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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Real-time SARS-CoV-2 diagnostic and variants tracking over multiple candidates using nanopore DNA sequencing
This article has 4 authors:Reviewed by ScreenIT
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Metformin is associated with favorable outcomes in patients with COVID-19 and type 2 diabetes mellitus
This article has 4 authors:Reviewed by ScreenIT
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The Easy-to-Use SARS-CoV-2 Assembler for Genome Sequencing: Development Study
This article has 7 authors:Reviewed by ScreenIT
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AnmO 2 l : An open-source pulse-dose oxygen conserving device for the COVID-19 crisis
This article has 8 authors:Reviewed by ScreenIT
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Evaluation of silver nanoparticles for the prevention of SARS-CoV-2 infection in health workers: In vitro and in vivo
This article has 11 authors:Reviewed by ScreenIT
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Release of infectious virus and cytokines in nasopharyngeal swabs from individuals infected with non-alpha or alpha SARS-CoV-2 variants: an observational retrospective study
This article has 27 authors:Reviewed by ScreenIT
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COVID-19 Mitigation Practices and COVID-19 Rates in Schools: Report on Data from Florida, New York and Massachusetts
This article has 5 authors:Reviewed by ScreenIT
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A model framework for projecting the prevalence and impact of Long-COVID in the UK
This article has 5 authors:Reviewed by ScreenIT
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Assessing the impact of human mobility to predict regional excess death in Ecuador
This article has 8 authors:Reviewed by ScreenIT
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Efficacy Estimates for Various COVID-19 Vaccines: What we Know from the Literature and Reports
This article has 6 authors:Reviewed by ScreenIT