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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The role of endotheliitis in COVID‐19: Real‐world experience of 11 190 patients and literature review for a pathophysiological map to clinical categorisation
This article has 5 authors:Reviewed by ScreenIT
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COVID-19 Serology Control Panel Using the Dried-Tube Specimen Method
This article has 20 authors:Reviewed by ScreenIT
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Examining Medical Student Volunteering During The COVID-19 Pandemic As A Prosocial Behavior During An Emergency
This article has 8 authors:Reviewed by ScreenIT
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Incidence of SARS-CoV-2 infection among previously infected or vaccinated employees
This article has 5 authors:Reviewed by ScreenIT
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Anxiety and depression symptoms after COVID-19 infection: results from the COVID Symptom Study app
This article has 25 authors:Reviewed by ScreenIT
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Monitoring SARS-CoV-2 variants alterations in Nice neighborhoods by wastewater nanopore sequencing
This article has 15 authors:Reviewed by ScreenIT
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Defining the Innate Immune Responses for SARS-CoV-2-Human Macrophage Interactions
This article has 13 authors:Reviewed by ScreenIT
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All-cause and in-hospital mortality after aspirin use in patients hospitalized with COVID-19: a systematic review and meta-analysis
This article has 13 authors:Reviewed by ScreenIT
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Transient Positive SARS-CoV-2 PCR without Induction of Systemic Immune Responses
This article has 5 authors:Reviewed by ScreenIT
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High-Resolution Linear Epitope Mapping of the Receptor Binding Domain of SARS-CoV-2 Spike Protein in COVID-19 mRNA Vaccine Recipients
This article has 12 authors:Reviewed by ScreenIT