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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Antiviral Resistance against Viral Mutation: Praxis and Policy for SARS-CoV-2
This article has 1 author:Reviewed by ScreenIT
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The Host Interactome of Spike Expands the Tropism of SARS-CoV-2
This article has 6 authors:Reviewed by ScreenIT
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Development and pre-clinical evaluation of Newcastle disease virus-vectored SARS-CoV-2 intranasal vaccine candidate
This article has 38 authors:Reviewed by ScreenIT
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Machine Learning Identifies Ponatinib as a Potent Inhibitor of SARS-CoV2-induced Cytokine Storm
This article has 5 authors:Reviewed by ScreenIT
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Autoantibodies stabilize neutrophil extracellular traps in COVID-19
This article has 11 authors:Reviewed by ScreenIT
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In Silico Investigation of the New UK (B.1.1.7) and South African (501Y.V2) SARS-CoV-2 Variants with a Focus at the ACE2–Spike RBD Interface
This article has 4 authors:Reviewed by ScreenIT
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Perceptions on undertaking regular asymptomatic self-testing for COVID-19 using lateral flow tests: a qualitative study of university students and staff
This article has 14 authors:Reviewed by ScreenIT
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Identification of guanylyltransferase activity in the SARS-CoV-2 RNA polymerase
This article has 5 authors:Reviewed by ScreenIT
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A Newcastle Disease Virus (NDV) Expressing a Membrane-Anchored Spike as a Cost-Effective Inactivated SARS-CoV-2 Vaccine
This article has 16 authors:Reviewed by ScreenIT
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The IgA in milk induced by SARS-CoV-2 infection is comprised of mainly secretory antibody that is neutralizing and highly durable over time
This article has 8 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT