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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Predictors of COVID-19 Vaccine Hesitancy in South African Local Communities: The VaxScenes Study
This article has 18 authors:Reviewed by ScreenIT
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Alterations in SARS-CoV-2 Omicron and Delta peptides presentation by HLA molecules
This article has 10 authors:Reviewed by ScreenIT
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Understanding the immunological landscape of England during SARS-CoV2 Omicron variant wave
This article has 4 authors:Reviewed by ScreenIT
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Targeted Down Regulation Of Core Mitochondrial Genes During SARS-CoV-2 Infection
This article has 43 authors:Reviewed by ScreenIT
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SARS-CoV-2 Viroporins Activate The NLRP3-Inflammasome Via The Mitochondrial Permeability Transition Pore
This article has 6 authors:Reviewed by ScreenIT
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Modular capsid decoration boosts adenovirus vaccine-induced humoral immunity against SARS-CoV-2
This article has 11 authors:Reviewed by ScreenIT
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Bayesian emulation and history matching of JUNE
This article has 14 authors:Reviewed by ScreenIT
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Scottish COVID CAncer iMmunity Prevalence (SCCAMP) - a longitudinal study of patients with cancer receiving active anti-cancer treatment during the COVID-19 pandemic
This article has 18 authors:Reviewed by ScreenIT
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Identification and differential usage of a host metalloproteinase entry pathway by SARS-CoV-2 Delta and Omicron
This article has 21 authors:Reviewed by ScreenIT
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Efficient recall of Omicron-reactive B cell memory after a third dose of SARS-CoV-2 mRNA vaccine
This article has 38 authors:Reviewed by ScreenIT