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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State-by-State estimates of R0 at the start of COVID-19 outbreaks in the USA
This article has 2 authors:Reviewed by ScreenIT
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Microscopic dynamics modeling unravels the role of asymptomatic virus carriers in SARS-CoV-2 epidemics at the interplay between biological and social factors
This article has 2 authors:Reviewed by ScreenIT
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Active learning tools improve the learning outcomes, scientific attitude, and critical thinking in higher education: Experiences in an online course during the COVID ‐19 pandemic
This article has 6 authors:Reviewed by ScreenIT
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Engineered RNA biosensors enable ultrasensitive SARS-CoV-2 detection in a simple color and luminescence assay
This article has 9 authors:Reviewed by Review Commons, ScreenIT, preLights
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Estimating the risk on outbreak spreading of 2019-nCoV in China using transportation data
This article has 8 authors:Reviewed by ScreenIT
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UV light influences covid-19 activity through big data: trade offs between northern subtropical, tropical, and southern subtropical countries
This article has 4 authors:Reviewed by ScreenIT
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COMPARISON OF ARTIFICIAL INTELLIGENCE ENABLED METHODS IN THE COMPUTED TOMOGRAPHIC ASSESSMENT OF COVID-19 DISEASE
This article has 3 authors:Reviewed by ScreenIT
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IFN-γ and TNF-α drive a CXCL10+ CCL2+ macrophage phenotype expanded in severe COVID-19 lungs and inflammatory diseases with tissue inflammation
This article has 10 authors:Reviewed by ScreenIT
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A Comparison of Methylprednisolone and Dexamethasone in Intensive Care Patients With COVID-19
This article has 6 authors:Reviewed by ScreenIT
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Estimates of global SARS-CoV-2 infection exposure, infection morbidity, and infection mortality rates in 2020
This article has 6 authors:Reviewed by ScreenIT