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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Community-level evidence for SARS-CoV-2 vaccine protection of unvaccinated individuals
This article has 11 authors:Reviewed by ScreenIT
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ChAdOx1 nCoV-19 vaccination prevents SARS-CoV-2 pneumonia in rhesus macaques
This article has 35 authors:Reviewed by ScreenIT
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Prefusion conformation of SARS-CoV-2 receptor-binding domain favours interactions with human receptor ACE2
This article has 9 authors:Reviewed by ScreenIT
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Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients
This article has 35 authors:Reviewed by ScreenIT
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Short Time Effect of COVID 19 Pandemic on HbA1c and Acute Metabolic Complications in Children with Type 1 Diabetes
This article has 4 authors:Reviewed by ScreenIT
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SARS-CoV-2 spike-protein D614G mutation increases virion spike density and infectivity
This article has 15 authors:Reviewed by ScreenIT
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COVID-19 in Children with Brain-Based Developmental Disabilities: A Rapid Review Update
This article has 15 authors:Reviewed by ScreenIT
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Immune characterization and profiles of SARS-CoV-2 infected patients reveals potential host therapeutic targets and SARS-CoV-2 oncogenesis mechanism
This article has 4 authors:Reviewed by ScreenIT
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Cost-effectiveness of Coronavirus Disease 2019 Vaccination in Low- and Middle-Income Countries
This article has 9 authors:Reviewed by ScreenIT
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Distinct SARS-CoV-2 antibody reactivity patterns in coronavirus convalescent plasma revealed by a coronavirus antigen microarray
This article has 14 authors:Reviewed by ScreenIT