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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Association of Country-wide Coronavirus Mortality with Demographics, Testing, Lockdowns, and Public Wearing of Masks
This article has 6 authors:Reviewed by ScreenIT
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BRD2 inhibition blocks SARS-CoV-2 infection by reducing transcription of the host cell receptor ACE2
This article has 27 authors:Reviewed by ScreenIT
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City-level SARS-CoV-2 sewage surveillance
This article has 30 authors:Reviewed by ScreenIT
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Neutralizing antibodies targeting the SARS‐CoV‐2 receptor binding domain isolated from a naïve human antibody library
This article has 5 authors:Reviewed by ScreenIT
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Global absence and targeting of protective immune states in severe COVID-19
This article has 66 authors:Reviewed by ScreenIT
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Planning for the aftershocks: a model of post-acute care needs for hospitalized COVID-19 patients
This article has 9 authors:Reviewed by ScreenIT
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Easing or tightening control strategies: determination of COVID-19 parameters for an agent-based model
This article has 6 authors:Reviewed by ScreenIT
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COVID-19Predict – Predicting Pandemic Trends
This article has 4 authors:Reviewed by ScreenIT
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Impact of public sentiments on the transmission of COVID-19 across a geographical gradient
This article has 7 authors:Reviewed by ScreenIT
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The SARS-CoV-2 Spike protein disrupts human cardiac pericytes function through CD147 receptor-mediated signalling: a potential non-infective mechanism of COVID-19 microvascular disease
This article has 17 authors:Reviewed by ScreenIT