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Spring 2026

Digital Repositories

This course offers a complete immersion into the world of Data Science, Information Systems, and Digital Repositories. It is designed to give students a deep understanding of the theoretical concepts and practical skills needed to navigate and lead in the field of information technology.

Spring 2026

Google Classroom
Meeting Time
Mondays and Wednesdays, 8:00–10:00 AM
Location
Room A101

Staff

Dr. Arturo López Pineda

Dr. Arturo López Pineda

Instructor

arturolp@enesmorelia.unam.mx

Syllabus

Download the full syllabus as a PDF.

Syllabus (PDF)

Topics

Real-World Data

  • Introduction to Digital Repositories
  • Real-World Data
  • Interoperability and Metadata Standards
  • Repository Evaluation

Data Science

  • Exploratory Data Analysis
  • Exploratory Data Analysis
  • Data Visualization
  • Machine Learning

Organizing Information

  • Taxonomies and Hierarchies for Data Organization
  • Ontologies
  • Metadata Standards
  • Folksonomies and Tagging
  • Data Cataloging Processes
  • International Metadata Standards

Information Systems Architecture

  • Fundamentals of Systems Architecture
  • Hardware and Software Components of an Information System
  • Systems Design and Modeling
  • Databases and Data Storage
  • Current and Future Trends in Systems Architecture
  • Systems Integration and Data Migration

Computing in Digital Repositories

  • Introduction to Computing in Digital Repositories
  • Data Processing and Analysis in Repositories
  • Computational Methods for Large-Scale Data
  • Distributed Computing in Data Science
  • Tools and Frameworks for Computing in Repositories
  • Data Visualization and Reporting

Legal and Ethical Aspects

  • Intellectual Property Protection in Data Science
  • Data Privacy and Security
  • Ethical Considerations in Data Handling

Funding Management and Strategies

  • Fundamentals of Information Technology Funding
  • Cost Models for Information Systems and Digital Repositories
  • Crowdfunding and Collaborative Financing
  • Business Models and Revenue Generation for Digital Repositories

Grading

  • Assignments

    Short exercises and case studies related to the course topics.

    20%

  • Student Presentations

    Presentations on specific course topics.

    20%

  • Quizzes

    Mid-course assessments.

    15%

  • Final Exam

    Written evaluation at the end of the course covering all topics.

    20%

  • Practical Project

    Projects involving the practical application of the course's concepts.

    25%

Policies

  • Attendance carries no direct percentage weight in the final grade. However, a minimum of 80% attendance is required to be eligible for continuous assessment and the final ordinary exam.
  • All submitted work (assignments, presentations, and projects) must be original. Generative AI tools may be used only for research, brainstorming, or grammar review, but not to draft the final content. The instructor reserves the right to request an in-person oral defense of any submitted work; if the student cannot demonstrate mastery of the topic or authorship of the text during that defense, the activity will be voided (a grade of zero).

Readings