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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Crowding Effects Dominate Demographic Attributes in COVID-19 Cases
This article has 2 authors:Reviewed by ScreenIT
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Hospital outcomes of community-acquired COVID-19 versus influenza: Insights from the Swiss hospital-based surveillance of influenza and COVID-19
This article has 21 authors:Reviewed by ScreenIT
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COVID 19 in Bangladesh: Assumption of possible infection and death
This article has 1 author:Reviewed by ScreenIT
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Association between population density and infection rate suggests the importance of social distancing and travel restriction in reducing the COVID-19 pandemic
This article has 11 authors:Reviewed by ScreenIT
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Syncytia formation by SARS‐CoV‐2‐infected cells
This article has 14 authors:Reviewed by ScreenIT
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Forecasting new daily confirmed cases infected by COVID-19 in Italy from April 9 th to May 18 th 2020
This article has 4 authors:Reviewed by ScreenIT
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Prison Population Reductions and COVID-19: A Latent Profile Analysis Synthesizing Recent Evidence From the Texas State Prison System
This article has 4 authors:Reviewed by ScreenIT
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Glycan reactive anti-HIV-1 antibodies bind the SARS-CoV-2 spike protein but do not block viral entry
This article has 3 authors:Reviewed by ScreenIT
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Temporal omics analysis in Syrian hamsters unravel cellular effector responses to moderate COVID-19
This article has 25 authors:Reviewed by ScreenIT
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Diaphragm dysfunction in severe COVID‐19 as determined by neuromuscular ultrasound
This article has 9 authors:Reviewed by ScreenIT