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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SARS-CoV-2 infecting the inner ear results in potential hearing damage at the early stage or prognosis of COVID-19 in rodents
This article has 9 authors:Reviewed by ScreenIT
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Instantaneous R calculation for COVID-19 epidemic in Brazil
This article has 2 authors:Reviewed by ScreenIT
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Discovery of COVID-19 Inhibitors Targeting the SARS-CoV-2 Nsp13 Helicase
This article has 3 authors:Reviewed by ScreenIT
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Competing Bioaerosols May Influence the Seasonality of Influenza-Like Illnesses, including COVID-19. The Chicago Experience
This article has 6 authors:Reviewed by ScreenIT
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Tracheal aspirate RNA sequencing identifies distinct immunological features of COVID-19 ARDS
This article has 117 authors:Reviewed by ScreenIT
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IMPACT OF A SARS-COV-2 INFECTION IN PATIENTS WITH CELIAC DISEASE
This article has 13 authors:Reviewed by ScreenIT
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Reciprocal association between voting and the epidemic spread of COVID-19: observational and dynamic modeling study
This article has 6 authors:Reviewed by ScreenIT
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Quantitative Fit Evaluation of N95 Filtering Facepiece Respirators and Coronavirus Inactivation Following Heat Treatment
This article has 8 authors:Reviewed by ScreenIT
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Role of Asymptomatic COVID-19 Cases in Viral Transmission: Findings From a Hierarchical Community Contact Network Model
This article has 5 authors:Reviewed by ScreenIT
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Projected spread of COVID-19’s second wave in South Africa under different levels of lockdown
This article has 2 authors:Reviewed by ScreenIT