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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Prediction of Covid-19 Infections Through December 2020 for 10 US States Incorporating Outdoor Temperature and School Re-Opening Effects-September Update
This article has 1 author:Reviewed by ScreenIT
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The successful introduction of a hand dermatitis clinic to reduce occupational dermatoses during the covid-19 pandemic
This article has 2 authors:Reviewed by ScreenIT
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A new approach to the dynamic modeling of an infectious disease
This article has 2 authors:Reviewed by ScreenIT
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COVID‐19 in pregnancy—characteristics and outcomes of pregnant women admitted to hospital because of SARS‐CoV‐2 infection in the Nordic countries
This article has 16 authors:Reviewed by ScreenIT
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Non-permissive SARS-CoV-2 infection in human neurospheres
This article has 28 authors:Reviewed by ScreenIT
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Tracking smell loss to identify healthcare workers with SARS-CoV-2 infection
This article has 21 authors:Reviewed by ScreenIT
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Brilacidin, a COVID-19 Drug Candidate, Exhibits Potent In Vitro Antiviral Activity Against SARS-CoV-2
This article has 8 authors:Reviewed by ScreenIT
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Globally local: Hyper-local modeling for accurate forecast of COVID-19
This article has 11 authors:Reviewed by ScreenIT
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Structural and Functional Comparison of SARS-CoV-2-Spike Receptor Binding Domain Produced in Pichia pastoris and Mammalian Cells
This article has 31 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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Use of non-steroidal anti-inflammatory drugs and risk of death from COVID-19: an OpenSAFELY cohort analysis based on two cohorts
This article has 32 authors:Reviewed by ScreenIT