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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COVID-19 infection and attributable mortality in UK care homes: cohort study using active surveillance and electronic records (March–June 2020)
This article has 8 authors:Reviewed by ScreenIT
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Viral genomes reveal patterns of the SARS-CoV-2 outbreak in Washington State
This article has 43 authors:Reviewed by ScreenIT
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Discovery of five HIV nucleoside analog reverse-transcriptase inhibitors (NRTIs) as potent inhibitors against the RNA-dependent RNA polymerase (RdRp) of SARS-CoV and 2019-nCoV
This article has 1 author:Reviewed by ScreenIT
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Indications for healthcare surge capacity in European countries facing an exponential increase in coronavirus disease (COVID-19) cases, March 2020
This article has 3 authors:Reviewed by ScreenIT
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The necessary cooperation between governments and public in the fight against COVID-19: why non-pharmaceutical interventions may be ineffective
This article has 4 authors:Reviewed by ScreenIT
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The Psychological Impact of Hypertension During COVID-19 Restrictions: Retrospective Case-Control Study
This article has 12 authors:Reviewed by ScreenIT
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Longitudinal COVID-19 Surveillance and Characterization in the Workplace with Public Health and Diagnostic Endpoints
This article has 12 authors:Reviewed by ScreenIT
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Stepwise school opening online and off-line and an impact on the epidemiology of COVID-19 in the pediatric population
This article has 5 authors:Reviewed by ScreenIT
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Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria
This article has 3 authors:Reviewed by ScreenIT
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Machine Learning Models for Government to Predict COVID-19 Outbreak
This article has 4 authors:Reviewed by ScreenIT