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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Phenotype and kinetics of SARS-CoV-2–specific T cells in COVID-19 patients with acute respiratory distress syndrome
This article has 14 authors:Reviewed by ScreenIT
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Analysis of the mitigation strategies for COVID-19: From mathematical modelling perspective
This article has 3 authors:Reviewed by ScreenIT
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Patients presenting for hospital‐based screening for the coronavirus disease 2019: Risk of disease, and healthcare access preferences
This article has 8 authors:Reviewed by ScreenIT
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Predictions, Role of Interventions and Effects of a Historic National Lockdown in India's Response to the the COVID-19 Pandemic: Data Science Call to Arms
Reviewed by ScreenIT
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Estimating the COVID-19 infection rate: Anatomy of an inference problem
This article has 2 authors:Reviewed by ScreenIT
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Meteorological factors and domestic new cases of coronavirus disease (COVID-19) in nine Asian cities: A time-series analysis
This article has 8 authors:Reviewed by ScreenIT
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Digital herd immunity and COVID-19
This article has 4 authors:Reviewed by ScreenIT
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Benefits and Risks of Chloroquine and Hydroxychloroquine in The Treatment of Viral Diseases: A Meta-Analysis of Placebo Randomized Controlled Trials
This article has 3 authors:Reviewed by ScreenIT
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Estimating the early death toll of COVID-19 in the United States
This article has 11 authors:Reviewed by ScreenIT
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Social Isolation as a predictor for mortality: Implications for COVID-19 prognosis
This article has 4 authors:Reviewed by ScreenIT