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Transforming research (and public engagement) through citizen science

Published online by Cambridge University Press:  03 March 2020

Samantha Blickhan ,
Laura Trouille  and
Chris J. Lintott
Samantha Blickhan
Affiliation:
Department of Citizen Science, The Adler Planetarium 1300 South Lake Shore Dr. Chicago, IL60605, USA emails: [email protected], [email protected]
Laura Trouille
Affiliation:
Department of Citizen Science, The Adler Planetarium 1300 South Lake Shore Dr. Chicago, IL60605, USA emails: [email protected], [email protected]
Chris J. Lintott
Affiliation:
Department of Physics, The University of Oxford Keble Road, Oxford, OX1 3RH, UK email: [email protected]
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Abstract

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Processing our increasingly large datasets poses a bottleneck for producing real scientific outcomes and citizen science - engaging the public in research - provides a solution, particularly when coupled with automated routines. In this talk we will provide a broad overview of citizen science approaches and best practices. We will also highlight in particular recent advances through Zooniverse, the world’s largest platform for online citizen science, engaging more than 1.7 million volunteers in tasks including discovering exoplanets, identifying features on Mars’ surface, transcribing artist’s notebooks, and tracking resistance to antibiotics.

Keywords

citizen sciencepublic engagementmachine learning
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 14 , Symposium A30: Astronomy in Focus XXX , August 2018 , pp. 518 - 523
Copyright
© International Astronomical Union 2020

References

Anderson, TM, et al. 2016, Philosophical Transactions of the Royal Society B: Biological Sciences 371(1703):20150314 Google Scholar
Areta, C, et al. 2016, in European Conference on Computer Vision 483–98Google Scholar
Barr, AJ, et al. 2016, arXiv:1610.02214 [physics.soc-ph]Google Scholar
Beck, MR, et al. 2018, MNRAS 476(4): 5516–34CrossRefGoogle Scholar
Bowyer, A, et al. 2015, in Human Computation and Crowdsourcing: Works in Progress and Demonstrations. An Adjunct to the Proceedings of the Third AAAI Conference on Human Computation and Crowdsourcing Google Scholar
Boyajian, TS, et al. 2016, MNRAS 457(4): 3988–4004 CrossRefGoogle Scholar
Cardamone, C, et al. 2009, MNRAS 399(3): 1191–1205 CrossRefGoogle Scholar
Christiansen, JL, et al. 2018, AJ 155(2): 57 CrossRefGoogle Scholar
Fortson, L, et al. 2012, in Advances in Machine Learning and Data Mining for Astronomy (CRC Press): 214–33Google Scholar
Grayson, RS 2016, British Journal for Military History 2(2): 160–85Google Scholar
Hennon, CC, et al. 2015, Bul. Am. Met. Soc. 96(4): 591–607 CrossRefGoogle Scholar
Ivezic, Z, et al. 2018, arXiv:0805.2366v5 [astro-ph]Google Scholar
Johnson, LC, et al. 2015, ApJ 802(2): 127 Google Scholar
Lintott, CJ, et al. 2008, MNRAS 389(3): 1179–89Google Scholar
Lintott, CJ, et al. 2009, MNRAS 399(1): 129–40CrossRefGoogle Scholar
Luczak-Roesch, M, et al. 2014, in ICWSM aaai.org, http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/viewFile/8092/8136 Google Scholar
Marshall, PJ, et al. 2015, ARAA 53: 247–78CrossRefGoogle Scholar
Marshall, PJ, et al. 2016, MNRAS 455: 1171–90 Google Scholar
Matsunaga, A, et al. 2016, Future Generation Computer Systems 56: 526–36CrossRefGoogle Scholar
Mugar, G, et al. 2014, in: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing (New York, NY, USA: ACM): 109–19Google Scholar
dos Reis, FJC, et al. 2015, EBioMedicine 2(7): 681–89 Google Scholar
Rosenthal, IS, et al. 2018, in press Google Scholar
Schwamb, ME, et al. 2012, apj 754(2): 129–146 CrossRefGoogle Scholar
Swanson, A, et al. 2012, Conservation Biology 30(3): 520–31CrossRefGoogle Scholar
Trouille, L, et al. 2018, accepted to PNAS Google Scholar
Willi, M, et al. 2018, submitted to Methods in Ecology and Evolution Google Scholar
Williams, AC, et al. 2014, in IEEE International Conference on Big Data 100–105Google Scholar
Wright, D, et al. 2017, in Monthly Notices of the Royal Astronomical Society 472(2): 1315–23Google Scholar
Zevin, M, et al. 2017, Classical and Quantum Gravity 34(6): 064003 CrossRefGoogle Scholar