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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Case Study: Using Facebook Data to Monitor Adherence to Stay-at-home Orders in Colorado and Utah
This article has 5 authors:Reviewed by ScreenIT
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Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic
This article has 6 authors:Reviewed by ScreenIT
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Association between SARS-CoV-2 infection, exposure risk and mental health among a cohort of essential retail workers in the USA
This article has 4 authors:Reviewed by ScreenIT
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Therapeutic Effectiveness of Interferon-α2b Against COVID-19: The Cuban Experience
This article has 10 authors:Reviewed by ScreenIT
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Casual Sex among Men Who Have Sex with Men (MSM) during the Period of Sheltering in Place to Prevent the Spread of COVID-19
This article has 11 authors:Reviewed by ScreenIT
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A dynamic COVID-19 immune signature includes associations with poor prognosis
This article has 50 authors:Reviewed by ScreenIT
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SHELTER IN PLACE ORDER CONTAINED COVID-19 GROWTH RATE IN GREECE
This article has 5 authors:Reviewed by ScreenIT
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Identification of multiple large deletions in ORF7a resulting in in-frame gene fusions in clinical SARS-CoV-2 isolates
This article has 8 authors:Reviewed by ScreenIT
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Concerns and Misconceptions About the Australian Government’s COVIDSafe App: Cross-Sectional Survey Study
This article has 5 authors:Reviewed by ScreenIT
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Indian Publications on SARS-CoV-2: A bibliometric study of WHO COVID-19 database
This article has 2 authors:Reviewed by ScreenIT