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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First computational design of Covid-19 coronavirus vaccine using lambda superstrings
This article has 9 authors:Reviewed by ScreenIT
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Efficacy of contact tracing for the containment of the 2019 novel coronavirus (COVID-19)
This article has 3 authors:Reviewed by ScreenIT
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Ad26.COV2.S-elicited immunity protects against G614 spike variant SARS-CoV-2 infection in Syrian hamsters and does not enhance respiratory disease in challenged animals with breakthrough infection after sub-optimal vaccine dosing
This article has 27 authors:Reviewed by ScreenIT
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A Saudi G6PD Deficient Girl Died with Pediatric Multisystem Inflammatory Syndrome-COVID-19
This article has 7 authors:Reviewed by ScreenIT
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The neutralization effect of montelukast on SARS-CoV-2 is shown by multiscale in silico simulations and combined in vitro studies
This article has 26 authors:Reviewed by ScreenIT
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Impact on mental health care and on mental health service users of the COVID-19 pandemic: a mixed methods survey of UK mental health care staff
This article has 44 authors:Reviewed by ScreenIT
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A Plasma-Generating N-95 Respirator Decontamination Unit Created from a Microwave Oven
This article has 7 authors:Reviewed by ScreenIT
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Predicting Health Disparities in Regions at Risk of Severe Illness to Inform Health Care Resource Allocation During Pandemics: Observational Study
This article has 1 author:Reviewed by ScreenIT
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Clinical risk factors for mortality in an analysis of 1375 patients admitted for COVID treatment
This article has 3 authors:Reviewed by ScreenIT
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Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: A population-based cohort study from Italy
This article has 10 authors:Reviewed by ScreenIT