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 minimal model for household-based testing and tracing in epidemics
This article has 8 authors:Reviewed by ScreenIT
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Dosing of thromboprophylaxis and mortality in critically ill COVID-19 patients
This article has 12 authors:Reviewed by ScreenIT
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Outcome of SARS-CoV-2 infection is linked to MAIT cell activation and cytotoxicity
This article has 35 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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SARS-CoV-2 antibody prevalence in health care workers: Preliminary report of a single center study
This article has 9 authors:Reviewed by ScreenIT
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A steady trickle-down from metro districts and improving epidemic-parameters characterized the increasing COVID-19 cases in India
This article has 5 authors:Reviewed by ScreenIT
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SARS-CoV-2 RNA concentrations in primary municipal sewage sludge as a leading indicator of COVID-19 outbreak dynamics
This article has 13 authors:Reviewed by ScreenIT
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Modeling the Waves of Covid-19
This article has 1 author:Reviewed by ScreenIT
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SIR-simulation of Corona pandemic dynamics in Europe
This article has 1 author:Reviewed by ScreenIT
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A novel fermented Yi traditional medicine efficiently suppresses the replication of SARS-CoV-2 in vitro
This article has 6 authors:Reviewed by ScreenIT
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Efficacy of chloroquine or hydroxychloroquine in COVID-19 patients: a systematic review and meta-analysis
This article has 10 authors:Reviewed by ScreenIT