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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Time series forecasting of COVID-19 confirmed cases with ARIMA model in the South East Asian countries of India and Thailand: a comparative case study
This article has 1 author:Reviewed by ScreenIT
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Preliminary evaluation of COVID-19 disease outcomes, test capacities and management approaches among African countries
This article has 17 authors:Reviewed by ScreenIT
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Causal Modeling of Twitter Activity during COVID-19
This article has 2 authors:Reviewed by ScreenIT
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Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening
This article has 8 authors:Reviewed by ScreenIT
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Comparative Analysis of the Application of Behavioural Insights of 33 Worldwide Governments on the Landing Pages of their COVID-19 Official Websites and their Impact on the Growth Scale of the Pandemic
This article has 4 authors:Reviewed by ScreenIT
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Early assessment of knowledge, attitudes, anxiety and behavioral adaptations of Connecticut residents to COVID-19
This article has 4 authors:Reviewed by ScreenIT
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Predicting SARS-CoV-2 Infection Trend Using Technical Analysis Indicators
This article has 2 authors:Reviewed by ScreenIT
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Feasibility of Reusing Surgical Mask Under Different Disinfection Treatments
This article has 6 authors:Reviewed by ScreenIT
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Preventive behavior of Vietnamese people in response to the COVID-19 pandemic
This article has 8 authors:Reviewed by ScreenIT
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Modeling the Effects of Nonpharmaceutical Interventions on COVID-19 Spread in Kenya
This article has 4 authors:Reviewed by ScreenIT