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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Association of lipid-lowering drugs with COVID-19 outcomes from a Mendelian randomization study
This article has 4 authors:This article has been curated by 1 group: -
COVID-19 in hospitalized patients in 4 hospitals in San Isidro, Buenos Aires, Argentina
This article has 13 authors:Reviewed by ScreenIT
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Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia
This article has 7 authors:Reviewed by ScreenIT
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COVID-19 outcomes in hospitalized puerperal, pregnant, and neither pregnant nor puerperal women
This article has 5 authors:Reviewed by ScreenIT
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COVID-ONE-humoral immune: The One-stop Database for COVID-19-specific Antibody Responses and Clinical Parameters
This article has 28 authors:Reviewed by ScreenIT
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Profiling lockdown adherence and poor coping responses towards the COVID-19 crisis in an international cross-sectional survey
This article has 4 authors:Reviewed by ScreenIT
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‘I don't want my son to be part of a giant experiment’: public attitudes towards COVID-19 vaccines in children
This article has 1 author:Reviewed by ScreenIT
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Identification of COVID-19–Associated Hepatitis in Children as an Emerging Complication in the Wake of SARS-CoV-2 Infections: Ambispective Observational Study
This article has 5 authors:Reviewed by ScreenIT
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Tracking cryptic SARS-CoV-2 lineages detected in NYC wastewater
This article has 22 authors:Reviewed by ScreenIT
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Epidemics of chikungunya, Zika, and COVID-19 reveal bias in case-based mapping
This article has 24 authors:Reviewed by ScreenIT