Digital Humanities 2023 “The research is happening in the text fields” – Are Linked Open Data and Art History a good match? Nanni, Giacomo giacomo.nanni@fh-potsdam.de UCLAB, University of Applied Sciences Potsdam Freyberg, Linda l.freyberg@dipf.de DIPF Leibniz-Institut für Bildungsforschung und Bildungsinformation, Germany de Günther, Sabine sabine.de.guenther@fh-potsdam.de UCLAB, University of Applied Sciences Potsdam Dörk, Marian marian.doerk@fh-potsdam.de UCLAB, University of Applied Sciences Potsdam Introduction The scientific discourse in object-focused and historical disci- plines follows certain methods and builds a narration with objects, events, evidence and conclusions. In Digital Humanities (DH) research this process and its speci- fic structure has to be represented on a data level. Linked Open Data, Semantic Web ontologies and, in particular, reference mo- dels like CIDOC CRM 1 (2022) offer a precise structural vocabu- lary to describe cultural heritage objects and their relations. These frameworks can support GLAM 2 institutions to make the data on their objects findable, accessible and interoperable. However, in research projects the course of developing argu- ments and describing findings is crucial. We use Art History rese- arch, in particular, the project “Restaging Fashion - Digital con- textualization of vestimentary sources” (Refa), as one example to show how emerging discourses within the research process can be mapped in a knowledge organization system and which aspects may not be formally represented. A recurring question is: What should be modeled as linked open data (LOD) and what should be entered as free-form text?Despite the proven utility of data stan- dards, vocabularies, and authority files providing for accuracy, context, and unambiguity, there is also a need for an open space to formulate and present tentative observations. With this contri- bution we present how we bridge LOD and full text descriptions through information visualization. Semantic data modeling in Muse- ums Semantic data modeling of a collection during an early stage of the research prompts a trade-off to be made between data stan- dardization and semantic accuracy.Sophisticated ontologies like CIDOC CRM encourage the detailed description of an object through an event-driven structure, which makes it a very powerful tool for the cultural heritage sector by providing for the possibility of adding meaning to the original data. Since 2006, when the term Linked Data was coined (see Ber- ners-Lee 2006), according to Daquinoet al."only a few pioneers have abandoned legacy cataloguing and archiving systems to fully embrace the Semantic Web paradigm and manage their catalogues through LOD-native management systems" (2022). When considering the LOD datasets published by The British Museum and the Rijksmuseum, who also create tools such as Re- searchSpace (Oldman / Tanase 2018) and dedicated platforms to engage with their collections, it appears that these museums only share relatively shallow data models for their artworks on the web. The Rijksmuseum provides basic metadata on objects structured in DC 3 , EDM 4 and LIDO 5 schemas. Only one example is modeled deeply: Linked Art van Gogh object metadata download. But the creators clearly state that: “both the implementation of the model as well as the use of identifiers will be subject to change” (Rijks- Data 2022). The British Museum, on the other hand, structured its entire collection using CIDOC CRM, but was unable to preserve its database in a consistent and accessible manner (Lincoln, 2015). Data that should be linked together is no longer accessible, making the research of scholars no longer available ( see Programming- historian 2017). The potential of these applications is obvious: LOD allows data to be reused and redistributed, revealing relationships with other data on the Web (Berners-Lee 2006). The resources involved, however, are somewhat excessive both from a technological per- spective (see De Decker 2015)and regarding the cataloging of in- formation. Manual tagging can be performed by domain experts in a very precise manner, but it cannot be performed manually to large volumes of already existing content due to the lack of re- sources (Simou et al. 2017) or may not be conducted automati- cally due to inconsistencies and/or the lack of standardization in the datasets. This leads institutions to publish only a subset of the available information, exposing minimal metadata (Doerr et al. 2010), which might prevent researchers from using the data and constrains them to conventional research methods. Data-driven art historical discourse Art historical research on and with collections is deeply concer- ned with the history of objects and their discourse such as pro- venance history, history of interpretation, dating or attribution ba- sed on comparable works. This can be expressed with the event structure of CIDOC CRM including descriptive information on the objects like dimensions, inscriptions, and alternative titles also linked to relevant vocabularies like ICONCLASS 6 and AAT 7 . But a crucial part of humanities research consists of annotating that basic formal information, by elaborating and contextualizing the history of the artworks and their interpretation, often concluding in the form of a paper or monograph. 1