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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Battle with COVID-19 Under Partial to Zero Lockdowns in India
This article has 2 authors:Reviewed by ScreenIT
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Persistence of serum and saliva antibody responses to SARS-CoV-2 spike antigens in COVID-19 patients
This article has 36 authors:Reviewed by ScreenIT
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Decentralized governance may lead to higher infection levels and sub-optimal releases of quarantines amid the COVID-19 pandemic
This article has 1 author:Reviewed by ScreenIT
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A pneumonia outbreak associated with a new coronavirus of probable bat origin
This article has 29 authors:Reviewed by ScreenIT
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Geographic access to COVID-19 healthcare in Brazil using a balanced float catchment area approach
This article has 7 authors:Reviewed by ScreenIT
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Forecasting COVID-19 pandemic Severity in Asia
This article has 2 authors:Reviewed by ScreenIT
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A Bayesian susceptible-infectious-hospitalized-ventilated-recovered model to predict demand for COVID-19 inpatient care in a large healthcare system
This article has 6 authors:Reviewed by ScreenIT
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Evaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation
This article has 8 authors:Reviewed by ScreenIT
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An Early Pandemic Analysis of SARS-CoV-2 Population Structure and Dynamics in Arizona
This article has 23 authors:Reviewed by ScreenIT
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End-Stage Renal Disease Patients on Chronic Hemodialysis Fare Better With COVID-19: A Retrospective Cohort Study From the New York Metropolitan Region
This article has 11 authors:Reviewed by ScreenIT