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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Early public adherence with and support for stay-at-home COVID-19 mitigation strategies despite adverse life impact: a transnational cross-sectional survey study in the United States and Australia
This article has 7 authors:Reviewed by ScreenIT
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Prevalence of putative invasive pulmonary aspergillosis in critically ill patients with COVID-19
This article has 5 authors:Reviewed by ScreenIT
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The Impact of Changes in Diagnostic Testing Practices on Estimates of COVID-19 Transmission in the United States
This article has 8 authors:Reviewed by ScreenIT
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Impact of Lockdown on the Epidemic Dynamics of COVID-19 in France
This article has 5 authors:Reviewed by ScreenIT
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An adaptive, interacting, cluster-based model for predicting the transmission dynamics of COVID-19
This article has 7 authors:Reviewed by ScreenIT
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Estimated inequities in COVID-19 infection fatality rates by ethnicity for Aotearoa New Zealand
This article has 11 authors:Reviewed by ScreenIT
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COVID-19 Epidemic in Sri Lanka: A Mathematical and Computational Modelling Approach to Control
This article has 3 authors:Reviewed by ScreenIT
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Clinical Characteristics and Risk factors for developed COVID-19 patients transferring to designated hospital from Jianghan Fangcang shelter Hospital: a retrospective, observational study
This article has 11 authors:Reviewed by ScreenIT
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Association between coronavirus disease 2019 (COVID-19) and long-term exposure to air pollution: Evidence from the first epidemic wave in China
This article has 12 authors:Reviewed by ScreenIT
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COVID-19 and Inequity: a Comparative Spatial Analysis of New York City and Chicago Hot Spots
This article has 3 authors:Reviewed by ScreenIT