Projects

Odeuropa will apply state-of-the-art AI techniques to cultural heritage text and image datasets spanning four centuries of European history, to identify and trace how ‘smell’ was expressed in different languages, with what places it was associated, what kinds of events and practices it characterised, and to what emotions it was linked. This multi-modal information will be curated, following semantic web standards, stored in the ‘European Olfactory Knowledge Graph’ (EOKG), and then drawn on to create new ‘storylines’ informed by cultural history research. The storyline resources will be prepared in different formats for different audiences: as an online ‘Encyclopaedia of European Smell Heritage’, as ‘interactive notebook’ demonstrators, and in the form of toolkits and training documentation describing best-practices in olfactory museology. We will develop new, evidence-based methodologies to quantify the impact of multisensory visitor engagement, and use this data to support the implementation of policy recommendations for the recognition, promotion, presentation and safe-guarding of our olfactory heritage.

The SociAl BIas Observatory (SABIO) project is aimed at investigating bias in the digital collections of the members of the Dutch Digital Heritage Network. In this project, we investigate how collection managers and curators create and add metadata to collection objects, and how bias in these metadata can be detected using statistical models. We aim to create a knowledge graph on top of existing collection databases that makes prejudices and imbalances in the data explicit such that they can be addressed, as well as taken into account by users of the data. Funded by the Dutch Digital Heritage Network.

The AI:CULT project addresses the gap between AI and our digital cultural heritage. Cultural heritage data is rarely objective data. The very reasons for certain heritage data to be preserved, its interpretation throughout time, and the way heritage data is accessed after digitalisation is all subject to strong biases. The inherent richness, subjectivity and polyvocal nature of cultural heritage data limits and often even rules out the responsible use of AI. How do we model that “Seventeenth Century” and “The Golden Age” refer to the same era, yet are not fully synonymous and carry different semantic payloads? Current state of the art AI cannot deal with these subtleties in a way that does justice to the important role of the heritage institute as a trusted source of information. Thus, the heritage sector is under threat to be left out of the current global success of AI. AI:CULT will allow heritage institutes to use AI in ways that align with their role in society: transparent, inclusive, and keeping the user in control. The project addresses two case studies with societal parties tasked with providing access to national heritage, and who have voiced their vested interest in using AI for their workflows: the National Library (KB) and the Institute for Sound and Vision (NISV): (i) automatically analysing and enriching object-level descriptions and (ii) creating data stories and narratives from raw collection data. Both institutions acknowledge that the straightforward application of AI reflects biases present in the training data. In the AI:CULT project bias detection and filtering methods will be developed that will be directly tested on the heritage institutions’ workfloors.

The two Ph.D. students on this project will be based at KNAW Humanities Cluster and CWI in Amsterdam.

Funded by NWO.

Global apple pie investigates the relationship between sugar import and export and recipes as well as opinions on health. This project is a collaboration across HuC institutes with Ulbe Bosma (IISG) and Rebeca Ibañez-Martín (Meertens Institute). Involvement DHLab: Marieke van Erp

Autumn 2020 - Summer 2021 The SociAl BIas Observatory (SABIO) project is aimed at investigating bias in the digital collections of the members of the Dutch Digital Heritage Network. In this project, we investigate how collection managers and curators create and add metadata to collection objects, and how bias in these metadata can be detected using statistical models. We aim to create a knowledge graph on top of existing collection databases that makes prejudices and imbalances in the data explicit such that they can be addressed, as well as taken into account by users of the data. This project is a collaboration with National Library of the Netherlands, Netherlans Institute for Sound and Vision, Nationaal Museum van Wereldculturen, and Centrum voor Wiskunde en Informatica. Involvement DHLab: Marieke van Erp is the principal investigator on this project Funded by:

The main aim of this Action is to promote synergies across Europe between linguists, computer scientists, terminologists, and other stakeholders in industry and society, in order to investigate and extend the area of linguistic data science. We understand linguistic data science as a subfield of the emerging “data science”, which focuses on the systematic analysis and study of the structure and properties of data at a large scale, along with methods and techniques to extract new knowledge and insights from it. Linguistic data science is a specific case, which is concerned with providing a formal basis to the analysis, representation, integration and exploitation of language data (syntax, morphology, lexicon, etc.). In fact, the specificities of linguistic data are an aspect largely unexplored so far in a big data context. In order to support the study of linguistic data science in the most efficient and productive way, the construction of a mature holistic ecosystem of multilingual and semantically interoperable linguistic data is required at Web scale. Such an ecosystem, unavailable today, is needed to foster the systematic cross-lingual discovery, exploration, exploitation, extension, curation and quality control of linguistic data. We argue that linked data (LD) technologies, in combination with natural language processing (NLP) techniques and multilingual language resources (LRs) (bilingual dictionaries, multilingual corpora, terminologies, etc.), have the potential to enable such an ecosystem that will allow for transparent information flow across linguistic data sources in multiple languages, by addressing the semantic interoperability problem. Involvement DHLab: Marieke van Erp leads WG2

This project investigates scene detection to enrich a historical press photo collection. DHLab involvement: Melvin Wevers (main applicant) Funded by NWO. More info: https://www.nwo.nl/en/research-and-results/research-projects/i/33/34433.html

Newspaper advertisements contain valuable information on many socio-economic historical developments. In Digital History research, advertisements are mostly used to study goods, products and consumer society. Advertisements, however, were not only used to sell, but also to ask. Job advertisements feature frequently in the nineteenth century. This projects aims to computationally extract job advertisements from the nineteenth century digitized newspapers provided by the Royal Dutch Library. The goal of this project is to aggregate the wages that were mentioned in this advertisements to gain a better insight in the economic development of ‘keukenmeiden’ and ‘dienstbodes’. Involvement DHLab: Ruben Ros, Marieke van Erp

May 2017 – December 2019 Historically, some animals have been perceived as threats by humans. These species were believed to carry diseases or harm crops and farm animals. SERPENS and its ATHENA extension aimed to study the historical impact of pest and nuisance species on human practices and changes in the public perception of these animals. Involvement DHLab: Marieke van Erp Funding:

October 2017 – March 2019 Although oral history and the study of ego documents both value these individual perspectives on history and its meaning, these research fields tend to operate separately. EviDENce explores new ways of analysing and contextualising historical sources by applying event modelling and semantic web technologies. Involvement DHLab: Marieke van Erp Funded by:

April 2018 – January 2019 The Amsterdam Time Machine (ATM) is a research and development platform on the history of Amsterdam. DHLab member involved: Marieke van Erp Funded by: