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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Effect of D614G Spike Variant on Immunoglobulin G, M, or A Spike Seroassay Performance
This article has 9 authors:Reviewed by ScreenIT
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Clinical, behavioural and social factors associated with racial disparities in COVID-19 patients from an integrated healthcare system in Georgia: a retrospective cohort study
This article has 7 authors:Reviewed by ScreenIT
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Revealing fine-scale spatiotemporal differences in SARS-CoV-2 introduction and spread
This article has 24 authors:Reviewed by ScreenIT
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A case-control and cohort study to determine the relationship between ethnic background and severe COVID-19
This article has 18 authors:Reviewed by ScreenIT
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SARS-CoV-2 Seroprevalence Among Parturient Women
This article has 30 authors:Reviewed by ScreenIT
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Comparison of SARS-CoV-2 serological tests with different antigen targets
This article has 5 authors:Reviewed by ScreenIT
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Predictors of Anxiety Regarding The COVID-19 Pandemic Among Health-care Workers in a Hospital Not Assigned to Manage COVID-19 Patients in Nepal
This article has 11 authors:Reviewed by ScreenIT
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Analysis of SARS-CoV-2 vertical transmission during pregnancy
This article has 16 authors:Reviewed by ScreenIT
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Sleep quality, mental health and circadian rhythms during COVID lockdown – Results from the SleepQuest Study
This article has 8 authors:Reviewed by ScreenIT
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SARS-CoV-2 spread across the Colombian-Venezuelan border
This article has 24 authors:Reviewed by ScreenIT