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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Epidemiology of COVID-19 and Predictors of Recovery in the Republic of Korea
This article has 2 authors:Reviewed by ScreenIT
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Rapid and Extraction-Free Detection of SARS-CoV-2 from Saliva by Colorimetric Reverse-Transcription Loop-Mediated Isothermal Amplification
This article has 13 authors:Reviewed by ScreenIT
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Psychological morbidities and fatigue in patients with confirmed COVID-19 during disease outbreak: prevalence and associated biopsychosocial risk factors
This article has 15 authors:Reviewed by ScreenIT
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Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies
This article has 11 authors:Reviewed by ScreenIT
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Coronavirus disease 2019 (COVID-19): an evidence map of medical literature
This article has 18 authors:Reviewed by ScreenIT
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Deactivation of SARS-CoV-2 with pulsed-xenon ultraviolet light: Implications for environmental COVID-19 control
This article has 14 authors:Reviewed by ScreenIT
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A Mathematical Model Approach for Prevention and Intervention Measures of the COVID-19 Pandemic in Uganda
This article has 7 authors:Reviewed by ScreenIT
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Double Power Law for COVID-19: Prediction of New Cases and Death Rates in Italy and Spain
This article has 3 authors:Reviewed by ScreenIT
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Negative Nasopharyngeal SARS-CoV-2 PCR Conversion in Response to Different Therapeutic Interventions
This article has 6 authors:Reviewed by ScreenIT
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Transmission in Latent Period Causes A Large Number of Infected People in the United States
This article has 11 authors:Reviewed by ScreenIT