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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OBSERVATIONAL STUDY ON CLINICAL FEATURES, TREATMENT AND OUTCOME OF COVID 19 IN A TERTIARY CARE CENTRE IN INDIA - A RETROSPECTIVE CASE SERIES
This article has 6 authors:Reviewed by ScreenIT
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Temporal Analysis of COVID-19 Peak Outbreak
This article has 1 author:Reviewed by ScreenIT
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AutoSEIR: Accurate Forecasting from Real-time Epidemic Data Using Machine Learning
This article has 4 authors:Reviewed by ScreenIT
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Social Patterning and Stability of Intention to Accept a COVID-19 Vaccine in Scotland: Will Those Most at Risk Accept a Vaccine?
This article has 6 authors:Reviewed by ScreenIT
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rSWeeP: A R/Bioconductor package deal with SWeeP sequences representation
This article has 7 authors:Reviewed by ScreenIT
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Targeting the Pentose Phosphate Pathway for SARS-CoV-2 Therapy
This article has 10 authors:Reviewed by ScreenIT
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Obesity, old age and frailty are the true risk factors for COVID-19 mortality and not chronic disease or ethnicity in Croydon
This article has 5 authors:Reviewed by ScreenIT
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MODELLING PRESYMPTOMATIC INFECTIOUSNESS IN COVID-19
This article has 4 authors:Reviewed by ScreenIT
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COVID-19 Seroprevalence in Baixada Santista Metropolitan Area – Brazil
This article has 8 authors:Reviewed by ScreenIT
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Multimodal Single-Cell Omics Analysis of COVID-19 Sex Differences in Human Immune Systems
This article has 11 authors:Reviewed by ScreenIT