Syllabus

Author

Affiliation

Julien A. Raemy

Swiss Federal Archives

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;
• practical examination and exploration of online services;
• assignments

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

Course Outline (2024-2025)

Date	Content
17.02.2025	Course Overview, Characteristics of Open Data, Associated Movements, Exercise 1
24.02.2025	Associated Principles, Exercise 2, Open Data Platforms and Organisations, Exercise 3
03.03.2025	Assessment, Data Quality, and Best Practices, Techniques, Software, and Tools, Exercise 4
10.03.2025	Showcases, Assignment Workshop

Exercises

Exercise 1

Find/Define your assignment!

What’s possible to choose from:

• The deletion of datasets from data.gov: Since Trump has once again been elected President of the United States, health and climate information/datasets have disappeared from federal websites [see https://www.theverge.com/news/604484/donald-trumps-data-purge-has-begun and https://fediscience.org/@petersuber/113874200243958520].
• Comparing CKAN, uData and Piveau: Compare these three software and their components
• (…)
• Find it yourself: Select one or more datasets from one or several platforms discussed during the course (if several datasets are selected, there must be a common thread).

Exercise 2

Identify the Movements and Principles

Relate the movements and principles (OA, Open Data, Open Science, FLOSS, FAIR, CARE, Collections as Data, LOUD) to the following propositions (multiple answers possible).

1. Software development
2. Image dissemination
3. Metadata dissemination
4. Publication of scientific articles
5. Persistent identifier assignment
6. Digital object interoperability
7. Development and maintenance of APIs
8. Documentation
9. Open license
10. Inclusivity
11. Machine-readable (meta)data
12. Collaboration
13. Ethical commitment
14. Semantic interoperability
15. Reusability

Exercise 3

Create a dashboard on visualize.admin.ch

Exercise 4

OpenRefine

Getting started

1. Install the software (https://openrefine.org/docs)
2. Run it locally (accessible at http://127.0.0.1:3333/)
3. Have a look at the different pages and functionalities
4. Create a new project by importing any supported files

Create a project with an extract from the CAS photographic archives

5. Create a project by importing the data extract from the CAS photographic archives: ekws_extract.csv
6. Review the dataset
7. Clean the dataset by removing unnecessary columns
8. Undertake some reconciliation with external services for agents (people and institutions).

A step further…

9. Create a new project by importing a dataset from one of the ORD/OGD portals
10. Analyse and curate the dataset

Alternatively: go through this tutorial from Library Carpentry: https://librarycarpentry.org/lc-open-refine/

Course Assessment

• Analyse, describe and identify the use case or the (potential) uses of the dataset(s).
• Between 900 and 1,100 words (excluding references) in an open format, preferably .qmd. A template will soon be provided…
• Short paper to be submitted either to the lecturer or via a pull request on the GitHub repository (for those who wish to make it public) by Friday 14 March, 7pm.

This assignment is weighted at 20% of the 7C2-CT module.

Criteria

Assessment Criteria	Points
Introduction and Contextualisation	5
Analysis and Argumentation	20
Structure and Writing	10
Presentation and Referencing	5
Total	40

Additional details will be discussed during the course.