@mastersthesis {2019, title = {Characterizing Novelty as a Motivator in Online Citizen Science}, year = {2019}, school = {Syracuse University}, type = {PhD dissertation}, address = {Syracuse, NY, USA}, abstract = {

Citizen science projects rely on the voluntary contribution of nonscientists to take part in scientific research projects. Projects taking place exclusively over the Internet face significant challenges, chief among them is the attracting and keeping the critical mass of volunteers needed to conduct the work outlined by the science team. The extent to which platforms can design experiences that positively influence volunteers{\textquoteright} motivation can help address the contribution challenges. Consequently, project organizers need to develop strategies to attract new participants and keep existing ones. One strategy to encourage participation is implementing features, which re-enforce motives known to change people{\textquoteright}s attitudes towards contributing positively. The literature in psychology noted that novelty is an attribute of objects and environments that occasion curiosity in humans leading to exploratory behaviors, e.g., prolonged engagement with the object or environment. This dissertation described the design, implementation, and evaluation of an experiment conducted in three online citizen science projects. Volunteers received novelty cues when they classified data objects that no other volunteer had previously seen. The hypothesis was that exposure to novelty cues while classifying data positively influences motivational attitudes leading to increased engagement in the classification task and increased retention. The experiments resulted in mixed results. In some projects, novelty cues were universally salient, and in other projects, novelty cues had no significant impact on volunteers{\textquoteright} contribution behaviors. The results, while mixed, are promising since differences in the observed behaviors arise because of individual personality differences and the unique attributes found in each project setting. This research contributes to empirically grounded studies on motivation in citizen science with analyses that produce new insights and questions into the functioning of novelty and its impact on volunteers{\textquoteright} behaviors.

}, url = {https://surface.syr.edu/etd/1046/}, author = {Corey Brian Jackson} } @article {2019, title = {Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning}, journal = {Physical Review D}, volume = {99}, year = {2019}, pages = {082002}, abstract = {

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project Gravity Spy has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO{\textquoteright}s second observing run, we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program.

}, issn = {2470-0010}, doi = {10.1103/PhysRevD.99.082002}, author = {Scott Coughlin and Sara Bahaadini and Neda Rohani and Michael Zevin and Patane, Oli and Mahboobeh Harandi and Corey Brian Jackson and Noroozi, V. and Sarah Allen and Areeda, J. and Coughlin, M. and Ruiz, P. and Berry, C. P. L. and Kevin Crowston and Aggelos Katsaggelos and Andrew Lundgren and Carsten {\O}sterlund and Joshua Smith and Laura Trouille and Vicky Kalogera} } @article {9999, title = {Coordinating Advanced Crowd Work: Extending Citizen Science}, journal = {Citizen Science: Theory and Practice}, volume = {4}, year = {2019}, pages = {1{\textendash}12}, abstract = {

Crowdsourcing work with high levels of coupling between tasks poses challenges for coordination. This paper presents a study of two online citizen science projects that involved volunteers in such tasks: not just analyzing bulk data but also interpreting data and writing a paper for publication in one project and identifying new classes of data in the other. However, extending the reach of citizen science adds tasks with more dependencies, which calls for more elaborate coordination mechanisms but the relationship between the project and volunteers limits how work can be coordinated. Contrariwise, a mismatch between dependencies and available coordination mechanisms can be expected to lead to performance problems. The results of the study offer recommendations for design of citizen science projects for advanced tasks.

}, doi = {10.5334/cstp.166}, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/Quench\%20to\%20share.pdf}, author = {Kevin Crowston and Mitchell, Erica Michelle and Carsten {\O}sterlund} } @proceedings {9999, title = {Coordinating advanced crowd work: Extending citizen science}, year = {2018}, edition = {51st}, abstract = {Crowdsourcing work with high levels of coupling between tasks poses challenges for coordination. This paper presents a study of an online citizen science project that involved volunteers in such tasks: not just analyzing bulk data but also interpreting data and writing a paper for publication. However, extending the reach of citizen science adds tasks with more dependencies, which calls for more elaborate coordination mechanisms but the relationship between the project and volunteers limits how work can be coordinated. Contrariwise, a mismatch between dependencies and available coordination mechanisms can be expected to lead to performance problems. The results of the study offer recommendations for design of crowdsourcing of more complex tasks. }, doi = {10.24251/HICSS.2018.212}, url = {http://hdl.handle.net/10125/50099}, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/Quench\%20to\%20distribute.pdf}, author = {Kevin Crowston and Mitchell, Erica Michelle and Carsten {\O}sterlund} } @proceedings {9999, title = {Citizen science system assemblages: Understanding the technologies that support crowdsourced science}, year = {2012}, month = {2/2012}, address = {Toronto, Ontario}, abstract = {We explore the nature of technologies to support citizen science, a method of inquiry that leverages the power of crowds to collect and analyze scientific data. We evaluate these technologies as system assemblages, collections of interrelated functionalities that support specific activities in pursuit of overall project goals. The notion of system assemblages helps us to explain how different citizen science platforms may be comprised of widely varying functionalities, yet still support relatively similar goals. Related concepts of build vs. buy and web satisfiers vs. web motivators are used to explore how different citizen science functionalities may lead to successful project outcomes. Four detailed case studies of current citizen science projects encompassing a cross-section of varying project sizes, resource levels, technologies, and approaches to inquiry help us to answer the following research questions: 1) What do typical system assemblages for citizen science look like? 2) What factors influence the composition of a system assemblage for citizen science? 3) What effect does the assemblage composition have on scientific goals, participant support, motivation, and satisfaction? and 4) What are the design implications for the system assemblage perspective on citizen science technologies?}, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/citizensciencesystemassemblage.pdf}, author = {Nathan Prestopnik and Kevin Crowston} } @article {374, title = {Citizen science system assemblages: Toward greater understanding of technologies to support crowdsourced science}, year = {2011}, month = {06/2011}, publisher = {Syracuse University School of Information Studies}, type = {Unpublished working paper}, abstract = {We explore the nature of technologies to support citizen science, a method of inquiry that leverages the power of crowds to collect and analyze scientific data. We evaluate these technologies as system assemblages, collections of interrelated functionalities that support specific activities in pursuit of overall project goals. The notion of system assemblages helps us to explain how different citizen science platforms may be comprised of widely varying functionalities, yet still support relatively similar goals. Related concepts of build vs. buy, support for science vs. support for participants, and web satisfiers vs. web motivators are used to explore how different citizen science functionalities may lead to successful project outcomes. Four detailed case studies of current citizen science projects encompassing a cross-section of varying project sizes, resource levels, technologies, and approaches to inquiry help us to answer the following research questions: 1) What factors influence the composition of a system assemblage for citizen science? 2) What do typical system assemblages for citizen science look like? 3) What effect does the assemblage composition have on scientific goals, participant support, motivation, and satisfaction? and 4) What are the design implications for the system assemblage perspective on citizen science technologies?}, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/system_assemblage.pdf}, author = {Nathan Prestopnik and Kevin Crowston} } @article {192, title = {Citizen Science System Assemblages: Toward Greater Understanding of Technologies to Support Crowdsourced Science}, year = {2011}, month = {6/2011}, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/system_assemblage_0.pdf}, author = {Nathan Prestopnik and Kevin Crowston} } @unpublished {2010, title = {Crowdsourcing Science: Organizing Virtual Participation in Knowledge Production}, year = {2010}, month = {05/2010}, publisher = {Syracuse University}, type = {PhD Dissertation Proposal}, address = {Syracuse, NY}, abstract = {Citizen science projects involve the public with scientists in collaborative research. When conducted via information technologies, they can yield massive virtual collaborations based on voluntary contributions by diverse participants. As the popularity of citizen science increases, scientists need a more thorough understanding of how research design and implementation decisions affect scientific outcomes in citizen science. In particular, designing information systems to support virtual citizen science requires understanding the relationships between virtuality, technology, and the processes of organizing and participation. These inputs and processes produce scientific outcomes in successful projects, leading to the specific research questions for the proposed study: How do virtuality and technology alter organizing in citizen science? How do virtuality and technology shape participation in citizen science? How do organizing and participation influence scientific outcomes in citizen science? The goal of the research is to develop a theoretical model of virtual organizing and participation in citizen science that can inform practice and provide a conceptual foundation for adaptation to other contexts of virtual participation. In the proposed study, a multiple case study research design will be employed to investigate the influence of virtuality and technology on the processes of organizing and participation, and the resulting impacts on scientific outcomes in citizen science projects. The expected contributions include a multilevel theoretical model of virtual participation processes and comparative analysis of citizen science projects yielding insights that can inform design and management.}, author = {Wiggins, Andrea} }