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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Dysregulation in mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS-CoV-2 infected cells
This article has 14 authors:Reviewed by ScreenIT
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Broad-spectrum antiviral activity of naproxen: from Influenza A to SARS-CoV-2 Coronavirus
This article has 12 authors:Reviewed by ScreenIT
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SARS-CoV-2 genomes recovered by long amplicon tiling multiplex approach using nanopore sequencing and applicable to other sequencing platforms
This article has 16 authors:Reviewed by ScreenIT
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Distinct Inductions of and Responses to Type I and Type III Interferons Promote Infections in Two SARS-CoV-2 Isolates
This article has 12 authors:Reviewed by ScreenIT
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Mutational spectra of SARS‐CoV‐2 orf1ab polyprotein and signature mutations in the United States of America
This article has 5 authors:Reviewed by ScreenIT
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SARS-CoV-2 spike protein predicted to form complexes with host receptor protein orthologues from a broad range of mammals
This article has 17 authors:Reviewed by ScreenIT
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Fitting SIR model to COVID-19 pandemic data and comparative forecasting with machine learning
This article has 1 author:Reviewed by ScreenIT
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Review of trials currently testing treatment and prevention of COVID-19
This article has 12 authors:Reviewed by ScreenIT
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Knowledge, Attitudes, and Practices (KAP) Towards COVID-19: An Online Cross-Sectional Survey of Tanzanian Residents
This article has 3 authors:Reviewed by ScreenIT
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Effectiveness of the Strategies Implemented in Sri Lanka for Controlling the COVID-19 Outbreak
This article has 4 authors:Reviewed by ScreenIT