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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Long COVID and the role of physical activity: a qualitative study
This article has 4 authors:Reviewed by ScreenIT
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CovidArray: A Microarray-Based Assay with High Sensitivity for the Detection of Sars-Cov-2 in Nasopharyngeal Swabs
This article has 9 authors:Reviewed by ScreenIT
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Tobacco, but Not Nicotine and Flavor-Less Electronic Cigarettes, Induces ACE2 and Immune Dysregulation
This article has 7 authors:Reviewed by ScreenIT
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Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics
This article has 14 authors:Reviewed by ScreenIT
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Fear of exponential growth in Covid19 data of India and future sketching
This article has 2 authors:Reviewed by ScreenIT
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SARS-CoV-2 Epitope Mapping on Microarrays Highlights Strong Immune-Response to N Protein Region
This article has 12 authors:Reviewed by ScreenIT
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SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment
This article has 35 authors:Reviewed by ScreenIT
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SARS-CoV-2 and Stroke Characteristics
This article has 114 authors:Reviewed by ScreenIT
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Diagnostic performance of the combined nasal and throat swab in patients admitted to hospital with suspected COVID-19
This article has 15 authors:Reviewed by ScreenIT
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Estimation of real-infection and immunity against SARS-CoV-2 in Indian populations
This article has 72 authors:Reviewed by ScreenIT