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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Forecasting daily COVID-19 confirmed, deaths and recovered cases using univariate time series models: A case of Pakistan study
This article has 2 authors:Reviewed by ScreenIT
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Immunoreactive peptide maps of SARS-CoV-2
This article has 16 authors:Reviewed by ScreenIT
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The impact of the COVID-19 pandemic on rabies reemergence in Latin America: The case of Arequipa, Peru
This article has 9 authors:Reviewed by ScreenIT
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Detection of SARS-CoV-2 within the healthcare environment: a multi-centre study conducted during the first wave of the COVID-19 outbreak in England
This article has 19 authors:Reviewed by ScreenIT
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Predictors of fatality including radiographic findings in adults with COVID-19
This article has 8 authors:Reviewed by ScreenIT
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Equity in Paediatric Emergency Departments during COVID-19
This article has 6 authors:Reviewed by ScreenIT
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Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data
This article has 17 authors:Reviewed by ScreenIT
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Translational design for limited resource settings as demonstrated by Vent-Lock, a 3D-printed ventilator multiplexer
This article has 20 authors:Reviewed by ScreenIT
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Respiratory Syncytial Virus Infections in Young Children Presenting to Primary Care in Catalonia During the COVID-19 Pandemic
This article has 8 authors:Reviewed by ScreenIT
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Alternative Qualitative Fit Testing Method for N95 Equivalent Respirators in the Setting of Resource Scarcity at the George Washington University
This article has 7 authors:Reviewed by ScreenIT