ReviewBioinformatics (Oxford, England)2013
Crowdsourcing for bioinformatics.
Review in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed, 148 citations in OpenAlex.
- Using semantic search to find publicly available gene-expression datasets.Bioinformatics (Oxford, England) · 2026Article
- Creating and validating a scholarly knowledge graph using natural language processing and microtask crowdsourcing.International journal on digital libraries · 2024Article
- Crowdsourcing Skin Demarcations of Chronic Graft-Versus-Host Disease in Patient Photographs: Training Versus Performance Study.JMIR dermatology · 2023Article
- Results and lessons learned from the sbv IMPROVER metagenomics diagnostics for inflammatory bowel disease challenge.Scientific reports · 2023Article
- A proteomics sample metadata representation for multiomics integration and big data analysis.Nature communications · 2021Review
- Collective view: mappingPeerJ. Computer science · 2021Article
- Deep Lake Explorer: A web application for crowdsourcing the classification of benthic underwater video from the Laurentian Great Lakes.Journal of Great Lakes research · 2020Article
- Research data management in health and biomedical citizen science: practices and prospects.JAMIA open · 2020Article
- Scientific Discovery Games for Biomedical Research.Annual review of biomedical data science · 2019Article
- Automated assessment of biological database assertions using the scientific literature.BMC bioinformatics · 2019Article
- Mining data and metadata from the gene expression omnibus.Biophysical reviews · 2019Review
- The CrowdWater game: A playful way to improve the accuracy of crowdsourced water level class data.PloS one · 2019Article
- OC-2-KB: integrating crowdsourcing into an obesity and cancer knowledge base curation system.BMC medical informatics and decision making · 2018Article
- Exploring applications of crowdsourcing to cryo-EM.Journal of structural biology · 2018Article
- webpic: A flexible web application for collecting distance and count measurements from images.PloS one · 2018Article
- A Review on the Applications of Crowdsourcing in Human Pathology.Journal of pathology informatics · 2018Review
- NoGOA: predicting noisy GO annotations using evidences and sparse representation.BMC bioinformatics · 2017Article
- Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop.Journal of biomedical informatics · 2017Article
- Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis.GigaScience · 2017Article
- Use it or lose it: citations predict the continued online availability of published bioinformatics resources.Nucleic acids research · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
motivationBioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base population and protein structure determination all benefit from human input. In some cases, people are needed in vast quantities, whereas in others, we need just a few with rare abilities. Crowdsourcing encompasses an emerging collection of approaches for harnessing such distributed human intelligence. Recently, the bioinformatics community has begun to apply crowdsourcing in a variety of contexts, yet few resources are available that describe how these human-powered systems work and how to use them effectively in scientific domains.
resultsHere, we provide a framework for understanding and applying several different types of crowdsourcing. The framework considers two broad classes: systems for solving large-volume 'microtasks' and systems for solving high-difficulty 'megatasks'. Within these classes, we discuss system types, including volunteer labor, games with a purpose, microtask markets and open innovation contests. We illustrate each system type with successful examples in bioinformatics and conclude with a guide for matching problems to crowdsourcing solutions that highlights the positives and negatives of different approaches.
Indexed as
Identifiers
What OpenQuestion holds
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.