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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SARS-CoV-2 antibody immunoassays in serial samples reveal earlier seroconversion in acutely ill COVID-19 patients developing ARDS
This article has 9 authors:Reviewed by ScreenIT
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Impact of clade specific mutations on structural fidelity of SARS-CoV-2 proteins
This article has 6 authors:Reviewed by ScreenIT
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A longitudinal survey for genome-based identification of SARS-CoV-2 in sewage water in selected lockdown areas of Lahore city, Pakistan; a potential approach for future smart lockdown strategy
This article has 29 authors:Reviewed by ScreenIT
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Empty Streets, Speeding and Motor Vehicle Collisions during Covid-19 Lockdowns: Evidence from Northern Ireland
This article has 2 authors:Reviewed by ScreenIT
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Rates of COVID-19–Related Outcomes in Cancer Compared With Noncancer Patients
This article has 16 authors:Reviewed by ScreenIT
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Resolution of viral load in mild COVID-19 patients is associated with both innate and adaptive immune responses
This article has 10 authors:Reviewed by ScreenIT
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Appealing for efficient, well organized clinical trials on COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Rapid Assessment of SARS-CoV-2 Transmission Risk for Fecally Contaminated River Water
This article has 8 authors:Reviewed by ScreenIT
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SARS-CoV-2 Testing Service Preferences of Adults in the United States: Discrete Choice Experiment
This article has 14 authors:Reviewed by ScreenIT
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Peripheral and lung resident memory T cell responses against SARS-CoV-2
This article has 26 authors:Reviewed by ScreenIT