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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A simple, SIR-like but individual-based epidemic model: Application in comparison of COVID-19 in New York City and Wuhan
This article has 1 author:Reviewed by ScreenIT
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COVID-19 Trends in Florida – August 10 – November 14, 2020
This article has 2 authors:Reviewed by ScreenIT
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Association of Simulated COVID-19 Vaccination and Nonpharmaceutical Interventions With Infections, Hospitalizations, and Mortality
This article has 11 authors:Reviewed by ScreenIT
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The basic reproduction number can be accurately estimated within 14 days after societal lockdown: The early stage of the COVID-19 epidemic in Denmark
This article has 3 authors:Reviewed by ScreenIT
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Dynamics and significance of the antibody response to SARS-CoV-2 infection
This article has 35 authors:Reviewed by ScreenIT
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The effects of India's COVID-19 lockdown on critical non-COVID health care and outcomes: Evidence from dialysis patients
This article has 2 authors:Reviewed by ScreenIT
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A randomized trial on the regular use of potent mouthwash in COVID-19 treatment
This article has 16 authors:Reviewed by ScreenIT
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Ribavirin shows antiviral activity against SARS-CoV-2 and downregulates the activity of TMPRSS2 and the expression of ACE2 in vitro
This article has 22 authors:Reviewed by ScreenIT
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Mapping SARS-CoV-2 Antibody Epitopes in COVID-19 Patients with a Multi-Coronavirus Protein Microarray
This article has 23 authors:Reviewed by ScreenIT
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The ACE2-binding Interface of SARS-CoV-2 Spike Inherently Deflects Immune Recognition
This article has 10 authors:Reviewed by ScreenIT