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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Clinical trials in COVID-19 management & prevention: A meta-epidemiological study examining methodological quality
This article has 19 authors:Reviewed by ScreenIT
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Aerosol filtering efficiency of respiratory face masks used during the COVID-19 pandemic
This article has 3 authors:Reviewed by ScreenIT
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Modeling return of the epidemic: Impact of population structure, asymptomatic infection, case importation and personal contacts
This article has 1 author:Reviewed by ScreenIT
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The Impact of Armed Conflict on the Epidemiological Situation of COVID-19 in Libya, Syria and Yemen
This article has 1 author:Reviewed by ScreenIT
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The relatively young and rural population may limit the spread and severity of Covid-19 in Africa: a modelling study
This article has 4 authors:Reviewed by ScreenIT
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Thirty-Day Mortality and Morbidity in COVID-19 Positive vs. COVID-19 Negative Individuals and vs. Individuals Tested for Influenza A/B: A Population-Based Study
This article has 5 authors:Reviewed by ScreenIT
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The Coronavirus Health and Impact Survey (CRISIS) reveals reproducible correlates of pandemic-related mood states across the Atlantic
This article has 13 authors:Reviewed by ScreenIT
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SARS-CoV-2 specific memory B cells frequency in recovered patient remains stable while antibodies decay over time
This article has 7 authors:Reviewed by ScreenIT
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Positive correlation between long term emission of several air pollutants and COVID-19 deaths in Sweden
This article has 1 author:Reviewed by ScreenIT
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High throughput detection and genetic epidemiology of SARS-CoV-2 using COVIDSeq next-generation sequencing
This article has 57 authors:Reviewed by ScreenIT