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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Government Restriction Efficiency on Curbing COVID-19 Pandemic Transmission in Western Europe
This article has 3 authors:Reviewed by ScreenIT
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Evolutionary analysis of genomes of SARS-CoV-2-related bat viruses suggests old roots, constant effective population size, and possible increase of fitness
This article has 3 authors:Reviewed by ScreenIT
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Time-Dependent Increase in Susceptibility and Severity of Secondary Bacterial Infection during SARS-CoV-2 Infection
This article has 11 authors:Reviewed by ScreenIT
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COVID-19 testing: disparity between national and institution-based case detection
This article has 5 authors:Reviewed by ScreenIT
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Vaccine hesitancy strongly correlates with COVID-19 deaths underreporting
This article has 3 authors:Reviewed by ScreenIT
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Effect of booster vaccination against Delta and Omicron SARS-CoV-2 variants in Iceland
This article has 13 authors:Reviewed by ScreenIT
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An Immunobridging Study to Evaluate the Neutralizing Antibody Titer in Adults Immunized with Two Doses of Either ChAdOx1-nCov-19 (AstraZeneca) or MVC-COV1901
This article has 10 authors:Reviewed by ScreenIT
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Rapid review of government issued documents relevant to mitigation of COVID-19 in the US food manufacturing and processing industry
This article has 4 authors:Reviewed by ScreenIT
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A compartmental Mathematical model of COVID-19 intervention scenarios for Mumbai
This article has 2 authors:Reviewed by ScreenIT
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RK-33, a small molecule inhibitor of host RNA helicase DDX3, suppresses multiple variants of SARS-CoV-2
This article has 9 authors:Reviewed by ScreenIT