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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Diminishing Marginal Benefit of Social Distancing in Balancing COVID-19 Medical Demand-to-Supply
This article has 3 authors:Reviewed by ScreenIT
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Transmission routes of Covid-19 virus in the Diamond Princess Cruise ship
This article has 8 authors:Reviewed by ScreenIT
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Efficacy of remdesivir in patients with COVID-19: a protocol for systematic review and meta-analysis of randomised controlled trials
This article has 3 authors:Reviewed by ScreenIT
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Analysis of factors associated early diagnosis in coronavirus disease 2019 (COVID-19)
This article has 16 authors:Reviewed by ScreenIT
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Forecasting the scale of the COVID-19 epidemic in Kenya
This article has 10 authors:Reviewed by ScreenIT
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Forecasting Covid-19 Outbreak Progression in Italian Regions: A model based on neural network training from Chinese data
This article has 5 authors:Reviewed by ScreenIT
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Relationship between Average Daily Temperature and Average Cumulative Daily Rate of Confirmed Cases of COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Myocyte Specific Upregulation of ACE2 in Cardiovascular Disease: Implications for SARS-CoV-2 Mediated Myocarditis
This article has 13 authors:Reviewed by ScreenIT
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An agent-based epidemic model REINA for COVID-19 to identify destructive policies
This article has 6 authors:Reviewed by ScreenIT
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Analysing recovery from pandemics by Learning Theory: the case of CoVid-19
This article has 2 authors:Reviewed by ScreenIT