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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The antibody response to SARS-CoV-2 infection persists over at least 8 months in symptomatic patients
This article has 10 authors:Reviewed by ScreenIT
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Estimation of COVID-19 transmission rates in California and the U.S. with reporting delays
This article has 7 authors:Reviewed by ScreenIT
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Staff–pupil SARS-CoV-2 infection pathways in schools in Wales: a population-level linked data approach
This article has 13 authors:Reviewed by ScreenIT
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An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques
This article has 3 authors:Reviewed by ScreenIT
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The impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China
This article has 9 authors:Reviewed by ScreenIT
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Anti-vaccine attitudes and risk factors for not agreeing to vaccination against COVID-19 amongst 32,361 UK adults: Implications for public health communications
This article has 3 authors:Reviewed by ScreenIT
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A HYBRID KNOWLEDGE-BASED AND MODIFIED REGRESSION ANALYSIS APPROACH FOR COVID-19 TRACKING IN USA
This article has 1 author:Reviewed by ScreenIT
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Elucidation of Genome Polymorphisms in Emerging SARS-CoV-2
This article has 4 authors:Reviewed by ScreenIT
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Impact of COVID-19 Attributable Deaths on Longevity, Premature Mortality and DALY: Estimates of USA, Italy, Sweden and Germany
This article has 4 authors:Reviewed by ScreenIT
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pH and Receptor Induced Conformational Changes-Implications Towards S1 Dissociation of SARS-CoV2 Spike Glycoprotein
This article has 4 authors:Reviewed by ScreenIT