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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Population density and basic reproductive number of COVID-19 across United States counties
This article has 3 authors:Reviewed by ScreenIT
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A study on the appropriate use of topdown approach for stepping up economic activities in districts of different States/Union Territories in India
This article has 2 authors:Reviewed by ScreenIT
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Interregional SARS-CoV-2 spread from a single introduction outbreak in a meat-packing plant in northeast Iowa
This article has 5 authors:Reviewed by ScreenIT
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Humoral Immune Response to SARS-CoV-2
This article has 5 authors:Reviewed by ScreenIT
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Retarded Logistic Equation as a Universal Dynamic Model for the Spread of COVID-19
This article has 2 authors:Reviewed by ScreenIT
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The effectiveness of social bubbles as part of a Covid-19 lockdown exit strategy, a modelling study
This article has 10 authors:Reviewed by ScreenIT
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Jinhua Qinggan granule, a Chinese herbal medicine against COVID‑19, induces rapid changes in the neutrophil/lymphocyte ratio and plasma levels of IL‑6 and IFN‑γ: An open‑label, single‑arm pilot study
This article has 7 authors:Reviewed by ScreenIT
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Factors associated with admission to intensive care units in COVID-19 patients in Lyon-France
This article has 17 authors:Reviewed by ScreenIT
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The arrival and spread of SARS‐CoV‐2 in Colombia
This article has 24 authors:Reviewed by ScreenIT
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Use of face coverings in public during the COVID-19 pandemic: an observational study
This article has 10 authors:Reviewed by ScreenIT