90157 - OPEN SCIENCE (1) (LM)

Conoscenze e abilità da conseguire

At the end of the course, the students knows the theoretical and social principles characterizing the Open Science movement, as well as the most common practices to set up and develop Open Science projects. The student is able to understand and use the main tools and techniques for gathering, analyzing, modifying, investigating open data of different domains, and to communicate the results of research works and experiments by means of typical and effective Open Science channels, such as the Web and Semantic Publishing technologies

Contenuti

The course is organised in nine lectures plus a final workshop.

Each lecture is organised in two parts. In the first part, I provide a theoretical and historical introduction about the specific topic of the lecture. In the second part, I run an hands-on session based on existing tools that enable the experimentation and/or implementation of the topic introduced in the first part.

Topics of the lectures

1. Introduction to Open Science
2. Reproducibility
3. FAIR and Open Data
4. Open Methodology
5. Open Peer Review
6. Open Source Software
7. Open Access
8. Open Metrics
9. Open Infrastructures
   

Testi/Bibliografia

Open access articles and papers will be made freely available to students in the GitHub repository of the course during the course. Slides and any additional material will be made also available in the same repository.

The following open access book could be helpful to students as background material, in order to practice basic terminologies of the course:

Bartling, S., Friesike, S. (2014). Opening Science. Cham, Switzerland: Springer. ISBN: 978-3-319-00026-8. DOI: https://doi.org/10.1007/978-3-319-00026-8

Due to the practical focus of the course, preliminary knowledge and practice on computational thinking (e.g. algorithms, data structures, and algorithmic techniques) and programming topics (e.g. Python and basics of Javascript libraries for web and data development) is highly recommended.

A minimal bibliography on the two topics mentioned above is:

• Peroni, S. (2020). Computational Thinking and Programming book. https://comp-think.github.io
  
• Tagliaferri, L. (2018). How To Code in Python. ISBN: 978-0999773017. Full text available online.
• Gans, M., Hodges, T., Wilson, G. (2020). JavaScript for Data Science. ISBN: 978-0367422486. Full text available online.
• Majorek, J. (2019). 14 Popular JavaScript Libraries for Data Visualization in 2020. https://www.monterail.com/blog/javascript-libraries-data-visualization

Metodi didattici

Face-to-face classes for 30 hours. Active participation by students is strongly recommended. The dates of the lectures are as follows:

1. 24 March 2021, 13:00-16:00
2. 31 March 2021, 13:00-16:00
3. 1 April 2021, 12:30-15:30
4. 8 April 2021, 12:30-15:30
5. 14 April 2021, 13:00-16:00
6. 15 April 2021, 12:30-15:30
7. 21 April 2021, 13:00-16:00
8. 22 April 2021, 12:30-15:30
9. 28 April 2021, 13:00-16:00
10. 5 May 2021, 13:00-16:00

Modalità di verifica e valutazione dell'apprendimento

The exam consists of:

1. the presentation of an Open Science project, chosen among those introduced by the professor during the first lecture;
2. a written examination held after the lectures on the topics of the course.

Students are mandatorily asked to organise themself in at least two groups of 3-4 people for preparing the project. The personal contribution of each member of a group will be assessed during the final workshop.

The final evaluation of the student is based on the scores gained for each of the aforementioned points. In particular:

• excellent evaluation: reaching an in-depth view of all the course topics + active involvement in the development of the project following all the theoretical principles and practical guidelines provided to the student during the lectures;
• sufficient evaluation: reaching a partial view of the course topics + providing a minor contribution to the development of the project;
• insufficient evaluation: either not reaching even partial view on the course topics or not providing any contribution to the project.

Even if discouraged, it is possible to follow the course as non attender. For non attenders, the topic of the project should be discussed with the professor in advance.

Strumenti a supporto della didattica

Classes are held in a classroom equipped with personal computers connected to the Intranet and Internet.

Theoretical introductions to Open Science topics will always be accompanied by practical parts which include several hands-on sessions. All the material of the course - including the papers to study and the slides - will be made available in the GitHub repository of the course. A mailing-list or a group in a free messaging application will be set up so as to allow all the students of the course to communicate directly with each other and with the professor.

Link ad altre eventuali informazioni

https://github.com/open-sci/2020-2021

Orario di ricevimento

Consulta il sito web di Silvio Peroni