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Workshop Title:
Computing Access, Practical Large-Scale Data Analysis and AI Tools in Scientific Research: Empowering Latin American Researchers**
Overview:
This intensive 8-hour hands-on workshop, divided into four 2-hour sessions, is designed for Latin American physics students and researchers.
The goal is to engage participants immediately in practical, real-time, large-scale data analysis using open data from sources such as CERN, NASA, ESA, Zenodo, and arXiv.
Participants will learn how to access, analyze, and visualize large datasets using enterprise-grade and industrial tools like GitHub, GitHub Actions, Jupyter Notebooks, and cloud-based platforms.
The workshop emphasizes collaborative data analysis, workflow automation, and online report publication.
Additionally, we will explore how to harness machine learning (ML) and large language models (LLMs) such as LLaMA in Retrieval-Augmented Generation (RAG) workflows for literature review, merging multiple papers, and conducting meta-analyses.
We will highlight how researchers can use ML, AI, and RAG tools to write, read, and evaluate scientific papers, thereby enhancing their global competitiveness.
Learning Objectives:
- Real-Time Large-Scale Data Analysis: Perform hands-on data analysis from the outset, utilizing industrial tools to process and analyze data efficiently.
- Enterprise-Grade Tools: Use Git and GitHub for version control and collaboration, automate workflows with GitHub Actions, and publish professional reports on GitHub Pages.
- Access Open Datasets: Work with datasets from CERN, NASA, ESA, Zenodo, and arXiv, applying particle physics and imaging data analysis using Jupyter Notebooks and Python.
- Advanced Machine Learning Techniques: Apply ML techniques for classification, regression, and clustering tasks.
- AI and LLMs in Research: Use LLMs in RAG workflows for literature reviews and meta-analyses, understanding their impact on writing, reading, and evaluating scientific papers.
- Optimize Computational Resources: Access free and low-cost computational resources for scientific data collection, analysis, and reporting, and set up personal computing environments using cloud platforms.
- Enhance Global Competitiveness: Apply best practices in open science, focusing on reproducibility, transparency, and ethical data sharing.
Instructor Information:
- Arturo Sánchez Pineda - linkedin.com/in/arturo-sanchez-pineda
Creative Commons Venezuela - Lead IT Infrastructure at https://inait.ai
Materials and Resources:
All workshop materials, including code examples, datasets, tutorials, and additional resources, will be available through GitHub: https://github.com/laa-hecap
You can access the detailed version of the workshop proposal at the following link: https://github.com/laa-hecap/workshop-silafae-2024/blob/main/README.md
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