This file collects thoughts around Citizen science and the FAIR Principles, as the topic keeps popping up in multiple contexts that I am involved in.
The FAIR Principles are a set of recommendations around research data management and stewardship. In short, these principles describe how data arising from research as well as the associated metadata can be made Findable, Accessible, Interoperable and Reusable for both humans and machines. Building on pioneering efforts within individual domains of research, they take a cross-disciplinary perspective on research data and are gaining traction in multiple corners of the research landscape, including in international research policy contexts.
It has yet to be explored in detail how the FAIR Principles align with best practices around data and metadata generated in the framework of citizen science projects from a range of disciplines and locations around the globe. This would require considering implications from the perspectives of research quality, research policy, research infrastructure and the communities around citizen science projects. In doing so, it will raise questions like how can citizen scientists find projects to contribute and vice versa, how can data from different citizen science projects be combined on a technical level, what policy issues need to be addressed, and how to identify best data stewardship practices in citizen science.
Of particular interest is the relationship between the FAIR Principles and the Ten Principles of Citizen Science as well as the potential of the FAIR Principles for enhancing interactions between citizen science projects and the wider research and data landscapes, and open science efforts in particular.
