ScreenIT
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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Structural and computational insights into the SARS-CoV-2 Omicron RBD-ACE2 interaction
This article has 15 authors:Reviewed by ScreenIT
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A synthetic bispecific antibody capable of neutralizing SARS-CoV-2 Delta and Omicron
This article has 14 authors:Reviewed by ScreenIT
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The SARS-CoV-2 infection in Thailand: analysis of spike variants complemented by protein structure insights
This article has 4 authors:Reviewed by ScreenIT
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Maternal cytokine response after SARS-CoV-2 infection during pregnancy
This article has 24 authors:Reviewed by ScreenIT
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Comparison of infectious SARS-CoV-2 from the nasopharynx of vaccinated and unvaccinated individuals
This article has 15 authors:Reviewed by ScreenIT
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Shielding individuals at high risk of COVID-19: a micro-simulation study
This article has 3 authors:Reviewed by ScreenIT
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Vaccines elicit highly conserved cellular immunity to SARS-CoV-2 Omicron
This article has 13 authors:Reviewed by ScreenIT
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Mathematical modelling of COVID-19 vaccination strategies in Kyrgyzstan
This article has 12 authors:Reviewed by ScreenIT
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Estimating the state of the Covid-19 epidemic curve in Mayotte during the period without vaccination
This article has 3 authors:Reviewed by ScreenIT
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Cognitive predictors of COVID-19 mitigation behaviors in vaccinated and unvaccinated general population members
This article has 5 authors:Reviewed by ScreenIT