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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Post-lockdown detection of SARS-CoV-2 RNA in the wastewater of Montpellier, France
This article has 7 authors:Reviewed by ScreenIT
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KN95 and N95 Respirators Retain Filtration Efficiency despite a Loss of Dipole Charge during Decontamination
This article has 6 authors:Reviewed by ScreenIT
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Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases
This article has 2 authors:Reviewed by ScreenIT
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Digestive Manifestations in Patients Hospitalized With Coronavirus Disease 2019
This article has 125 authors:Reviewed by ScreenIT
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Diagnostic and prognostic value of hematological and immunological markers in COVID-19 infection: A meta-analysis of 6320 patients
This article has 10 authors:Reviewed by ScreenIT
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Risk factors prediction, clinical outcomes, and mortality in COVID‐19 patients
This article has 17 authors:Reviewed by ScreenIT
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Predictive Analysis for COVID-19 Spread in India by Adaptive Compartmental Model
This article has 2 authors:Reviewed by ScreenIT
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High levels of SARS-CoV-2–specific T cells with restricted functionality in severe courses of COVID-19
This article has 13 authors:Reviewed by ScreenIT
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High excess mortality in areas with young and socially vulnerable populations during the COVID-19 outbreak in Stockholm Region, Sweden
This article has 6 authors:Reviewed by ScreenIT
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COVID-19 mortality effects of underlying health conditions in India: a modelling study
This article has 4 authors:Reviewed by ScreenIT