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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COVID-19 vaccines dampen genomic diversity of SARS-CoV-2: Unvaccinated patients exhibit more antigenic mutational variance
This article has 17 authors:Reviewed by ScreenIT
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Perceptions and acceptance of COVID-19 vaccine among pregnant and lactating women in Singapore: A cross-sectional study
This article has 10 authors:Reviewed by ScreenIT
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Allotypic variation in antigen processing controls antigenic peptide generation from SARS-CoV-2 S1 spike glycoprotein
This article has 5 authors:Reviewed by ScreenIT
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An engineered SARS-CoV-2 receptor-binding domain produced in Pichia pastoris as a candidate vaccine antigen
This article has 23 authors:Reviewed by ScreenIT
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Convalescent plasma for hospitalized patients with COVID-19 and the effect of plasma antibodies: a randomized controlled, open-label trial
This article has 36 authors:Reviewed by ScreenIT
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A model for network-based identification and pharmacological targeting of aberrant, replication-permissive transcriptional programs induced by viral infection
This article has 16 authors:Reviewed by ScreenIT
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Subgenomic and negative sense RNAs are not markers of active replication of SARS-CoV-2 in nasopharyngeal swabs
This article has 3 authors:Reviewed by ScreenIT
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Vaccine Breakthrough Infections by SARS-CoV-2 Variants after ChAdOx1 nCoV-19 Vaccination in Healthcare Workers
This article has 23 authors:Reviewed by ScreenIT
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Longitudinal testing for SARS-CoV-2 RNA in day care centers in Hesse, Germany, during increased local incidence and with VOC Alpha as dominant variant: Results of the SAFE KiDS 2 and SAFE KiDS 3 study
This article has 14 authors:Reviewed by ScreenIT
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Characterizing parametric differences between the two waves of COVID-19 in India
This article has 1 author:Reviewed by ScreenIT