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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Missing science: A scoping study of COVID-19 epidemiological data in the United States
This article has 3 authors:Reviewed by ScreenIT
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A Data-Driven Simulation of the Exposure Notification Cascade for Digital Contact Tracing of SARS-CoV-2 in Zurich, Switzerland
This article has 5 authors:Reviewed by ScreenIT
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Determinants of psychological distress during the COVID-19 pandemic and the lockdown measures: a nationwide on-line survey in Greece and Cyprus
This article has 7 authors:Reviewed by ScreenIT
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Extensive Testing May Reduce COVID-19 Mortality: A Lesson From Northern Italy
This article has 4 authors:Reviewed by ScreenIT
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Emetine as an antiviral agent suppresses SARS-CoV-2 replication by inhibitinginteraction of viral mRNAwith eIF4E: An in vitro study
This article has 9 authors:Reviewed by ScreenIT
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The effect of early-stage public health policies in the transmission of COVID-19 for South American countries
This article has 6 authors:Reviewed by ScreenIT
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Hydroxychloroquine for SARS-CoV-2 positive patients quarantined at home: The first interim analysis of a remotely conducted randomized clinical trial
This article has 20 authors:Reviewed by ScreenIT
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Event-specific interventions to minimize COVID-19 transmission
This article has 4 authors:Reviewed by ScreenIT
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SARS-CoV-2 infection of primary human lung epithelium for COVID-19 modeling and drug discovery
This article has 16 authors:Reviewed by ScreenIT
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Globalized low-income countries may experience higher COVID-19 mortality rates
This article has 3 authors:Reviewed by ScreenIT