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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How Many Intensive Care Beds are Justifiable for Hospital Pandemic Preparedness? A Cost-effectiveness Analysis for COVID-19 in Germany
This article has 1 author:Reviewed by ScreenIT
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Computing the daily reproduction number of COVID-19 by inverting the renewal equation using a variational technique
This article has 4 authors:Reviewed by ScreenIT
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COVID-19: Can early home treatment with Azithromycin alone or with Zinc help prevent hospitalisation, death, and long-COVID-19? A review
This article has 4 authors:Reviewed by ScreenIT
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Reduced access to care among older American adults during CoVID-19 pandemic: Results from a prospective cohort study
This article has 6 authors:Reviewed by ScreenIT
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COVID-19 Transmission Dynamics in India with Extended SEIR Model
This article has 3 authors:Reviewed by ScreenIT
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Seroprevalence of SARS-CoV-2-Specific IgG Antibodies Among Adults Living in Connecticut: Post-Infection Prevalence (PIP) Study
This article has 21 authors:Reviewed by ScreenIT
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Profile of Circulatory Cytokines and Chemokines in Human Coronaviruses: A Systematic Review and Meta-Analysis
This article has 4 authors:Reviewed by ScreenIT
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Performance Assessment of First-Generation Anti-SARS-CoV-2 Serological Assays
This article has 12 authors:Reviewed by ScreenIT
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Genomic epidemiology of SARS-CoV-2 reveals multiple lineages and early spread of SARS-CoV-2 infections in Lombardy, Italy
This article has 18 authors:Reviewed by ScreenIT
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Exponential Damping: The Key to Successful Containment of COVID-19
This article has 5 authors:Reviewed by ScreenIT