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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Chest computed tomography for the diagnosis of patients with coronavirus disease 2019 (COVID-19): a rapid review and meta-analysis
This article has 21 authors:Reviewed by ScreenIT
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Model calibration, nowcasting, and operational prediction of the COVID-19 pandemic
This article has 2 authors:Reviewed by ScreenIT
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Optimal Allocation of COVID-19 Test Kits Among Accredited Testing Centers in the Philippines
This article has 5 authors:Reviewed by ScreenIT
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Optimizing RT-PCR detection of SARS-CoV-2 for developing countries using pool testing
This article has 7 authors:Reviewed by ScreenIT
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Multi‐chain Fudan‐CCDC model for COVID‐19 ‐‐ a revisit to Singapore’s case
This article has 8 authors:Reviewed by ScreenIT
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COVID-19 pandemic: A Hill type mathematical model predicts the US death number and the reopening date
This article has 1 author:Reviewed by ScreenIT
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A High-Throughput Assay for Circulating Antibodies Directed Against the S Protein of Severe Acute Respiratory Syndrome Coronavirus 2
This article has 14 authors:Reviewed by ScreenIT
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The Estimated Time-Varying Reproduction Numbers during the Ongoing Epidemic of the Coronavirus Disease 2019 (COVID-19) in China
This article has 2 authors:Reviewed by ScreenIT
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Lopinavir/ritonavir for COVID-19: A living systematic review
This article has 4 authors:Reviewed by ScreenIT
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The Effect of Recombinant Human Interferon Alpha Nasal Drops to Prevent COVID-19 Pneumonia for Medical Staff in an Epidemic Area
This article has 8 authors:Reviewed by ScreenIT