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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Nested pool testing strategy for the reliable identification of individuals infected with SARS-CoV-2
This article has 6 authors:Reviewed by ScreenIT
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An effect of the COVID-19 pandemic: Significantly more complicated appendicitis due to delayed presentation of patients!
This article has 6 authors:Reviewed by ScreenIT
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Unprecedented reduction in births of very low birthweight (VLBW) and extremely low birthweight (ELBW) infants during the COVID-19 lockdown in Ireland: a ‘natural experiment’ allowing analysis of data from the prior two decades
This article has 8 authors:Reviewed by ScreenIT
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FASN inhibitor TVB-3166 prevents S-acylation of the spike protein of human coronaviruses
This article has 14 authors:Reviewed by ScreenIT
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Initial experience in Mexico with convalescent plasma in COVID-19 patients with severe respiratory failure, a retrospective case series
This article has 28 authors:Reviewed by ScreenIT
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Systematic discovery and functional interrogation of SARS-CoV-2 viral RNA-host protein interactions during infection
This article has 16 authors:Reviewed by ScreenIT
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Collider bias undermines our understanding of COVID-19 disease risk and severity
This article has 14 authors:Reviewed by ScreenIT
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Feasibility of neighborhood and building scale wastewater-based genomic epidemiology for pathogen surveillance
This article has 3 authors:Reviewed by ScreenIT
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A Meta-analysis on the Role of Children in Severe Acute Respiratory Syndrome Coronavirus 2 in Household Transmission Clusters
This article has 14 authors:Reviewed by ScreenIT
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A prospective study of risk factors associated with seroprevalence of SARS-CoV-2 antibodies in healthcare workers at a large UK teaching hospital
This article has 41 authors:Reviewed by ScreenIT