Papers
. (2011).
designing citizen science games.pdf (1 MB)

Gamers, citizen scientists, and data: Exploring participant contributions in two games with a purpose. Computers In Human Behavior, 68, 254–268. https://doi.org/10.1016/j.chb.2016.11.035
. (2017). 
Citizen science system assemblages: Understanding the technologies that support crowdsourced science. In iConference 2012.
. (2012). 
The future of citizen science: emerging technologies and shifting paradigms. Frontiers In Ecology And The Environment, 10(6), 298–304. https://doi.org/10.1890/110294
. (2012). Planet Hunters and Seafloor Explorers: Legitimate Peripheral Participation Through Practice Proxies in Online Citizen Science. In 17th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2014). https://doi.org/10.1145/2531602.2531721
. (2014). 
Being Present in Online Communities: Learning in Citizen Science. In 7th International Conference on Communities and Technologies. https://doi.org/10.1145/2768545.2768555
. (2015). 
Preserving the margins: Supporting creativity and resistance on digital participatory platforms. Proceedings Of The Acm: Human-Computer Interaction, 1(CSCW). https://doi.org/10.1145/3134718
. (2017). 
Recruiting messages matter: Message strategies to attract citizen scientists. In ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2017). https://doi.org/10.1145/3022198.3026335
. (2017). 
Appealing to different motivations in a message to recruit citizen scientists: results of a field experiment. Journal Of Science Communication, 17. https://doi.org/10.22323/2.17010202
. (2018). 
Folksonomies to support coordination and coordination of folksonomies. Computer Supported Cooperative Work, 27(3–6), 647–678. https://doi.org/10.1007/s10606-018-9327-z
. (2018). 
Teaching Citizen Scientists to Categorize Glitches using Machine-Learning-Guided Training. Computers In Human Behavior, 105, 106198. https://doi.org/10.1016/j.chb.2019.106198
. (2020). 
. (2020).
Novelty_Motivator_2020.pdf (1.15 MB)

Did they login? Patterns of anonymous contributions to online communities. Proceedings Of The Acm On Human-Computer Interaction, 2(CSCW), Article 77. https://doi.org/10.1145/3274346
. (2018). 
Shifting forms of Engagement: Volunteer Learning in Online Citizen Science. Proceedings Of The Acm On Human-Computer Interaction, (CSCW), 36. https://doi.org/10.1145/3392841
. (2020). 
Characterizing Novelty as a Motivator in Online Citizen Science (Syracuse University). In School of Information Studies. Retrieved de https://surface.syr.edu/etd/1046/
. (2019). Linguistic adoption in online citizen science: A structurational perspective. In International Conference on Information Systems. Retrieved de https://aisel.aisnet.org /icis2019/crowds_social/crowds_social/28/
. (2019). 
“Guess what! You’re the first to see this event”: Increasing Contribution to Online Production Communities. In ACM Group. https://doi.org/10.1145/2957276.2957284
. (2016). Encouraging Work in Citizen Science: Experiments in Goal Setting and Anchoring. In ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW). https://doi.org/10.1145/2818052.2869129
. (2016). 
Which Way Did They Go? Newcomer Movement through the Zooniverse. In 19th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW). https://doi.org/10.1145/2818048.2835197
. (2016). 
Motivations for sustained participation in crowdsourcing: The role of talk in a citizen science case study. In Proceedings of the Forty-eighth Hawai'i International Conference on System Sciences (HICSS-48).
. (2015). 
Learning at the Seafloor, Looking at the Sky: The Relationship Between Individual Tasks and Collaborative Engagement in Two Citizen Science Projects. In Computer Supported Collaborative Learning. Presented at the Computer Supported Collaborative Learning .
. (2013). 

Occasional Groups in Crowdsourcing Platforms. In School of Information Studies. Syracuse University, Syracuse, NY, USA.
. (2021). 
The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning. In Hawai'i International Conference on System Science. https://doi.org/10.24251/HICSS.2020.719
. (2020). 
Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications. Classical And Quantum Gravity, 40(6). https://doi.org/10.1088/1361-6382/acb633
. (2023). Artificial Intelligence and the Future of Citizen Science. Citizen Science: Theory And Practice, 9(1), 32. https://doi.org/10.5334/cstp.812
. (2024).