative and efficient research environment which is kept as free as possible from legacy traditional research structures.

We believe that people with scientific interest should be stimulated and supported to pursue their curiosity, also as a part-time effort beside other occupations or as a full-time scientist working independently from the existing institutional model. This would leverage the enormous potential talent that is available, as is already done in open source software communities.

We want to achieve this by creating a new type of autonomous, decentralised research organisation that is committed to the highest standards of the scientific method, helps scientists thrive as part of a global community, stimulates new connections, fosters open research collaborations and provides key infrastructure (decentral governance, finance, computational and IT resources). This organisation will also strive to include non-traditional scientists: people who are eager to learn, participate in and contribute to science but have not followed, or have left a career in academia or commercial research.

4.1 Values for Better Science

  • Scientific output should be diverse and not limited to publications. Documented datasets, code, educational material, public outreach, and other dissemination outlets (e.g. blogs, protocols, peer-review reports) are just as valuable and necessary. We believe all these outputs should be equally acknowledged.
  • Scientific output should be openly licensed and accessible. Common licenses (CC, MIT) and repositories (OSF, GitHub, Zenodo, figshare) should be used widely.
  • Scientific results should be fully and independently reproducible. If data can’t be shared for privacy or legal reasons, a simulated dataset that mimics the original data should be provided with the open source analysis code. Reproducibility should be easy to carry out and should be well documented.