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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Impact of the Timing of Stay-at-Home Orders and Mobility Reductions on First-Wave COVID-19 Deaths in US Counties
This article has 5 authors:Reviewed by ScreenIT
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Six-month antibody response to SARS-CoV-2 in healthcare workers assessed by virus neutralisation and commercial assays
This article has 16 authors:Reviewed by ScreenIT
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Self-Rated Smell Ability Enables Highly Specific Predictors of COVID-19 Status: A Case–Control Study in Israel
This article has 11 authors:Reviewed by ScreenIT
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PRAK-03202: A triple antigen virus-like particle vaccine candidate against SARS CoV-2
This article has 28 authors:Reviewed by ScreenIT
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Endpoint PCR Detection of Sars-CoV-2 RNA
This article has 9 authors:Reviewed by ScreenIT
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Potential Biases Arising From Epidemic Dynamics in Observational Seroprotection Studies
This article has 5 authors:Reviewed by ScreenIT
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“SARS-CoV-2 antibody seroprevalence and stability in a tertiary care hospital-setting”
This article has 10 authors:Reviewed by ScreenIT
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Structural basis for broad coronavirus neutralization
This article has 15 authors:Reviewed by ScreenIT
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Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations
This article has 10 authors:Reviewed by ScreenIT
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The Effects of Stringent and Mild Interventions for Coronavirus Pandemic
This article has 9 authors:Reviewed by ScreenIT