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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Prophylaxis against covid-19: living systematic review and network meta-analysis
This article has 35 authors:Reviewed by ScreenIT
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Analysis of the Long-Term Impact on Cellular Immunity in COVID-19-Recovered Individuals Reveals a Profound NKT Cell Impairment
This article has 26 authors:Reviewed by ScreenIT
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Different mutations in SARS-CoV-2 associate with severe and mild outcome
This article has 3 authors:Reviewed by ScreenIT
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Frontline Healthcare Workers’ Knowledge and Perception of COVID-19, and Willingness to Work during the Pandemic in Nepal
This article has 18 authors:Reviewed by ScreenIT
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Evidence for transmission of COVID-19 prior to symptom onset
This article has 10 authors:Reviewed by ScreenIT
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Immunoinformatic based analytics on T-cell epitope from spike protein of SARS-CoV-2 concerning Indian population
This article has 2 authors:Reviewed by ScreenIT
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Comparative analysis of variation in the quality and completeness of local outbreak control plans for SARS-CoV-2 in English local authorities
This article has 5 authors:Reviewed by ScreenIT
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No evidence of association between schools and SARS-CoV-2 second wave in Italy
This article has 6 authors:Reviewed by ScreenIT
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Dynamic Analysis of Social Distancing Ratio, Isolation Rate and Transmission Coefficient in COVID-19 Epidemic for Many Countries by SIQR Model
This article has 1 author:Reviewed by ScreenIT
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Simple Mathematics on Covid-19 Expansion
This article has 1 author:Reviewed by ScreenIT