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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Controlling COVID-19 via test-trace-quarantine
This article has 17 authors:Reviewed by ScreenIT
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Risk and protective factors of SARS-CoV-2 infection – Meta-regression of data from worldwide nations
This article has 7 authors:Reviewed by ScreenIT
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“An exploratory Integrated Moving Average Time Series Model of the initial outbreak of COVID-19 in six (6) significantly impacted Countries”
This article has 2 authors:Reviewed by ScreenIT
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Scrutinizing the heterogeneous spreading of COVID-19 outbreak in large territorial countries
This article has 3 authors:Reviewed by ScreenIT
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Emergence and expansion of SARS-CoV-2 B.1.526 after identification in New York
This article has 13 authors:Reviewed by ScreenIT
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Evaluation of convalescent plasma versus standard of care for the treatment of COVID-19 in hospitalized patients: study protocol for a phase 2 randomized, open-label, controlled, multicenter trial
This article has 15 authors:Reviewed by ScreenIT
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A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates
This article has 2 authors:Reviewed by ScreenIT
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Epidemiological characteristics of SARS-COV-2 in Myanmar
This article has 7 authors:Reviewed by ScreenIT
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Sequencing of SARS CoV2 in local transmission cases through oxford nanopore MinION platform from Karachi Pakistan
This article has 7 authors:Reviewed by ScreenIT
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Protocol for a sequential, prospective meta-analysis to describe coronavirus disease 2019 (COVID-19) in the pregnancy and postpartum periods
This article has 69 authors:Reviewed by ScreenIT