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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Emerging SARS-CoV-2 variants of concern evade humoral immune responses from infection and vaccination
This article has 29 authors:Reviewed by ScreenIT
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Infectious Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Virus in Symptomatic Coronavirus Disease 2019 (COVID-19) Outpatients: Host, Disease, and Viral Correlates
This article has 25 authors:Reviewed by ScreenIT
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Characterization of SARS2 Nsp15 nuclease activity reveals it's mad about U
This article has 11 authors:Reviewed by ScreenIT
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The potential impact of vaccine passports on inclination to accept COVID-19 vaccinations in the United Kingdom: Evidence from a large cross-sectional survey and modeling study
This article has 3 authors:Reviewed by ScreenIT
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Absolute quantification of SARS-CoV-2 with Clarity Plus™ digital PCR
This article has 4 authors:Reviewed by ScreenIT
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Admission criteria in critically ill COVID-19 patients: A physiology-based approach
This article has 9 authors:Reviewed by ScreenIT
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First evidence of SARS-CoV-2 genome detection in zebra mussel (Dreissena polymorpha)
This article has 13 authors:Reviewed by ScreenIT
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Epidemiological profile study of COVID-19 in West African countries: Nigeria, Senegal, Mauritania, Cape Verde and Mali
This article has 8 authors:Reviewed by ScreenIT
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College students’ COVID-19 vaccine beliefs and intentions: Implications for interventions
This article has 6 authors:Reviewed by ScreenIT
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Progression of COVID-19 Pandemic in India: A Linear Functional Concurrent Regression Analysis Approach
This article has 2 authors:Reviewed by ScreenIT