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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Clinical predictors of donor antibody titre and correlation with recipient antibody response in a COVID‐19 convalescent plasma clinical trial
This article has 34 authors:Reviewed by ScreenIT
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Chest X-ray Classification Using Deep Learning for Automated COVID-19 Screening
This article has 7 authors:Reviewed by ScreenIT
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Behavioral changes before lockdown and decreased retail and recreation mobility during lockdown contributed most to controlling COVID-19 in Western countries
This article has 4 authors:Reviewed by ScreenIT
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Experiences of frontline healthcare workers and their views about support during COVID-19 and previous pandemics: a systematic review and qualitative meta-synthesis
This article has 5 authors:Reviewed by ScreenIT
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Estimation of undetected symptomatic and asymptomatic cases of COVID‐19 infection and prediction of its spread in the USA
This article has 3 authors:Reviewed by ScreenIT
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COVID-19 lockdown: if, when and how
This article has 7 authors:Reviewed by ScreenIT
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miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles
This article has 6 authors:Reviewed by ScreenIT
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Functional characterization of SARS-CoV-2 infection suggests a complex inflammatory response and metabolic alterations
This article has 12 authors:Reviewed by ScreenIT
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In vivo antiviral host transcriptional response to SARS-CoV-2 by viral load, sex, and age
This article has 15 authors:Reviewed by ScreenIT
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Design of a Companion Bioinformatic Tool to detect the emergence and geographical distribution of SARS-CoV-2 Spike protein genetic variants
This article has 8 authors:Reviewed by ScreenIT