@article {2024, title = {Artificial Intelligence and the Future of Citizen Science}, journal = {Citizen Science: Theory and Practice}, volume = {9}, year = {2024}, month = {11/2024}, pages = {32}, doi = {10.5334/cstp.812}, author = {Fortson, Lucy and Crowston, Kevin and Kloetzer, Laure and Ponti, Marisa} } @article {zevin2023gravity, title = {Gravity Spy: Lessons Learned and a Path Forward}, journal = {European Physical Journal Plus}, volume = {139}, year = {2024}, month = {01/2024}, pages = {Article 100}, abstract = {

The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine-learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine-learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine-learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.

}, doi = {10.1140/epjp/s13360-023-04795-4}, author = {Michael Zevin and Corey B. Jackson and Zoheyr Doctor and Yunan Wu and Carsten {\O}sterlund and L. Clifton Johnson and Christopher P. L. Berry and Kevin Crowston and Scott B. Coughlin and Vicky Kalogera and Sharan Banagiri and Derek Davis and Jane Glanzer and Renzhi Hao and Aggelos K. Katsaggelos and Oli Patane and Jennifer Sanchez and Joshua Smith and Siddharth Soni and Laura Trouille and Marissa Walker and Irina Aerith and Wilfried Domainko and Victor-Georges Baranowski and Gerhard Niklasch and Barbara T{\'e}gl{\'a}s} } @article {9998, title = {Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications}, journal = {Classical and Quantum Gravity}, volume = {40}, year = {2023}, keywords = {FOS: Physical sciences, General Relativity and Quantum Cosmology (gr-qc), Instrumentation and Methods for Astrophysics (astro-ph.IM)}, doi = {10.1088/1361-6382/acb633}, author = {Glanzer, J and Banagiri, S and Coughlin, S B and Soni, S and Zevin, M and Berry, C P L and Patane, O and Bahaadini, S and Rohani, N and Crowston, K and {\O}sterlund, C} } @article {soni2021discovering, title = {Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning}, journal = {Classical and Quantum Gravity}, volume = {38}, year = {2021}, doi = {10.1088/1361-6382/ac1ccb}, author = {S Soni and C P L Berry and S B Coughlin and M Harandi and C B Jackson and K Crowston and C {\O}sterlund and O Patane and A K Katsaggelos and L Trouille and V-G Baranowski and W F Domainko and K Kaminski and M A Lobato Rodriguez and U Marciniak and P Nauta and G Niklasch and R R Rote and B T{\'e}gl{\'a}s and C Unsworth and C Zhang} } @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 = {Gamers, citizen scientists, and data: Exploring participant contributions in two games with a purpose}, journal = {Computers in Human Behavior}, volume = {68}, year = {2017}, pages = {254{\textendash}268}, abstract = {

Two key problems for crowd-sourcing systems are motivating contributions from participants and ensuring the quality of these contributions. Games have been suggested as a motivational approach to encourage contribution, but attracting participation through game play rather than intrinsic interest raises concerns about the quality of the contributions provided. These concerns are particularly important in the context of citizen science projects, when the contributions are data to be used for scientific research. To assess the validity of concerns about the effects of gaming on data quality, we compare the quality of data obtained from two citizen science games, one a {\textquotedblleft}gamified{\textquotedblright} version of a species classification task and one a fantasy game that used the classification task only as a way to advance in the game play. Surprisingly, though we did observe cheating in the fantasy game, data quality (i.e., classification accuracy) from participants in the two games was not significantly different. As well, data from short-time contributors was also at a usable level of accuracy. Finally, learning did not seem to affect data quality in our context. These findings suggest that various approaches to gamification can be useful for motivating contributions to citizen science projects.

}, doi = {10.1016/j.chb.2016.11.035}, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/chb2016.pdf}, author = {Nathan Prestopnik and Kevin Crowston and Wang, Jun} } @proceedings {9999, title = {Exploring data quality in games with a purpose}, year = {2014}, month = {3/2014}, address = {Berlin, Germany}, abstract = {

A key problem for crowd-sourcing systems is motivating contributions from participants and ensuring the quality of these contributions. Games have been suggested as a motivational approach to encourage contribution, but attracting participation through game play rather than scientific interest raises concerns about the quality of the data provided, which is particularly important when the data are to be used for scientific research. To assess whether these concerns are justified, we compare the quality of data obtained from two citizen science games, one a {\textquotedblleft}gamified{\textquotedblright} version of a species classification task and one a fantasy game that used the classification task only as a way to advance in the game play. Surprisingly, though we did observe cheating in the fantasy game, data quality (i.e., classification accuracy) from participants in the two games was not significantly different. As well, the quality of data from short-time contributors was at a usable level of accuracy. These findings suggest that various approaches to gamification can be useful for motivating contributions to citizen science projects.

}, doi = {10.9776/14066}, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/gamedataquality_cameraready_4.pdf}, author = {Nathan Prestopnik and Kevin Crowston and Wang, Jun} } @conference {2013, title = {Forgotten island: A story-driven citizen science adventure}, booktitle = {CHI {\textquoteright}13 Extended Abstracts on Human Factors in Computing Systems}, year = {2013}, month = {4/2013}, pages = {2643{\textendash}2646}, publisher = {ACM Press}, organization = {ACM Press}, address = {Paris, France}, abstract = {

Forgotten Island, a citizen science video game, is part of an NSF-funded design science research project, Citizen Sort. It is a mechanism to help life scientists classify photographs of living things and a research tool to help HCI and information science scholars explore storytelling, engagement, and the quality of citizenproduced data in the context of citizen science.

}, isbn = {9781450319522}, doi = {10.1145/2468356.2479484}, url = {http://delivery.acm.org/10.1145/2480000/2479484/p2643-prestopnik.pdf}, author = {Nathan Prestopnik and Souid, Dania} } @conference {Crowston:2013, title = {Motivation and data quality in a citizen science game: A design science evaluation}, booktitle = {Forty-sixth Hawai{\textquoteright}i International Conference on System Sciences (HICSS-46)}, year = {2013}, month = {1/2013}, address = {Wailea, HI}, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/hicss2013citizensort_cameraready.pdf}, author = {Kevin Crowston and Nathan Prestopnik} } @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} } @conference {Prestopnik:2012a, title = {Purposeful gaming \& socio-computational systems: A citizen science design case}, booktitle = {Group {\textquoteright}12 Conference}, year = {2012}, month = {10/2012}, address = {Sanibel Island, FL, USA}, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/citizensort_cameraready.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 {9999, title = {Exploring Collective Intelligence Games With Design Science: A Citizen Science Design Case}, year = {2011}, abstract = {Citizen science is a form of collective intelligence where members of the public are recruited to contribute to scientific investigations. Citizen science projects often use web-based systems to support collaborative scientific activities, but finding ways to attract participants and confirm the veracity of the data produced by non-scientists are key research questions. We describe a series of web-based tools and games currently under development to support taxonomic classification of organisms in photographs collected by citizen science projects. In the design science tradition, the systems are purpose-built to test hypotheses about participant motivation and techniques for ensuring data quality. Findings from preliminary evaluation and the design process itself are discussed.}, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/designing\%20citizen\%20science\%20games.pdf}, author = {Nathan Prestopnik and Kevin Crowston} }