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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Transcriptional profiling reveals potential involvement of microvillous TRPM5-expressing cells in viral infection of the olfactory epithelium
This article has 12 authors:Reviewed by ScreenIT
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Broad sarbecovirus neutralizing antibodies define a key site of vulnerability on the SARS-CoV-2 spike protein
This article has 37 authors:Reviewed by ScreenIT
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ACE2, TMPRSS2, and Furin expression in the nose and olfactory bulb in mice and human
This article has 6 authors:Reviewed by ScreenIT
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Analysis of SARS-CoV-2 RNA-Sequences by Interpretable Machine Learning Models
This article has 7 authors:Reviewed by ScreenIT
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CoV-AbDab: the coronavirus antibody database
This article has 4 authors:Reviewed by ScreenIT
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Analytical and Clinical Comparison of Three Nucleic Acid Amplification Tests for SARS-CoV-2 Detection
This article has 6 authors:Reviewed by ScreenIT
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Favipiravir strikes the SARS-CoV-2 at its Achilles heel, the RNA polymerase
This article has 13 authors:Reviewed by ScreenIT
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A single dose SARS-CoV-2 simulating particle vaccine induces potent neutralizing activities
This article has 17 authors:Reviewed by ScreenIT
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Yeast-expressed SARS-CoV recombinant receptor-binding domain (RBD219-N1) formulated with aluminum hydroxide induces protective immunity and reduces immune enhancement
This article has 13 authors:Reviewed by ScreenIT
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Structural analysis of the SARS-CoV-2 methyltransferase complex involved in RNA cap creation bound to sinefungin
This article has 4 authors:Reviewed by ScreenIT