ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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assayM: a web application to monitor mutations in COVID-19 diagnostic assays
This article has 2 authors:Reviewed by ScreenIT
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Robot dance: a city-wise automatic control of Covid-19 mitigation levels
This article has 3 authors:Reviewed by ScreenIT
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Continued proportional age shift of confirmed positive COVID-19 incidence over time to children and young adults: Washington State March—August 2020
This article has 3 authors:Reviewed by ScreenIT
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COVID-19 infection among bartenders and waiters before and after pub lockdown
This article has 3 authors:Reviewed by ScreenIT
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Model-based projections for COVID-19 outbreak size and student-days lost to closure in Ontario childcare centres and primary schools
This article has 4 authors:Reviewed by ScreenIT
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Simulated Identification of Silent COVID-19 Infections Among Children and Estimated Future Infection Rates With Vaccination
This article has 5 authors:Reviewed by ScreenIT
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Impact of Meteorological Parameters on the COVID-19 Incidence: The Case of the City of Oran, Algeria
This article has 3 authors:Reviewed by ScreenIT
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Global convergence of COVID-19 basic reproduction number and estimation from early-time SIR dynamics
This article has 5 authors:Reviewed by ScreenIT
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Household transmission in people infected with SARS-CoV-2 (COVID-19) in Metropolitan Lima
This article has 6 authors: -
Estimating the Changing Infection Rate of COVID-19 Using Bayesian Models of Mobility
This article has 9 authors:Reviewed by ScreenIT