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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Potential neutralizing antibodies discovered for novel corona virus using machine learning
This article has 3 authors:Reviewed by ScreenIT
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Scientometric trends for coronaviruses and other emerging viral infections
This article has 3 authors:Reviewed by GigaScience, ScreenIT
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Development and Validation of a Rapid, Single-Step Reverse Transcriptase Loop-Mediated Isothermal Amplification (RT-LAMP) System Potentially to Be Used for Reliable and High-Throughput Screening of COVID-19
This article has 12 authors:Reviewed by ScreenIT
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A brief review of antiviral drugs evaluated in registered clinical trials for COVID-19
This article has 6 authors:Reviewed by ScreenIT
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Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach
This article has 7 authors:Reviewed by ScreenIT
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Multiplex reverse transcription loop-mediated isothermal amplification combined with nanoparticle-based lateral flow biosensor for the diagnosis of COVID-19
This article has 13 authors:Reviewed by ScreenIT
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Fear, Access, and the Real-Time Estimation of Etiological Parameters for Outbreaks of Novel Pathogens
This article has 6 authors:Reviewed by ScreenIT
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A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19): A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China
This article has 12 authors:Reviewed by ScreenIT
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Spatial Visualization of Cluster-Specific COVID-19 Transmission Network in South Korea During the Early Epidemic Phase
This article has 1 author:Reviewed by ScreenIT
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Estimating the Risk of Death from COVID-19 in Adult Cancer Patients
This article has 9 authors:Reviewed by ScreenIT