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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Immune response to vaccine candidates based on different types of nanoscaffolded RBD domain of the SARS-CoV-2 spike protein
This article has 25 authors:Reviewed by ScreenIT
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Inferring the effectiveness of government interventions against COVID-19
This article has 19 authors:Reviewed by ScreenIT
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Evaluation of vertical transmission of SARS-CoV-2 in utero: Nine pregnant women and their newborns
This article has 8 authors:Reviewed by ScreenIT
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Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations
This article has 5 authors:Reviewed by ScreenIT
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Strong impact of closing schools, closing bars and wearing masks during the COVID-19 pandemic: results from a simple and revealing analysis
This article has 2 authors:Reviewed by ScreenIT
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Nowcasting and Forecasting the Spread of COVID-19 and Healthcare Demand in Turkey, a Modeling Study
This article has 3 authors:Reviewed by ScreenIT
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Characterization and structural basis of a lethal mouse-adapted SARS-CoV-2
This article has 30 authors:Reviewed by ScreenIT
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Pulmonary post-mortem findings in a series of COVID-19 cases from northern Italy: a two-centre descriptive study
This article has 15 authors:Reviewed by ScreenIT
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Host-to-host airborne transmission as a multiphase flow problem for science-based social distance guidelines
This article has 5 authors:Reviewed by ScreenIT
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SARS-CoV-2 serosurvey in health care workers of the Veneto Region
This article has 9 authors:Reviewed by ScreenIT