Open Science has emerged into the mainstream, primarily due to concerted efforts from various individuals, institutions, and initiatives. This small, focused gathering brought together several of those community leaders. The purpose of the meeting was to define common goals, discuss common challenges, and coordinate on common efforts.
We had good discussions about several issues at the intersection of technology and social hacking including badging, improving standards for scientific APIs, and developing shared infrastructure. We also talked about coordination challenges due to the rapid growth of the open science community. At least three collaborative projects emerged from the meeting as concrete outcomes to combat the coordination challenges.
A repeated theme was how to make the value proposition of open science more explicit. Why should scientists become more open, and why should institutions and funders support open science? We agreed that incentives in science are misaligned with practices, and we identified particular pain points and opportunities to nudge incentives.
We focused on providing information about the benefits of open science to researchers, funders, and administrators, and emphasized reasons aligned with each stakeholders’ interests. We also discussed industry interest in ‘open’, both in making good use of open data, and also in participating in the open ecosystem. One of the collaborative projects emerging from the meeting is a paper or papers to answer the question “Why go open?” for researchers.
Many groups are providing training for tools, statistics, or workflows that could improve openness and reproducibility. We discussed methods of coordinating training activities, such as a training ‘decision tree’ defining potential entry points and next steps for researchers. For example, Center for Open Science offers statistics consulting, rOpenSci offers training on tools, and Software Carpentry, Data Carpentry, and Mozilla Science Lab offer training on workflows. A federation of training services could be mutually reinforcing and bolster collective effectiveness, and facilitate sustainable funding models.
The challenge of supporting training efforts swas linked to the larger challenge of funding the so-called ‘glue’ – the technical infrastructure that is only noticed when it fails to function. There is little glory in training and infrastructure, but both are essential elements for providing knowledge to enable change, and tools to enact change.
Another repeated theme was the ‘open science bubble’. Many participants felt that they were failing to reach people outside of the open science community. Training in data science and software development was recognized as one way to introduce people to open science. For example, data integration and techniques for reproducible computational analysis naturally connect to discussions of data availability and open source.
Re-branding was also discussed as a solution – rather than “post preprints!”, say “get more citations!” Another important realization was that researchers who engage with open practices need not, and indeed may not want to, self-identify as ‘open scientists’ per se. The identity and behavior need not be the same.
A number of concrete actions and collaborative activities emerged at the end, including a more coordinated effort around badging, collaboration on API connections between services and producing an article on best practices for scientific APIs, and the writing of an opinion paper outlining the value proposition of open science for researchers. While several proposals were advanced for ‘next meetings’ such as hackathons, no decision has yet been reached. But, a more important decision was clear – the open science community is emerging, strong, and ready to work in concert to help the daily scientific practice live up to core scientific values.
- Tal Yarkoni, University of Texas at Austin
- Kara Woo, NCEAS
- Andrew Updegrove, Gesmer Updegrove and org
- Kaitlin Thaney, Mozilla Science Lab
- Jeffrey Spies, Center for Open Science
- Courtney Soderberg, Center for Open Science
- Andrew Sallans, Center for Open Science
- Karthik Ram, rOpenSci and Berkeley Institute for Data Science
- Min Ragan-Kelley, IPython and UC Berkeley
- Brian Nosek, Center for Open Science and University of Virginia
- Erin C, McKiernan, Wilfrid Laurier University
- Jennifer Lin, PLOS
- Amye Kenall, BioMed Central
- Mark Hahnel, figshare
- Titus Brown, UC Davis
- Sara D. Bowman, Center for Open Science