Open Science Cloud
FEATURES
What this citizen science is not primarily designed for is outreach or education – which is not to say that it can ’ t inspire or guide volunteers into deeper engagement with science , but if your main goal is to inspire or to educate , then my consistent advice is not to do citizen science . Rather , spend your efforts directly on inspirational public engagement and education . But if you have a complex data mining problem , then citizen science should be among your tools . It ' s a missed opportunity , and a fundamental misunderstanding , when citizen science is said to be synonymous with outreach .
Meanwhile , during the burgeoning successes of citizen science , there has also been a simultaneous growth of open science initiatives . The European Commission in particular has earmarked well over a quarter of a billion Euros on the development and deployment of the European Open Science Cloud , or EOSC . The citizen community is one of the strategic priorities of the Strategic Research and Innovation Agenda of the EOSC ( https :// eosc . eu / sria ). Even so , only a very small fraction of the resource is being deployed on their engagement with the European Open Science Cloud .
Citizen science nevertheless involves an enormously larger and more diverse scientific user community with the European Open Science Cloud . For example , our Galaxy Zoo Clump Scout project had a science team of just three academics but a community of nearly fourteen thousand volunteers , contributing nearly two million classifications . In the Clump Scout project , as with all our citizen science projects , contextual educational and training resources are embedded into the volunteer workflows . This allows non-specialist volunteers to gain enough subject specialist knowledge for more comprehensive explorations of the data , and indeed on many projects there are explicit links to external professional tools for this deeper engagement . There is also evidence for volunteers acquiring new scientific terminology that was not provided in training material , i . e . there is evidence that the activity has stimulated independent study ( e . g . Luczak-Roesch et al . 2014 , International AAAI Conference on Web and Social Media ; Oesterlund et al . 2017 , Academy of Management Annual Meeting Proceedings ). Nevertheless , the education is part of the supporting structure and is not the primary goal ; it is always exclusively in the context of volunteers participating in projects with clear science goals , which drive and define the data mining activities .
A central vision of the European Open Science Cloud is to make scientific data FAIR , that is Findable , Accessible , Interoperable and Reusable . Implicit in this vision is that the FAIR data should also be useful , but this is far from being guaranteed , especially given its inter-disciplinary and multi-disciplinary remit . For example , Daylan et al . 2016 ( Physics of the Dark Universe , Volume 12 , p . 1-23 ) reanalysed public sky survey data from the Fermi gamma-ray telescope , and interpreted a gamma-ray excess
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