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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Model-free estimation of COVID-19 transmission dynamics from a complete outbreak
This article has 6 authors:Reviewed by ScreenIT
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Psychological preparedness for pandemic (COVID-19) management: Perceptions of nurses and nursing students in India
This article has 7 authors:Reviewed by ScreenIT
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Implementation of an in-house real-time reverse transcription-PCR assay for the rapid detection of the SARS-CoV-2 Marseille-4 variant
This article has 17 authors:Reviewed by ScreenIT
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A simple criterion to design optimal non-pharmaceutical interventions for mitigating epidemic outbreaks
This article has 5 authors:Reviewed by ScreenIT
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Explaining Causal Influence of External Factors on Incidence Rate of Covid-19
This article has 3 authors:Reviewed by ScreenIT
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Ultrarapid On-Site Detection of SARS-CoV-2 Infection Using Simple ATR-FTIR Spectroscopy and an Analysis Algorithm: High Sensitivity and Specificity
This article has 10 authors:Reviewed by ScreenIT
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COVID-19 among Nursing Staff: Settings and Regional Differences
This article has 3 authors:Reviewed by ScreenIT
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Workplace measures against COVID-19 during the winter third wave in Japan: company size-based differences
This article has 10 authors:Reviewed by ScreenIT
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Estimation of COVID-19 dynamics in the different states of the United States using Time-Series Clustering
This article has 3 authors:Reviewed by ScreenIT
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Structural and functional analysis of female sex hormones against SARS-Cov2 cell entry
This article has 9 authors:Reviewed by ScreenIT