ScreenIT
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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Characteristics and Outcomes of Over 300,000 Patients with COVID-19 and History of Cancer in the United States and Spain
This article has 53 authors:Reviewed by ScreenIT
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Detecting SARS-CoV-2 variants with SNP genotyping
This article has 12 authors:Reviewed by ScreenIT
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Transmission Dynamics of Coronavirus Disease 2019 (COVID-19) in the World: The Roles of Intervention and Seasonality
This article has 16 authors:Reviewed by ScreenIT
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Six scenarios of non-medical interventions in the SARS-CoV-2 epidemic
This article has 1 author:Reviewed by ScreenIT
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Adjusted dynamics of COVID-19 pandemic due to herd immunity in Bangladesh
This article has 5 authors:Reviewed by ScreenIT
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Research on Recognition Method of COVID-19 Images Based on Deep Learning
This article has 4 authors:Reviewed by ScreenIT
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Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20–28 January 2020
This article has 3 authors:Reviewed by ScreenIT
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A Novel Multi-ventilation Technique to Split Ventilators
This article has 3 authors:Reviewed by ScreenIT
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Clinical Evaluation of Roche SD Biosensor Rapid Antigen Test for SARS-CoV-2 in Municipal Health Service Testing Site, the Netherlands
This article has 12 authors:Reviewed by ScreenIT
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Impact of B.1.1.7 variant mutations on antibody recognition of linear SARS-CoV-2 epitopes
This article has 5 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT