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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SARS-CoV-2 Seroprevalence and Clinical Features of COVID-19 in a German Liver Transplant Recipient Cohort: A Prospective Serosurvey Study
This article has 9 authors:Reviewed by ScreenIT
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The role of children in the spread of COVID-19: Using household data from Bnei Brak, Israel, to estimate the relative susceptibility and infectivity of children
This article has 10 authors:Reviewed by ScreenIT
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COVID-19 Myocardial Pathology Evaluation in Athletes With Cardiac Magnetic Resonance (COMPETE CMR)
This article has 10 authors:Reviewed by ScreenIT
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Agent-Based Simulation of Covid-19 Vaccination Policies in CovidSIMVL
This article has 2 authors:Reviewed by ScreenIT
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Asymptomatic cases and limited transmission of SARS-CoV-2 in residents and healthcare workers in three Dutch nursing homes
This article has 6 authors:Reviewed by ScreenIT
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12-lead Electrocardiogram in Hospitalized COVID 19 Patients
This article has 12 authors:Reviewed by ScreenIT
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Role of Weather Factors in COVID-19 Deaths in Tropical Climate: A Data-Driven Study Focused on Brazil Manuscript
This article has 1 author:Reviewed by ScreenIT
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Influenza Vaccination and COVID-19 Mortality in the USA: An Ecological Study
This article has 7 authors:Reviewed by ScreenIT
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Exploring Drugs and Vaccines Associated with Altered Risks and Severity of COVID-19: A UK Biobank Cohort Study of All ATC Level-4 Drug Categories Reveals Repositioning Opportunities
This article has 3 authors:Reviewed by ScreenIT
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The Current COVID-19 Spread Pattern in India
This article has 1 author:Reviewed by ScreenIT