Data sources

Subject description

1. General issues about data sources (Characteristics of data sources. Different kinds of access to data. Creative use of data sources.)

2. Big data and the future of data sources.

3. Data confidentiality and data protection. Statistical disclosure control.

4. Data visualization.

5. Data in official statistics:

  • Typology of data sources in official statistics.
  • Quality. Statistical standards. Metadata.
  • Accessibility, tools and approaches for obtaining and using statistical data.
  • Visualisation and exploratory data analysis in official statistics.

6. Data search and secondary analysis of data from scientific data archives:

  • General data archives (Social Sciences Data Archive etc.) and specialised data archives (e.g. qualitative, networks).
  • Access to international data and different disciplinary data (CESSDA, ICPSR, international research projects, organisations).
  • Preparation of data for analysis: merging from different sources and formats, ex-post harmonisation of variables, data cleaning and documentation.
  • Advanced options for exploiting existing data (multilevel analysis, comparative analysis, longitudinal research etc.).
  • Research data management and planning.
  • FAIR data assessment.
  • Ethical and legal aspects of data management.
  • Open science ecosystem.

7. Specific data sources from the fields of social and natural sciences (e.g. commercial databases, data sources in public and private sector, data sources in specific scientific fields such as public health and medicine etc.).

Other relevant topics.

The subject is taught in programs

Objectives and competences

Course objectives are to:

  • Introduce students to the most important national and foreign sources of statistical data and possibilities of their use.
  • Enable students for quality assessment and an efficient use of statistical data sources, also in their research field (statistical and data literacy).
  • Introduce students to the basics of data access and data management.
  • Introduce students to the basics of data visualization.


  • Ability to judge the quality and usefulness of data sources for creating value added of statistical analysis.
  • Knowledge of approaches to obtaining, searching, accessing, managing and visualizing statistical data.

Teaching and learning methods

Lectures, tutorials, homework, assignment, presentation.

Part of the pedagogical process will be carried out with the help of ICT technologies and the opportunities they offer.

Expected study results

Students will deepen and extend their knowledge of data sources from various scientific disciplines and understanding of their quality, importance and role in statistical analysis. They will get the latest knowledge about their availability, access and possibilities of exploitations, and visualization.

Basic sources and literature

• Odbor Evropskega statističnega sistema (2017). Kodeks ravnanja evropske statistike. []
• Gradiva na spletnih straneh SURS, Eurostata, mednarodnih ustanov (UNECE, OECD itd.), AJPES, NIJZ, GURS, Informacijskega pooblaščenca RS in drugih relevantnih inštitucij.
• Hundepool, A. et al. (2012). Statistical Disclosure Control, John Wiley & Sons.
• Inter-university Consortium for Political and Social Research (ICPSR). (2018). Guide to Social Science Data Preparation and Archiving: Best Practice Throughout the Data Life Cycle (6th edition). Ann Arbor, MI. []
• CESSDA Data Management Expert Guide. []
• Gradiva združenj RDA, IASSIST, CESSDA in ADP.
• Schwabish, J. (2021). Better Data Visualization. A guide for Scholars, Researchers, and Wonks. Columbia University Press.
• Zadnik, V. et al. (2017). Cancer burden in Slovenia with the time trends analysis. Radiol Oncol, 51(1): 47-55.
• Žagar, T., Primic Žakelj, M., & Zadnik, V. (2007). Pretok in uporaba informacij v registru raka – najstarejšem zdravstvenem registru v Sloveniji. Informatika, 23(3), 105-110.

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