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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A Rapid COVID-19 RT-PCR Detection Assay for Low Resource Settings
This article has 5 authors:Reviewed by ScreenIT
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Validation of N95 Filtering Facepiece Respirator Decontamination Methods Available at a Large University Hospital
This article has 20 authors:Reviewed by ScreenIT
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Bayesian inference of COVID-19 spreading rates in South Africa
This article has 2 authors:Reviewed by ScreenIT
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ACE2 and TMPRSS2 expression by clinical, HLA, immune, and microbial correlates across 34 human cancers and matched normal tissues: implications for SARS-CoV-2 COVID-19
This article has 4 authors:Reviewed by ScreenIT
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Estimation of the Actual Incidence of Coronavirus Disease (COVID-19) in Emergent Hotspots: The Example of Hokkaido, Japan during February–March 2020
This article has 6 authors:Reviewed by ScreenIT
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Students’ mental health problems before, during, and after COVID-19 lockdown in Italy
This article has 8 authors:Reviewed by ScreenIT
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Fractional SIR epidemiological models
This article has 4 authors:Reviewed by ScreenIT
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Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models
This article has 7 authors:Reviewed by ScreenIT
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Emergency Medical Services resource capacity and competency amid COVID-19 in the United States: preliminary findings from a national survey
This article has 3 authors:Reviewed by ScreenIT
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Estimating COVID-19 Prevalence in the United States: A Sample Selection Model Approach
This article has 3 authors:Reviewed by ScreenIT