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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Diagnostic performance and clinical implications of rapid SARS-CoV-2 antigen testing in Mexico using real-world nationwide COVID-19 registry data
This article has 8 authors:Reviewed by ScreenIT
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Novel ELISA Protocol Links Pre-Existing SARS-CoV-2 Reactive Antibodies With Endemic Coronavirus Immunity and Age and Reveals Improved Serologic Identification of Acute COVID-19 via Multi-Parameter Detection
This article has 23 authors:Reviewed by ScreenIT
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Air Recirculation Role in the Spread of COVID-19 Onboard the Diamond Princess Cruise Ship during a Quarantine Period
This article has 1 author:Reviewed by ScreenIT
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Lessons learned from Vietnam’s COVID-19 response: the role of adaptive behavior change and testing in epidemic control
This article has 15 authors:Reviewed by ScreenIT
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The social experience of participation in a COVID-19 vaccine trial: Subjects’ motivations, others’ concerns, and insights for vaccine promotion
This article has 2 authors:Reviewed by ScreenIT
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Direct RNA Nanopore Sequencing of SARS-CoV-2 Extracted from Critical Material from Swabs
This article has 18 authors:Reviewed by ScreenIT
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Epidemiological Differences in the Impact of COVID-19 Vaccination in the United States and China
This article has 5 authors:Reviewed by ScreenIT
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Comprehensive analysis of SARS‐CoV‐2 antibody dynamics in New Zealand
This article has 19 authors:Reviewed by ScreenIT
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Accuracy of rapid point-of-care antibody test in patients with suspected or confirmed COVID-19
This article has 15 authors:Reviewed by ScreenIT
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Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach
This article has 4 authors:Reviewed by ScreenIT