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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Projections and early-warning signals of a second wave of the COVID-19 epidemic in Illinois
This article has 6 authors:Reviewed by ScreenIT
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Role of SARS-CoV-2 in altering the RNA binding protein and miRNA directed post-transcriptional regulatory networks in humans
This article has 4 authors:Reviewed by ScreenIT
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Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity
This article has 5 authors:Reviewed by ScreenIT
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Comparison of three TaqMan real-time reverse transcription-PCR assays in detecting SARS-CoV-2
This article has 8 authors:Reviewed by ScreenIT
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Comparison of Transgenic and Adenovirus hACE2 Mouse Models for SARS-CoV-2 Infection
This article has 9 authors:Reviewed by ScreenIT
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N- and O-Glycosylation of the SARS-CoV-2 Spike Protein
This article has 3 authors:Reviewed by ScreenIT
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Discovery of Drug-Like Ligands for the Mac1 Domain of SARS-CoV-2 Nsp3
This article has 7 authors:Reviewed by ScreenIT
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Identification of Potent and Safe Antiviral Therapeutic Candidates Against SARS-CoV-2
This article has 12 authors:Reviewed by ScreenIT
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Animal Model Prescreening: Pre-exposure to SARS-CoV-2 impacts responses in the NHP model
This article has 19 authors:Reviewed by ScreenIT
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Renal Carcinoma Is Associated With Increased Risk of Coronavirus Infections
This article has 4 authors:Reviewed by ScreenIT