90157 - Open Science (1) (LM)

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Industry, innovation and infrastructure

Academic Year 2021/2022

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 ten lectures and requires an active and intensive participation of the students for its entire duration.

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
  10. Citizen Science

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:

Teaching methods

Face-to-face classes for 30 hours. The dates of the lectures are as follows:

  1. 23 March 2021, 12:30-15:30
  2. 24 March 2021, 12:30-15:30
  3. 30 March 2021, 12:30-15:30
  4. 31 March 2021, 12:30-15:30
  5. 6 April 2021, 12:30-15:30
  6. 7 April 2021, 12:30-15:30
  7. 13 April 2021, 12:30-15:30
  8. 20 April 2021, 12:30-15:30
  9. 21 April 2021, 12:30-15:30
  10. 27 April 2021, 12:30-15:30

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 of the course.

Assessment methods

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 based on the material (i.e. articles, books, and other publications) made available for each lecture of course.

1. Project. Students are mandatorily asked to organise themself in groups of 3-4 people for preparing the project. The results of the project will be presented during a final workshop that will be organised in a date agreed with all the students and that will be held after the end of the course. The material that should be produced by each group includes at least:

  • diaries of all group members describing the activity they addressed 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 the description of the methodology proposed by other groups of the course;
  • a software used to gather and analyse data relevant for the project;
  • the data gathered by running the methodology;
  • a short 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 each group during the course, while the rest should be submitted a few days before the final workshop. The diaries, instead, should be kept updated until the finalisation of the project. The personal contribution of each member of a group will be assessed. If necessary for reaching the maximum score for the project, I can ask to each group to improve the quality of one or more outcomes delivered according to my personal assessment 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 four distinct questions (including multiple-choice and free-text answers), which are based on the material made available in each lecture of the course.

Final evaluation. The final evaluation of each 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, before the first lecture.

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/2021-2022

Office hours

See the website of Silvio Peroni