90157 - Open Science (1) (LM)

Learning outcomes

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

Course contents

The course is organised in nine lectures and a final wrap-up session, and requires an active and intensive participation of the students for its entire duration.

Each lecture is organised in three parts. In the first part (45 minutes), a theoretical and historical introduction about the specific topic of the lecture is provided. In the second part (45 minutes), a structured discussion on a scientific article provided by the professor in the previous lecture is organised. In the third part (one hour an half), all students and professor participate in an hands-on session based on existing tools that enable the experimentation and/or implementation of the project.

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
10. Wrap-up session
    

The dates and times of all the lectures above are available in the section "Schedule" of the GitHub repository of the course.

Students with specific learning disorders (SLD) or temporary/permanent disabilities should contact the appropriate University office immediately and agree with the teacher the most effective strategies for attending the lectures and preparing for the exam.

Readings/Bibliography

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 GitHub repository of the Open Science course held in the previous year and 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. (2020). 19 Popular JavaScript Libraries for Data Visualization in 2021. https://www.monterail.com/blog/javascript-libraries-data-visualization
  

Teaching methods

Face-to-face classes for 30 hours.

Active participation by students is strongly recommended, due to the direct involvement required to students during the course. In particular, students are required to address every week specific action items concerning the topics and the project of the course.

Assessment methods

The exam consists of:

1. the presentation of the Open Science project proposed by the professor during the first lecture - maximum score: 26 points;
2. a (non-mandatory) written examination based on the articles introduced (one per lecture) by the professor as part of the material to study and the slides of all the lectures - maximum score: 6 points.

1. Project. Students are organise in a big group coordinated by the professor who act as Project Manager. The results of the project will be presented by the students during a final workshop that will be organised in a date agreed with all the students and that will be held about one month after the end of the course. The material that should be produced by the group includes at least:

• a diary describing the activities addressed by group members to fulfil the project goals;
• a textual abstract presenting the project methodologies, values, and results;
  
• a data management plan;
• a description of the methodology followed to address the project goals;
• reviews of the data management plan and of the description of the methodology;
  
• a software used to gather and analyse data relevant for the project;
• the data gathered by running the methodology;
• a scientific article presenting the research conducted to address the project goals;
• the slides used to present the project outcomes during the final workshop.
  

Part of the material mentioned above (i.e. textual abstract, data management plan, methodology, reviews) should be developed and released by the group during the course. The rest, instead, will be under-development during the lectures and should be kept updated until the finalisation of the project. The personal contribution of each member of the group will be assessed. If necessary for reaching the maximum score for the project, the professor can ask to the group to improve the quality of one or more outcomes delivered according to personal assessments and as a consequence of workshop discussions.

2. Written examination. The written examination is a test that will be held using the Esami On Line platform. It is a one hour examination that comprises six distinct questions (including multiple-choice and free-text answers), which are based on the articles (one per lecture) introduced by the professor as part of the material to study and the slides of the course.

Final evaluation. The final evaluation of the student is the sum of the scores gained for each of the aforementioned points (any score greater than 30 will be registered as 30 cum laude). In particular:

• excellent evaluation (final score greater than 26): 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 (final score between 18 and 26): reaching a partial view of the course topics + providing a minor contribution to the development of the project;
• insufficient evaluation (final score lesser than 18): either not reaching even partial view on the course topics or not providing any contribution to the project.

It is strongly suggested to attend the course in person considering the overall organisation of the course, which has several hands-on sessions during each lecture, short workshop dedicated to project development, and also collegial discussions with the professor and the other students. However, even if discouraged, it is possible to follow the course as non attender. For non attenders, the organisation of the course should be discussed with the professor in advance, before the first lecture.

Students with specific learning disorders (SLD) or temporary/permanent disabilities should contact the appropriate University office in advance. The office will be responsible for proposing adaptations to interested students. Such adaptations must be submitted to the teacher for approval at least 15 days before the exam session. The teacher will also evaluate the adaptations regarding the training objectives of teaching.

Teaching tools

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.

Links to further information

https://github.com/open-sci/2024-2025

Office hours

See the website of Silvio Peroni