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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In silico design and validation of commercial kit GPS™ CoVID-19 dtec-RT-qPCR Test under criteria of UNE/EN ISO 17025:2005 and ISO/IEC 15189:2012
This article has 6 authors:Reviewed by ScreenIT
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CovidNLP : A Web Application for Distilling Systemic Implications of COVID-19 Pandemic with Natural Language Processing
This article has 8 authors:Reviewed by ScreenIT
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A prototype for decision support tool to help decision-makers with the strategy of handling the COVID-19 UK epidemic
This article has 7 authors:Reviewed by ScreenIT
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Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide
This article has 7 authors:Reviewed by ScreenIT
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COVID-19 in India: Statewise Analysis and Prediction
This article has 3 authors:Reviewed by ScreenIT
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Role of Drugs Used for Chronic Disease Management on Susceptibility and Severity of COVID‐19: A Large Case‐Control Study
This article has 17 authors:Reviewed by ScreenIT
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An evaluation of COVID-19 serological assays informs future diagnostics and exposure assessment
This article has 16 authors:Reviewed by ScreenIT
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A Novel Protein Drug, Novaferon, as the Potential Antiviral Drug for COVID-19
This article has 24 authors:Reviewed by ScreenIT
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Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study
This article has 4 authors:Reviewed by ScreenIT
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Development and Potential Usefulness of the COVID-19 Ag Respi-Strip Diagnostic Assay in a Pandemic Context
This article has 22 authors:Reviewed by ScreenIT