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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Detection of COVID-19 and age-dependent dysosmia with paired crushable odorant ampules
This article has 4 authors:Reviewed by ScreenIT
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Response to COVID-19 booster vaccinations in seronegative people with multiple sclerosis
This article has 24 authors:Reviewed by ScreenIT
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Analysis of immunization time, amplitude, and adverse events of seven different vaccines against SARS-CoV-2 across four different countries
This article has 22 authors:Reviewed by ScreenIT
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Omicron SARS-CoV-2 epidemic in England during February 2022: A series of cross-sectional community surveys
This article has 21 authors:Reviewed by ScreenIT
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Surveillance of COVID-19 cases associated with dental settings using routine health data from the East of Scotland with a description of efforts to break chains of transmission from October 2020 to December 2021
This article has 2 authors:Reviewed by ScreenIT
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Quantitative Trend Analysis of SARS-CoV-2 RNA in Municipal Wastewater Exemplified with Sewershed-Specific COVID-19 Clinical Case Counts
This article has 17 authors:Reviewed by ScreenIT
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Duration of mRNA vaccine protection against SARS-CoV-2 Omicron BA.1 and BA.2 subvariants in Qatar
This article has 24 authors:Reviewed by ScreenIT
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Development and implementation of a simple and rapid extraction-free saliva SARS-CoV-2 RT-LAMP workflow for workplace surveillance
This article has 16 authors:Reviewed by ScreenIT
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Interstitial lung damage following COVID-19 hospitalisation: an interim analysis of the UKILD Post-COVID study
This article has 79 authors:Reviewed by ScreenIT
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Rare Variants in Inborn Errors of Immunity Genes Associated With Covid-19 Severity
This article has 8 authors:Reviewed by ScreenIT