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From Novice to Expert: Supporting All Levels of Computational Expertise in Reproducible Research Methods

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From Novice to Expert: Supporting All Levels of Computational Expertise in Reproducible Research Methods

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Presentation from PEARC20 (Practice & Experience in Advanced Research Computing) by Hertweck and Strasser, published article here: https://dl.acm.org/doi/abs/10.1145/3311790.3396655

Training and documentation for on-premises infrastructure represent the foundation of most institutional support for computational researchers. For most academic research institutions, however, these approaches fall short of meeting the needs of diverse researchers with different levels of experience with data-intensive research. We describe a framework for characterizing levels of computational expertise and relate this model to informational support provided for biomedical researchers at a non-profit/academic research center. Our model differentiates between novice, competent practitioner, and expert users of reproducible computational methods, and is related to the composition and needs of an entire research community. We specify methods best suited for researchers with different levels of expertise, including formally structured short courses, code examples/templates, and online wiki-style documentation. We provide recommendations to encourage the development and deployment of these resources, and suggest methods for assessing their effectiveness. Supporting multiple types of informational resources for researchers with different computational needs can be labor-intensive, but ideally increases computational ability for the entire institution.

Presentation from PEARC20 (Practice & Experience in Advanced Research Computing) by Hertweck and Strasser, published article here: https://dl.acm.org/doi/abs/10.1145/3311790.3396655

Training and documentation for on-premises infrastructure represent the foundation of most institutional support for computational researchers. For most academic research institutions, however, these approaches fall short of meeting the needs of diverse researchers with different levels of experience with data-intensive research. We describe a framework for characterizing levels of computational expertise and relate this model to informational support provided for biomedical researchers at a non-profit/academic research center. Our model differentiates between novice, competent practitioner, and expert users of reproducible computational methods, and is related to the composition and needs of an entire research community. We specify methods best suited for researchers with different levels of expertise, including formally structured short courses, code examples/templates, and online wiki-style documentation. We provide recommendations to encourage the development and deployment of these resources, and suggest methods for assessing their effectiveness. Supporting multiple types of informational resources for researchers with different computational needs can be labor-intensive, but ideally increases computational ability for the entire institution.

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From Novice to Expert: Supporting All Levels of Computational Expertise in Reproducible Research Methods

  1. 1. From novice to expert: Supporting all levels of computational expertise in reproducible research methods Kate Hertweck and Carly Strasser Fred Hutchinson Cancer Research Center @k8hert
  2. 2. How do I do [some task]? Kate Hertweck (@k8hert), Supporting all levels of expertise realityexpectation help(print)
  3. 3. Kate Hertweck (@k8hert), Supporting all levels of expertise fredhutch.io thecoop.fredhutch.org
  4. 4. Kate Hertweck (@k8hert), Supporting all levels of expertise thecoop.fredhutch.org The Coop Community Biomedical and clinical research experts Variable acceptance of open science principles Customizable and flexible solutions required
  5. 5. Kate Hertweck (@k8hert), Supporting all levels of expertise Novice Competent practitioner Expert Application of computational skills to own research Routine application of skills and adoption of best practices Levels of computational expertise
  6. 6. Kate Hertweck (@k8hert), Supporting all levels of expertise
  7. 7. Kate Hertweck (@k8hert), Supporting all levels of expertise What are the pressure points for adoption of reproducible computational methods? What do people need to learn? + How are they most likely to learn?
  8. 8. Kate Hertweck (@k8hert), Supporting all levels of expertise Types of support: short courses fredhutch.io/resources carpentries.org Materials adapted to suit the specific needs of our community
  9. 9. Kate Hertweck (@k8hert), Supporting all levels of expertise Types of support: code examples/templates Application of code to common workflows Code that works, but is also aligned with best practices
  10. 10. Kate Hertweck (@k8hert), Supporting all levels of expertise Types of support: demos and tutorials Bridge between reference documentation and developing code for a specific project
  11. 11. Kate Hertweck (@k8hert), Supporting all levels of expertise Types of support: SciWiki sciwiki.fredhutch.org
  12. 12. SciWiki pageviews Kate Hertweck (@k8hert), Supporting all levels of expertise SciWiki users sciwiki.fredhutch.org
  13. 13. The usefulness of training and documentation depends on both what information is provided, and how it’s delivered. Kate Hertweck (@k8hert), Supporting all levels of expertise Learning technical skills is “easy.” Learning to apply technical skills to your own research problems is much more difficult.
  14. 14. thecoop.fredhutch.org/ carpentries.org @k8hert katehertweck.com slideshare.net/katehertweck Hertweck and Strasser, 2020, PEARC Carly Strasser Best practices in online teaching for Spanish speakers metadocencia.netlify.app/ MetaDocencia

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