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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Evaluation of High-Throughput SARS-CoV-2 Serological Assays in a Longitudinal Cohort of Patients with Mild COVID-19: Clinical Sensitivity, Specificity, and Association with Virus Neutralization Test
This article has 20 authors:Reviewed by ScreenIT
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Forecasting COVID-19 cases at the Amazon region: a comparison of classical and machine learning models
This article has 3 authors:Reviewed by ScreenIT
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Cardiopulmonary exercise testing in COVID-19 patients at 3 months follow-up
This article has 17 authors:Reviewed by ScreenIT
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Detection of a SARS-CoV-2 variant of concern in South Africa
This article has 47 authors:Reviewed by ScreenIT
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Impact of school closures on the health and well-being of primary school children in Wales UK: a routine data linkage study using the HAPPEN Survey (2018–2020)
This article has 5 authors:Reviewed by ScreenIT
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Forecasting Hospitalizations Due to COVID-19 in South Dakota, USA
This article has 5 authors:Reviewed by ScreenIT
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COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model
This article has 11 authors:Reviewed by ScreenIT
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Recurrent SARS-CoV-2 RNA positivity after COVID-19: a systematic review and meta-analysis
This article has 7 authors:Reviewed by ScreenIT
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Development of a SARS-CoV-2 nucleocapsid specific monoclonal antibody
This article has 7 authors:Reviewed by ScreenIT
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Community end-of-life care during the COVID-19 pandemic: findings of a UK primary care survey
This article has 9 authors:Reviewed by ScreenIT