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Digital data on tangible and intangible cultural assets is an essential part of daily life, communication and experience. It has a lasting influence on the perception of cultural identity as well as on the interactions between research, the cultural economy and society. Throughout the last three decades, many cultural heritage institutions have contributed a wealth of digital representations of cultural assets (2D digital reproductions of paintings, sheet music, 3D digital models of sculptures, monuments, rooms, buildings), audio-visual data (music, film, stage performances), and procedural research data such as encoding and annotation formats. The long-term preservation and FAIR availability of research data from the cultural heritage domain is fundamentally important, not only for future academic success in the humanities but also for the cultural identity of individuals and society as a whole. Up to now, no coordinated effort for professional research data management on a national level exists in Germany. NFDI4Culture aims to fill this gap and create a user-centered, research-driven infrastructure that will cover a broad range of research domains from musicology, art history and architecture to performance, theatre, film, and media studies. The research landscape addressed by the consortium is characterized by strong institutional differentiation. Research units in the consortium's community of interest comprise university institutes, art colleges, academies, galleries, libraries, archives and museums. This diverse landscape is also characterized by an abundance of research objects, methodologies and a great potential for data-driven research. In a unique effort carried out by the applicant and co-applicants of this proposal and ten academic societies, this community is interconnected for the first time through a federated approach that is ideally suited to the needs of the participating researchers. To promote collaboration within the NFDI, to share knowledge and technology and to provide extensive support for its users have been the guiding principles of the consortium from the beginning and will be at the heart of all workflows and decision-making processes. Thanks to these principles, NFDI4Culture has gathered strong support ranging from individual researchers to high-level cultural heritage organizations such as the UNESCO, the International Council of Museums, the Open Knowledge Foundation and Wikimedia. On this basis, NFDI4Culture will take innovative measures that promote a cultural change towards a more reflective and sustainable handling of research data and at the same time boost qualification and professionalization in data-driven research in the domain of cultural heritage. This will create a long-lasting impact on science, cultural economy and society as a whole.

This talk outlines a vision for Common European Data Spaces, proposed by the European Commission, where FAIR principles (Wilkinson et al. 2016) and FAIR Digital Objects (FDOs) (De Smedt et al. 2020, Schwardmann 2020) can play a role in bringing together research infrastructures, data aggregators and other stakeholders working with curated objects in museums, herbaria, libraries and archives. The organisations and stakeholders involved represent a wide range of disciplines and data types including biodiversity, ecology, anthropology, archaeology, cultural history, digital storytelling, art conservation, and history of science among others (ICEDIG 2020, Ortolja-Baird and Nyhan 2021). The context and the history of the curated objects also span the natural sciences and cultural heritage domains (Nadim 2021, Weber 2021). Despite this heterogeneity, various common themes in the area of digital curation, open access, and data usage (Tasovac et al. 2020) appear where FDOs and Common European Data Spaces can be a useful venue for supporting the European Strategy for Data. In particular, FDOs, as an abstraction mechanism to structure and describe digital artefacts from a specific domain yet at the same time provide interoperability (De Smedt et al. 2020), can help realise the vision behind a common data space to “bring together relevant data infrastructures and governance frameworks in order to facilitate data pooling and sharing” (European Commission 2022:2). A May 2022 report on the challenges and opportunities of European Common Data Spaces highlights the following points: Open data holders have extensive experience in data publishing, metadata management, data quality, dataset discovery, data federation, as well as tried-and-tested standards (e.g. DCAT) and technologies. There seems to be very little knowledge/technology transfer from the open data community to the data spaces community, which is a missed opportunity. Data space implementations should not reinvent wheels that the open data community has already developed, tested, and used extensively. Whether the data is private, shared, or open, using data from multiple sources requires interoperability at several levels, from identifiers to vocabularies. The question of which data intermediaries will act as neutral agents to ensure interoperability is underexplored in the data space context. Public administrations, building on their experience of publishing open data, are best placed to take on such roles Open data holders have extensive experience in data publishing, metadata management, data quality, dataset discovery, data federation, as well as tried-and-tested standards (e.g. DCAT) and technologies. There seems to be very little knowledge/technology transfer from the open data community to the data spaces community, which is a missed opportunity. Data space implementations should not reinvent wheels that the open data community has already developed, tested, and used extensively. Whether the data is private, shared, or open, using data from multiple sources requires interoperability at several levels, from identifiers to vocabularies. The question of which data intermediaries will act as neutral agents to ensure interoperability is underexplored in the data space context. Public administrations, building on their experience of publishing open data, are best placed to take on such roles Building on previous conversations facilitated by DiSSCo, DARIAH, Europeana, and Archives Portal Europe Foundation, (Europeana Conference 2021, DARIAH Annual Event 2022), this talk will address the above points from the perspective of bringing together the domains of natural history museums, cultural heritage, and digital humanities. Within our collaboration, we have identified several common areas such as data discoverability, linking, and providing contextual information, which align with the goal of FDO implementation. DiSSCo and DARIAH as European infrastructures, on the one hand, and Europeana and Archives Portal as data aggregators, on the other hand, are involved in improving access to data and the researchers' capacity to work with heterogeneous data sources. One of the biggest shared challenges across the diverse workflows in the arts and humanities and natural history domains is that the data curation processes form a natural continuum between a range of different actors working either in cultural heritage institutions or in academia. In reality, these different layers of curation, enrichment and analysis are separated by legal, institutional, infrastructural and even funding silos (as in many countries, these institutions belong to different ministries, and fall under different legislative frameworks). How can this continuum, from a scholarly point of view, be supported within common data space and FDO framework? At the same time, implementing a common data space requires not just interoperability but stewardship and strategy for sharing resources (Keller 2021). The data infrastructure and FAIR related activities explored in our collaboration are of strategic importance to help Europe and the rest of the world deal with important societal issues. Therefore, bringing this collaboration within the context of FDO provides an ideal avenue to explore potential data, policy, and implementation matters, in order to address the two gaps outlined above for Common Data Spaces. Furthermore, the ideas expressed in Common European Data Space for Cultural Heritage (with Europeana as the core stakeholder) and Green Deal Data Spaces need further clarification concerning implementation planning and most importantly, how multiple commons would work together. With DARIAH coming from the humanities and DiSSCo from the natural sciences side, such collaborations and synergy should align with the Common Data Spaces vision. The philosophy and ideas behind data and digital commons are not new (Fuchs 2020, Kashwan et al. 2021). However, it is crucial to contextualise the implementation strategy and benefits within data intensive, multidisciplinary research and FAIR principles. Given that curated objects are informational resources for the researchers, but can also provide contexts, and make visible the relationships between artefacts, people, publications, organisations, provenance, and events, it is important to think of them as much more than just records in a database. Additionally, FDOs as the digital representations of the curated objects have the potential of fostering cross-disciplinary collaborations (such as between biology, history, art or anthropology) and of providing a wider lens for understanding materiality and the role of data (Ribes 2019). As interdisciplinarity and data-driven foci are gaining traction via applications of artificial intelligence and machine learning, it is vital to understand what FDO adoption and implementation can contribute to common data spaces. We believe FDOs can be a successful foundation for Common European Data Spaces because they can can connect multiple commons -- from Green Deal to Cultural Heritage -- in order to drive forward the vision for interdisciplinary collaboration.

FAIR Digital Objects (FDOs) are typed by a well described set of attributes, where attributes are key value pairs with a key, which refers to a syntactic description of the value. Often the description of the set of attributes is called also profile. The exact description of the attribute keys is obviously crucial for machine actionability on one hand. On the other hand an exact description of attributes can be a way to allow also human readability of the used keys. Furthermore it can often integrate legacy attribute sets that are provided inside repositories for the description of their digital objects. In the following we show two examples of FDO types with their attributes from different viewpoints. The two examples are: the Persistent Identifiers (PID) for Instruments example and the DARIAH (see https://de.dariah.eu) use case. In both cases the Handle system is used for the persistent identifiers, the FDO record is provided by the Handle record of the PID and the attributes can be found here as type-data pairs in the phrasing of the Handle system. 1 Example 1: PID for Instruments The PID for instrument example goes back to the development of kernel metadata, which is seen as minimally required to reference and describe scientific instruments Stocker et al. 2020. The value space for the attributes here often contains hierarchical objects and can also be lists of attributes. An example of such an attribute definition is that of a single manufacturer of an instrument that occurs in a list of manufacturers here: http://dtr-test.pidconsortium.eu/#objects/21.T11148/7adfcd13b3b01de0d875. 1.1. The Handle Record for a Full PID for Instruments In this case one uses the references to the attribute definitions as keys for the values, which are often lists or objects. The Handle Record for a full attribute list of a PID for Instruments can be obtained from the Handle Proxy with https://hdl.handle.net/21.T11998/0000-001A-3905-1?noredirect The structure of this FDO record is defined as a type definition at the ePIC Date Type Registry Schwardmann 2020 with http://dtr-test.pidconsortium.eu/objects/21.T11148/17ce618137e697852ea6 . This way we can also refer to this structure definition in a qualified key value pair like TYPE/0.TYPE and then use as keys in the FDO record the names as they are given for keys in this structure. This way an FDO record becomes a human readable form without loosing any machine readability. For further details see: https://hdl.handle.net/21.T11998/0000-001A-3905-8?noredirect In both cases the full instrument descriptions are completely stored in the Handle database of the Handle PID service. The PID itself is a metadata object and can be seen as an FDO of its own. 1.2. Type for a PID4Inst based on Attributes The type for such FDOs is given via proxy https://hdl.handle.net/21.T11148/17ce618137e697852ea6 in the ePIC DTR 1.3. PID4Inst in a Repository Another option is to store the metadata objects of instrument descriptions in repositories. In this case a schema is needed to describe the metadata elements that are needed for the description. The existing attribute definitions could be bundled here into a single complex definition, which is syntactically almost identical to the type definition for instruments. From such a complex definition one could derive a schema for the repository entries. In this case the schema was directly derived from the type, which is conceptually different from attribute definitions, but syntactically similar and therefore exploitable by the same services. The result of the schema derivation can then be directly fed into the ingest module of repositories, in the following figure for example into the CORDRA schema module for the definition of attribute types: https://hdl.handle.net/21.T11148/c2c8c452912d57a44117 An example of such a PID for instrument object in a repository is given at https://vm11.pid.gwdg.de:8445/objects/21.11145/8fefa88dea40956037ec 2. Example 2: The DARIAH Use Case This example evolved in the humanities in the DARIAH project about five years ago with the DARIAH repository (Kálmán et al. 2016, DARIAH-DE 2016). The Handle record structure was created far before FDO records have been discussed. It uses key value pairs with human readable keys as the type and provides relatively atomic values. For humans the key here is a description for the value space that can be expected: https://hdl.handle.net/21.11113/0000-000B-CA4C-D?noredirect The use of human readable keys does however not match the goal of machine readability of this description. Additionally it has the risk of uncertainty and ambiguity. 2.1. Attribute Definitions In order to make these attributes machine readable, attribute definitions for the allowed value spaces were needed and can be found in the ePIC data type registries. The following basic information type for an email address can be used as the reference key for the value space given for the 'RESPONSIBLE' type above for instance: https://dtr-test.pidconsortium.eu/#objects/21.T11148/e117a4a29bfd07438c1e Attribute definitions for all attributes used in the DARIAH example are given in the ePIC data type registrie. This way one is able to define a type for the DARIAH Handle records. 2.2. An FDO Type of Legacy Repository Records Such a type definition is given at: https://dtr-test.pidconsortium.eu/#objects/21.T11148/f1eea855587d8b1f66da If this type is the known type of all objects in the DARIAH repository, the references to the keys are named very similar the human readable form of the Handle record. Usually and as we have seen in the previous PID4Inst example the type of the FDO would be another attribute of the FDO. This would require an adaption of the attributes of all digital objects of the DARIAH repository. But since all digital objects of the DARIAH repository follow the same profile and all its digital objects have the same PID prefix, it would be sufficient to implement this additional attribute at the prefix level. Together with a rule that attributes on a lower level dominate attributes on a higher level, this additional prefix attribute makes FDOs out of legacy digital objects that have been defined a long time ago. 3. Presentation In the presentation we will describe based on the two examples above how machine and human readability can be supported at the same time by FDO types and discuss the implementation of hierarchical type definitions as the basic infrastructure for FAIRness of data in more detail.