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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Diagnostic Performance of Chest CT for SARS-CoV-2 Infection in Individuals with or without COVID-19 Symptoms
This article has 10 authors:Reviewed by ScreenIT
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COVID19-world: a shiny application to perform comprehensive country-specific data visualization for SARS-CoV-2 epidemic
This article has 4 authors:Reviewed by ScreenIT
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Mathematical framework to model Covid-19 daily deaths
This article has 3 authors:Reviewed by ScreenIT
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Exploring the spread dynamics of COVID-19 in Morocco
This article has 1 author:Reviewed by ScreenIT
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Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel
This article has 19 authors:Reviewed by ScreenIT
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Convalescent plasma treatment of severe COVID-19: a propensity score–matched control study
This article has 30 authors:Reviewed by ScreenIT
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Impacts of remdesivir on dynamics and efficacy stratified by the severity of COVID- 19: a simulated two-arm controlled study
This article has 7 authors:Reviewed by ScreenIT
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Acute cardiac injury in patients with COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Differential patterns of cross-reactive antibody response against SARS-CoV-2 spike protein detected for chronically ill and healthy COVID-19 naïve individuals
This article has 21 authors:Reviewed by ScreenIT
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Going by the numbers : Learning and modeling COVID-19 disease dynamics
This article has 2 authors:Reviewed by ScreenIT