Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3311790.3396655acmconferencesArticle/Chapter ViewAbstractPublication PagespearcConference Proceedingsconference-collections
research-article

From novice to expert: Supporting all levels of computational expertise in reproducible research methods

Published: 26 July 2020 Publication History

Abstract

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.

Supplemental Material

MP4 File
Presentation video

References

[1]
Erin Alison Becker, Christina Koch, and Karen Word. 2019. Instructor Training. The Carpentries. https://doi.org/10.5281/zenodo.3258398
[2]
Wiki Contributors. 2020. Fred Hutch Biomedical Data Science Wiki. Fred Hutchinson Cancer Research Center. Retrieved February 14, 2020 from https://sciwiki.fredhutch.org
[3]
Gabriel A Devenyi, Rémi Emonet, Rayna M Harris, Kate L Hertweck, Damien Irving, Ian Milligan, and Greg Wilson. 2018. Ten simple rules for collaborative lesson development. PLoS Computational Biology 14, 3 (2018). https://doi.org/10.1371/journal.pcbi.1005963
[4]
Stuart E Dreyfus and Hubert L Dreyfus. 1980. A five-stage model of the mental activities involved in directed skill acquisition. Technical Report. California Univ Berkeley Operations Research Center.
[5]
International Society for Technology in Education. [n.d.]. Computational Thinking Competencies. Retrieved February 14, 2020 from https://www.iste.org/standards/computational-thinking
[6]
fredhutch.io. 2020. fh.io: Education and access to computational methods at the Fred Hutchinson Cancer Research Center. Fred Hutchinson Cancer Research Center. Retrieved February 14, 2020 from http://www.fredhutch.io
[7]
Jo Erskine Hannay, Carolyn MacLeod, Janice Singer, Hans Petter Langtangen, Dietmar Pfahl, and Greg Wilson. 2009. How do scientists develop and use scientific software?. In 2009 ICSE Workshop on Software Engineering for Computational Science and Engineering. IEEE, 1–8. https://doi.org/10.1109/SECSE.2009.5069155
[8]
Sarah L. R. Stevens, Mateusz Kuzak, Carlos Martinez, Aurelia Moser, Petra Bleeker, and Marc Galland. 2018. Building a local community of practice in scientific programming for life scientists. PLOS Biology 16, 11 (11 2018), 1–10. https://doi.org/10.1371/journal.pbio.2005561
[9]
The Coop Team. 2020. The Fred Hutch Bioinformatics and Data Science Cooperative (Coop) Blog. Fred Hutchinson Cancer Research Center. Retrieved February 14, 2020 from https://fredhutch.github.io/coop/
[10]
The Coop Team. 2020. Fred Hutch Bioinformatics and Data Science Cooperative (The Coop). Fred Hutchinson Cancer Research Center. Retrieved February 14, 2020 from https://thecoop.fredhutch.org
[11]
Greg Wilson. 2016. Software Carpentry: Lessons learned. F1000Research 3, 62 (2016), 62. https://doi.org/10.12688/f1000research.3-62.v2
[12]
Greg Wilson, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, and Tracy K Teal. 2017. Good enough practices in scientific computing. PLoS Computational Biology 13, 6 (2017). https://doi.org/10.1371/journal.pcbi.1005510

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
PEARC '20: Practice and Experience in Advanced Research Computing 2020: Catch the Wave
July 2020
556 pages
ISBN:9781450366892
DOI:10.1145/3311790
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 July 2020

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. community organization
  2. documentation
  3. reproducible research
  4. training

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • NIH/NCI

Conference

PEARC '20
Sponsor:

Acceptance Rates

Overall Acceptance Rate 133 of 202 submissions, 66%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 63
    Total Downloads
  • Downloads (Last 12 months)13
  • Downloads (Last 6 weeks)2
Reflects downloads up to 14 Dec 2024

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media