Towards High-Value Datasets Determination for Data-Driven Development: A Systematic Literature Review
Abstract
Open government data (OGD) is seen as a political and socio-economic phenomenon that promises to promote civic engagement and stimulate public sector innovations in various areas of public life. To bring the expected benefits, data must be reused and transformed into value-added products or services. This, in turn, sets another precondition for data that are expected to not only be available and comply with open data principles, but also be of value, i.e., of interest for reuse by the end-user. This refers to the notion of “high-value dataset” (HVD), recognized by the European Data Portal as a key trend in the OGD area in 2022. While there is progress in this direction, e.g., the Open Data Directive, incl. identifying 6 key categories, a list of HVDs and arrangements for their publication and re-use, they can be seen as “core” / “base” datasets aimed at increasing interoperability of public sector data with a high priority, contributing to the development of a more mature OGD initiative. Depending on the specifics of a region and country - geographical location, social, environmental, economic issues, cultural characteristics, (under)developed sectors and market specificities, more datasets can be recognized as of high value for a particular country. However, there is no standardized approach to assist chief data officers in this, and there is a clear lack of conceptualizations for the determination of HVD and systematic oversight. In this paper, we present a systematic review of existing literature on HVD determination, which is expected to form an initial knowledge base for this process, including used approaches and indicators to de-termine them, data, stakeholders
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- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Nikiforova A., Rizun N., Ciesielska M., Alexopoulos C. H., Miletić A.: Towards High-Value Datasets Determination for Data-Driven Development: A Systematic Literature Review// / : , 2023,
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-031-41138-0_14
- Sources of funding:
-
- Nie ma
- Verified by:
- Gdańsk University of Technology
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