Cool Neighbors: Combining Artificial Intelligence and Citizen Science to Chart the Sun’s Cosmic Neighborhood
In this case study, we describe the design and implementation of the Backyard Worlds: Cool Neighbors citizen science project, which combines image-level deep learning with Zooniverse-hosted online crowdsourcing to mine large astronomical sky maps for rare celestial objects called “brown dwarfs.” Spe...
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| Format: | Article |
| Language: | English |
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Ubiquity Press
2024-12-01
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| Series: | Citizen Science: Theory and Practice |
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| Online Access: | https://account.theoryandpractice.citizenscienceassociation.org/index.php/up-j-cstp/article/view/737 |
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| author | Aaron Meisner Dan Caselden Austin Humphreys Grady Robbins Eden Schapera J. Davy Kirkpatrick Adam Schneider L. Clifton Johnson Marc Kuchner Jacqueline Faherty Sarah Casewell Federico Marocco Adam Burgasser Daniella Bardalez Gagliuffi |
| author_facet | Aaron Meisner Dan Caselden Austin Humphreys Grady Robbins Eden Schapera J. Davy Kirkpatrick Adam Schneider L. Clifton Johnson Marc Kuchner Jacqueline Faherty Sarah Casewell Federico Marocco Adam Burgasser Daniella Bardalez Gagliuffi |
| author_sort | Aaron Meisner |
| collection | DOAJ |
| description | In this case study, we describe the design and implementation of the Backyard Worlds: Cool Neighbors citizen science project, which combines image-level deep learning with Zooniverse-hosted online crowdsourcing to mine large astronomical sky maps for rare celestial objects called “brown dwarfs.” Specifically, Cool Neighbors uses machine learning to pre-select the sky images shown to volunteers. Cool Neighbors represents an excellent opportunity to interrogate the effects of incorporating artificial intelligence into a citizen science project; its sibling project, Backyard Worlds: Planet 9, uses no artificial intelligence, providing a natural point of comparison for participant engagement metrics. Through analysis of more than 10 million total Zooniverse classifications from the combination of Cool Neighbors and Backyard Worlds: Planet 9, among other results, we find (1) Cool Neighbors volunteers perform ~3x more classifications per unit of time invested than Backyard Worlds: Planet 9 volunteers, and (2) each registered Cool Neighbors participant performs ~2–5x more classifications than each registered Backyard Worlds: Planet 9 participant. We also discuss our measured approach to presenting the complementarity of machine learning and citizen science in volunteer-facing Cool Neighbors materials. Finally, we present a survey of advanced Backyard Worlds participants, which indicates that these citizen scientists are by and large not dissuaded from participating in Cool Neighbors because of its usage of artificial intelligence. |
| format | Article |
| id | doaj-art-122c378549a84b31a3ddb968abc9ffa5 |
| institution | Kabale University |
| issn | 2057-4991 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ubiquity Press |
| record_format | Article |
| series | Citizen Science: Theory and Practice |
| spelling | doaj-art-122c378549a84b31a3ddb968abc9ffa52025-01-08T07:54:40ZengUbiquity PressCitizen Science: Theory and Practice2057-49912024-12-0191393910.5334/cstp.737719Cool Neighbors: Combining Artificial Intelligence and Citizen Science to Chart the Sun’s Cosmic NeighborhoodAaron Meisner0https://orcid.org/0000-0002-1125-7384Dan Caselden1https://orcid.org/0000-0001-7896-5791Austin Humphreys2https://orcid.org/0000-0002-8142-6948Grady Robbins3https://orcid.org/0009-0000-0638-6520Eden Schapera4https://orcid.org/0000-0002-4318-7173J. Davy Kirkpatrick5https://orcid.org/0000-0003-4269-260XAdam Schneider6https://orcid.org/0000-0002-6294-5937L. Clifton Johnson7https://orcid.org/0000-0001-6421-0953Marc Kuchner8https://orcid.org/0000-0002-2387-5489Jacqueline Faherty9https://orcid.org/0000-0001-6251-0573Sarah Casewell10https://orcid.org/0000-0003-2478-0120Federico Marocco11https://orcid.org/0000-0001-7519-1700Adam Burgasser12https://orcid.org/0000-0002-6523-9536Daniella Bardalez Gagliuffi13https://orcid.org/0000-0001-8170-7072NSF NOIRLabAmerican Museum of Natural HistoryUniversity of Maryland College ParkUniversity of FloridaEmory UniversityCaltech/IPACUnited States Naval Observatory, Flagstaff StationNorthwestern UniversityNASAAmerican Museum of Natural HistoryUniversity of LeicesterCaltech/IPACUniversity of California San DiegoAmherst CollegeIn this case study, we describe the design and implementation of the Backyard Worlds: Cool Neighbors citizen science project, which combines image-level deep learning with Zooniverse-hosted online crowdsourcing to mine large astronomical sky maps for rare celestial objects called “brown dwarfs.” Specifically, Cool Neighbors uses machine learning to pre-select the sky images shown to volunteers. Cool Neighbors represents an excellent opportunity to interrogate the effects of incorporating artificial intelligence into a citizen science project; its sibling project, Backyard Worlds: Planet 9, uses no artificial intelligence, providing a natural point of comparison for participant engagement metrics. Through analysis of more than 10 million total Zooniverse classifications from the combination of Cool Neighbors and Backyard Worlds: Planet 9, among other results, we find (1) Cool Neighbors volunteers perform ~3x more classifications per unit of time invested than Backyard Worlds: Planet 9 volunteers, and (2) each registered Cool Neighbors participant performs ~2–5x more classifications than each registered Backyard Worlds: Planet 9 participant. We also discuss our measured approach to presenting the complementarity of machine learning and citizen science in volunteer-facing Cool Neighbors materials. Finally, we present a survey of advanced Backyard Worlds participants, which indicates that these citizen scientists are by and large not dissuaded from participating in Cool Neighbors because of its usage of artificial intelligence.https://account.theoryandpractice.citizenscienceassociation.org/index.php/up-j-cstp/article/view/737astronomycitizen scienceneural networksimage analysiszooniversebrown dwarfs |
| spellingShingle | Aaron Meisner Dan Caselden Austin Humphreys Grady Robbins Eden Schapera J. Davy Kirkpatrick Adam Schneider L. Clifton Johnson Marc Kuchner Jacqueline Faherty Sarah Casewell Federico Marocco Adam Burgasser Daniella Bardalez Gagliuffi Cool Neighbors: Combining Artificial Intelligence and Citizen Science to Chart the Sun’s Cosmic Neighborhood Citizen Science: Theory and Practice astronomy citizen science neural networks image analysis zooniverse brown dwarfs |
| title | Cool Neighbors: Combining Artificial Intelligence and Citizen Science to Chart the Sun’s Cosmic Neighborhood |
| title_full | Cool Neighbors: Combining Artificial Intelligence and Citizen Science to Chart the Sun’s Cosmic Neighborhood |
| title_fullStr | Cool Neighbors: Combining Artificial Intelligence and Citizen Science to Chart the Sun’s Cosmic Neighborhood |
| title_full_unstemmed | Cool Neighbors: Combining Artificial Intelligence and Citizen Science to Chart the Sun’s Cosmic Neighborhood |
| title_short | Cool Neighbors: Combining Artificial Intelligence and Citizen Science to Chart the Sun’s Cosmic Neighborhood |
| title_sort | cool neighbors combining artificial intelligence and citizen science to chart the sun s cosmic neighborhood |
| topic | astronomy citizen science neural networks image analysis zooniverse brown dwarfs |
| url | https://account.theoryandpractice.citizenscienceassociation.org/index.php/up-j-cstp/article/view/737 |
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