Exploring the role of citizen science volunteers in scientific discovery
A recent study supported by the EU-funded ASTERICS, ESCAPE and EOSC Future projects offers new recommendations on how to use citizen science to enhance research outcomes. The study aids researchers by shedding valuable light on citizen science’s potential impact.
Citizens versus machine
The volume and complexity of scientific data used in various research fields has rocketed in recent years. Although machine learning technologies can do much to handle the avalanche of data sourced for research purposes, humans are still often much better at classifying some data. This happens especially in cases where subjective assessment is needed, or for rare subjects of interest or small sample sizes. With these challenges in mind, more and more EU-funded research projects are adopting a new way of doing science. They are crowdsourcing their data with the help of citizen science volunteers. This approach is applied across diverse fields, ranging from social sciences and genomics to astronomical imaging. “With a suitably carefully curated interaction with FAIR data, members of the public can make a genuine and valuable participation in scientific discovery,” researchers from The Open University, United Kingdom, report in their paper published in ‘The European Physical Journal Plus’. The authors summarise the citizen science in the ASTERICS and ESCAPE projects, which engaged hundreds of thousands of volunteers in providing millions of data mining classifications. They also outline the work performed with EOSC Future funding to develop services for the European Open Science Cloud (EOSC). Referring to the influence of citizen science, the authors state: “Not only does this have enormously more scientific and societal impact than conventional dissemination, but it facilitates the direct research involvement of what is often arguably the most neglected stakeholder group in Horizon projects, the science-inclined public.”
Lessons and ambitions
Certain lessons were learned from the ASTERICS (Astronomy ESFRI and Research Infrastructure Cluster), ESCAPE (European Science Cluster of Astronomy & Particle physics ESFRI research infrastructures), and EOSC Future (EOSC Future) projects. First, interoperability between citizen science services is not automatic. Second, creating tutorials, notebook resources and other training materials also requires funding. Third, even with abundant online training materials, “support scientists” are still needed to assist in the creation, operation and science exploitation of citizen science projects. Last, all the citizen science demonstrators created in ESCAPE were driven by fundamental science questions and had specialist science teams that were motivated to find answers to the science questions. “Science team engagement with volunteers is one of the key factors influencing success in citizen science projects,” the researchers write, adding however: “the lesson in EOSC is deeper: technical developments are, and must be, precipitated bottom-up by the science use cases. Features that are ‘nice to have’ are not built unless they also have science drivers. This is consistently true in citizen science contexts …” With these lessons in mind, ambitions for the future include closer cooperation between citizen science platforms and the EOSC, deeper technology integration and greater development of open data standards in citizen science. The researchers conclude with valuable recommendations for using crowdsourced data mining in the physical sciences, noting that the primary goal is always the fundamental research question. They clarify that if public engagement is a project’s primary goal, then other, more targeted approaches may be more effective than citizen science. For more information, please see: ASTERICS project website ESCAPE project website EOSC Future project website
Keywords
ASTERICS, ESCAPE, EOSC Future, citizen science, citizen, science, research, European Open Science Cloud, EOSC, data
