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 previous epidemics enable timelier COVID-19 responses: an empirical study using organisational memory theory
This article has 1 author:Reviewed by ScreenIT
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Impact of population density on Covid-19 infected and mortality rate in India
This article has 3 authors:Reviewed by ScreenIT
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Summer vacation and COVID-19: effects of metropolitan people going to summer provinces
This article has 2 authors:Reviewed by ScreenIT
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Evaluation of nafamostat mesylate safety and inhibition of SARS-CoV-2 replication using a 3-dimensional human airway epithelia model
This article has 2 authors:Reviewed by ScreenIT
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How will this continue? Modelling interactions between the COVID-19 pandemic and policy responses
This article has 2 authors:Reviewed by ScreenIT
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Positive selection within the genomes of SARS-CoV-2 and other Coronaviruses independent of impact on protein function
This article has 3 authors: -
Plasma irradiation efficiently inactivates the coronaviruses mouse hepatitis virus and SARS-CoV-2
This article has 6 authors:Reviewed by ScreenIT
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How effective was Newfoundland & Labrador’s travel ban to prevent the spread of COVID-19? An agent-based analysis
This article has 8 authors:Reviewed by ScreenIT
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Characteristics of SARS-CoV-2 testing for rapid diagnosis of COVID-19 during the initial stages of a global pandemic
This article has 16 authors:Reviewed by ScreenIT
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A Neutralizing Antibody-Conjugated Photothermal Nanoparticle Captures and Inactivates SARS-CoV-2
This article has 11 authors:Reviewed by ScreenIT