JarvisBulldogTeamFacHackGW23

Jarvis Bulldog’s Team FacultyHack@Gateways23

This repository is for Jarvis Bulldog Team of FacultyHack@Gateways23 with SGX3.

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Team Member: Widodo Samyono, PhD
Email: wsamyono@jarvis.edu
LinkedIn: https://www.linkedin.com/in/widodosamyono/

Team Mentor: Je’aime Powell.
Email: jpowell@tacc.utexas.edu
LinkedIn: https://www.linkedin.com/in/jeaimehp/

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Resources

HPC support / accounts for our course (include URL and a brief description)

The team TACC Account information:
Account: wsamyono. Email: wsamyono@jarvis.edu. Institution: Jarvis Christian University (jarvis.edu).
The TACC HPC resources is from the following link: https://portal.tacc.utexas.edu/allocations-overview

Needs: 1) accesing and using the Jupiter Notebook for the team and students for the course in Computational and Mathematical Biology. 2) data storage. 3) other resources: GPU, Jetstream, etc.

List of 3-4 Gateway references (include the URL of the Gateway and a brief description)

• Welcome to Colaboratory. https://colab.research.google.com/notebooks/intro.ipynb The website instroduces to Google Colab (Colaboratory). What is Colab? Getting started with Colab. Sample Applications. More Resources.
• Overview of Colaboratory Features. https://colab.research.google.com/notebooks/basic_features_overview.ipynb The website explains the Colab features: Cells, Working with Python, and Integration with Drive (Google Drive).
• An Introduction to Programming for Bioscientists: A Python-Based Primer, Berk Ekmekci ,Charles E. McAnany ,Cameron Mura. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004867#sec031 We used this journal as the basis for our course for the computational biology.
• Jetstreams Resources: https://docs.jetstream-cloud.org/general/resources/

List of HPC tools used (include URL of tutorials or training)

• Scikit-Learn (sklearn) Tutorials: https://scikit-learn.org/stable/tutorial/index.html
• TACC Training: https://www.tacc.utexas.edu/use-tacc/training/

The revised course syllabus

Spring2024-MATH3390-CourseSyllabus.pdf

The next step suggestions from the community mentor

• The mentor suggested to check DesignSafe: https://www.designsafe-ci.org/user-guide/usecases/generalusecases/
• Science Gateways Catalog:https://catalog.sciencegateways.org/#/home

The 2-year Course implementation schedule (Spring 2024 - Fall 2025)

Spring 2024, Summer 2024, Spring 2025, Summer 2025

Team’s Poster, which conforms to the template provided

• The link to the team poster: https://github.com/wsamyono/JarvisBulldogTeamFacHackGW23/blob/main/posterlastversion.pdf

The 2-page blog post (Include specific HPC resources and Gateways usage).Jarvis

• The link to the 2-page blog post:

Description of your ongoing needs from SGX3.

• Resourses and data for compuational and mathematical biology
• Parallel computing for solving problems in mathematics and biology.

Jupyter Notebook Code:

• JSEP2022Week1.ipynb
• JSEP2022Week2.ipynb
• JSEP2022Week3.ipynb
• JSEP2022Week4.ipynb

The Targeted Course MATH 3390 Computational and Mathematical Biology in Canvas:

https://canvas.instructure.com/courses/7289468

Implementation Schedule

• Spring 2024, January 8, 2024 – May 3, 2024, GitHub, Colab
• Summer 2024, June 3, 2024 – June 28, 2024, TACC.
• Spring 2025, January 6, 2025 – May 2, 2025, TACC.
• Summer 2025, January 2, 2025 – June 27, 2025, Design Safe.

Resource Needs/List

1) Google Colab. The students use it for programming in Python to introduce the students with Python coding without installing Python in their computers. https://colab.research.google.com/
2) Jupyter Notebooks. The students may use TACC and Jetstream 2 Resources for using the Jupyter Notebooks. Texas Advanced Computing Center Jetstream2
3) Anaconda Navigator. The students may experience installing Python in their own computers. The link is this: https://docs.anaconda.com/free/navigator/index.html
4) Python. The students need to learn this popular language to solve problems in biology. This is the link: https://www.python.org/
5) SciPy. The students need to learn algorithms in solving biological problems. This is the link: https://scipy.org/
6) scikit-learn (sklearn). Simple and efficient tools for machine learning. They students may use it in the future. The link is this: https://scikit-learn.org/stable/
7) GitHub. The students need to collaborate in solving biological problems, so they can use it to share codes. The link to our GitHub repository is this: https://github.com/wsamyono/BulldogTeamFacHackGW23