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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Nanodroplet-Benzalkonium Chloride Formulation Demonstrates In Vitro and Ex-Vivo Broad-Spectrum Antiviral Activity Against SARS-CoV-2 and other Enveloped Viruses
This article has 6 authors:Reviewed by ScreenIT
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SARS-CoV-2 Serologic Assays in Control and Unknown Populations Demonstrate the Necessity of Virus Neutralization Testing
This article has 11 authors:Reviewed by ScreenIT
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Massachusetts general hospital Covid-19 registry reveals two distinct populations of hospitalized patients by race and ethnicity
This article has 13 authors:Reviewed by ScreenIT
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The influence of climate factors on COVID-19 transmission in Malaysia: An autoregressive integrated moving average (ARIMA) model
This article has 5 authors:Reviewed by ScreenIT
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The misleading illusion of COVID-19 confirmed case data: alternative estimates and a monitoring tool
This article has 2 authors:Reviewed by ScreenIT
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Rule of thumb in human intelligence for assessing the COVID-19 outbreak in Japan
This article has 3 authors:Reviewed by ScreenIT
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Impact of COVID-19 pandemic on cancer care delivery : A Real World Experience
This article has 8 authors:Reviewed by ScreenIT
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Targeting TGF-β pathway with COVID-19 Drug Candidate ARTIVeda/PulmoHeal Accelerates Recovery from Mild-Moderate COVID-19
This article has 7 authors:Reviewed by ScreenIT
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Application of pooled testing in estimating the prevalence of COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Seroprevalence of SARS-CoV-2 IgG Antibodies in Corsica (France), April and June 2020
This article has 9 authors:Reviewed by ScreenIT