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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Evaluation of the role of home rapid antigen testing to determine isolation period after infection with SARS-CoV-2
This article has 13 authors:Reviewed by ScreenIT
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A mechanistic understanding of the modes of Ca ion binding to the SARS-CoV-1 fusion peptide and their role in the dynamics of host membrane penetration
This article has 9 authors:Reviewed by ScreenIT
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COVID-19 Hospitalisation in Portugal, the first year: Results from hospital discharge data
This article has 6 authors:Reviewed by ScreenIT
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Zinc pyrithione is a potent inhibitor of PL Pro and cathepsin L enzymes with ex vivo inhibition of SARS-CoV-2 entry and replication
This article has 9 authors:Reviewed by ScreenIT
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Reasons underlying the intention to vaccinate children aged 5-11 against COVID-19: A cross-sectional study of parents in Israel, November 2021
This article has 9 authors:Reviewed by ScreenIT
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Sero-surveillance for IgG to SARS-CoV-2 at antenatal care clinics in three Kenyan referral hospitals: Repeated cross-sectional surveys 2020–21
This article has 42 authors:Reviewed by ScreenIT
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COVID-19 Exposure Assessment Tool (CEAT): Exposure quantification based on ventilation, infection prevalence, group characteristics, and behavior
This article has 11 authors:Reviewed by ScreenIT
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Association between COVID-19 risk-mitigation behaviors and specific mental disorders in youth
This article has 8 authors:Reviewed by ScreenIT
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Precision recruitment for high-risk participants in a COVID-19 research study
This article has 9 authors:Reviewed by ScreenIT
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The relationship between BMI and COVID-19: exploring misclassification and selection bias in a two-sample Mendelian randomisation study
This article has 9 authors:Reviewed by ScreenIT