When the National Science Foundation recently announced their FAIROS RCN opportunity in January 2022, and more recently the new Open-Source Science Initiative (OSSI) from NASA with Transform to the OPen Science (TOPS) Mission embodying a shift in research culture to Open Science, there was an overall excitement in the research community, particularly from groups working to advance Open Science and FAIR practices.
AWESOMENESS: a new NSF solicitation (NSF 22-553) for Findable Accessible Interoperable Reusable Open Science Research Coordination Networks (FAIROS RCNs). Deadline for full proposals is April 12, 2022. https://t.co/pHqyP0EC1f cc @ESIPfed @EarthCube @ShelleyStall #FAIRData— Dr. Dawn Wright 🇺🇦 (@deepseadawn) January 13, 2022
We, AGU, UNC RENCI, and ESIP, were excited as well and interested in aligning a concept that we had been discussing initially with CUAHSI and the hydrology community. Our intention is to use the idea of a “design pattern”, a re-usable template for solving common problems originally developed in the software world, and applying it to Open Science and FAIR practice development. In our view, the problem is not the availability of standards or leading practices. Instead, the challenge is filtering through all the available information to figure out what to do and when, and to overcome negative opinions related to the perceived costs of implementing Open Science and FAIR practices in relation to the known benefits. Open Science is not a destination, but a journey.
Our full approach, described below, represents a community-led, sustainable approach to enhancing Open Science and FAIR practices at the discipline-specific level in meaningful, and measurable ways.
Community FAIR: Open data and software interoperability through cross-discipline shared protocols
We envision developing a Community FAIR Consortium that establishes discipline-based, community protocols for Open Science and FAIR-er data and software. Working directly with scientific disciplines, starting with the Earth, space, and environmental science, we will use a design patterns-approach to create reusable templates analogous to laboratory or experiment protocols that will guide researchers in best practices for implementing FAIR data, software, and sample metadata in their work (we group these collectively as “FAIR data” or data+). The lack of actionable, community-accepted, FAIR data practices within specific disciplines has been the fundamental challenge in realizing a vision of interoperability and reusability. Our envisaged process has four parts: 1) creating the design pattern protocols; 2) testing them in several pilot disciplines; 3) extending this template widely; and 4) facilitating adoption and use of them. The design patterns are both proven and novel for this application that will address, as appropriate, elements of FAIR and Open Science, providing guidance in areas such as repositories and metadata for disciplines to enable interoperability, and eventually will be made machine readable. The design patterns need to be developed with and by the discipline-specific communities. These communities, along with a team to facilitate, would identify effective ways to facilitate adoption and use of the protocols. Central to success is leveraging researcher relationships with society-organized domain communities together with repositories, research services and infrastructure providers supporting Open Science, and sharing data+. We believe that this approach will go a long way to addressing the key barrier for widespread adoption of Open Science and FAIR data practices.
In the spirit of being open, we are making this available:
Stall, Shelley, Lenhardt, W. Christopher, Erdmann, Christopher, Shingledecker, Susan, & Lyon, Laura. (2022). Community FAIR. Zenodo. https://doi.org/10.5281/zenodo.6585767
The hydrology community is on board for being the first pilot, with support from Jared Bales, CUAHSI, and others.
If you are interested in working with us and taking Community FAIR further, please join our mailing list!