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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Crystal Structure of the SARS-CoV-2 Non-structural Protein 9, Nsp9
This article has 4 authors:Reviewed by ScreenIT
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Triage assessment of cardiorespiratory risk status based on measurement of the anaerobic threshold, and estimation by activity limitation in patients with pulmonary arteriovenous malformations and hereditary haemorrhagic telangiectasia
This article has 9 authors:Reviewed by ScreenIT
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SARS‐CoV ‐2 asymptomatic and symptomatic patients and risk for transfusion transmission
This article has 9 authors:Reviewed by ScreenIT
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Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT Scan Images
This article has 10 authors:Reviewed by ScreenIT
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Orthogonal genome-wide screens of bat cells identify MTHFD1 as a target of broad antiviral therapy
This article has 23 authors:Reviewed by ScreenIT
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SARS-CoV-2 detection from nasopharyngeal swab samples without RNA extraction
This article has 7 authors:Reviewed by ScreenIT
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Comparative Genomic Analysis of Rapidly Evolving SARS-CoV-2 Reveals Mosaic Pattern of Phylogeographical Distribution
This article has 17 authors:Reviewed by ScreenIT
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The psychological distress and coping styles in the early stages of the 2019 coronavirus disease (COVID-19) epidemic in the general mainland Chinese population: A web-based survey
This article has 8 authors:Reviewed by ScreenIT
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Changed transmission epidemiology of COVID-19 at early stage: A nationwide population-based piecewise mathematical modelling study
This article has 16 authors:Reviewed by ScreenIT
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Evaluations of the serological test in the diagnosis of 2019 novel coronavirus (SARS-CoV-2) infections during the COVID-19 outbreak
This article has 12 authors:Reviewed by ScreenIT