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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A monoclonal antibody against staphylococcal enterotoxin B superantigen inhibits SARS-CoV-2 entry in vitro
This article has 10 authors:Reviewed by ScreenIT
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Choice of assemblers has a critical impact on de novo assembly of SARS-CoV-2 genome and characterizing variants
This article has 7 authors:Reviewed by ScreenIT
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Optimizing ventilation cycles to control airborne transmission risk of SARS-CoV2 in school classrooms
This article has 2 authors:Reviewed by ScreenIT
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Preliminary estimates of the reproduction number of the coronavirus disease (COVID-19) outbreak in Republic of Korea and Italy by 5 March 2020
This article has 9 authors:Reviewed by ScreenIT
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A Novel Heuristic Global Algorithm to Predict the COVID-19 Pandemic Trend
This article has 9 authors:Reviewed by ScreenIT
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Snake venom phospholipases A2 possess a strong virucidal activity against SARS-CoV-2 in vitro and block the cell fusion mediated by spike glycoprotein interaction with the ACE2 receptor
This article has 8 authors:Reviewed by ScreenIT
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Inadequate preparedness for response to COVID-19 is associated with stress and burnout among healthcare workers in Ghana
This article has 8 authors:Reviewed by ScreenIT
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Examining the persistence of human Coronavirus 229E on fresh produce
This article has 4 authors:Reviewed by ScreenIT
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Global, regional, and national estimates of target population sizes for covid-19 vaccination: descriptive study
This article has 11 authors:Reviewed by ScreenIT
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Estimating Unreported Deaths from Natural Causes during COVID-19
This article has 5 authors:Reviewed by ScreenIT