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.

About Followers

Latest preprint reviews

  1. Smoking and the risk of COVID-19 infection in the UK Biobank Prospective Study

    1. Eo Rin Cho
    2. Prabhat Jha
    3. Arthur S. Slutsky

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  2. On the Progression of COVID-19 in Portugal: A Comparative Analysis of Active Cases Using Non-linear Regression

    1. Ana Milhinhos
    2. Pedro M. Costa

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  3. Evaluation of the Current Therapeutic Approaches for COVID-19: A Systematic Review and a Meta-analysis

    1. Zeinab Abdelrahman
    2. Qian Liu
    3. Shanmei Jiang
    4. Mengyuan Li
    5. Qingrong Sun
    6. Yue Zhang
    7. Xiaosheng Wang

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  4. Clinical performance of the Abbott Panbio with nasopharyngeal, throat, and saliva swabs among symptomatic individuals with COVID-19

    1. William Stokes
    2. Byron M. Berenger
    3. Danielle Portnoy
    4. Brittney Scott
    5. Jonas Szelewicki
    6. Takshveer Singh
    7. Allison A. Venner
    8. LeeAnn Turnbull
    9. Kanti Pabbaraju
    10. Sandy Shokoples
    11. Anita A. Wong
    12. Kara Gill
    13. Tracy Guttridge
    14. Dustin Proctor
    15. Jia Hu
    16. Graham Tipples

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  5. Efficacy of Face Masks Used in Uganda: A Laboratory-Based Inquiry during the COVID-19 Pandemic

    1. Gerald Mboowa
    2. Derrick Semugenze
    3. Hellen Nakabuye
    4. Douglas Bulafu
    5. Dickson Aruhomukama

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  6. Indirect benefits are a crucial consideration when evaluating SARS-CoV-2 vaccine candidates

    1. Molly E. Gallagher
    2. Andrew J. Sieben
    3. Kristin N. Nelson
    4. Alicia N. M. Kraay
    5. Walter A. Orenstein
    6. Ben Lopman
    7. Andreas Handel
    8. Katia Koelle

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  7. SARS-CoV-2 genome diversity at the binding sites of oligonucleotides used for COVID-19 diagnosis

    1. Renan Valieris
    2. Michał B. Kowalski
    3. Alina Frolova
    4. Witold Wydmański
    5. Johnathan Foox
    6. Giovana T. Torrezan
    7. Ewelina Pośpiech
    8. Wojciech Branicki
    9. Kasthuri Venkateswaran
    10. Bharath Prithiviraj
    11. Ramasamy Dhamodharan
    12. Klas I. Udekwu
    13. Diana N. Nunes
    14. Dirce M. Carraro
    15. Christopher Mason
    16. Paweł P. Łabaj
    17. Israel Tojal da Silva
    18. Emmanuel Dias-Neto

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  8. Predicting the number of reported and unreported cases for the COVID-19 epidemic in South Korea, Italy, France and Germany

    1. P. Magal
    2. G. Webb

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  9. Can pollen explain the seasonality of flu-like illnesses in the Netherlands?

    1. Martijn J. Hoogeveen
    2. Eric C.M. van Gorp
    3. Ellen K. Hoogeveen

    Reviewed by ScreenIT

    2 evaluationsAppears in 1 listLatest version Latest activity

  10. Clinical and historical features associated with severe COVID-19 infection: a systematic review

    1. JL Pigoga
    2. A Friedman
    3. M Broccoli
    4. S Hirner
    5. AV Naidoo
    6. S Singh
    7. K Werner
    8. LA Wallis

    Reviewed by ScreenIT

    2 evaluationsAppears in 1 listLatest version Latest activity
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