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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Eco-epidemiological assessment of the COVID-19 epidemic in China, January–February 2020
This article has 1 author:Reviewed by ScreenIT
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A novel benchmark for COVID-19 pandemic testing effectiveness enables the accurate prediction of new Intensive Care Unit admissions
This article has 3 authors:Reviewed by ScreenIT
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Integrated characterization of SARS-CoV-2 genome, microbiome, antibiotic resistance and host response from single throat swabs
This article has 16 authors:Reviewed by ScreenIT
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Surveillance by age-class and prefecture for emerging infectious febrile diseases with respiratory symptoms, including COVID-19
This article has 7 authors:Reviewed by ScreenIT
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One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features
This article has 1 author:Reviewed by ScreenIT
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Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity across viruses
This article has 17 authors:Reviewed by ScreenIT
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Social network-based cohorting to reduce the spread of SARS-CoV-2 in secondary schools: A simulation study in classrooms of four European countries
This article has 3 authors:Reviewed by ScreenIT
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Identification, mapping and relative quantitation of SARS-CoV-2 Spike glycopeptides by Mass-Retention Time Fingerprinting
This article has 11 authors:Reviewed by ScreenIT
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Evolution of COVID-19 cases in selected low- and middle-income countries: past the herd immunity peak?
This article has 3 authors:Reviewed by ScreenIT
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Enforced inactivity in the elderly and diabetes risk: initial estimates of the burden of an unintended consequence of COVID-19 lockdown
This article has 4 authors:Reviewed by ScreenIT