Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour

The often opportunistic nature of biological recording via citizen science leads to taxonomic, spatial and temporal biases which add uncertainty to biodiversity estimates. However, such biases may also give valuable insight into volunteers’ recording behaviour. Using Greater London as a case-study we examined the composition of three citizen science datasets – from Greenspace Information for Greater London CIC, iSpot and iRecord - with respect to recorder contribution and spatial and taxonomic biases, i.e. when, where and what volunteers record. We found most volunteers contributed few records and were active for just one day. Each dataset had its own taxonomic and spatial signature suggesting that volunteers’ personal recording preferences may attract them towards particular schemes. There were also patterns across datasets: species’ abundance and ease of identification were positively associated with number of records, as was plant height. We found clear hotspots of recording activity, the 10 most popular sites containing open water. We note that biases are accrued as part of the recording process (e.g. species’ detectability) as well as from volunteer preferences. An increased understanding of volunteer behaviour gained from analysing the composition of records could thus enhance the fit between volunteers’ interests and the needs of scientific projects

What Do We Know about Young Volunteers? An Exploratory Study of Participation in Zooniverse

Citizen Science (CS) is an increasingly popular activity enacted either in the field or online. Volunteers participate in research activities such as data processing and analysis by, for example, identifying plants and animals. In this paper we examine young people’s participation in online CS projects hosted on the Zooniverse platform. This is an exploratory study, the first of its kind that focuses on young people, mainly 16–19 years old. It uses data analytics and visualisation techniques to capture participation in online CS, and in particular to answer the following questions: (a) What does young people’s participation look like in CS projects? (b) What Zooniverse projects do young people choose to participate in? and (3) What Zooniverse projects do young people choose together? Findings revealed five distinct engagement profiles characterising young people’s participation and identified certain projects as been more popular across participants. Implications for the design of online citizen science projects targeting young people are discussed

Characterising Volunteers' Task Execution Patterns Across Projects on Multi-Project Citizen Science Platforms

Citizen science projects engage people in activities that are part of a scientific research effort. On multi-project citizen science platforms, scientists can create projects consisting of tasks. Volunteers, in turn, participate in executing the project's tasks. Such type of platforms seeks to connect volunteers and scientists' projects, adding value to both. However, little is known about volunteer's cross-project engagement patterns and the benefits of such patterns for scientists and volunteers. This work proposes a Goal, Question, and Metric (GQM) approach to analyse volunteers' cross-project task execution patterns and employs the Semiotic Inspection Method (SIM) to analyse the communicability of the platform's cross-project features. In doing so, it investigates what are the features of platforms to foster volunteers' cross-project engagement, to what extent multi-project platforms facilitate the attraction of volunteers to perform tasks in new projects, and to what extent multi-project participation increases engagement on the platforms. Results from analyses on real platforms show that volunteers tend to explore multiple projects, but they perform tasks regularly in just a few of them; few projects attract much attention from volunteers; volunteers recruited from other projects on the platform tend to get more engaged than those recruited outside the platform. System inspection shows that platforms still lack personalised and explainable recommendations of projects and tasks. The findings are translated into useful claims about how to design and manage multi-project platforms.Comment: XVIII Brazilian Symposium on Human Factors in Computing Systems (IHC'19), October 21-25, 2019, Vit\'oria, ES, Brazi

Design processes of a citizen inquiry community

As with other online communities, it is important to design elements of citizen inquiry projects that will attract and engage members. This chapter describes the process of designing an online community for citizen inquiry. It builds on design principles of inquiry learning, citizen inquiry and other online communities. The ‘Weather-it’ citizen inquiry community is intended to engage and support people in initiating and joining sustainable citizen-led investigations. The findings indicate some successful mechanisms for the design of effective and sustainable citizen inquiry communities and ways to sustain them

I’m fine with collecting data: Engagement profiles differ depending on scientific activities in an online community of a citizen science project

Digital technologies facilitate collaboration between citizens and scientists in citizen science (CS) projects. Besides the facilitation of data transmission and access, digital technologies promote novel formats for education in CS by including citizens in the process of collecting, analyzing, and discussing data. It is usually assumed that citizens profit more from CS the more they participate in the different steps of the scientific process. However, it has so far not been analyzed whether citizens actually engage in these steps. Therefore, we investigated citizens’ actual engagement in different scientific steps online (i.e., data collection and data analysis) in two field studies of a CS project. We then compared them with other CS projects. We analyzed behavioral engagement patterns of N = 273 participants with activity logs and cluster analyses. Opportunities to engage in different steps of the scientific process increased participants’ overall commitment compared to contributory CS projects. Yet, despite their increased commitment, participants’ engagement was only more active for data collection but not for data analysis. We discuss how participants’ perceived role as data collectors influenced their actual engagement in the scientific steps. To conclude, citizens may need support to change their role from data collectors to data inquirers

The Sense-it App: A Smartphone Toolkit for Citizen Inquiry Learning

The authors describe the design and formative evaluation of a sensor toolkit for Android smartphones and tablets that supports inquiry-based science learning. The Sense-it app enables a user to access all the motion, environmental and position sensors available on a device, linking these to a website for shared crowd-sourced investigations. The authors describe the four investigations with the toolkit: environmental noise, sunlight levels, air pressure and rainfall, and the speed of lifts (elevators). These show a variety of methods to initiate, orchestrate and conclude inquiry-based science learning. Two of the missions are in the context of a study to develop a community of inquiry around weather and meteorology. The others are intended to engage members of the public in practical science activities. Analysis of the missions and the associated online discussions reveals that the Sense-it toolkit can be adopted for engaging science investigations, though the practical issue of calibrating sensors on personal devices needs to be addressed