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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Is tracking and modeling Covid-19 infection dynamics for Bangladesh using daily data feasible?
This article has 3 authors:Reviewed by ScreenIT
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The Ensembl COVID-19 resource: ongoing integration of public SARS-CoV-2 data
This article has 27 authors:Reviewed by ScreenIT
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The impact of the COVID-19 pandemic on wellbeing and cognitive functioning of older adults
This article has 6 authors:Reviewed by ScreenIT
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Estimating the impact of influenza on the epidemiological dynamics of SARS-CoV-2
This article has 4 authors:Reviewed by ScreenIT
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A phenomics approach for in vitro antiviral drug discovery
This article has 9 authors:Reviewed by ScreenIT
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Development of Wastewater Pooled Surveillance of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) from Congregate Living Settings
This article has 12 authors:Reviewed by ScreenIT
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Comparison of three nasopharyngeal swab types and the impact of physiochemical properties for optimal SARS-CoV-2 detection
This article has 4 authors:Reviewed by ScreenIT
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Altered kidney function induced by SARS-CoV-2 infection and acute kidney damage markers predict survival outcomes of COVID-19 patients: a prospective pilot study
This article has 12 authors:Reviewed by ScreenIT
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Sharing a household with children and risk of COVID-19: a study of over 300 000 adults living in healthcare worker households in Scotland
This article has 15 authors:Reviewed by ScreenIT
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Harnessing testing strategies and public health measures to avert COVID-19 outbreaks during ocean cruises
This article has 4 authors:Reviewed by ScreenIT