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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Interaction of Human ACE2 to Membrane-Bound SARS-CoV-1 and SARS-CoV-2 S Glycoproteins
This article has 8 authors:Reviewed by ScreenIT
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Serologic Surveillance and Phylogenetic Analysis of SARS-CoV-2 Infection Among Hospital Health Care Workers
This article has 22 authors:Reviewed by ScreenIT
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Analysis of the COVID-19 epidemic in french overseas department Mayotte based on a modified deterministic and stochastic SEIR model
This article has 2 authors:Reviewed by ScreenIT
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Longitudinal analysis of the humoral response to SARS-CoV-2 spike RBD in convalescent plasma donors
This article has 10 authors:Reviewed by ScreenIT
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COVID-19 detection on IBM quantum computer with classical-quantum transfer learning
This article has 2 authors:Reviewed by ScreenIT
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Bat and pangolin coronavirus spike glycoprotein structures provide insights into SARS-CoV-2 evolution
This article has 9 authors:Reviewed by ScreenIT
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Association between Alzheimer’s disease and COVID-19: A bidirectional Mendelian randomization
This article has 9 authors:Reviewed by ScreenIT
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Spotlight on the dark figure: Exhibiting dynamics in the case detection ratio of COVID-19 infections in Germany
This article has 4 authors:Reviewed by ScreenIT
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Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic
This article has 6 authors:Reviewed by ScreenIT
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Early Start of Oral Clarithromycin Is Associated with Better Outcome in COVID-19 of Moderate Severity: The ACHIEVE Open-Label Single-Arm Trial
This article has 31 authors:Reviewed by ScreenIT