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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Modeling shield immunity to reduce COVID-19 epidemic spread
This article has 13 authors:Reviewed by ScreenIT
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Meteorological factors correlate with transmission of 2019-nCoV: Proof of incidence of novel coronavirus pneumonia in Hubei Province, China
This article has 6 authors:Reviewed by ScreenIT
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Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States
This article has 3 authors: -
Early perceptions and behavioural responses during the COVID-19 pandemic: a cross-sectional survey of UK adults
This article has 7 authors:Reviewed by ScreenIT
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The protein expression profile of ACE2 in human tissues
This article has 6 authors:Reviewed by ScreenIT
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In silico approach for designing of a multi-epitope based vaccine against novel Coronavirus (SARS-COV-2)
This article has 2 authors:Reviewed by ScreenIT
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LxxIxE-like Motif in Spike Protein of SARS-CoV-2 that is Known to Recruit the Host PP2A-B56 Phosphatase Mimics Artepillin C, an Immunomodulator, of Brazilian Green Propolis
This article has 1 author:Reviewed by ScreenIT
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Structural Basis for Designing Multiepitope Vaccines Against COVID-19 Infection: In Silico Vaccine Design and Validation
This article has 9 authors:Reviewed by ScreenIT
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SARS-CoV-2 neutralizing serum antibodies in cats: a serological investigation
This article has 16 authors:Reviewed by ScreenIT
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Deducing the N- and O-glycosylation profile of the spike protein of novel coronavirus SARS-CoV-2
This article has 4 authors:Reviewed by ScreenIT