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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coronapp : A Web Application to Annotate and Monitor SARS-CoV-2 Mutations
This article has 5 authors:Reviewed by ScreenIT
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Reconstructed signaling and regulatory networks identify potential drugs for SARS-CoV-2 infection
This article has 9 authors:Reviewed by ScreenIT
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Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCov literature
This article has 2 authors:Reviewed by ScreenIT
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Impact of emerging mutations on the dynamic properties the SARS-CoV-2 main protease: an in silico investigation
This article has 3 authors:Reviewed by ScreenIT
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Dose-dependent response to infection with SARS-CoV-2 in the ferret model: evidence of protection to re-challenge
This article has 49 authors:Reviewed by ScreenIT
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SARS-CoV-2 infects and induces cytotoxic effects in human cardiomyocytes
This article has 22 authors:Reviewed by ScreenIT
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Minimal system for assembly of SARS-CoV-2 virus like particles
This article has 8 authors:Reviewed by ScreenIT
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Targeted Intracellular Degradation of SARS-CoV-2 RBD via Computationally-Optimized Peptide Fusions
This article has 3 authors:Reviewed by ScreenIT
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ACE2 and SCARF expression in human dorsal root ganglion nociceptors: implications for SARS-CoV-2 virus neurological effects
This article has 10 authors:Reviewed by ScreenIT
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Experimental infection of domestic dogs and cats with SARS-CoV-2: Pathogenesis, transmission, and response to reexposure in cats
This article has 10 authors:Reviewed by ScreenIT