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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Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model
This article has 5 authors:Reviewed by ScreenIT
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Potent neutralization of 2019 novel coronavirus by recombinant ACE2-Ig
This article has 7 authors:Reviewed by ScreenIT
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Early dynamics of transmission and control of COVID-19: a mathematical modelling study
This article has 22 authors:Reviewed by ScreenIT
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Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak
This article has 8 authors:Reviewed by ScreenIT
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Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases
This article has 9 authors:Reviewed by ScreenIT
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Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China
This article has 25 authors:Reviewed by ScreenIT
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Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates
This article has 5 authors: -
Causes of death in newborn C57BL/6J mice
This article has 5 authors:Reviewed by ScreenIT
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Trends in SARS-CoV-2 infection prevalence during England’s roadmap out of lockdown, January to July 2021
This article has 14 authors:Reviewed by ScreenIT