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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A Genotype-to-Phenotype Modeling Framework to Predict Human Pathogenicity of Novel Coronaviruses
This article has 2 authors:Reviewed by ScreenIT
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Protocol: A two-wave cross-sectional study in England investigating suicidal behaviour and self-harm amongst healthcare workers during the Covid-19 pandemic
This article has 5 authors:Reviewed by ScreenIT
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Distributed Counterfactual Modeling Approach for Investigating Hospital-Associated Racial Disparities in COVID-19 Mortality
This article has 6 authors:Reviewed by ScreenIT
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Evolution of COVID-19 mortality over time: results from the Swiss hospital surveillance system (CH-SUR)
This article has 34 authors:Reviewed by ScreenIT
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SARS-CoV-2 Variants in Paraguay: Detection and Surveillance with an Economical and Scalable Molecular Protocol
This article has 16 authors:Reviewed by ScreenIT
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Environmental factors and mobility predict COVID-19 seasonality in the Netherlands
This article has 3 authors:Reviewed by ScreenIT
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Planning and Conducting an Online Conference at the time of COVID-19: Lessons Learned from EGREPA 2021
This article has 4 authors:Reviewed by ScreenIT
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A Community Study of SARS-CoV-2 Detection by RT-PCR in Saliva: A Reliable and Effective Method
This article has 27 authors:Reviewed by ScreenIT
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Correlates of COVID-19 vaccination status among college students
This article has 16 authors:Reviewed by ScreenIT
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Spatiotemporal analyses illuminate the competitive advantage of a SARS-CoV-2 variant of concern over a variant of interest
This article has 7 authors:Reviewed by ScreenIT