Introduction to Open Data
  • Home
  • Syllabus
  • Exercises
    • Exercise 1: OA Deep Dive
    • Exercise 2: Up do date with Linked Data
    • Exercise 3: Movements and Principles
    • Exercise 4: The Reuser’s Perspective (OGD)
    • Exercise 5: Reading Assignment
    • Exercise 6: Open Refine
    • Exercise 7: IIIF & ML
  • Course Sections
    • Characteristics of Open Data
    • Associated Movements
    • Associated Principles
    • Open Data Platforms and Organisations
    • Assessment, Data Quality, and Best Practices
    • Techniques, Software, and Tools
    • Showcases
  • References
  • About

On this page

  • Learning Objectives
  • Content
  • Methods
  • Course Outline (2025-2026)
  • Edit this page
  • Report an issue

Syllabus

Author
Affiliations

Julien A. Raemy

docuteam SA

University of Bern

Published

December 12, 2024

Modified

February 1, 2026

Learning Objectives

This course introduces the foundational principles of Open Data, equipping undergraduate students in Information Science with the knowledge and skills to find, analyse, and effectively reuse open datasets. Through a combination of lectures, practical activities, and assignments, students will explore the benefits and challenges of Open Data and develop a deeper understanding of its potential applications and implications. It is designed with the following objectives:

  1. To gain an understanding of Open Data, its essential aspects, and the principles of opening data;
  2. to learn how to find, analyse, and reuse open datasets;
  3. to learn the processes involved in preparing and publishing open datasets.

These objectives provide the foundation for the course and guide the learning outcomes.

Content

The topics covered in this course include:

  • Background on open data;
  • Benefits and risks of opening data;
  • Models related to using and publishing open data.

Methods

The course employs the following methods to support learning:

  • Course presentations;
  • Exploration of online services;
  • Hands-on exercises;
  • Engagement with scholarly literature.

These methods are intended to foster both theoretical understanding and practical application of the course material.

Course Outline (2025-2026)

An exam will be held at the end of the fourth and final session. It will consist of multiple-choice and open-ended questions, will last a maximum of one hour, and will be conducted through Cyberlearn. All course notes and access to the internet are permitted. However, LLM and communication tools are not permitted.

Date Content
18.02.2026 Course Overview
Characteristics of Open Data
Exercise 1: OA Deep Dive
Associated Movements
Associated Principles
Exercise 2: Up to date with Linked Data
25.02.2026 Exercise 3: Movements and Principles
Open Data Platforms and Organisations
Exercise 4: The Reuser’s Perspective
Exercise 5: Reading Assignment
04.03.2026 Exercise 5: Reading Assignment (Follow-up)
Assessment, Data Quality, and Best Practices
Techniques, Software, and Tools
Exercise 6: Open Refine
Exercise 7: IIIF & ML
11.03.2026 Exercise 7: IIIF & ML (Follow-up)
Showcases
Course Recap
Examination (multiple-choice and open-ended)
Missed Examination Policy

If a student is absent during the last session and misses the examination for legitimate reasons, a substitute assignment will be organised by the tutor.

Back to top

Reuse

CC BY 4.0

Julien A. Raemy | Introduction to Open Data

 
  • Edit this page
  • Report an issue

Content is published under a Creative Commons Attribution 4.0 International licence