Page 74 - JOURNAL OF LIBRARY SCIENCE IN CHINA 2018 Vol. 44

ZHAO Yuxiang / A preliminary exploration on citizen science projects based on scientific crowdsourcing perspectives: 073
Conceptualization, pattern design and research opportunities

be driven by institutions, so as to further reflect the characteristics of “institutional view” in the
operation model of citizen science projects. Incentive mechanism, thereinto, is a series of incentive
design work carried out on the platform in order to better attract volunteers to participate in citizen
science projects. At present, some gamification studies on citizen science projects are devoted to
solving this problem (Preece, 2016; Prestopnik & Tang, 2015; Prestopnik, Crowston, &Wang,
2017). Collaborative mechanism is a series of direct or indirect, online or offline activities carried
out by institutions to better connect research teams and volunteers, being aimed at making the two
subjects better understand each other’s demands, eliminate stereotypes, and thus generate more
efficient collaboration and cooperation. The evaluation mechanism is to help the scientific research
team to better evaluate and control the quality of the feedback collected. By introducing this
mechanism, the work burden of the research team can be reduced and the results of citizen science
projects can also be evaluated more objectively, that is, assessment indicators such as education
goals and social goals can be integrated into the evaluation system more comprehensively in
addition to the assessment of scientific goals. Data management and protection mechanism refer
to the scientific research data supervision and maintenance of the whole life cycle of the tasks on
the platform, and on this basis, the corresponding knowledge discovery and knowledge innovation
services are carried out.

4 Citizen science projects: What can we do?

In the light of theoretical research, domestic scholars have created few achievements in directly
exploring citizen science projects, but recent two years have witnessed a series of studies on
scientific crowdsourcing with respect to concept induction (Zeng, 2016), typical cases (Y. Liu,
2016), operation mode (Wei, Jiang, Tao, G.F. Xie, & Tan, 2015), business process (Pang &
Z.Y. Liu, 2015, 2016), innovation mechanism of network mode (S.L. Zhang & Zheng, 2016),
and so on. In terms of practical exploration, most of the citizen science projects that have been
carried out in China are in the preliminary data collection stage, and they mainly focus on the
field of natural science, laying rather insufficient emphasis on how to conduct citizen science
projects in the humanities and social sciences. Sullivan et al. (2009) indicate that citizen science
projects should extend from traditional data collection to more businesses, including community
participation, data management and protection, data integration and analysis, pattern recognition
and visualization, and the promotion of results. The authors hold that a great many research
issues demand prompt solution both theoretically and practically in the emerging citizen science.
First of all, the development and implementation of citizen science depend heavily on collective
wisdom and group participation. Therefore, as the theoretical basis of citizen science, the scientific
crowdsourcing model calls for effective planning and design. At present, the majority of domestic
and foreign researches on the scientific crowdsourcing model directly draw lessons from the
crowdsourcing model in the commercial environment, but they do not carry out the corresponding