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. Substantial underestimation of SARS-CoV-2 infection in the United States

    1. Sean L. Wu
    2. Andrew N. Mertens
    3. Yoshika S. Crider
    4. Anna Nguyen
    5. Nolan N. Pokpongkiat
    6. Stephanie Djajadi
    7. Anmol Seth
    8. Michelle S. Hsiang
    9. John M. Colford
    10. Art Reingold
    11. Benjamin F. Arnold
    12. Alan Hubbard
    13. Jade Benjamin-Chung

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  2. Proinflammatory IgG Fc structures in patients with severe COVID-19

    1. Saborni Chakraborty
    2. Joseph Gonzalez
    3. Karlie Edwards
    4. Vamsee Mallajosyula
    5. Anthony S. Buzzanco
    6. Robert Sherwood
    7. Cindy Buffone
    8. Nimish Kathale
    9. Susan Providenza
    10. Markus M. Xie
    11. Jason R. Andrews
    12. Catherine A. Blish
    13. Upinder Singh
    14. Haley Dugan
    15. Patrick C. Wilson
    16. Tho D. Pham
    17. Scott D. Boyd
    18. Kari C. Nadeau
    19. Benjamin A. Pinsky
    20. Sheng Zhang
    21. Matthew J. Memoli
    22. Jeffery K. Taubenberger
    23. Tasha Morales
    24. Jeffrey M. Schapiro
    25. Gene S. Tan
    26. Prasanna Jagannathan
    27. Taia T. Wang

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  4. Evaluation of the U.S. governors' decision when to issue stay‐at‐home orders

    1. Benjamin Djulbegovic
    2. David J. Weiss
    3. Iztok Hozo

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  6. Early experience with COVID-19 patients at academic hospital in Southwestern United States

    1. Rahul Shekhar
    2. Abu Baker Sheikh
    3. Shubhra Upadhyay
    4. Jeanette Atencio
    5. Devika Kapuria

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  7. Interpretable Artificial Intelligence for COVID-19 Diagnosis from Chest CT Reveals Specificity of Ground-Glass Opacities

    1. Anmol Warman
    2. Pranav I. Warman
    3. Ayushman Sharma
    4. Puja Parikh
    5. Roshan Warman
    6. Narayan Viswanadhan
    7. Lu Chen
    8. Subhra Mohapatra
    9. Shyam S Mohapatra
    10. Guillermo Sapiro

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  8. Establishment of an African green monkey model for COVID-19

    1. Courtney Woolsey
    2. Viktoriya Borisevich
    3. Abhishek N. Prasad
    4. Krystle N. Agans
    5. Daniel J. Deer
    6. Natalie S. Dobias
    7. John C. Heymann
    8. Stephanie L. Foster
    9. Corri B. Levine
    10. Liana Medina
    11. Kevin Melody
    12. Joan B. Geisbert
    13. Karla A. Fenton
    14. Thomas W. Geisbert
    15. Robert W. Cross

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  9. Nrf2 Activator PB125® as a Potential Therapeutic Agent against COVID-19

    1. Joe M. McCord
    2. Brooks M. Hybertson
    3. Adela Cota-Gomez
    4. Kara P. Geraci
    5. Bifeng Gao

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  10. REMBRANDT: A high-throughput barcoded sequencing approach for COVID-19 screening

    1. Dario Palmieri
    2. Jalal Siddiqui
    3. Anne Gardner
    4. Richard Fishel
    5. Wayne O. Miles

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity
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