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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Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection
This article has 11 authors:Reviewed by ScreenIT
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Clinical characteristics and COVID-19 outcomes in a regional cohort of pediatric patients with rheumatic diseases
This article has 13 authors:Reviewed by ScreenIT
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Presence of a SARS-COV-2 protein enhances Amyloid Formation of Serum Amyloid A
This article has 3 authors:Reviewed by ScreenIT
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Virtually in synch: a pilot study on affective dimensions of dancing with Parkinson’s during COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Impact of leadership on the nursing workforce during the COVID-19 pandemic
This article has 7 authors:Reviewed by ScreenIT
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CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images
This article has 7 authors:Reviewed by ScreenIT
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SARS-CoV-2 infection among educational staff in Berlin, Germany, June to December 2020
This article has 15 authors:Reviewed by ScreenIT
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Adaptive immune determinants of viral clearance and protection in mouse models of SARS-CoV-2
This article has 7 authors:Reviewed by ScreenIT
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One Year of SARS-CoV-2: Genomic Characterization of COVID-19 Outbreak in Qatar
This article has 33 authors:Reviewed by ScreenIT
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Integrating Health Behavior Theories to Predict COVID-19 Vaccine Acceptance: Differences between Medical Students and Nursing Students
This article has 2 authors:Reviewed by ScreenIT