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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Symptoms and syndromes associated with SARS-CoV-2 infection and severity in pregnant women from two community cohorts
This article has 18 authors:Reviewed by ScreenIT
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HLA-C * 04:01 is a Genetic Risk Allele for Severe Course of COVID-19
This article has 50 authors:Reviewed by ScreenIT
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Genome-wide CRISPR screening identifies TMEM106B as a proviral host factor for SARS-CoV-2
This article has 19 authors:Reviewed by ScreenIT
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Hydroxychloroquine: mechanism of action inhibiting SARS-CoV2 entry
This article has 4 authors:Reviewed by ScreenIT
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Inhaled corticosteroids downregulate SARS-CoV-2-related genes in COPD: results from a randomised controlled trial
This article has 11 authors:Reviewed by ScreenIT
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The effect of international travel restrictions on internal spread of COVID-19
This article has 7 authors:Reviewed by ScreenIT
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Trajectory of COVID-19 epidemic in Europe
This article has 5 authors:Reviewed by ScreenIT
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COVID-19 projections for reopening Connecticut
This article has 3 authors:Reviewed by ScreenIT
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The Effect of Early Hydroxychloroquine-based Therapy in COVID-19 Patients in Ambulatory Care Settings: A Nationwide Prospective Cohort Study
This article has 22 authors:Reviewed by ScreenIT
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Risk Factors of SARS-CoV-2 Infection: Global Epidemiological Study
This article has 1 author:Reviewed by ScreenIT