CORE042 Data – the Answer to the Ultimate Question of Life, the Universe, and Everything...

Pan-university studies
spring 2023
Extent and Intensity
2/0/0. 3 credit(s). Type of Completion: k (colloquium).
Taught in person.
RNDr. Michal Růžička, Ph.D. (lecturer)
RNDr. Tomáš Rebok, Ph.D. (lecturer)
doc. Ing. Štěpán Mikula, Ph.D. (lecturer)
doc. Mgr. Pavel Rychlý, Ph.D. (lecturer)
Mgr. Hynek Cígler, Ph.D. (lecturer)
RNDr. Stanislava Bezdíček Králová, Ph.D. (lecturer)
PhDr. Michal Lorenz, Ph.D. (lecturer)
Mgr. Ing. Lubomír Prokeš, Ph.D. (lecturer)
Mgr. Lukáš Hamřík, Ph.D. (lecturer)
Mgr. et Mgr. Matěj Búřil (lecturer)
Mgr. Pavel Tomančák, PhD. (lecturer)
Mgr. Michal Bozděch, Ph.D. (lecturer)
Guaranteed by
RNDr. Michal Růžička, Ph.D.
Data management – Open Science – CERIT SC – Institute of Computer Science
Contact Person: RNDr. Michal Růžička, Ph.D.
Supplier department: Data management – Open Science – CERIT SC – Institute of Computer Science
Wed 10:00–11:50 KOM 200
Course Enrolment Limitations
The course is offered to students of any study field.
The capacity limit for the course is 130 student(s).
Current registration and enrolment status: enrolled: 61/130, only registered: 0/130, only registered with preference (fields directly associated with the programme): 0/130
Course objectives
The course aims to provide students with a broad view of different forms of contemporary research and different methods of approaching it in various scientific fields. The common denominator of research in the 21st century across disciplines is research data, which is the cornerstone of scientific work in different disciplines. Scientific data is now coming to the fore in terms of applicability, communication and credibility of the research. However, the view of, access to, and principles for working with scientific data vary greatly depending on the scientific discipline.
You can look forward to lectures by successful MU researchers across research disciplines who will introduce you to practical research issues and the use of data in their research. You will be able to compare what research practice entails in your home faculty's disciplines and how researchers in other fields approach research and work with data. Understanding their ways of thinking and needs will allow you to better understand the world and research outside of your domain. It may help future collaborations with colleagues with different work and interests.
Learning outcomes
Upon completion of the course, graduates will have an overview of
– the life cycle of research data,
– characteristics of FAIR data and how to implement them in practice,
– specific practical examples of the use of data in research and the transfer of results into practice/commerce,
– the scientific method and good and bad scientific practices,
– similarities and differences in approaches to working with research data and
– the use of data in practice/commerce across different research disciplines.
As a result, students will not only be better prepared to work with data in their studies or research but will also be better able to understand and collaborate with colleagues from other disciplines.
  • 1. Introduction to research data, its life cycle, and the concept of FAIR Data.
  • 2. Big data processing, data modelling and evaluation of experiments; satellite data, earth research.
  • 3. Data-driven decisions: about mice and humans – how can government use data to manage and control the effectiveness of spending in education, health, transport, ...
  • 4. Language data corpora in natural language machine processing – why corpora are helpful and how they are used across disciplines, how to build corpora (harvesting sources, filtering unwanted content, processing) and where and how to make them available.
  • 5. Data as a source of confidence in scientific results – lessons from the “credibility crisis” in psychological science.
  • 6. Data in microbiology – addressing each stage of the data life cycle in microbiology.
  • 7. Data and science communication – philosophy of data, how trustworthy it is, what role it plays in knowledge, changes in communication, how digital data is stored and how data management is addressed.
  • 8. Data for education – how to collect and interpret data correctly.
  • 9. Legal and ethical aspects of working with data in research – ethical standards and norms, how ethics affect working with data in research, ethical and legal challenges for research in the 21st century.
  • 10. From Academic Research to Practice – Commercialization of Scientific Results or Doing Business with Data; Legal Issues, Different Ways Researchers Collaborate with Commercial Companies.
  • 11. The scientific process.
  • 12. Data for machine learning and artificial intelligence – how data and AI help athletes.
  • 13. Course wrap-up – summary of lectures, highlighting differences, similarities, and possible interdisciplinary collaborations; essay assignments for the end-of-course colloquium.
Teaching methods
Lectures by successful researchers from most MU faculties and institutes.
Assessment methods
Final essay on the chosen topic (selection of about 42 topics across the lecture areas).
Language of instruction
Further Comments
Study Materials
The course is taught each semester.
The course is also listed under the following terms Autumn 2022, Autumn 2023, spring 2024.
  • Enrolment Statistics (spring 2023, recent)
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