Course “Open government data in a data-driven world”

In 2021 I have developed and launched a course for master and doctoral students entitled “Open government data in a data-driven world”.

Its aim is to provide basic knowledge of the concept of Open Government Data (OGD), its popularity and potential for the development of technological and economic processes, as well as the latest trends from theoretical basics (methodologies, etc.) to practical applications, by promoting the development and maintenance of sustainable open data ecosystem.

At the beginning of the course, an overview of the concept of open government data, the principles of open government data and other related issues is given. Then it:

  • takes a detailed look at the concept of open government data and their uses (Latvian, European and global),
  • discusses in detail the principles of open government data, preparing data for their publication as open government data, including metadata preparation, anonymizing data, selecting a license, etc.,
  • looks at existing and potentially possible re-use of Latvian open government data,
  • discusses in detail the concept of open data quality, including their specific features, compared to the classical concept of “data quality”,
  • performs an analysis of the quality of the Latvian open government data,
  • discusses in detail the objectives, tasks and level of performance of open data portals (Latvian, European and global), with particular attention to the usability of the portal, looking at good and bad practices,
  • discusses the role of open data portals in the context of smart city.

At the end of the course, the most recent studies in the field of open government data are being discussed, further research topics for scientific research, including master’s work, are being formulated.

  1. Ķikuts, J. (2021). Scraper development for Latvian Open data portal analysis// Rasmotāja izstrāde Latvijas Atvērto datu portāla analīzei.
  2. Začests, L. (2021). Assessing data quality analysis tools and identifying their shortcomings for developing an online data quality tool // Datu kvalitātes analīzes rīku piemērotības novērtējums un to trūkumu noteikšana tiešsaistes datu kvalitātes rīka izstrādei.
  3. Plaude, P. (2021). Identification and analysis of security risks of the Information system and website// Informācijas sistēmas un tīmekļvietnes drošības risku noteikšana un to analīze.
  4. Cvetkovs, J. (2021). Appropriatness of recommending systems for open data portals to improve the user experience // Rekomendētājsistēmu piemērotība atvērto datu portāliem lietotāja pieredzes uzlabošanai.
  5. Bukša, D. (2021). Machine learning based data quality tools for open data quality improvement // Mašīnmācīšanās balstītu datu kvalitātes rīku izmantošana atvērto datu uzlabošanai.
  6. Beizaka, B. (2021). Analysis of the Latvian open data portal and quality of its data sets // Latvijas atvērto datu portāla un tā datu kopu kvalitātes analīze.
  7. Baldere, I. (2021). Analysis of Latvian Open Data and classification of the identified quality problems // Latvijas atvērto datu kvalitātes analīze un identificēto kvalitātes trūkumu klasificēšana.
  8. Vradijs, V. (2021). Big data driven analysis of energy data for European Union countries// Lielo datu virzīta enerģijas datu analīze Eiropas Savienības valstīm.
  9. Neiders, K. (2020). Analysis and comparison of European and Latvian open data portals // Eiropas un Latvijas atvērto datu portālu analīze un salīdzinājums.