ScreenIT
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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Forecasting COVID-19 cases and deaths in epidemic-mitigating European countries by Richards function-based regression analyses
This article has 1 author:Reviewed by ScreenIT
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The changing characteristics of COVID-19 presentations. A regional comparison of SARS-CoV-2 hospitalised patients during the first and second wave
This article has 18 authors:Reviewed by ScreenIT
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JUE Insight: COVID-19, race, and gender
This article has 2 authors:Reviewed by ScreenIT
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The SARS-CoV-2 antibody landscape is lower in magnitude for structural proteins, diversified for accessory proteins and stable long-term in children
This article has 16 authors:Reviewed by ScreenIT
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Clinico-pathological features in fatal COVID-19 infection: a preliminary experience of a tertiary care center in North India using postmortem minimally invasive tissue sampling
This article has 41 authors:Reviewed by ScreenIT
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Understanding COVID-19 dynamics and the effects of interventions in the Philippines: A mathematical modelling study
This article has 13 authors:Reviewed by ScreenIT
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Quantifying Covid19-vaccine location strategies for Germany
This article has 7 authors:Reviewed by ScreenIT
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Will an imperfect vaccine curtail the COVID-19 pandemic in the U.S.?
This article has 3 authors:Reviewed by ScreenIT
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Paired heavy- and light-chain signatures contribute to potent SARS-CoV-2 neutralization in public antibody responses
This article has 34 authors:Reviewed by ScreenIT
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Rapid detection of SARS-CoV2 by Ambient Mass Spectrometry Techniques
This article has 15 authors:Reviewed by ScreenIT