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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Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile
This article has 6 authors:Reviewed by ScreenIT
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Temporal Trends in COVID-19 associated AKI from March to December 2020 in New York City
This article has 12 authors:Reviewed by ScreenIT
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Immunogenicity and crossreactivity of antibodies to the nucleocapsid protein of SARS-CoV-2: utility and limitations in seroprevalence and immunity studies
This article has 19 authors:Reviewed by ScreenIT
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Rt2: computing and visualising COVID-19 epidemics temporal reproduction number
This article has 4 authors:Reviewed by ScreenIT
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AI aided design of epitope-based vaccine for the induction of cellular immune responses against SARS-CoV-2
This article has 12 authors:Reviewed by ScreenIT
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Estandarización y validación de una prueba molecular RT-LAMP in house para el diagnóstico de SARS-CoV-2
This article has 15 authors:Reviewed by ScreenIT
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An assessment of efficacy of Iodine complex (Renessans) against SARS-CoV-2 in non-human primates ( Rhesus macaque )
This article has 22 authors:Reviewed by ScreenIT
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Safe and effective two-in-one replicon-and-VLP minispike vaccine for COVID-19
This article has 10 authors:Reviewed by ScreenIT
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CAN-NPI: A Curated Open Dataset of Canadian Non-Pharmaceutical Interventions in Response to the Global COVID-19 Pandemic
This article has 11 authors:Reviewed by ScreenIT
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The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19
This article has 12 authors:Reviewed by ScreenIT