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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“I Walk around Like My Hands are Covered in Mud”: Food Safety and Hand Hygiene Behaviors of Canadians during the COVID-19 Pandemic
This article has 4 authors:Reviewed by ScreenIT
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Awareness, knowledge and trust in the Greek authorities towards COVID-19 pandemic: results from the Epirus Health Study cohort
This article has 23 authors:Reviewed by ScreenIT
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Recommendations for accurate genotyping of SARS-CoV-2 using amplicon-based sequencing of clinical samples
This article has 29 authors:Reviewed by ScreenIT
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Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning
This article has 30 authors:Reviewed by ScreenIT
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Effects of non-pharmaceutical interventions on COVID-19: A Tale of Three Models
This article has 4 authors:Reviewed by ScreenIT
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On an optimal testing strategy for workplace settings operating during the COVID-19 pandemic
This article has 2 authors:Reviewed by ScreenIT
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Relation of severe COVID-19 to polypharmacy and prescribing of psychotropic drugs: the REACT-SCOT case-control study
This article has 18 authors:Reviewed by ScreenIT
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High neutralizing potency of swine glyco‐humanized polyclonal antibodies against SARS‐CoV‐2
This article has 29 authors:Reviewed by ScreenIT
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Group IIA secreted phospholipase A2 is associated with the pathobiology leading to COVID-19 mortality
This article has 24 authors:Reviewed by ScreenIT
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Transmission of SARS‐CoV‐2 by inhalation of respiratory aerosol in the Skagit Valley Chorale superspreading event
This article has 10 authors:Reviewed by ScreenIT