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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Distinct Features and Functions of Systemic and Mucosal Humoral Immunity Among SARS-CoV-2 Convalescent Individuals
This article has 12 authors:Reviewed by ScreenIT
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Time-Varying COVID-19 Reproduction Number in the United States
This article has 2 authors:Reviewed by ScreenIT
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Clinical correlations of SARS-CoV-2 antibody responses in patients with COVID-19 infection
This article has 7 authors:Reviewed by ScreenIT
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Longitudinal observation and decline of neutralizing antibody responses in the three months following SARS-CoV-2 infection in humans
This article has 42 authors:Reviewed by ScreenIT
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A national cohort study of COVID-19 in-hospital mortality in South Africa: the intersection of communicable and non-communicable chronic diseases in a high HIV prevalence setting
This article has 32 authors:Reviewed by ScreenIT
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Non-medical COVID-19-related personal impact in medical ecological perspective: A global multileveled, mixed method study
This article has 14 authors:Reviewed by ScreenIT
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Pooled Sample Testing for SARS-CoV-2 Using Rapid RT-PCR COVID-19 Tests
This article has 3 authors:Reviewed by ScreenIT
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SARS-CoV-2 antibody seroprevalence in Tbilisi, the capital city of country of Georgia
This article has 8 authors:Reviewed by ScreenIT
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Evaluation of efficiency and sensitivity of 1D and 2D sample pooling strategies for SARS-CoV-2 RT-qPCR screening purposes
This article has 5 authors:Reviewed by ScreenIT
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Immunological characteristics govern the transition of COVID-19 to endemicity
This article has 3 authors:Reviewed by ScreenIT