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1. Ramine Tinati, Markus Luczak-Roesch, Kate Lyle, [Amy Robinson (External, Eyewire.org)
WSI Stimulus Fund, Mid-term Presentation. September 9th 2016
Centre for longitudinal studies of
online citizen participation systems

2. Project Motivations
The use of crowdsourcing techniques is increasingly being used to support complex
computational tasks
Citizen Science has become one of the forefronts for developing crowdsourcing techniques.
How do you get non-experts to complete highly scientific tasks?
Citizen Science projects, which request individuals to volunteer their time to complete small
tasks, is now being used across a broad spectrum of domains
However, Research on Citizen Science activity has shown running a ‘successful projects’ is a
complex social and technical negotiation between design and community management.

3. Project Background
Studying society in the digital age is a matter looked at by a number of communities interested in
knowledge production in online communities
And, Social networks, online communities, human computation systems, and citizen science or
peer-production platforms are the focus of an increasing number of research communities across
many disciplines
Much of this research aims explore the relations between humans and new forms of technology
and the implications of this for future societies.
We draw upon expertise and literature associated with both the engineering and production of
technology, and the study of technology in practice.

4. Project Aim
To improve the quality and diversity of research into online citizen
participation systems, with the long-term goal of establishing a centre for
longitudinal studies that will coordinate and support interdisciplinary
research in the field.

5. Project Activities
a) Performing a systematic review and survey of the methods and techniques used within the
observation and discovery of citizen-led scientific findings.
b) Review of existing citizen science platforms. Quantitative and qualitative exploration of a number
of different platforms to explore how individuals interact and use the platforms.
c) Development of a citizen science toolkit and prototype application to allow for cross-platform
analysis of citizen science projects.

6. Survey of Citizen Science Methods
Literature review of motivational and user analysis studies conducted on citizen science platforms.
Predominantly from the computation sciences (CHI, CSCW, Web Science, WWW).
In summary, studies aim to either explore why people volunteer their free time to explore the
platforms (interviews, user log data analysis), or examine how to improve their performance (user
modelling, predictive studies). The problems we identified:
there is a disconnect between the combination of methods used to provide a richer
understanding on why individuals participant on citizen science platforms.
For example, can you really measure motivations based on a Likert scale?
There is also little understanding of the work of citizen science and how it is achieved. We
know the community interacts, but we do not know how they produce the work.
And there is little work being done to explore motivations of users across multiple platforms.

7. Global Citizen Science Activities
Article presented at the Winter Symposium for Computational Social Science Research (December 2015)
Collected data from 2 platforms (40
projects), > 4 million records
Exploring the global presence of citizen
science activities and engagement 
Initial findings show that the ‘global
activity’ is predominantly located in the
western world (and Aus…).

8. Mixed-Methods Research on EyeWire
“Because science is awesome”. Web Science 2016.
Eyewire is a gamified citizen science platform where players get to complete puzzles, which are
made from 3D-rendered MRI images of neurons in the human brain
We explored the use of mixed methods to examine player interactions and EyeWire platform.
what drives them to participant, how do they communicate, how do they function as a
community.
Worked with students from the social sciences, both with expertise in qualitative coding of survey
responses
Based on a player-wide survey, we completed a qualitative review of 1,400 responses asking
questions based on how and why player’s participate.
Hand-coded over 4,000 chat messages (2-week duration) to examine “what” and “how” people
really interact with each other.

9. Citizen Science Data Collection Toolkit
Data collection harvesters have been developed for both the EyeWire and Zooniverse
platform.
These capture the user interactions, communications, and current status of the project
(e.g. the number of overall daily classifications)
These are available via the git/bitbucket Repos:
Eyewire - https://bitbucket.org/SaudAljaloud/eyewire.scapper
Zooniverse - https://github.com/raminetinati/Zooniverse-Harvester
(they’re not user friendly… yet)

10. Cross-platform citizen science visualisations (alpha)

11. Cross-platform citizen science visualisations (alpha)

12. Long term project aims
Provide a catalogue of research data focusing longitudinal studies of online citizen
participation systems
Provide toolkit and infrastructure to support research collaboration and dissemination;
Develop ethical principles and guidelines for researchers and research funders in the
field and enforce these through a reputable ethics board.
We want to improves the quality and reproducibility of research in this space, and limits
redundancy in funding, consolidating activities, and helps to rigorously resolve scientific
conflicts and debates across the boundaries of individual discipline

13. Long term impact
Establish the University of Southampton as a centre for longitudinal citizen science
studies. To become a worldwide hub for citizen science related research
Establish a network of partner institutions and a community of researchers. Engage with
studies which may not initially considered within the scope of citizen science related
research.
Publications and events produced through the centre will provide evidence to support
further funding applications related to the study of citizen science and crowdsourcing
activities.
We hope that by engaging with doctoral students within this project and the Web
Science doctoral training programme, we aim to feedback these methodological
developments into the training programme for Web Science graduates