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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Household Food Insecurity Scores are Higher among Adults Infected with COVID-19: A Cross-Sectional Online Study among an Iranian Population
This article has 4 authors:Reviewed by ScreenIT
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Estimation of Final Size of COVID-19 Epidemic in Balochistan Province, Pakistan
This article has 3 authors:Reviewed by ScreenIT
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Evaluation of spike protein antigens for SARS-CoV-2 serology
This article has 12 authors:Reviewed by ScreenIT
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No evidence that plasmablasts transdifferentiate into developing neutrophils in severe COVID‐19 disease
This article has 3 authors:Reviewed by ScreenIT
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Probing the Dynamic Structure–Function and Structure-Free Energy Relationships of the Coronavirus Main Protease with Biodynamics Theory
This article has 3 authors:Reviewed by ScreenIT
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Modelling the Occurrence of the Novel Pandemic COVID-19 Outbreak– A Box and Jenkins Approach
This article has 3 authors:Reviewed by ScreenIT
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Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach
This article has 3 authors:Reviewed by ScreenIT
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Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020
This article has 7 authors:Reviewed by ScreenIT
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Expanding Covid-19 Testing: Mathematical Guidelines for the Optimal Sample Pool Size Given Positive Test Rate
This article has 1 author:Reviewed by ScreenIT
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The impact of COVID-19 infection on maternal and reproductive health care services in governmental health institutions of Dessie town, North-East Ethiopia, 2020 G.C.
This article has 3 authors:Reviewed by ScreenIT