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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Evaluating temperature and humidity gradients of COVID-19 infection rates in light of Non-Pharmaceutical Interventions
This article has 11 authors:Reviewed by ScreenIT
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Th1 Dominant Nucleocapsid and Spike Antigen-Specific CD4+ and CD8+ Memory T Cell Recall Induced by hAd5 S-Fusion + N-ETSD Infection of Autologous Dendritic Cells from Patients Previously Infected with SARS-CoV-2
This article has 20 authors:Reviewed by ScreenIT
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Biomathematical models for genetic diversity analyses in complete genomes of SARS-CoV-2
This article has 5 authors:Reviewed by ScreenIT
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Optimal timing for social distancing during an epidemic
This article has 1 author:Reviewed by ScreenIT
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Sample pooling is a viable strategy for SARS-CoV-2 detection in low-prevalence settings
This article has 6 authors:Reviewed by ScreenIT
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A critical update on the role of mild and serious vitamin D deficiency prevalence and the COVID-19 epidemic in Europe
This article has 2 authors:Reviewed by ScreenIT
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Modeling COVID-19 dynamics in the sixteen West African countries
This article has 3 authors:Reviewed by ScreenIT
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Tracking the introduction and spread of SARS-CoV-2 in coastal Kenya
This article has 26 authors:Reviewed by ScreenIT
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Control dynamics of the COVID-19 pandemic in China and South Korea
This article has 1 author:Reviewed by ScreenIT
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Multinational Prevalence of Neurological Phenotypes in Patients Hospitalized with COVID-19
This article has 23 authors:Reviewed by ScreenIT