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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IL-2 and IFN-γ are biomarkers of SARS-CoV-2 specific cellular response in whole blood stimulation assays
This article has 15 authors:Reviewed by ScreenIT
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Adaptive immunity to human coronaviruses is widespread but low in magnitude
This article has 22 authors:Reviewed by ScreenIT
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Near Term Predictions of Covid-19 Cases in West Bengal, Maharashtra, Delhi and Tamil Nadu in India Based on Basu Model
This article has 1 author:Reviewed by ScreenIT
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Prediction of peak and termination of novel coronavirus COVID-19 epidemic in Iran
This article has 3 authors:Reviewed by ScreenIT
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Beneficial effects of colchicine for moderate to severe COVID-19: a randomised, double-blinded, placebo-controlled clinical trial
This article has 34 authors:Reviewed by ScreenIT
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COVID-19 associated autoimmunity is a feature of severe respiratory disease - a Bayesian analysis
This article has 7 authors:Reviewed by ScreenIT
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Online biophysical predictions for SARS-CoV-2 proteins
This article has 6 authors:Reviewed by ScreenIT
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A SEIR-like model with a time-dependent contagion factor describes the dynamics of the Covid-19 pandemic
This article has 1 author:Reviewed by ScreenIT
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Strategies for Controlling the Spread of COVID-19
This article has 2 authors:Reviewed by ScreenIT
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Preventing within household transmission of Covid-19: is the provision of accommodation to support self-isolation feasible and acceptable?
This article has 5 authors:Reviewed by ScreenIT