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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Retrospective study of COVID-19 seroprevalence among tissue donors at the onset of the outbreak before implementation of strict lockdown measures in France
This article has 7 authors:Reviewed by ScreenIT
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Do latitude and ozone concentration predict Covid-2019 cases in 34 countries?
This article has 1 author:Reviewed by ScreenIT
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Three pictures of COVID-19 behavior in Italy: similar growth and different degrowth
This article has 3 authors:Reviewed by ScreenIT
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Scrutinizing the spread of COVID-19 in Madagascar
This article has 2 authors:Reviewed by ScreenIT
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The kinetic variations of anti-nucleocapsid antibody in SARS-CoV-2 infection
This article has 7 authors:Reviewed by ScreenIT
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First Data-Set on SARS-CoV-2 Detection for Istanbul Wastewaters in Turkey
This article has 6 authors:Reviewed by ScreenIT
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SARS-CoV-2 S protein:ACE2 interaction reveals novel allosteric targets
This article has 12 authors:This article has been curated by 1 group: -
The effect of whey protein on viral infection and replication of SARS-CoV-2 and pangolin coronavirus in vitro
This article has 14 authors:Reviewed by ScreenIT
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Forecasting PPE Consumption during a Pandemic: The Case of Covid-19
This article has 13 authors:Reviewed by ScreenIT
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A bacterial artificial chromosome (BAC)-vectored noninfectious replicon of SARS-CoV-2
This article has 5 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT