The research team promotes the use of open data to support transparency, innovation, and evidence-based policy. Our work in this area explores the technical, institutional, and societal dimensions of data openness and reuse.
We focus on how public sector data can be published, enriched, and linked to maximize its value for citizens, businesses, researchers, and civil society. Our team combines semantic technologies, visualization tools, and participatory methods to advance open data practices.
Key Research Topics:
Linked Open Data (LOD) combines the principles of Linked Data with Open Data, meaning the data is not only structured and interconnected but also freely available for anyone to use, reuse, and distribute. Key technologies underpinning LOD include Uniform Resource Identifiers (URIs) to uniquely identify entities, HTTP to retrieve these resources, and the Resource Description Framework (RDF) as a graph-based data model to structure and link the data. SPARQL is the query language used to retrieve and manipulate data stored in RDF format. The aim is to create a web of data where information from diverse sources can be easily connected, shared, and processed by machines, leading to richer insights and new applications.
Semantic enrichment and metadata standards refer to the processes and frameworks used to enhance digital content with meaningful, structured information that improves discoverability, interoperability, and contextual understanding. Semantic enrichment involves linking data to controlled vocabularies, ontologies, or taxonomies, while metadata standards provide consistent formats for describing content across systems. The primary objectives are to enable more efficient information retrieval, facilitate integration across platforms, and support advanced applications such as linked data and intelligent search. Effective implementation requires collaboration across domains, adherence to established standards, and ongoing governance to ensure data quality and relevance in diverse digital environments.
Open statistical data and visualization, including multidimensional Linked Open Data (LOD), refer to the publication and representation of structured statistical information in accessible, reusable, and machine-readable formats. By applying semantic web technologies and linking datasets across domains, multidimensional LOD enables more dynamic exploration, integration, and analysis of complex data. Visualization tools further enhance comprehension by transforming raw figures into interactive charts, maps, and dashboards. The main goals are to promote transparency, support evidence-based policymaking, and empower users—from researchers to citizens—to draw meaningful insights. Successful implementation relies on open standards, interoperability, and user-centered design to ensure that data is both technically robust and practically usable.
Open data ecosystems and governance models refer to the frameworks and collaborative environments that enable the sharing, management, and reuse of public data across sectors. These ecosystems are built on principles of transparency, interoperability, and accessibility, encouraging participation from government agencies, private entities, academia, and civil society. Governance models define the roles, responsibilities, and policies that ensure data quality, ethical use, and long-term sustainability. The primary objectives are to unlock the value of data for innovation, accountability, and public benefit. Effective implementation requires clear legal frameworks, institutional coordination, and continuous stakeholder engagement to maintain trust and maximize the impact of open data initiatives.
Civic uses of open government data involve leveraging publicly available datasets to empower citizens, foster transparency, and support community-driven initiatives. By making government information accessible and reusable, individuals, nonprofits, and local organizations can analyze data to address social issues, promote accountability, and encourage informed public participation. The main goals are to enhance civic engagement, enable data-driven decision-making at the grassroots level, and stimulate innovation in public services. Successful adoption depends on ensuring data quality, usability, and ongoing collaboration between government bodies and the civic sector to maximize social impact.
Selected Projects:
Focused on modernization of public administration through the use of linked open statistical data.
Innovative Open Data Education and Training based on PBL and Learning Analytics
Designed and tested tools for publishing and analyzing open statistical data.
Used open data and social media analytics to support youth engagement and communication.
Beyond these projects, our team is actively pursuing research into automated data publishing pipelines, semantic enrichment techniques, and the role of linked data in smart governance. We are particularly interested in the integration of real-time open data streams, the development of data quality assessment frameworks, and impact evaluation methods for open data initiatives. Further topics include open algorithms, data cooperatives, and data stewardship models that empower citizens while respecting privacy and data sovereignty. We are committed to advancing both the technical and institutional dimensions of the open data ecosystem.
Linked Open Data Technologies (Linked Open Data)
Linked Open Data (LOD) combines the principles of Linked Data with Open Data, meaning the data is not only structured and interconnected but also freely available for anyone to use, reuse, and distribute. Key technologies underpinning LOD include Uniform Resource Identifiers (URIs) to uniquely identify entities, HTTP to retrieve these resources, and the Resource Description Framework (RDF) as a graph-based data model to structure and link the data. SPARQL is the query language used to retrieve and manipulate data stored in RDF format. The aim is to create a web of data where information from diverse sources can be easily connected, shared, and processed by machines, leading to richer insights and new applications.
Business Processes CSD
In the context of financial markets, CSD stands for Central Securities Depository. A CSD is a financial market infrastructure that plays a crucial role in the post-trade environment. Its core business processes involve operating a securities settlement system, which facilitates the transfer of ownership of securities (like stocks and bonds) between parties, often on a delivery versus payment (DvP) basis. CSDs also handle the initial recording of newly issued securities in a book-entry system (notary service) and provide and maintain securities accounts at the top-tier level (central maintenance service). Essentially, they act as a central point for holding securities, ensuring the integrity of securities issues, and enabling efficient settlement of transactions.
Multidimensional Linked Open Data
Multidimensional Linked Open Data refers to publishing statistical or complex datasets as Linked Open Data, often using a data cube model. This approach leverages the RDF Data Cube Vocabulary (QB), a W3C recommendation, to represent data with multiple dimensions (e.g., time, geography, product type) and measures (e.g., sales, population). By structuring data in this way and linking it to other relevant datasets on the web, it becomes easier to perform complex analyses, such as On-Line Analytical Processing (OLAP) operations like slicing, dicing, and drilling down. This enhances the potential for data integration, allows for richer querying across disparate sources, and supports the discovery of new insights from openly available information.