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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Serological surveys to inform SARS-CoV-2 epidemic curve: a cross-sectional study from Odisha, India
This article has 47 authors:Reviewed by ScreenIT
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Does Lockdown Decrease the Protective Role of Ultraviolet-B (UVB) Radiation in Reducing COVID-19 Deaths?
This article has 2 authors:Reviewed by ScreenIT
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Increase in suicide following an initial decline during the COVID-19 pandemic in Japan
This article has 2 authors:Reviewed by ScreenIT
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Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation
This article has 2 authors:Reviewed by ScreenIT
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Multiple-Organ Complement Deposition on Vascular Endothelium in COVID-19 Patients
This article has 10 authors:Reviewed by ScreenIT
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Early analysis of the Australian COVID-19 epidemic
This article has 13 authors:Reviewed by ScreenIT
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Use of dried blood spot samples for SARS-CoV-2 antibody detection using the Roche Elecsys ® high throughput immunoassay
This article has 16 authors:Reviewed by ScreenIT
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Who is wearing a mask? Gender-, age-, and location-related differences during the COVID-19 pandemic
This article has 8 authors:Reviewed by ScreenIT
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Zinc-Embedded Polyamide Fabrics Inactivate SARS-CoV-2 and Influenza A Virus
This article has 7 authors:Reviewed by ScreenIT
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Use of convalescent plasma in patients with coronavirus disease (Covid-19): Systematic review and meta-analysis
This article has 5 authors:Reviewed by ScreenIT