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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Performance of serum apolipoprotein-A1 as a sentinel of Covid-19
This article has 25 authors:Reviewed by ScreenIT
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High food insecurity in Latinx families and associated COVID-19 infection in the Greater Bay Area, California
This article has 5 authors:Reviewed by ScreenIT
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Modelling to Predict Hospital Bed Requirements for Covid-19 Patients in California
This article has 1 author:Reviewed by ScreenIT
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Two New Models for Epidemics with Application to the COVID-19 Pandemic in the United States, Italy, and the United Kingdom
This article has 1 author:Reviewed by ScreenIT
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Neutralizing antibody responses 10 months after mild and moderately-severe SARS-CoV-2 infection
This article has 5 authors:Reviewed by ScreenIT
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Early transmissibility assessment of the N501Y mutant strains of SARS-CoV-2 in the United Kingdom, October to November 2020
This article has 5 authors:Reviewed by ScreenIT
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SARS-CoV-2 nucleocapsid protein binds host mRNAs and attenuates stress granules to impair host stress response
This article has 16 authors:Reviewed by ScreenIT
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Predicting the Impact of the COVID-19 Pandemic for the Low- and Middle-Income Countries
This article has 7 authors:Reviewed by ScreenIT
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Face mask wearing rate predicts COVID-19 death rates across countries
This article has 2 authors:Reviewed by ScreenIT
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Risk factors for outcomes of COVID-19 patients: an observational study of 795 572 patients in Russia
This article has 10 authors:Reviewed by ScreenIT