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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Validating and modeling the impact of high-frequency rapid antigen screening on COVID-19 spread and outcomes
This article has 18 authors:Reviewed by ScreenIT
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Temporal Patterns in the Evolutionary Genetic Distance of SARS-CoV-2 during the COVID-19 Pandemic
This article has 10 authors:Reviewed by ScreenIT
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Comparative Evaluation of Three Serologic Assays for the Identification of SARS-CoV-2 Antibodies
This article has 4 authors:Reviewed by ScreenIT
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Molecular mechanism of inhibiting the SARS-CoV-2 cell entry facilitator TMPRSS2 with camostat and nafamostat
This article has 9 authors:Reviewed by ScreenIT
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Age-adjusted Charlson comorbidity index score is the best predictor for severe clinical outcome in the hospitalized patients with COVID-19 infection
This article has 7 authors:Reviewed by ScreenIT
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Quantitative microbial risk assessment of SARS-CoV-2 for workers in wastewater treatment plants
This article has 8 authors:Reviewed by ScreenIT
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The SARS-CoV-2 Spike harbours a lipid binding pocket which modulates stability of the prefusion trimer
This article has 13 authors:Reviewed by ScreenIT
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Disparity in the quality of COVID-19 data reporting across India
This article has 5 authors:Reviewed by ScreenIT
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Genomics, social media and mobile phone data enable mapping of SARS-CoV-2 lineages to inform health policy in Bangladesh
This article has 22 authors:Reviewed by ScreenIT
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Persistently Increased Systemic ACE2 Activity Is Associated With an Increased Inflammatory Response in Smokers With COVID-19
This article has 4 authors:Reviewed by ScreenIT