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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Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform
This article has 9 authors:Reviewed by ScreenIT
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Features of C-reactive protein in COVID-19 patients within various period: a cohort study
This article has 9 authors:Reviewed by ScreenIT
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Statistical power in COVID-19 case-control host genomic study design
This article has 11 authors:Reviewed by ScreenIT
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Low awareness of past SARS-CoV-2 infection in healthy plasma donors
This article has 11 authors:Reviewed by ScreenIT
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An epidemic model integrating direct and fomite transmission as well as household structure applied to COVID-19
This article has 6 authors:Reviewed by ScreenIT
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A Deferred-Vaccination Design to Assess Durability of COVID-19 Vaccine Effect After the Placebo Group Is Vaccinated
This article has 30 authors:Reviewed by ScreenIT
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The date predicted 200.000 cases of Covid-19 in Spain
This article has 2 authors:Reviewed by ScreenIT
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Social disparities in the first wave of COVID-19 incidence rates in Germany: a county-scale explainable machine learning approach
This article has 3 authors:Reviewed by ScreenIT
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Unbiased Screens Show CD8+ T Cells of COVID-19 Patients Recognize Shared Epitopes in SARS-CoV-2 that Largely Reside outside the Spike Protein
This article has 19 authors:Reviewed by ScreenIT
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Potent SARS-CoV-2 neutralizing antibodies directed against spike N-terminal domain target a single supersite
This article has 23 authors:Reviewed by ScreenIT