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The Evolving Landscape of Citizen Science
 

The Evolving Landscape of Citizen Science

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Presentation for the USGS Community Data Integration workshop on Citizen Science

Presentation for the USGS Community Data Integration workshop on Citizen Science

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  • Thanks for having me! I ’ m currently a postdoc with D1 at UNM and CLO at Cornell, and my work focuses on data management and technologies for citizen science. I ’ m kicking off the discussion of citizen science engagement by talking about the many flavors of citizen science.
  • Many labels for PPSR have emerged over time, often in different fields that do not communicate with one another. There are also a variety of related research practices with similarities to citizen science. So what are we really talking about here?
  • In this case, we ’ re focusing on citizen science and volunteer monitoring, which is now often called citizen science. In these projects, volunteers help do scientific work, rather than talking about it or deciding things about it. Notably the three forms listed right under citizen science & volunteer monitoring could also be considered citizen science by the simple definition of including the public in doing scientific work, and one of the biggest differences there is just the research fields in which they ’ re practiced.
  • Lawrence - Power, knowledge, & participation from literature in STS, looking at rolesCAISE - participation tasks from case studies in ISE Wiggins 2011 - Explicit goals based on landscape sample Wiggins 2012 - Survey analyzed on participation tasks and protocols
  • CAISE model - based on several prior similar models. Classifies projects according to who does which scientific tasks in the project. Most apparent point of differentiation between many projects, and easy to assess. This is really one of the more useful ways to divide citizen science projects up into categories.
  • Another way to think about these tasks - and this isn ’ t from any particular typology - is whether volunteers are Sharing, Working, or Playing when they participate. This perspective also focuses on the tasks, but instead looks at them from the perspective of participant experience.
  • In the typologies we generated from survey data, using algorithmic clustering, we basically found that there were more interesting associations between these clusters when they were related to goals than if they were based on common tasks. For example, the two science-focused clusters had higher average budgets (until you take out outliers) but had distinctly different goals with respect to using scientific data for restoration, management and action, versus straight-up science and monitoring. When we looked at projects focused primarily on education and outreach goals, we found that they were no more likely than others to have online learning materials. In fact, what came to light is that the scale of the project and degree of localization versus distributed participation had more to do with training and learning resources. Local projects actually had more, and that makes sense because the distributed projects use simpler protocols to get good data out of a larger number of people.
  • But when we think about engaging people in citizen science, especially from a project design standpoint, there are a number of other important factors that we can ’ t ignore, and they all vary based on the project goals and tasks. There are certainly additional relevant points of comparison, but these are the ones I hear brought up over and over.
  • So taking that easy-to-use typology from the CAISE report, let ’ s look at the relative pros and cons for each of those models of participation based on implications for those critical factors. Contributory: most scalable but needs IT & numbers to succeed; low complexity tasks reduces training, improves data quality; greatest potential spatiotemporal spread Co-created: least scalable but also least IT-dependent; higher complexity increases training needs; most localized, needs most organizer time as ratio to participants Collaborative: negotiate more tradeoffs; more unknowns For all projects, data quality and sustainability vary across the board. Data quality varies because it ’ s a function of the intersection of all of the design factors, while sustainability varies based primarily on project resources.
  • So what I want you to take away from this talk are four simple points. They may seem obvious because they are essentially common-sense, but they are important to deliberately consider when designing or even just comparing citizen science projects. [READ OFF]
  • And here are the references for some of the typologies, for anyone who is interested in looking them up...

The Evolving Landscape of Citizen Science The Evolving Landscape of Citizen Science Presentation Transcript