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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Enisamium Inhibits SARS-CoV-2 RNA Synthesis
This article has 15 authors:Reviewed by ScreenIT
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COVID-19: Short term prediction model using daily incidence data
This article has 8 authors:Reviewed by ScreenIT
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Risk of Corona virus disease 2019 (COVID-19) among spectacles wearing population of Northern India
This article has 1 author:Reviewed by ScreenIT
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A cell phone data driven time use analysis of the COVID-19 epidemic
This article has 4 authors:Reviewed by ScreenIT
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The common interests of health protection and the economy: evidence from scenario calculations of COVID-19 containment policies
This article has 12 authors:Reviewed by ScreenIT
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Hydroxyzine inhibits SARS-CoV-2 Spike protein binding to ACE2 in a qualitative in vitro assay
This article has 3 authors:Reviewed by ScreenIT
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Act early, save lives: managing COVID-19 in Greece
This article has 3 authors:Reviewed by ScreenIT
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Early impact of school closure and social distancing for COVID-19 on the number of inpatients with childhood non-COVID-19 acute infections in Japan
This article has 7 authors:Reviewed by ScreenIT
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Cardiometabolic risks of SARS-CoV-2 hospitalization using Mendelian Randomization
This article has 2 authors:Reviewed by ScreenIT
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Adult stem cell-derived complete lung organoid models emulate lung disease in COVID-19
This article has 21 authors:This article has been curated by 1 group: