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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Cremation based estimates suggest significant under- and delayed reporting of COVID-19 epidemic data in Wuhan and China
This article has 4 authors:Reviewed by ScreenIT
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Who is more susceptible to Covid-19 infection and mortality in the States?
This article has 6 authors:Reviewed by ScreenIT
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Single-Dilution COVID-19 Antibody Test with Qualitative and Quantitative Readouts
This article has 41 authors:Reviewed by ScreenIT
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Early sample tagging and pooling enables simultaneous SARS-CoV-2 detection and variant sequencing
This article has 17 authors:Reviewed by ScreenIT
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Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic
This article has 2 authors:Reviewed by ScreenIT
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Global analysis of daily new COVID-19 cases reveals many static-phase countries including the United States potentially with unstoppable epidemic
This article has 3 authors:Reviewed by ScreenIT
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Novel RT-ddPCR Assays for determining the transcriptional profile of SARS-CoV-2
This article has 8 authors:Reviewed by ScreenIT
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Delayed Interventions, Low Compliance, and Health Disparities Amplified the Early Spread of COVID-19
This article has 9 authors:Reviewed by ScreenIT
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Space-time covid-19 Bayesian SIR modeling in South Carolina
This article has 2 authors:Reviewed by ScreenIT
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Cell-type apoptosis in lung during SARS-CoV-2 infection
This article has 14 authors:Reviewed by ScreenIT