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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Analytical Validation of a COVID-19 qRT-PCR Detection Assay Using a 384-well Format and Three Extraction Methods
This article has 15 authors:Reviewed by ScreenIT
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Data-driven estimate of SARS-CoV-2 herd immunity threshold in populations with individual contact pattern variations
This article has 6 authors:Reviewed by ScreenIT
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Association of HIV infection with outcomes among adults hospitalized with COVID-19
This article has 7 authors:Reviewed by ScreenIT
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Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark
This article has 8 authors:Reviewed by ScreenIT
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Trajectories of clinical and laboratory characteristics associated with COVID ‐19 in hemodialysis patients by survival
This article has 14 authors:Reviewed by ScreenIT
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Rapid generation of potent antibodies by autonomous hypermutation in yeast
This article has 18 authors:Reviewed by ScreenIT
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Changing patterns of sickness absence among healthcare workers in England during the COVID-19 pandemic
This article has 8 authors:Reviewed by ScreenIT
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Patterns of SARS-CoV-2 aerosol spread in typical classrooms
This article has 5 authors:Reviewed by ScreenIT
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Force-dependent stimulation of RNA unwinding by SARS-CoV-2 nsp13 helicase
This article has 8 authors:Reviewed by ScreenIT
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In silico analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs
This article has 5 authors:Reviewed by ScreenIT