Europeana Foundation

Global biodiversity is in crisis, with wildlife populations declining 69% since 1970 (WWF). Preserving and restoring ecosystems is essential for sustaining life on Earth. However, many countries rely on market-based instruments like biodiversity offsets, despite little evidence of their effectiveness. This study critically examines biodiversity offsets, identifying institutional, data, ecological, economic, and social failures that undermine their success. Using Indonesia, a global biodiversity hotspot, as a case study, we develop an econometric model to analyze key drivers of deforestation. The findings reveal that biodiversity offset schemes are fundamentally flawed: they lack scientific credibility, rely on arbitrary ratios, lack auditing and transparency, create value conflicts, and fail to achieve “No Net Loss” even over a 100-year timeframe. Offsets do not compensate for lost biodiversity, especially for affected communities, and are rarely supported by ecosystem mapping or robust valuation metrics. Without major reforms, they cannot halt or reverse biodiversity loss. A stronger, evidence-based approach is urgently needed. Rather than relying on ineffective offset schemes, the global community must prioritize genuine ecosystem restoration and sustainable conservation strategies to protect biodiversity for future generations.

PurposeThe purpose of this study is to offer a checklist that can be used for both creating and evaluating digital collections, which are also sometimes referred to as data sets as part of the collections as data movement, suitable for computational use.Design/methodology/approachThe checklist was built by synthesising and analysing the results of relevant research literature, articles and studies and the issues and needs obtained in an observational study. The checklist was tested and applied both as a tool for assessing a selection of digital collections made available by galleries, libraries, archives and museums (GLAM) institutions as proof of concept and as a supporting tool for creating collections as data.FindingsOver the past few years, there has been a growing interest in making available digital collections published by GLAM organisations for computational use. Based on previous work, the authors defined a methodology to build a checklist for the publication of Collections as data. The authors’ evaluation showed several examples of applications that can be useful to encourage other institutions to publish their digital collections for computational use.Originality/valueWhile some work on making available digital collections suitable for computational use exists, giving particular attention to data quality, planning and experimentation, to the best of the authors’ knowledge, none of the work to date provides an easy-to-follow and robust checklist to publish collection data sets in GLAM institutions. This checklist intends to encourage small- and medium-sized institutions to adopt the collection as data principles in daily workflows following best practices and guidelines.

Nowadays, vast amounts of multimedia content are being produced, archived, and digitized, resulting in great troves of data of interest. Examples include user-generated content, such as images, videos, text, and audio posted by users on social media and wikis, or content provided through official publishers and distributors, such as digital libraries, organizations, and online museums. This digital content can serve as a valuable source of inspiration to the creative industries, such as architecture and gaming, to produce new innovative assets or to enhance and (re-)use existing ones. However, in its current form, this content is difficult to be reused and repurposed due to the lack of appropriate solutions for its retrieval, analysis, and integration into the design process. In this article, we present V4Design, a novel framework for the automatic content analysis, linking, and seamless transformation of heterogeneous multimedia content to help architects and virtual reality game designers establish innovative value chains and end-user applications. By integrating and intelligently combining state-of-the-art technologies in computer vision, 3-D generation, text analysis, generation and semantic integration, and interlinking, V4Design provides architects and video game designers with innovative tools to draw inspiration from archive footage and documentaries, inspiring and eventually supporting the design process.

This article presents an observational study of the virtual graph formed by equivalence links between agent entities across 8 knowledge bases. To evaluate the potential of this linked data graph, we measured the equivalences that it could provide for a real dataset. We crawled the virtual graph by starting from references to agents we found in descriptions of objects collected from data of cultural heritage institutions in Europeana. Our study characterizes the current virtual equivalence graph, presenting statistics about the links, their type and origin. Crawling the equivalences for agent URIs required several crawling iterations on the virtual equivalence graph. The amount of gathered equivalences grows steeply in the first 3 crawling iterations and stabilizes on the 4th iteration. VIAF was the KB with the highest number of equivalences, reaching 60.7%, and it was followed by Wikidata with 34.5%.

Abstract The prevalence and impact of bleeding disorders in women is not sufficiently acknowledged, with the organisation of care traditionally biased towards boys and men with haemophilia. In 2017, the European Haemophilia Consortium surveyed women with bleeding disorders, national member organisations (NMOs) and treatment centres to assess the impact of bleeding disorders in women in four domains: physical activity, active life, romantic and social life, and reproductive life. Most women had von Willebrand disease (VWD) or described themselves as a carrier. All reported a negative impact on physical activity, active life and romantic and social life. Up to 70% of women in all groups reported that their bleeding disorder had a significant impact on their ability or willingness to have children, or prevented it. Heavy menstrual bleeding was reported as the having the most significant impact on daily life. Women face barriers to diagnosis and care, including difficulty obtaining a referral and lack of knowledge among general practitioners and gynaecologists. While bleeding disorders share many symptoms, including bleeding after minor injury and trauma, the link between heavy menstrual bleeding and a bleeding disorder often goes unrecognised and its severity is underestimated. Screening is not offered to all eligible women despite the availability of long-established management guidelines; clinical tools to estimate severity may be unreliable. Failure to recognise a bleeding disorder in a woman is a multifactorial problem that is partly due to cultural reluctance to discuss menstruation. Public awareness campaigns are seeking to correct this, and many NMOs involve women in their initiatives and provide women-centred activities. However, a transformation in diagnosis is needed to shift the focus of treatment centres beyond boys and men with haemophilia, and to acknowledge the prevalence and severity of bleeding disorders in women.

Wikidata is an outstanding data source with potential application in many scenarios. Wikidata provides its data openly in RDF. Our study aims to evaluate the usability of Wikidata as a data source for robots operating on the web of data, according to specifications and practices of linked data, the Semantic Web and ontology reasoning. We evaluated from the perspective of two use cases of data crawling robots, which are guided by our general motivation to acquire richer data for Europeana, a data aggregator from the Cultural Heritage domain. The first use case regards general data consumption applications based on RDF, RDF-Schema, OWL, SKOS and linked data. The second case regards applications that explore semantics relying on Schema.org and SKOS. We conclude that a human operator must assist linked data applications to interpret Wikidata’s RDF because of the choices that were taken at Wikidata in the definition of its expression in RDF. The semantics of the RDF output from Wikidata is “locked-in” by the usage of Wikidata’s own ontology, resulting in the need for human intervention. Wikidata is only a few steps away from high quality machine interpretation, however. It contains extensive alignment data to RDF, RDFS, OWL, SKOS and Schema.org, but a machine interpretation of those alignments can only be done if some essential Wikidata alignment properties are known.

This chapter explores the tenets and the practice of a transnational initiative to promote local entrepreneurial growth: the Building Global Innovators (BGI) model, a startup accelerator based in Lisbon (Portugal) and in Cambridge (Massachusetts, USA). We examine the pathways through which BGI's process and global network of experts helped two successful Portuguese technology startups grow and scale very quickly – Movvo and Veniam. We combine literatures from strategic management, marketing and economic geography to explore BGI's transnational acceleration model, which taps into global “pipelines” and distant entrepreneurial ecosystems, namely as a way to access the expertise, market opportunities and venture capital that is often unavailable in emergent and policy-sheltered local clusters. We discuss the relevance of such a transnational acceleration model for high-tech startups in peripheral economies.

In this paper, we present the overview of Europeana Cloud system, which is a new undertaking of Europeana Foundation and partnering institutions aimed to provide shared, cloud-based infrastructure for aggregation and exchange of cultural heritage metadata and content for European institutions.