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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High viral loads: what drives fatal cases of COVID-19 in vaccinees? – an autopsy study
This article has 22 authors:Reviewed by ScreenIT
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Systems immune profiling of variant-specific vaccination against SARS-CoV-2
This article has 8 authors:Reviewed by ScreenIT
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Predicting potential SARS-CoV-2 mutations of concern via full quantum mechanical modelling
This article has 12 authors:Reviewed by ScreenIT
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Heterogeneity of neutrophils and inflammatory responses in patients with COVID-19 and healthy controls
This article has 8 authors:Reviewed by ScreenIT
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The impact of COVID-19 on NO 2 and PM 2.5 levels and their associations with human mobility patterns in Singapore
This article has 7 authors:Reviewed by ScreenIT
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Online Phylogenetics using Parsimony Produces Slightly Better Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Approaches
This article has 9 authors:Reviewed by ScreenIT
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Introduction and community transmission of SARS-CoV-2 lineage A.2.5 in Florida with novel spike INDELS
This article has 8 authors:Reviewed by ScreenIT
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Seroprevalence of SARS-CoV-2 antibodies prior to the widespread introduction of vaccine programmes in the WHO European Region, January - December 2020: a systematic review
This article has 13 authors:Reviewed by ScreenIT
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Risk assessment of banknotes as a fomite of SARS‐CoV‐2 in cash payment transactions
This article has 6 authors:Reviewed by ScreenIT
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Protection and Waning of Natural and Hybrid Immunity to SARS-CoV-2
This article has 9 authors:Reviewed by ScreenIT