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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Short-Term Immune Response After Inactivated SARS-CoV-2 (CoronaVac®, Sinovac) And ChAdOx1 nCoV-19 (Vaxzevria®, Oxford-AstraZeneca) Vaccinations in Thai Health Care Workers
This article has 12 authors:Reviewed by ScreenIT
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Predictive factors of a viral neutralizing humoral response after a third dose of COVID-19 mRNA vaccine
This article has 7 authors:Reviewed by ScreenIT
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The effectiveness of SARS-CoV-2 vaccination in preventing severe illness and death – real-world data from a cohort of patients hospitalized with COVID-19
This article has 30 authors:Reviewed by ScreenIT
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Alternative COVID-19 mitigation measures in school classrooms: analysis using an agent-based model of SARS-CoV-2 transmission
This article has 5 authors:Reviewed by ScreenIT
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Strikingly Different Roles of SARS-CoV-2 Fusion Peptides Uncovered by Neutron Scattering
This article has 16 authors:Reviewed by ScreenIT
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A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization
This article has 9 authors:Reviewed by ScreenIT
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Analysis of Reproduction Number R0 of COVID-19 Using Current Health Expenditure as Gross Domestic Product Percentage (CHE/GDP) across Countries
This article has 3 authors:Reviewed by ScreenIT
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Association between democratic governance and excess mortality during the COVID-19 pandemic: an observational study
This article has 3 authors:Reviewed by ScreenIT
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Sensitivity of wastewater-based epidemiology for detection of SARS-CoV-2 RNA in a low prevalence setting
This article has 9 authors:Reviewed by ScreenIT
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Early immune markers of clinical, virological, and immunological outcomes in patients with COVID-19: a multi-omics study
This article has 27 authors:This article has been curated by 1 group: