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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Effect of Balloon-Blowing on Dyspnea and Oxygenation in Hospitalized COVID-19 Patients: A Pilot Study
This article has 8 authors:Reviewed by ScreenIT
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Structures of SARS-CoV-2 B.1.351 neutralizing antibodies provide insights into cocktail design against concerning variants
This article has 10 authors:Reviewed by ScreenIT
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A Cross-Sectional Study of Psychosocial Factors and Sickness Presenteeism in Japanese Workers During the COVID-19 Pandemic
This article has 9 authors:Reviewed by ScreenIT
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Probiotic consortia improve anti-viral immunity to SARS-CoV-2 in Ferrets
This article has 9 authors:Reviewed by ScreenIT
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Immunization with synthetic SARS-CoV-2 S glycoprotein virus-like particles protects macaques from infection
This article has 31 authors:Reviewed by ScreenIT
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The Impact of Large Mobile Air Purifiers on Aerosol Concentration in Classrooms and the Reduction of Airborne Transmission of SARS-CoV-2
This article has 6 authors:Reviewed by ScreenIT
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Knowledge, Attitude, and Practice among the Healthcare Professionals regarding the myths on COVID-19 vaccination - Demystified
This article has 6 authors:Reviewed by ScreenIT
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Predictors of COVID-19 vaccination uptake and reasons for decline of vaccination: a systematic review
This article has 6 authors:Reviewed by ScreenIT
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Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar
This article has 8 authors:Reviewed by ScreenIT
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Opioid overdose decedent characteristics during COVID-19
This article has 7 authors:Reviewed by ScreenIT