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-CoV-2 suppresses mRNA expression of selenoproteins associated with ferroptosis, endoplasmic reticulum stress and DNA synthesis
This article has 13 authors:Reviewed by ScreenIT
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Prediction of COVID‐19 cases during Tokyo's Olympic and Paralympic Games
This article has 2 authors:Reviewed by ScreenIT
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Phylogenetic analysis of SARS-CoV-2 lineage development across the first and second waves in Eastern Germany, 2020
This article has 6 authors:Reviewed by ScreenIT
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Integrative approach identifies SLC6A20 and CXCR6 as putative causal genes for the COVID-19 GWAS signal in the 3p21.31 locus
This article has 7 authors:Reviewed by ScreenIT
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Gargle-Direct: Extraction-Free Detection of SARS-CoV-2 using Real-time PCR (RT-qPCR) of Saline Gargle Rinse Samples
This article has 10 authors:Reviewed by ScreenIT
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Actual conditions of person-to-object contact and a proposal for prevention measures during the COVID-19 pandemic
This article has 4 authors:Reviewed by ScreenIT
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Role of masks, testing and contact tracing in preventing COVID-19 resurgences: a case study from New South Wales, Australia
This article has 8 authors:Reviewed by ScreenIT
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Influenza viral particles harboring the SARS-CoV-2 spike RBD as a combination respiratory disease vaccine
This article has 7 authors:Reviewed by ScreenIT
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Mapping of UV-C dose and SARS-CoV-2 viral inactivation across N95 respirators during decontamination
This article has 7 authors:Reviewed by ScreenIT
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Modeling how antibody responses may determine the efficacy of COVID-19 vaccines
This article has 3 authors:Reviewed by ScreenIT