Abstract

Open Government Data (OGD) are seen as one of the trends that has the potential to benefit the economy, improve the quality, efficiency, and transparency of public administration, and change the lives of citizens, and the society as a whole facilitating efficient sustainability-oriented data-driven services. However, the quick achievement of these benefits is closely related to the “value” of the OGD, i.e., how useful, and reusable the data provided by public agencies are for creating value for the above stakeholder. This is where the notion of "high-value datasets" (HVD), defined by the European Commission in Open Data Directive, comes, referring to data that can create the most value for society, the economy, and the environment. This is even more so, considering the proliferation of Artificial Intelligence (AI) and machine learning (ML) applications in various domains. While there are some efforts in that direction, there is still no available framework for identifying country-specific high-value datasets (and their determinants). The objective of the workshop is to raise awareness and build a network of key stakeholders around the HVD issue, to allow each participant to think about how and whether the determination of HVD is taking place in their country, how this can be improved with the help of portal owners, data publishers, data owners, businesses and citizens, what are and can be determinants to be used for identifying HVDs, whether they are SMART. Our main motivation is that, as members of the dg.o community, we can collaboratively answer the above questions, and those raised during the previous two editions of this workshop at ICEGOV2022 and ICOD2022, forming an initial knowledge base, as well as assessing currently used indicators. In this 3rd edition of the workshop, previously obtained results, which make up a list of the most promising indicators, will be discussed, validated and possibly refined through live discussions with the workshop participants following the DELPHI method.

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Authors (4)