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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Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study
This article has 10 authors:Reviewed by ScreenIT
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The impact of coronavirus disease 2019 (COVID-19) on liver injury in China
This article has 6 authors:Reviewed by ScreenIT
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Preexisting Comorbidities Predicting COVID-19 and Mortality in the UK Biobank Community Cohort
This article has 7 authors:Reviewed by ScreenIT
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Weather Conditions and COVID-19 Transmission: Estimates and Projections
This article has 6 authors:Reviewed by ScreenIT
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Ethnicity, comorbidity, socioeconomic status, and their associations with COVID-19 infection in England: a cohort analysis of UK Biobank data
This article has 3 authors:Reviewed by ScreenIT
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Spatial network based model forecasting transmission and control of COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Studies of Novel Coronavirus Disease 19 (COVID-19) Pandemic: A Global Analysis of Literature
This article has 9 authors:Reviewed by ScreenIT
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A structured open dataset of government interventions in response to COVID-19
This article has 43 authors:Reviewed by ScreenIT
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The impact of Coronavirus disease 2019 (COVID-19) on health systems and household resources in Africa and South Asia
This article has 12 authors:Reviewed by ScreenIT
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ProgNet: COVID-19 Prognosis Using Recurrent and Convolutional Neural Networks
This article has 4 authors:Reviewed by ScreenIT