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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Predictive power of SARS-CoV-2 wastewater surveillance for diverse populations across a large geographical range
This article has 5 authors:Reviewed by ScreenIT
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The aging whole blood transcriptome reveals a potential role of FASLG in COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Projections and fractional dynamics of COVID-19 with optimal control strategies
This article has 3 authors:Reviewed by ScreenIT
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Implementation of novel and conventional outbreak control measures in managing COVID-19 outbreaks in a large UK prison
This article has 12 authors:Reviewed by ScreenIT
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Impact of COVID-19 on medical practice: A nationwide survey of dermatologists and health care providers in Iraq
This article has 6 authors:Reviewed by ScreenIT
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Adjusting to Disrupted Assessments, Placements and Teaching (ADAPT): a snapshot of the early response by UK medical schools to COVID-19
This article has 34 authors:Reviewed by ScreenIT
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Highland of COVID-19 outside Hubei: epidemic characteristics, control and projections of Wenzhou, China
This article has 7 authors:Reviewed by ScreenIT
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Effective screening of SARS-CoV-2 neutralizing antibodies in patient serum using lentivirus particles pseudotyped with SARS-CoV-2 spike glycoprotein
This article has 7 authors:Reviewed by ScreenIT
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Ethacridine inhibits SARS-CoV-2 by inactivating viral particles
This article has 11 authors:Reviewed by ScreenIT
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A rapid and low-cost protocol for the detection of B.1.1.7 lineage of SARS-CoV-2 by using SYBR Green-based RT-qPCR
This article has 5 authors:Reviewed by ScreenIT