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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First-in-human demonstration of splenic ultrasound stimulation for non-invasively controlling inflammation
This article has 19 authors:Reviewed by ScreenIT
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Using Real World Data to Understand HIV and COVID-19 in the U.S.A. and Spain: Characterizing Co-Infected Patients Across the Care Cascade
This article has 18 authors:Reviewed by ScreenIT
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In-Silico analysis reveals lower transcription efficiency of C241T variant of SARS-CoV-2 with host replication factors MADP1 and hnRNP-1
This article has 10 authors:Reviewed by ScreenIT
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The Potential Impact of Interruptions to HIV Services: A Modelling Case Study for South Africa
This article has 3 authors:Reviewed by ScreenIT
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Covid-19 Risk Among Airline Passengers: Should the Middle Seat Stay Empty?
This article has 1 author:Reviewed by ScreenIT
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Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature With Unsupervised Word Embeddings and Machine Learning: Evidence-Based Study
This article has 6 authors:Reviewed by ScreenIT
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Assessment of a Laboratory-Based SARS-CoV-2 Antibody Test Among Hemodialysis Patients: A Quality Improvement Initiative
This article has 21 authors:Reviewed by ScreenIT
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A Versatile Reporter System To Monitor Virus-Infected Cells and Its Application to Dengue Virus and SARS-CoV-2
This article has 9 authors:Reviewed by ScreenIT
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Preliminary Estimates of Years of Life Lost (YLL) Due to COVID-19 in India
This article has 3 authors:Reviewed by ScreenIT
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To isolate or not to isolate: the impact of changing behavior on COVID-19 transmission
This article has 10 authors:Reviewed by ScreenIT