Resources

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REMINDER

Hackathon Deliverables:

Deliverables Guide
  • GitHub Repository
    • README.md with project description and goals.
    • Source code with comments explaining functionality.
    • Project poster in PDF format.
    • Final presentation in PDF, including:
      • Team members’ names and photos.
      • Technology used (e.g., tools, HPC resources).
      • Scores for 2023 ISCE and 2024 Supercomputing papers.
      • Scorecard explanation covering:
        • Paper availability.
        • Code and software availability.
        • Dataset availability.
        • Computer and GPU requirements.
        • Three additional metrics (e.g., documentation, result reproducibility).
  • Website or Portal
    • Displays scorecard results for evaluated papers.
    • Explains scorecard metrics and their importance.
    • Lists team members with names, photos, GitHub, and LinkedIn profiles.
  • Proof of Poster Submission
    • Evidence of submitting the project poster to Gateways 2025.
    • Adds +5 points to judging score.


Information Posts

Sessions:

  1. [2025-06-23T10:00(-04:00) ] Hackathon Overview
  2. [2025-06-23T15:00(-04:00) ] Team Introductions
  3. [2025-06-24T10:00(-04:00) ] Project Plan
  4. [2025-06-24T15:00(-04:00) ] Team Progress
  5. [2025-06-25T10:00(-04:00) ] ADMI Welcome - Alfred Watkins
  6. [2025-06-25T10:00(-04:00) ] Team Progress
  7. [2025-06-25T15:00(-04:00) ] Team Progress
  8. [2025-06-26T10:00(-04:00) ] Agbeli Ameko - Educational Designer NCAR
  9. [2025-06-26T10:00(-04:00) ] Team Progress
  10. [2025-06-27T10:00(-04:00) ] Team Progress
  11. [2025-06-27T13:00(-04:00) ] Final Presentations

Hackathon:


References:


AI Platforms:



UI/UX:


Coding Platforms:

  • GitHub Codespaces ([Note:] Codespaces is available for free for individual use for up to 60 hours a month and comes with simple, pay-as-you-go pricing afterwards. (Last Checked 6/21/23 Ref. https://github.com/features/codespaces) - https://github.com/codespaces

Computation/Hosting:


Datasets:


Dashboards:

  • R for Data Science - R for Data Science. Code in R / concepts useful any language - had.co.nz
  • Tidy data for efficiency, reproducibility, and collaboration - openscapes.org
  • Tidy data - informal and example code heavy (in R) version - tidyverse.org