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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Impacts of people’s learning behavior in fighting the COVID-19 epidemic
This article has 2 authors:Reviewed by ScreenIT
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The COVID-19 Infection in Italy: A Statistical Study of an Abnormally Severe Disease
This article has 12 authors:Reviewed by ScreenIT
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Healthcare workers’ knowledge, attitude and practices during the COVID-19 pandemic response in a tertiary care hospital of Nepal
This article has 3 authors:Reviewed by ScreenIT
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Factors influencing self-harm thoughts and behaviours over the first year of the COVID-19 pandemic in the UK: longitudinal analysis of 49 324 adults
This article has 2 authors:Reviewed by ScreenIT
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The Immune-Buffer COVID-19 Exit Strategy that Protects the Elderly
This article has 4 authors:Reviewed by ScreenIT
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Is There a Correlation Between Pulmonary Inflammation Index With COVID-19 Disease Severity and Outcome?
This article has 8 authors:Reviewed by ScreenIT
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Temperature and Humidity Do Not Influence Global COVID-19 Incidence as Inferred from Causal Models
This article has 6 authors:Reviewed by ScreenIT
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Sequencing of SARS-CoV-2 genome using different nanopore chemistries
This article has 7 authors:Reviewed by ScreenIT
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A Simulation of a COVID-19 Epidemic Based on a Deterministic SEIR Model
This article has 4 authors:Reviewed by ScreenIT
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Quarantine and testing strategies in contact tracing for SARS-CoV-2: a modelling study
This article has 50 authors:Reviewed by ScreenIT