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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Mental wellbeing in the Bangladeshi healthy population during nationwide lockdown over COVID-19: an online cross-sectional survey
This article has 4 authors:Reviewed by ScreenIT
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SARS-CoV-2 seroprevalence trends in healthy blood donors during the COVID-19 Milan outbreak
This article has 16 authors:Reviewed by ScreenIT
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Afucosylated IgG characterizes enveloped viral responses and correlates with COVID-19 severity
This article has 101 authors:Reviewed by ScreenIT
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No SARS‐CoV‐2 carriage observed in children attending daycare centers during the intial weeks of the epidemic in Belgium
This article has 7 authors:Reviewed by ScreenIT
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Estimating the Global Infection Fatality Rate of COVID-19
This article has 2 authors:Reviewed by ScreenIT
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Retrospective Screening for SARS-CoV-2 RNA in California, USA, Late 2019
This article has 6 authors:Reviewed by ScreenIT
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Does the human placenta express the canonical cell entry mediators for SARS-CoV-2?
This article has 9 authors:Reviewed by ScreenIT
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Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T-cell memory formation after mild COVID-19 infection
This article has 13 authors: -
Repurposing of Miglustat to inhibit the coronavirus Severe Acquired Respiratory Syndrome SARS-CoV-2
This article has 9 authors:Reviewed by ScreenIT
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Neutralizing Antibody and Soluble ACE2 Inhibition of a Replication-Competent VSV-SARS-CoV-2 and a Clinical Isolate of SARS-CoV-2
This article has 25 authors:Reviewed by ScreenIT