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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SARS-CoV2 Testing: The Limit of Detection Matters
This article has 8 authors:Reviewed by ScreenIT
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Comparison of Mucosal and Intramuscular Immunization against SARS-CoV-2 with Replication-Defective and Replicating Single-cycle Adenovirus Vaccines
This article has 19 authors:Reviewed by ScreenIT
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In silico analysis of mutant epitopes in new SARS-CoV-2 lineages suggest global enhanced CD8+ T cell reactivity and also signs of immune response escape
This article has 5 authors:Reviewed by ScreenIT
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Assessing the impact of multiple comorbidities on fatal outcome in young COVID-19
This article has 4 authors:Reviewed by ScreenIT
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Prevalence and Longevity of SARS-CoV-2 Antibodies Among Health Care Workers
This article has 10 authors:Reviewed by ScreenIT
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The MERS-CoV receptor gene is among COVID-19 risk factors inherited from Neandertals
This article has 2 authors:Reviewed by ScreenIT
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Effect of Immunosuppression on the Immunogenicity of mRNA Vaccines to SARS-CoV-2
This article has 40 authors:Reviewed by ScreenIT
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Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery
This article has 32 authors:Reviewed by ScreenIT
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Teaching how to break bad news in Oncology: In-class vs. virtual peer role-plays
This article has 3 authors:Reviewed by ScreenIT
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A genetic barcode of SARS-CoV-2 for monitoring global distribution of different clades during the COVID-19 pandemic
This article has 13 authors:Reviewed by ScreenIT