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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Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis
This article has 4 authors:Reviewed by ScreenIT
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Understanding Spatial Heterogeneity of COVID-19 Pandemic Using Shape Analysis of Growth Rate Curves
This article has 2 authors:Reviewed by ScreenIT
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Sociodemographic Predictors of Outcomes in COVID-19: Examining the Impact of Ethnic Disparities in Northern Nevada
This article has 10 authors:Reviewed by ScreenIT
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Reaching collective immunity for COVID-19: an estimate with a heterogeneous model based on the data for Italy
This article has 4 authors:Reviewed by ScreenIT
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Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States
This article has 6 authors:Reviewed by ScreenIT
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Reopening businesses and risk of COVID-19 transmission
This article has 9 authors:Reviewed by ScreenIT
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Diligent Medical Activities of a Publicly Designated Medical Institution for Infectious Diseases Pave the Way for Overcoming COVID-19: A Positive Message to People Working at the Cutting Edge
This article has 7 authors:Reviewed by ScreenIT
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Standardization of ELISA protocols for serosurveys of the SARS-CoV-2 pandemic using clinical and at-home blood sampling
This article has 21 authors:Reviewed by ScreenIT
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Association between comorbidities and the risk of death in patients with COVID-19: sex-specific differences
This article has 8 authors:Reviewed by ScreenIT
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COVID-19 Outcomes in 4712 consecutively confirmed SARS-CoV2 cases in the city of Madrid
This article has 27 authors:Reviewed by ScreenIT