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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Real-Time Estimation of Rt for Supporting Public-Health Policies Against COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Modeling the Epidemic Growth of Preprints on COVID-19 and SARS-CoV-2
This article has 4 authors:Reviewed by ScreenIT
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Intermediate‐dose anticoagulation, aspirin, and in‐hospital mortality in COVID ‐19: A propensity score‐matched analysis
This article has 22 authors:Reviewed by ScreenIT
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Future scenarios for the SARS-CoV-2 epidemic in Switzerland: an age-structured model
This article has 7 authors:Reviewed by ScreenIT
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Impact of climatic parameters on coronavirus disease 2019 pandemic progression in India: Analysis and prediction
This article has 3 authors:Reviewed by ScreenIT
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Passive surveillance assesses compliance with COVID-19 behavioural restrictions in a rural US county
This article has 5 authors:Reviewed by ScreenIT
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Barriers to Online Learning in the Time of COVID-19: A National Survey of Medical Students in the Philippines
This article has 9 authors:Reviewed by ScreenIT
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Imprints of Lockdown and Treatment Processes on the Wastewater Surveillance of SARS-CoV-2: A Curious Case of Fourteen Plants in Northern India
This article has 14 authors:Reviewed by ScreenIT
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Effective design of barrier enclosure to contain aerosol emissions from COVID‐19 patients
This article has 12 authors:Reviewed by ScreenIT
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Estimating the presymptomatic transmission of COVID19 using incubation period and serial interval data
This article has 1 author:Reviewed by ScreenIT