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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What have we learned about COVID-19 volunteering in the UK? A rapid review of the literature
This article has 4 authors:Reviewed by ScreenIT
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Risk of thrombotic complications in influenza versus COVID‐19 hospitalized patients
This article has 16 authors:Reviewed by ScreenIT
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Local public health officials and COVID-19: evidence from China
This article has 2 authors:Reviewed by ScreenIT
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SARS-CoV-2 Fusion Peptide has a Greater Membrane Perturbating Effect than SARS-CoV with Highly Specific Dependence on Ca2+
This article has 2 authors:Reviewed by ScreenIT
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Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study
This article has 8 authors:Reviewed by ScreenIT
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Quality of Life and Depressive Symptoms Among Peruvian University Students During the COVID-19 Pandemic
This article has 4 authors:Reviewed by ScreenIT
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Bayesian estimation of the seroprevalence of antibodies to SARS-CoV-2
This article has 2 authors:Reviewed by ScreenIT
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Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England
This article has 48 authors:Reviewed by ScreenIT
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Passing the Test: A Model-Based Analysis of Safe School-Reopening Strategies
This article has 5 authors:Reviewed by ScreenIT