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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Identifiability of Infection Model Parameters Early in an Epidemic
This article has 6 authors:Reviewed by ScreenIT
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Mathematical perspective of Covid-19 pandemic: Disease extinction criteria in deterministic and stochastic models
This article has 3 authors:Reviewed by ScreenIT
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The role of asymptomatic and pre-symptomatic infection in SARS-CoV-2 transmission—a living systematic review
This article has 6 authors:Reviewed by ScreenIT
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Prevalence and stability of SARS-CoV-2 RNA on Bangladeshi banknotes
This article has 8 authors:Reviewed by ScreenIT
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Prolonged Severe Acute Respiratory Syndrome Coronavirus 2 Replication in an Immunocompromised Patient
This article has 11 authors:Reviewed by ScreenIT
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Comparison of longitudinal trends in self-reported symptoms and COVID-19 case activity in Ontario, Canada
This article has 14 authors:Reviewed by ScreenIT
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A bibliometric and co-occurrence analysis of COVID-19–related literature published between December 2019 and June 2020
This article has 1 author:Reviewed by ScreenIT
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Ascertainment rate of novel coronavirus disease (COVID-19) in Japan
This article has 3 authors:Reviewed by ScreenIT
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The quantitative landscape of the neutralizing antibody response to SARS-CoV-2
This article has 3 authors:Reviewed by ScreenIT
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Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning
This article has 2 authors:Reviewed by ScreenIT