The use of game elements within virtual citizen science is increasingly common, promising to bring increased user activity, motivation and engagement to large-scale scientific projects. However there is an ongoing debate about whether or not gamifying systems such as these is actually an effective means by which to increase motivation and engagement in the long term. While gamification itself is receiving a large amount of attention, there has been little beyond individual studies to assess its suitability or success for citizen science; similarly, while frameworks exist for assessing citizen science performance, they tend to lack any appreciation of the effects that game elements might have had. We therefore review the literature to determine what the trends are regarding the performance of particular game elements or characteristics in citizen science, and survey existing projects to assess how popular different game features are. Investigating this phenomenon further, we then present the results of a series of interviews carried out with the EyeWire citizen science project team to understand more about how gamification elements are introduced, monitored and assessed in a live project. Our findings suggest that projects use a range of game elements with points and leaderboards the most popular, particularly in projects that describe themselves as 'games'. Currently, gamification appears to be effective in citizen science for maintaining engagement with existing communities, but shows limited impact for attracting new players.

Online citizen science projects have been increasingly used in a variety of disciplines and contexts to enable large-scale scientific research. The successes of such projects have encouraged the development of customisable platforms to enable anyone to run their own citizen science project. However, the process of designing and building a citizen science project remains complex, with projects requiring both human computation and social aspects to sustain user motivation and achieve project goals. In this paper, we conduct a systematic survey of 48 citizen science projects to identify common features and functionality. Supported by online community literature, we use structured walkthroughs to identify different mechanisms used to encourage volunteer contributions across four dimensions: task visibility, goals, feedback, and rewards. Our findings contribute to the ongoing discussion on citizen science design and the relationship between community and microtask design for achieving successful outcomes.

PurposeCitizen Science – public participation in scientific projects – is becoming a global practice engaging volunteer participants, often non-scientists, with scientific research. Citizen Science is facing major challenges, such as quality and consistency, to reap open the full potential of its outputs and outcomes, including data, software and results. In this context, the principles put forth by Data Science and Open Science domains are essential for alleviating these challenges, which have been addressed at length in these domains. The purpose of this study is to explore the extent to which Citizen Science initiatives capitalise on Data Science and Open Science principles.Design/methodology/approachThe authors analysed 48 Citizen Science projects related to pollution and its effects. They compared each project against a set of Data Science and Open Science indicators, exploring how each project defines, collects, analyses and exploits data to present results and contribute to knowledge.FindingsThe results indicate several shortcomings with respect to commonly accepted Data Science principles, including lack of a clear definition of research problems and limited description of data management and analysis processes, and Open Science principles, including lack of the necessary contextual information for reusing project outcomes.Originality/valueIn the light of this analysis, the authors provide a set of guidelines and recommendations for better adoption of Data Science and Open Science principles in Citizen Science projects, and introduce a software tool to support this adoption, with a focus on preparation of data management plans in Citizen Science projects.

Virtual citizen science (VCS) projects have proven to be a highly effective method to analyse large quantities of data for scientific research purposes. Yet if these projects are to achieve their goals, they must attract and maintain the interest of sufficient numbers of active, dedicated volunteers. Although CSCW and HCI research has typically focussed on designing platforms to support long-term engagement, in recent years a new project format has been trialled -using short-term crowdsourcing activities lasting as little as 48 hours. In this paper, we explore two short-term projects to understand how they influence participant engagement in the task and discussion elements of VCS. We calculate descriptive statistics to characterise project participants. Additionally, using calculation of correlation coefficients and hypothesis testing, we identify factors influencing volunteer task engagement and the effect this has on project outcomes. Our findings contribute to the understanding of volunteer engagement in VCS.CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing.

15 2.4 Cellular Technologies 15 2.4.1 Emerging 5G Cellular Technology 16 2.5 Conclusion 18 References 18 3 E d g eC o m p u t i n g 21 Mohammad Hossein Zoualfaghari, Simon Beddus, and Salman Taherizadeh 3.1 Introduction 21 3.2 Edge Computing Fundamentals 22