FlyWire: online community for whole-brain connectomics.
Clicks: 184
ID: 275004
2022
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Emerging Content
6.3
/100
21 views
21 readers
Trending
AI Quality Assessment
Not analyzed
Abstract
Due to advances in automated image acquisition and analysis, whole-brain connectomes with 100,000 or more neurons are on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a Drosophila melanogaster brain and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based three-dimensional interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analyzing the connectome of mechanosensory neurons.
Abstract Quality Issue:
This abstract appears to be incomplete or contains metadata (152 words).
Try re-searching for a better abstract.
| Reference Key |
dorkenwald2022flywirenature
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Dorkenwald, Sven;McKellar, Claire E;Macrina, Thomas;Kemnitz, Nico;Lee, Kisuk;Lu, Ran;Wu, Jingpeng;Popovych, Sergiy;Mitchell, Eric;Nehoran, Barak;Jia, Zhen;Bae, J Alexander;Mu, Shang;Ih, Dodam;Castro, Manuel;Ogedengbe, Oluwaseun;Halageri, Akhilesh;Kuehner, Kai;Sterling, Amy R;Ashwood, Zoe;Zung, Jonathan;Brittain, Derrick;Collman, Forrest;Schneider-Mizell, Casey;Jordan, Chris;Silversmith, William;Baker, Christa;Deutsch, David;Encarnacion-Rivera, Lucas;Kumar, Sandeep;Burke, Austin;Bland, Doug;Gager, Jay;Hebditch, James;Koolman, Selden;Moore, Merlin;Morejohn, Sarah;Silverman, Ben;Willie, Kyle;Willie, Ryan;Yu, Szi-Chieh;Murthy, Mala;Seung, H Sebastian; |
| Journal | Nature Methods |
| Year | 2022 |
| DOI |
10.1038/s41592-021-01330-0
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.