ArticleJournal of structural biology2018
Exploring applications of crowdsourcing to cryo-EM.
Article in Journal of structural biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- Online citizen science with the Zooniverse for analysis of biological volumetric data.Histochemistry and cell biology · 2023Article
- Research data management in health and biomedical citizen science: practices and prospects.JAMIA open · 2020Article
- A guided approach for subtomogram averaging of challenging macromolecular assemblies.Journal of structural biology: X · 2020Article
- Scientific Discovery Games for Biomedical Research.Annual review of biomedical data science · 2019Article
- Securing the future of research computing in the biosciences.PLoS computational biology · 2019Article
- The crowd storms the ivory tower.Nature methods · 2018Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Extraction of particles from cryo-electron microscopy (cryo-EM) micrographs is a crucial step in processing single-particle datasets. Although algorithms have been developed for automatic particle picking, these algorithms generally rely on two-dimensional templates for particle identification, which may exhibit biases that can propagate artifacts through the reconstruction pipeline. Manual picking is viewed as a gold-standard solution for particle selection, but it is too time-consuming to perform on data sets of thousands of images. In recent years, crowdsourcing has proven effective at leveraging the open web to manually curate datasets. In particular, citizen science projects such as Galaxy Zoo have shown the power of appealing to users' scientific interests to process enormous amounts of data. To this end, we explored the possible applications of crowdsourcing in cryo-EM particle picking, presenting a variety of novel experiments including the production of a fully annotated particle set from untrained citizen scientists. We show the possibilities and limitations of crowdsourcing particle selection tasks, and explore further options for crowdsourcing cryo-EM data processing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.