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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Optimizing COVID-19 surveillance in long-term care facilities: a modelling study
This article has 9 authors:Reviewed by ScreenIT
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COVID-19 experience: first Italian survey on healthcare staff members from a Mother-Child Research Hospital using combined molecular and rapid immunoassays test
This article has 29 authors:Reviewed by ScreenIT
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Multivariate Analysis of Black Race and Environmental Temperature on COVID-19 in the US
This article has 11 authors:Reviewed by ScreenIT
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Community and Socioeconomic Factors Associated with COVID-19 in the United States: Zip code level cross sectional analysis
This article has 4 authors:Reviewed by ScreenIT
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Impacts of Early Interventions on the Age-Specific Incidence of COVID-19 in New York, Los Angeles, Daegu and Nairobi
This article has 3 authors:Reviewed by ScreenIT
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Tracking R of COVID-19: A new real-time estimation using the Kalman filter
This article has 4 authors:Reviewed by ScreenIT
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Dynamical asymmetry exposes 2019-nCoV prefusion spike
This article has 1 author:Reviewed by ScreenIT
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Rapid and quantitative detection of COVID-19 markers in micro-liter sized samples
This article has 5 authors:Reviewed by ScreenIT
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Structural Basis of SARS-CoV-2 Spike Protein Priming by TMPRSS2
This article has 7 authors:Reviewed by ScreenIT
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Seasonality and uncertainty in global COVID-19 growth rates
This article has 2 authors:Reviewed by ScreenIT