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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Risk, Clinical Course, and Outcome of Ischemic Stroke in Patients Hospitalized With COVID-19: A Multicenter Cohort Study
This article has 47 authors:Reviewed by ScreenIT
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A Case Study of Domestic Violence Arrests during the COVID-19 Pandemic in Miami-Dade County
This article has 3 authors:Reviewed by ScreenIT
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Genetics of symptom remission in outpatients with COVID-19
This article has 33 authors:Reviewed by ScreenIT
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Respiratory disease in rhesus macaques inoculated with SARS-CoV-2
This article has 17 authors:Reviewed by ScreenIT
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IN VITRO ANTI-VIRAL ACTIVITY OF HEXETIDINE (BACTIDOL ® ) ORAL MOUTHWASH AGAINST HUMAN CORONAVIRUS OC43 AND INFLUENZA A (H1N1) VIRUS
This article has 2 authors:Reviewed by ScreenIT
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Assessing the risk of cascading COVID-19 outbreaks from prison-to-prison transfers
This article has 2 authors:Reviewed by ScreenIT
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Citation needed? Wikipedia bibliometrics during the first wave of the COVID-19 pandemic
This article has 3 authors:Reviewed by GigaScience, preLights, ScreenIT
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High failure rate of ChAdOx1-nCoV19 immunization against asymptomatic infection in healthcare workers during a Delta variant surge
This article has 39 authors:Reviewed by ScreenIT
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New Zealand Emergency Department COVID-19 Preparedness Survey
This article has 3 authors:Reviewed by ScreenIT
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Deciphering the link between Diabetes mellitus and SARS-CoV-2 infection through differential targeting of microRNAs in the human pancreas
This article has 3 authors:Reviewed by ScreenIT