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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Interest in COVID-19 in Latin America and the Caribbean: an infodemiological study using Google Trends
This article has 5 authors:Reviewed by ScreenIT
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Projecting the impact of behaviour and isolation interventions and super spreader events from mass gatherings and international travel on Malaysia’s COVID-19 epidemic trajectories using an augmented SEIR model
This article has 3 authors:Reviewed by ScreenIT
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Cohorting of Non-Critically Ill COVID-19 Patients: A Multicenter Survey Study (COVID-COHORT)
This article has 2 authors:Reviewed by ScreenIT
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Evaluating the Effectiveness of Social Distancing Interventions to Delay or Flatten the Epidemic Curve of Coronavirus Disease
This article has 2 authors:Reviewed by ScreenIT
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SARS-CoV-2 infection in schools in a northern French city: a retrospective serological cohort study in an area of high transmission, France, January to April 2020
This article has 27 authors:Reviewed by ScreenIT
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Online respondent-driven detection for enhanced contact tracing of close-contact infectious diseases: benefits and barriers for public health practice
This article has 8 authors:Reviewed by ScreenIT
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Danish premature birth rates during the COVID-19 lockdown
This article has 11 authors:Reviewed by ScreenIT
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Comparison of media and standards for SARS-CoV-2 RT-qPCR without prior RNA preparation
This article has 6 authors:Reviewed by ScreenIT
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Automatic COVID-19 Detection from chest radiographic images using Convolutional Neural Network
This article has 2 authors:Reviewed by ScreenIT
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Forecasting COVID-19 cases: A comparative analysis between recurrent and convolutional neural networks
This article has 5 authors:Reviewed by ScreenIT