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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The SARS-CoV-2 RNA–protein interactome in infected human cells
This article has 21 authors:Reviewed by ScreenIT
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Impact of COVID-19 on Public Research Recruitment
This article has 9 authors:Reviewed by ScreenIT
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A Classification Approach for Predicting COVID-19 Patient's Survival Outcome with Machine Learning Techniques
This article has 9 authors:Reviewed by ScreenIT
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Persistence of SARS-CoV-2 antibodies and symptoms in an Irish Healthcare Worker (HCW) setting: Results of the COVID Antibody Staff Testing (CAST) Study
This article has 13 authors:Reviewed by ScreenIT
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Beneficial and harmful outcomes of tocilizumab in severe COVID‐19: A systematic review and meta‐analysis
This article has 10 authors:Reviewed by ScreenIT
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Multi-city modeling of epidemics using spatial networks: Application to 2019-nCov (COVID-19) coronavirus in India
This article has 2 authors:Reviewed by ScreenIT
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Monitoring the evolution of the COVID-19 pandemic in China, South Korea, Italy and USA through the net relative rate of infection of the total number of confirmed cases
This article has 1 author:Reviewed by ScreenIT
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Ultrafast Sample placement on Existing tRees (UShER) enables real-time phylogenetics for the SARS-CoV-2 pandemic
This article has 8 authors:Reviewed by ScreenIT
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Tuning intrinsic disorder predictors for virus proteins
This article has 3 authors:Reviewed by ScreenIT
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Severe Acute Respiratory Syndrome Coronavirus 2 Incidence and Risk Factors in a National, Community-Based Prospective Cohort of US Adults
This article has 15 authors:Reviewed by ScreenIT