Two major trends significantly changed the global Artificial Intelligence (AI) and Data landscape. Recent AI and Machine Learning developments are driving a paradigm shift to creating large task-agnostic foundation models pre-trained using web-scale data. Foundation models are then adapted to different downstream tasks via techniques such as fine-tuning. At the same time, we see a movement to the creation of large-scale data-sharing infrastructures. Data Spaces are an emerging approach to data management and sharing at the core of the European Data Strategy to provide access to high-quality data for AI. This paper brings together work on foundation models and data spaces into a holistic vision for Foundation Data Space Models. The paper highlights the data management requirements challenges for data spaces and details a high-level approach for foundation data space models together with a unified lifecycle for data spaces and foundation models. Finally, it sets out a research agenda.

When the global pandemic struck in 2020, most countries established task forces to meet a challenge that impacted governmental resources. It became apparent that data, intelligence gathering, and both modelling and predictive capabilities were required. While artificial intelligence (AI) based solutions had already begun to emerge within the public sector, the Covid-19 pandemic accelerated this process. In particular, modelling of case numbers with the development of predictive algorithms. The development of AI solutions for public sector organizations is inherently multidisciplinary. This is crucial to understanding how solutions can be developed, outputs understood, and the benefits and risks measured. Furthermore, the development of AI solutions often requires data which may not be accessible from a single location. In the case of Covid-19 modelling, data must be extracted from multiple locations to construct data assets. In this research, a collaborative approach to developing machine learning expertise for the public sector is presented. Using Covid-19 as a case study, the role of different government sectors when building data assets is examined along with the use of standard data models, and how this type of cooperation led to the development of a pipeline for data assets to underpin AI solutions for the public sector.

Amidst prevailing healthcare challenges, a dynamic solution emerges, fusing knowledge graph technology, clinical trials optimization, dataspace integration, and AI innovation. This unified approach tackles issues like limited patient insights, suboptimal trial designs, and imprecise treatments. By interlinking diverse data through knowledge graphs, this method illuminates disease trends, therapeutic efficacies, and patient prognoses. AI techniques, especially machine learning, contribute predictive power by unveiling hidden patterns for accurate diagnostics, prognostics, and personalized treatments. This multidisciplinary fusion transforms clinical trials, enhancing comprehensiveness and precision through real-world data analysis and subgroup identification. In reshaping healthcare, this proposition aims to accelerate treatment personalization, elevate therapeutic efficacy, and empower informed medical decisions, encompassing the essence of ’Advancing Healthcare through Innovation: Knowledge Graphs, Clinical Trials, Dataspace, and AI’.

Foundation Models are pivotal in advancing artificial intelligence, driving notable progress across diverse areas. When merged with dataspace, these models enhance our capability to develop algorithms that are powerful, predictive, and honor data sovereignty and quality. This paper highlights the potential benefits of a comprehensive repository of Foundation Models, contextualized within dataspace. Such an archive can streamline research, development, and education by offering a comparative analysis of various models and their applications. While serving as a consistent reference point for model assessment and fostering collaborative learning, the repository does face challenges like unbiased evaluations, data privacy, and comprehensive information delivery. The paper also notes the importance of the repository being globally applicable, ethically constructed, and user-friendly. We delve into the nuances of integrating Foundation Models within dataspace, balancing the repository’s strengths against its limitations.

The research shared on a digital social media has enabled us to measure the impact of academic entities beyond the conventional bibliometric community. We explored a diffusion-based metrics to measure the influence of academic entities in social media using 2-layered graph where the first layer is the graph between academic and social media entities and a second layer is the graph between social media entities. We employed the heat diffusion algorithms to measure the social impact of academic entities and evaluate them by (i) predicting links between academic entities and social media and (ii) suggesting memes for the academic entities. Our analysis on predicting links between scientist and social media entities showed the AUC-ROC score of 0.73 and the AUC-PR score of 0.30. Similarly, predicting links between scientific publications and social media entities showed the AUC-ROC score of 0.80 and the AUC-PR score of 0.19. Our approach also provides decent social media entities (memes) suggestion for scientific publications.

The growth in the alternative digital publishing is widening the breadth of scholarly impact beyond the conventional bibliometric community. Thus, research is becoming more reachable both inside and outside of academic institutions and are found to be shared, downloaded and discussed in social media. In this study, we linked the scientific articles found in mainstream news, weblogs and Stack Overflow to the citation database of peer-reviewed literature called Scopus. We then explored how standard graph-based influence metrics can be used to measure the social impact of scientific articles. We also proposed the variant of Katz centrality metrics called EgoMet score to measure the local importance of scientific articles in its ego network. Later we evaluated these computed graph-based influence metrics by predicting absolute citations. Our results of the prediction model describe 34% variance to predict citations from blogs and mainstream news and 44% variance to predict citations from Stack Overflow.

The realization that scholarly publications are discussed and have influence on discourse outside scientific and academic domains has given rise to area of scientometrics called alternative metrics or “altmetrics”. Furthermore, researchers in this field tend to focus primarily on measuring scientific activity on social media platforms such as Twitter, however these count-based metrics are vulnerable to gaming because they tend to lack concrete justification or reference to the primary source. In this collaboration with Elsevier, we extend the conventional citation graph to a heterogeneous graph of publications, scientists, venues, organizations and more authoritative media sources such as mainstream news and weblogs. Our approach consists of two parts: one is integrating the bibliometric data with the social data such as blogs, mainstream news. The other involves understanding how standard graph-based metrics can be used to predict the academic impact. Our result showed the computed graph-based metrics can reasonably predict the academic impact of early stage researchers.