ScreenIT
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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High resolution proximity statistics as early warning for US universities reopening during COVID-19
This article has 7 authors:Reviewed by ScreenIT
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Variant analysis of SARS-CoV-2 genomes in the Middle East
This article has 2 authors:Reviewed by ScreenIT
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Using surgical wrapping material for the fabrication of respirator masks
This article has 2 authors:Reviewed by ScreenIT
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Targeting conserved viral virulence determinants by single domain antibodies to block SARS-CoV2 infectivity
This article has 9 authors:Reviewed by ScreenIT
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Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave
This article has 12 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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SARS-CoV-2 infection among patients with multiple sclerosis; A cross-sectional study
This article has 6 authors:Reviewed by ScreenIT
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Dynamics of SARS-CoV-2-specific antibodies among COVID19 biobank donors in Argentina
This article has 4 authors:Reviewed by ScreenIT
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Ranking the effectiveness of worldwide COVID-19 government interventions
This article has 9 authors:Reviewed by ScreenIT
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Identification of low micromolar SARS-CoV-2 M pro inhibitors from hits identified by in silico screens
This article has 10 authors:Reviewed by ScreenIT
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Comparative analyses of SARS-CoV-2 binding (IgG, IgM, IgA) and neutralizing antibodies from human serum samples
This article has 11 authors:Reviewed by ScreenIT