Abstract
This article presents a work-in-progress version of a Dublin Core Application Profile (DCAP) developed to serve the Social and Solidarity Economy (SSE). Studies revealed that this community is interested in implementing both internal interoperability between their Web platforms to build a global SSE e-marketplace, and external interoperability among their Web platforms and external ones. The Dublin Core Application Profile for Social and Solidarity Economy (DCAP-SSE) serves this purpose. SSE organisations are submerged in the market economy but they have specificities not taken into account in this economy. The DCAP-SSE integrates terms from well-known metadata schemas, Resource Description Framework (RDF) vocabularies or ontologies, in order to enhance interoperability and take advantage of the benefits of the Linked Open Data ecosystem. It also integrates terms from the new essglobal RDF vocabulary which was created with the goal to respond to the SSE-specific needs. The DCAP-SSE also integrates five new Vocabulary Encoding Schemes to be used with DCAP-SSE properties. The DCAP development was based on a method for the development of application profiles (Me4MAP). We believe that this article has an educational value since it presents the idea that it is important to base DCAP developments on a method. This article shows the main results of applying such a method.

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Abstract
This chapter presents the process of developing a Metadata Application Profile for the Social and Solidarity Economy (DCAP-SSE) using Me4MAP, a method for developing Application Profiles that was being put forth by the authors. The DCAPSSE and Me4MAP were developed iteratively, feeding new developments into each other. This paper presents how the DCAP-SSE was developed showing the steps followed through the development of the activities and the techniques used, and the final deliverables obtained at the end of each activity. It also presents the work-team and how each profile of the team contributed for the DCAP-SSE development process. The DCAP-SSE has been endorsed by the SSE community and new perspectives of SSE activities have been defined for future enlargement of the DCAP-SSE. At the time of writing this chapter, Linked Open SSE Data is being published, they are the first examples of use of the DCAP-SSE. © 2017, IGI Global.

Abstract
The prevalence of data science has grown exponentially in recent years. Increases in data exchange have created the need for standards and formats on handling data from different sources. Developing Metadata Application Profiles is an innovative reference source that discusses the latest trends and techniques for effectively managing and exchanging metadata. Including a range of perspectives on schemas and application profiles, such as interoperability, ontology-based design, and model-driven approaches, this book is ideally designed for researchers, academics, professionals, graduate students, and practitioners actively engaged in data science.

Abstract
Networks appear across various domains, and identifying central nodes in temporal networks is more challenging than in static networks. Temporal betweenness centrality is the widely used method to assess the importance of the nodes. This method is based on shortest temporal path calculations. However, computing this centrality metrics value is computationally intensive, especially for large-scale networks. Various approximation algorithms exist, but they often lack efficiency or accuracy. We introduce TGNN-Bet, a temporal graph neural network model, to approximate temporal betweenness centrality. In TGNN-Bet, each node gathers features from multi-hop neighbors, enabling the model to simulate paths and capture the reachability of nodes. The model's effectiveness is validated using the Spearman correlation (?) performance metric and comparing system runtimes with the existing temporal betweenness centrality method. Experimental results on six real-world temporal networks demonstrate that TGNN-Bet strongly correlates with existing temporal betweenness centrality methods. The proposed TGNN-Bet model achieves an average computation time reduction of 94.216% compared to conventional temporal betweenness centrality methods. © 2025 IEEE.