Task area

Task Area 6: Participation and Steering

TA6 not only establishes the foundation for the collaborative work in the consortium – including handling administration, building the internal consortial infrastructure and setting policies – but also provides the space and structures for an overarching layer to foster synergies and collaborative efforts, enable open knowledge exchange, encourage participatory decisionmaking, maintain relationships with our community and with other consortia and coordinate joint contributions to the NFDI and the community.

Measures

Measure 1: Participation and steering architecturePlease click button to expand or collapse content

This measure formally integrates the community and ensures the consortium’s structural integrity and operational sustainability, including its RDM infrastructure and services. The community is integrated through formal procedures developed by the 4Memory Coordination Office and monitored jointly with the community, including proposals, partnerships, decision-making and collaborations to advance or complement the work programme. To enable participatory yet efficient steering, the spokespersons are continuously involved in strategy through formal, transparent processes documented and communicated within the consortium. Policy development – including compliance management – will lead to standardised workflows and fewer case-by-case analyses, ensuring greater equality and inclusion. The consortium’s sustainable operational model joins different models, reflecting various services and hosting institutions. Sustainable models for services derived from research projects, such as data collections without institutional curation, are also included (a need expressed in our problem stories.) Our community has key roles in ongoing needs assessment.

Measure 2: Academic coordinationPlease click button to expand or collapse content

Academic coordination facilitates collaboration among the TAs and initiates interactions among the consortial members, ensuring technical, organisational and social interoperability. The scope of coordinating activities includes organising and contributing to teamwork within the consortium and the community. The technical coordination ensures the interoperability and assessment of services on the technological level. Ethical and legal issues are addressed both specifically and in general. The transfer of the consortium’s key results is subject to multiple activities and publication formats, overseen by the editorial board.

Measure 3: Innovation framework and engagementPlease click button to expand or collapse content

4Memory will incorporate and support cutting-edge RDM methods, products and processes in its community, which are constantly being developed; at the same time, the consortium must maintain consistent liaison with relevant national and international networks and perspectives. For innovations arising in the first project phase, parts of the budget are reserved in an incubator fund for which awarding procedures are established. Apart from the 4Memory Coordination Office, many participants will serve as community agents embedded in their respective (sub-)communities – including societies and associations; citizen-science organisations; state RDM initiatives; humanities data centres; and collaborative projects and centres (CRCs, GRKs, Merian Centres) – keeping us up to date on the latest developments in our community. The scope of coordinating activities includes cross-consortial co-operations within the NFDI and contributions to and benefits from cross-cutting topics. Networking on a global level takes place through liaisons, joint projects and knowledge exchange with international partners.

Measure 4: Communication and community managementPlease click button to expand or collapse content

4Memory’s communication and community management activities will liaison with the community through meaningful outreach and inreach. Their guiding aims are establishing two-way communication and treating every request from the community as a chance to support and advance historical research. The establishment of an internal and external single point of contact – both technically and in the person of the MD – enables clear, consistent communication. Through organisational workflows and digital tools, the 4Memory Coordination Office’s officers ensure constant information flows, forming the core of the communication strategy. Public activities focus both on inreach and outreach, offering information to and input from the community. The 4Memory web portal will be a knowledge base and guidebook for the community.

Measure 5: Administration and proceduresPlease click button to expand or collapse content

4Memory’s administration aims for professional management and a comprehensive service portfolio to allow operational staff to efficiently implement the work programme and provide the (co-)spokespersons with effective, trustworthy and accountable workflows. The FO enables the 4Memory Coordination Office to set up reliable, responsive financial management. The 4Memory Coordination Office’s professional project and process management eases controlling and enables change management. A cloud-based project information and communication infrastructure meets the needs for digital tools in day-to-day work while enabling a secure data and information flow. 4Memory sets high standards for its own data management and documentation. Centralised monitoring of funding opportunities will initiate collaborations on joint project proposals.

Task Area 5: Data Culture

TA5 aims to encourage reflection upon and the (re)use of research data and corresponding digital methods and to foster a distinct data culture in our community. This requires a critical, shared discourse about the demands, pathways and consequences of digitalisation in our disciplines; incentivising the development of new competencies, methods and approaches; and expanding our instruments of information, opinion-formation and communication. TA5 thus aims to integrate long-established analogue research practices into new, data-driven approaches, anchoring the standards and services developed in the consortium in day-to-day disciplinary practice. Here, “data culture” has two senses. More broadly, it refers to the integration of data and data-driven methods and practices into the professional culture of historical disciplines, which requires increasing the understanding and acceptance of new digital methods and procedures and a wideranging discussion on how they change the production of historical knowledge. More narrowly, the term encompasses the day-to-day practices of dealing with research data and their associated responsibilities, including legal and ethical issues, questions of sustainability and the referencing and recognition of data as scholarly achievements. We require policies and guidelines to encourage a data culture that values research data; facilitates and promotes data sharing and (re)use to preserve, critically evaluate and advance historical knowledge; and ensures data security and reliability. It is based on the FAIR principles and accords with the CARE principles, ensuring that data is used not only academically but also ethically and responsibly.

Would you like to find out more about the 'Task Area Data Culutre'?
The lecture "Von Methoden, Epistemologien und Publikationskulturen im Zeitalter der rapiden Datafizierung der Geschichtswissenschaften" presents Task Area 5 "Data Culture" of the NFDI consortium 4Memory, and also, the epistemological and ontological dimensions of datafication arediscussed and very concrete approaches and measures for the promotion of a living data culture will be presented using the example of a concrete historical research process: https://youtu.be/CUesXneE5ng?feature=shared

Measures

Measure 1: Epistemologies, knowledge orders, disciplinary contextsPlease click button to expand or collapse content

We must encourage reflection on digitalisation’s consequences: changing epistemological foundations, the permeability and potential redrawing of disciplinary boundaries, and the role of citizen sciences in producing historical knowledge. Future research must account for the impact of the digital encoding of knowledge systems, e.g. cultural bias and constraints on long-term disciplinary development. Addressing these questions, M1 provides a basis for other measures. The goals are: bringing together experts and gatekeepers from across the community, but also representatives from neighbouring consortia such as 4Objects, to further a critical discourse on digital epistemologies and disseminate their findings to the wider community; engaging in a vibrant exchange with the community; enabling a mutual integration between our discourse and those of the relevant international communities; and helping early-career researchers join the debate. Specific topics will be determined by the expert group and may include: the datafication of historical sources, digitality and epistemology; AI and historical knowledge production; the impact of knowledge graphs and authority files on the knowledge order; born-digital sources and the loss of authenticity; and new disciplinary boundaries and professional profiles.

Measure 2: Methods Innovation LabPlease click button to expand or collapse content

Researchers will only create and provide research data if they know how to use them and are sure that others will be able to use them effectively. Relevant methods must be made known and accessible and anchored in practice. We first gain an overview of the state of the use of data and methods in historical studies, track community developments and trace 4Memory’s impact and the need for further action. We evaluate established and innovative methods, demonstrate their utility and make them available to researchers with the necessary data skills (TA4), while creating incentives for others to acquire them. By drawing on the consortium's infrastructure and data (with TA3), we create use case demonstrators. By organising hackathons, impulses will be gathered from a broader community, highlighting the public relevance of the critical function of research data and the digital transformation of historical studies. The information and competencies thus gained will be proactively made available to DH centres and coordinators at universities, research and memory institutions and, through them, anchored in practice; in return, their experiences will be reintegrated. Finally, we shift the focus from methods to research questions to incorporate those methods in current historical research, strengthen the link and dialogue between analogue and digital historians and showcase new methods and practices (such as co-authorship) in traditional publications. In doing so, we aim to boost disciplinary innovation processes while ensuring domain-specific accessibility to a variety of algorithms and methods to prevent monopolisation, which is essential for free and open research and use of research data.

Measure 3: Ethical and legal implicationsPlease click button to expand or collapse content

Responsibly using sources from contexts of injustice (such as dictatorships, colonial rule or genocide) has long been central to the historical study of entire eras, regions and themes. But the changes that come with the digital representation of these sources, born-digital sources and new technologies – such as the decontextualisation of data or their easier availability, duplicability and mutability – pose new challenges for our field and for society. We need a comprehensive discourse on the ethical principles and legal challenges arising from Machine Learning, Big Data and the emergence of AI (such as synthetic data, deep fakes or bias); on public responsibility for open access research data and its reuse by third parties with different agendas (such as private companies or government agencies); on data authenticity and falsification; on protecting personal data and privacy; on data security; on copyright issues and the legal framework for preserving and providing digital sources for future research (to avoid the loss of the history of the digital present); on issues of scientific freedom such as user tracking or free access to data and open software as well as the ecological footprints of our research data. Many in our community are unsure about these issues: moving towards a data culture thus requires a better understanding of and competence in addressing them. We will gather a range of expertise and also explore the legal and ethical challenges arising from technological developments to provide guidance to the community and the public and to highlight where steps must be taken. The often abstract and complex nature of such issues has limited the community's interest in addressing them. Thus, they need to be communicated as comprehensibly as possible. Building on this, the community needs basic information. Finally, since historical data and their handling are also societal issues, this discussion must extend beyond narrower academic circles into the public.

Measure 4: Publication and review culture with regard to research data and data-driven researchPlease click button to expand or collapse content

Researchers’ status and visibility depend on peer review, which ultimately defines “good” and “relevant” research. Increasing the acceptance of data-driven research in our field requires incorporating a review system for research data and data-driven research into practices and institutions established over centuries. Digital transformation and datafication are leading to even greater changes in our publication culture, as established genres expand via data publications, data papers or computational publications and digital publications of all kinds. These shifts entail new forms of authorship, publication technologies and citation methods. A new data culture requires a new publication culture and – especially for innovative young researchers – integrating research data into our established reputation system. M4 identifies challenges and introduces solutions, testing and implementing them via our own publications, exemplarily anchoring them in the publication culture. M4 focuses on integrating research data and data-driven research into the reputation system, requiring new assessment criteria that combine established, analogue standards of quality assessment with technical, data-driven methodological competencies. Changing review processes and their technological underpinnings must also be explored. Above all, changes must be visibly implemented in the key review publications, as is done here using the online platform H-Soz-Kult, published by Clio-online e.V., with its over 30,000 subscribers, over one million monthly pageviews by natural persons, and more than 1,000 reviews per year covering all fields of historically oriented humanities.

Measure 5: Creating impact, shaping change: enabling critical discourse in our communityPlease click button to expand or collapse content

What practical means will shift the historically oriented humanities towards a data culture, increase the acceptance of research data and support the next generation of digital researchers in working with research data? Other measures enable reflection, methodological innovation, discussion of ethical and legal issues and increase recognition for data-driven research: M5 aims at implementing these efforts among different groups, especially early-career researchers. It provides an infrastructure for informing, discussing, networking and collaborating, thus anchoring needed changes in our community. It includes creating a communicative hub on data culture; providing space and time for master's students, early career researchers and practitioners from academia, memory institutions and public history and citizen science projects to learn about digital transformation, discuss mutual expectations and needs and reflect upon new job profiles and qualification pathways; networking PhD students who are integrating digital methods and digital transformation in historical scholarship in order to strengthen their position in the field; offering short-term postdoc fellowships to share research data from their PhD or other projects, demonstrating the publication of research data; and communicating the digital transformation in historical scholarship and resulting shifts in our perspectives to the larger public.

Task Area 4: Data Literacy

TA4 develops specialised methodological innovation and activities to enhance data literacy (DL) in historically oriented research and teaching by addressing the challenges and methodological implications of the digital turn for historical scholarship described by our community. Nearly half of the collected problem stories were explicitly or implicitly concerned with issues of DL and sustainable RDM. TA4 will promote data literacy by developing a DL competence profile for historically oriented researchers and a sustainable and certified DL training catalogue with training courses and materials geared towards specific target groups. Following Ridsdale et al. 2015, we understand “data literacy” as “the ability to collect, manage, evaluate, and apply data in a critical manner”. Although DL and, specifically, data criticism are essential skills for nearly all sectors and disciplines of the global knowledge-based economy, DL distinctively affects the historically oriented disciplines’ methodological self-conception. Using the historical method to reconstruct and analyse past events in different world regions at varying geographic scales distinguishes history from fiction. Sound historical scholarship depends on a rigorous, careful application of the “critical historical method” as the basis for historical hermeneutics, historical knowledge production and communicating knowledge about the past.

If you want to reach out to us, please use this mail address: contact@4memory-dataliteracy.de

If you do not want to miss any activities of TA Data Literacy, sign in for our mailing list:
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Measures

Measure 1: Data Literacy Training LabPlease click button to expand or collapse content

Measure Leads: Prof. Dr. Martin Lücke, Dr. Brigitte Grote, Dr. Daniel Brandau, Prof. Dr. Stefan Schmunk, Marina Lemaire
M1 links experts in the field of PhD and doctoral education, historical didactics, teacher-training
institutions and secondary schools who collaborate in promoting curricular innovation and conceptual progress in teaching DL and RDM competencies. By transferring expert knowledge into academic study programs, school curricula and the curricular norms and standards defined by state ministries, M1 promotes a target-group-specific mainstreaming of data literacy education
and training for secondary school teachers, BA and MA students, PhD and postdoc researchers. This includes topics such as data organisation and manipulation, data conversion, metadata, data curation, reuse and preservation (in cooperation with TA1, TA2, TA3) as well as algorithm, interface and infrastructure criticism. M1 develops innovative DL-training modules and material
(T2), proposes a curricular model to mainstream DL-training in BA and MA study programmes (T3), and establishes a DL-accreditation service (T4) for quality management and advocacy with responsible political actors and agencies.

Measure 2: Historical Data Criticism and RDMPlease click button to expand or collapse content

Measure Leads: Prof. Dr. Ursula Lehmkuhl, Daniel Burckhardt, Katharina Hering, Simone Lässig
M2 establishes an expert group for historical data criticism and research data management, focusing on time and area-specific research-based data collection, production, management and
evaluation as well as the analysis and interpretation of historical data. M2 identifies challenges in using, presenting, managing and storing historical data on specific historical periods, from
antiquity to contemporary history, and on different cultural and knowledge contexts, developing guidelines for critically handling and (re)using data-driven research results. To reach out to our
community of interest and to secure the participation of national and international experts, M2 establishes a blog (via hypotheses.org) on “The historical method under digital conditions”. It
creates time- and area-specific syllabi as best practices demonstrating solutions to the challenges of teaching historical data criticism and sustainable RDM. It develops guidelines for the historical contextualisation of unstructured and/or heterogeneous historical data and innovative training material for using digitised sources in the history classroom. These deliverables feed into the publication of a textbook on historical data criticism and RDM covering a broad spectrum of historical topics and addressing politically relevant actors such as accreditation agencies, state
ministries, and educational boards.

Measure 3: Digital area historiesPlease click button to expand or collapse content

Measure Leads: Prof. Dr. Matthias Middell, Dr. Kathleen Schlütter, Eva Ommert
M3 addresses technical, legal and ethical dimensions of historical data criticism and RDM in area histories (African, Asian, European, East-European, North American, Latin American and Russian
History). Digital area histories face distinct challenges because creating and using research data must be coordinated with the national and regional institutional, technical and legal contexts in
which these data were primarily created, collected, made accessible and managed. Therefore, RDM in area histories need to consider issues of technical fit between different traditions of data
collection and storage as well as indexing through metadata standards. It must also account for divergent legal regimes of data access, research data use and good scholarly practice. Finally,
yet importantly, Europe’s colonial history and treatment of its colonial historical heritage as well as persistent global asymmetries of academic research opportunities, pose specific issues of
research ethics in area histories. M3’s central objective is twofold: (1) to map and monitor such differences and make available reliable and relevant information on the international development
of research data management for the German research and education landscape; (2) to initiate and document the negotiation of cooperative procedures for using data from other national and
regional contexts and to thus make this data accessible for German scholars in a “fair” and “careful” manner

Further Informations: https://recentglobe.uni-leipzig.de/forschung/forschungsprojekte/nfdi4memory

Measure 4: Digital research counselling service Please click button to expand or collapse content

Measure Leads: Prof. Dr. Ursula Lehmkuhl, Prof. Dr. Stefan Schmunk, Marina Lemaire
To empower historical researchers, instructors and trainees to conduct digitally driven historical research, M4 will establish a counselling service for digital methods, tools, instruments, research techniques and forms of collaborative research data management. The counselling service will connect researchers (a) with experts working on and with digital tools and (b) with professionals at libraries, archives and infrastructure partners. Based on a needs and qualification assessment, M4 will develop virtual training materials (webinars) and pilot regional training centres and contact offices to serve as the basis for establishing a virtual DL counselling service and helpdesk. Webinars and onsite peer-to-peer consultation support the training of future trainers and users. The training courses and training materials as well as information about digital tools, instruments, research techniques, local and regional contact offices and the helpdesk will be published on the 4Memory portal (DL virtual hub). The counselling service will become part of 4Memory’s service portfolio.

Task Area 3: Data Services

TA3 aims to provide a set of robust and reliable key services and link them with further data collections and tools in an interoperable “4Memory Data Space” via a knowledge graph and standardised technical interfaces. It will offer a technical counselling service to empower the 4Memory community to expand the Data Space with their own services. TA3 pursues five goals: (1) operating a set of key services in a reliable and sustainable manner, including those for storage and long-term archiving of research data for the historically oriented disciplines; (2) implementing a knowledge graph based on existing metadata as a representation of the new digital knowledge order; (3) making the existing repositories, DH tools and data offerings of memory organisations relevant to historically oriented disciplines interoperable and searchable by defining and setting-up a 4Memory Data Space; (4) combining data spheres of memory institutions and research projects in a virtual indexing layer with cooperative data enrichment; (5) ensuring the FAIRness of research data in 4memory.

If you do not want to miss any activities of TA Data Connectivity, sign in for our mailing list: https://lists.nfdi.de/postorius/lists/nfdi4memory-ta3-community.lists.nfdi.de/

Measures

Measure 1: Standardised and interoperable APIsPlease click button to expand or collapse content

Historians use heterogeneous data sources. The compilation of project-specific resource collections spanning multiple data sources is therefore a laborious intellectual process. Unified programming interfaces allow standardised, interoperable access to these heterogeneous data. M1 will therefore define such an interoperable API together with the providers of key data sources and foster their implementation. This is important preparatory work for the Data Space planned in M3. We will closely monitor international developments such as IIIF, SPARQL or GraphQL and coordinate with the other consortia of the MoU group as well as the “Common Infrastructure” section of the NFDI.

Measure 2: Technical implementation of a 4Memory Knowledge Graph Please click button to expand or collapse content

M2 builds the KG’s technical foundation alongside a distributed editorial system enabling federated maintenance: various projects will be able to participate and contribute. Among other benefits, distributed editing gives non-European communities decisive influence over the representation of their cultures in knowledge systems, supporting the observance of CARE principles. The federated approach will raise complex questions of data quality that are addressed in TA1. The KG’s core component is an ontology conceptualised in TA2 in line with international standards. It will cover specific concepts and knowledge domains relevant to historians, such as temporal fuzziness, the changing semantics of terms over time, hypotheses, and temporal and spatial networks. The KG enables the semantic linking of the heterogeneous data sources of the Data Space (M3), allowing for interlinking with external resources, e.g. from other NFDI consortia or internationally. All the KG’s data will be queryable via a SPARQL endpoint.

Measure 3: Creation of a 4Memory Data Space Please click button to expand or collapse content

M3 designs and implements the 4Memory Data Space, a federation of heterogeneous data sources from research and memory organisations, which we connect via common interfaces (M1) and a Knowledge Graph (M2). This allows researchers to perform cross-searches and compile corpora from different sources.

Measure 4: Technical counselling service on interoperability and reusability of dataPlease click button to expand or collapse content

To continuously expand 4Memory’s emerging ecosystem of services while increasing the interoperability of services and reusability of data, TA3, with the other TAs, establishes a counselling service to support researchers and project leaders in applying for and carrying out projects and to advise them on selecting suitable services and data for their research questions. The service will consider standards identified or developed in TA2 and become a part of the 4Memory helpdesk organised by TA6-M4.

Measure 5: 4Memory Data LabPlease click button to expand or collapse content

The 4Memory Data Lab provides a technical infrastructure for the evaluation and deployment of innovative technologies and applications for annotation, feature detection and data analysis as well as text recognition. It will combine seamless access to relevant data sets and corpora via the Data Space with access to a high-performance hardware cluster. Suitable applications and technologies based on machine learning algorithms are developed or defined in the Method Lab (TA5-M2), including support for researchers on how to employ these emerging methodologies. The resulting annotations and deep indexing will flow back to the data-providing institutions via the virtual indexing layer and be available to third parties. In the first funding phase, the technical design is defined and implemented along practical requirements. Subsequently, the Data Lab will be put into operation. In future, a distributed approach with further clusters and corresponding operators and institutions will be pursued.

Measure 6: Design and development of a virtual indexing layerPlease click button to expand or collapse content

TA3 works with research projects, memory organisations and citizen scientists to design and implement a virtual indexing layer, combining memory institutions’ indexing information (source descriptions) and deep indexing from projects (content annotation). The virtual layer enables the combined data’s comprehensive search and retrieval, fostering reuse of valuable indexing information from projects. It will enable cooperative indexing or enrichment of existing indexing data that can be reintegrated into the original systems. We aim for easy transfer of annotations and metadata generated within the 4Memory Data Lab (M5). In the first funding period, TA3 develops and coordinates a concept with the community: implementation occurs in the second funding phase.

Task Area 2: Data Connectivity

TA2 advances the linking and contextualising of research data in the historical domain to enable a coherent historical record in the digital age and provide the foundation for the planned 4Memory Data Space. It lays the ground for historically sensitive, unambiguous data descriptions; the integration of research-led historical categorisation and cataloguing by archives, libraries and museums; and contextualisation within larger data structures. Data connectivity is vital to achieving the LINKAGE objectives. Developing and improving authority files, taxonomies, metadata, ontologies and APIs will increase the linking of research, memory institutions and infrastructures via a knowledge graph, also adding to the representation of a knowledge order for the digital future of the past. Connecting sources, publication and research data advances the analogue / digital interface of historical source material and data, as both types of material will be accessible in one data space. Open, jointly developed authority files generate standards for historical research data and sustainability.

If you want to reach out to us, please use this mail address: ta2-nfdi4memory@geschichte.uni-halle.de

To learn more about the work of and on Data Connectivity, visit the blog: https://blogs.urz.uni-halle.de/nfdi4memory/

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Services

R:hovono - register for historical and object-related vocabularies and standard dataPlease click button to expand or collapse content

What is "R:hovono"?

The acronym R:hovono stands for the “Register for historical and object-related vocabularies and standard data”, a service that is intended to provide an overview of existing vocabularies, standard data, classifications, thesauri and ontologies to all the disciplines in the humanities and history. The register will comprehensively expand existing structures of information and thus facilitate the findability and categorisation of across-institutions. We are focusing on specific specialised information ranging from metadata, usage scenarios, access options, cross-concordances to technical details. Registering vocabularies is possible either via LimeSurvey or directly in the FactGrid database. The service aims to facilitate the use uses of vocabularies in the wide range of applications from linking and indexing data to data curation, information enrichment, information extraction and data analysis. Contributions are welcome from the entire field of historical disciplines. The editorial team of TA “Data Connectivity” will accompany the process with advice and with the curation of incoming entries.

The documentation on the register of historical and object-orientated vocabularies and authority data and vocabularies (R:hovono) is published on Zenodo.
The presentation of R:hovono is published on Zenodo.

Measures

Measure 1: Development framework for historical authority data Please click button to expand or collapse content

M1 analyses existing authority data approaches for the long-term development of graduated concepts for search strategies, data curation, data exploitation and quality requirements. This applies both to the subject-specific context of 4Memory and to interdisciplinary and international initiatives. In doing so, 4Memory takes into account authority data that contextualise historical entities in a long-term perspective. TA2 begins by working with four use cases (UC) selected through community meetings and problem stories, bringing together diverse actors from 4Memory’s international network. They cover concepts in different historical periods (UC1 Premodern, UC2 Modern/Contemporary History), historical geographical categories (UC3 Space) and personal relationships/activities and occupations (UC4 Activities). To this end, existing approaches are gathered and screened, analysed and modelled, as well as represented and described in a directory of subject-specific knowledge organisation systems, such as authority data and controlled vocabularies. At the same time, existing activities are networked more closely in order to develop common goals and guidelines in the field of authority data production, this applies to specific concepts (UC 4), especially for a more in-depth cooperation with NFDI4Objects and Berd@NFDI.

Measure 2: Working Group on integrating historical data into authority data Please click button to expand or collapse content

The international working group aims to integrate and link authority data to the GND and other standards. The concepts derive from selected subject-specific authority data provided as use cases from the participants, in order to meet the needs of both compact, largely standardised keywording and diversified processes of scholarly analysis with basically the same networked approaches and concepts. The aim is establishing a two-tiered, linked network of persistently addressable authority data (GND, metadata, search) in relation to dynamically changing data (taxonomies, ontologies, context data) geared towards scholarly analysis. This approach addresses an essential goal for producing FAIR data by maintaining data transparency, traceability and contextualisation from the (analogue or digital) source to the national/international authority data set: it is essential to integrating historical source criticism (contextualisation), developing standards and networking different process levels of RDM (i.e. data production, analysis, provision and reuse).

Measure 3: Data curation and quality measurement interfaces and tools Please click button to expand or collapse content

M3 addresses the conception, planning and preparation/development of tools for data curation, content quality measurement and the automated indexing or enrichment of research data (e.g. the definition of knowledge graphs, preparation for the Semantic Web or contextualisation of information) and providing the groundwork for their realisation in accompanying projects and/or the second NFDI funding phase. Together with TA1, criteria of a graded quality model for the authority data concepts of specific historical sub-areas are developed. Basic concepts for the normalisation and contextualisation of data as well as for its taxonomic preparation are subjected to quality control and prepared for transfer into tools and interfaces (TA1, TA3, TA4, TA5).

Measure 4: Metadata cataloguing and documentation of research data Please click button to expand or collapse content

After evaluating the status quo and its desiderata, expert groups develop common standards and rules to describe and document research data, easing data linkage and aggregation in the 4Memory community and its Data Space, as well as with international services. Scholars will have a shared set of descriptors, authority data and taxonomies to search for sources and research data. Service providers such as archives, libraries and museums will be given clear guidelines on cataloguing and documenting research data based on common standards and rules adapted to them. The inclusion of a defined set of research data – related to publications and as publications in their own right – into the German Historical Bibliography (GHB) and possibly other bibliographies provides a proof of concept for recommended formal descriptors, authority data and taxonomies. As described in the RDM-Strategy, 4Memory’s research community will benefit from the extended GHB by being able to use this new resource in an established environment with direct-access options.

Measure 5: Interoperable data linkage for the Data Space Please click button to expand or collapse content

For the exchange, integration, delivery and reuse of research data, a 4Memory ontology and a metadata schema must be developed by a metadata competence unit. This will allow linking the research data of the 4Memory community within our Data Space and connecting it to other aggregation services such as BASE and Google Dataset Search or future services in NFDI and EOSC. The developed data model must also integrate non-German languages as well as knowledge and organisation systems in order to represent research data from fields such as area studies, to introduce postcolonial perspectives and to secure the international connectivity and visibility of data curated and listed by 4Memory, thus enabling a truly transnational historiography. Additionally, persistent identifiers will allow addressing granular data, improving the citation and reuse of research data through, e.g. linking to micro-publications such as annotations or referencing concepts within taxonomies.

Task Area 1: Data Quality

TA1 assesses and advances the definition, measurement and implementation of research data quality in the historically oriented humanities, enabling further academic study and computational research through FAIR data. It addresses several LINKAGE objectives. Working groups and events Link research, memory institutions and infrastructures. Elaborating methods for data quality Integrates historical source criticism into data services. Assessing and implementing data quality practices strengthens the Network of historically oriented research communities. Quality assured data shape the Knowledge order for the digital future of the past, and quality criteria (e.g. provenance and processing information) Advance the analogue / digital interface of historical source material and data. Data-quality guidance Generates standards for historical research data and advancing sustainability. TA1 deals with content-related, administrative (e.g. legal documentation) and technical aspects of data quality: standards for research data in the historically oriented humanities are (further) developed and implemented jointly with other TAs and the participating research and infrastructure communities. TA1 relates data quality to the definition of research data in the 4Memory RDM Strategy and to both metadata and data: the line between them is not drawn firmly but rather according to need, depending on disciplinary practices and data types.

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Measures

Measure 1: Assessment of data-quality practicesPlease click button to expand or collapse content

The first step towards ensuring research data quality in historical research is assessing the status quo, allowing general guidelines and procedures for improving sustainable quality management to be developed. TA1 leads a working group to survey the needs and practices of – and available services for – data quality in our community. This effort includes different data types and all phases of the research data life cycle, considering existing guidelines and data-collection principles in RDM plans, methods, software, plausibility checks and data curation, provenance and ethical questions and data protection needs, data documentation, indexing with metadata, principles of standardised documentation, the use of metadata standards and normative vocabularies, the licensing and citation of data, and standards of scholarly practice. Together with TA4-M2, M1 also addresses the challenge of incorporating historical source criticism including hermeneutical and epistemological dimensions into data-quality measures. Apart from best-practices, obstacles to compliance with quality measures must be understood. Based on continuously collected problem stories, a systematisation of the surveyed requirements will further our understanding of what improvements are needed in data-quality practices. A tangible outcome of M1 will be a directory of existing technical recommendations, guidelines and standards of data processing for historical research.

Measure 2: Analysis of data-quality requirementsPlease click button to expand or collapse content

In M2, the data-quality status quo revealed in M1 enables analysing data requirements and evaluating actions for improving and ensuring their quality. Within this context, M2 also aims to make ethical issues an integral part of data-quality analysis via the FAIR and CARE-principles. This aspect will be complemented by NFDI4Objects in their Task Area 5 (Storage, Access and Dissemination) which will allow us to take into account both historical and archaeological research perspectives. A planned working group includes co-applicants, participants and other national and international partners (e.g. ICOM CIDOC) as intermediaries and experts for specific data-quality challenges. Data-quality requirements are analysed across the broad range of data types used in our community, based on problems named by our community and solutions developed in recent and ongoing projects within it. Also, data use and quality management issues in various contexts (editions, archives, collections, libraries, museums, memorial sites, etc.) are addressed. A catalogue of FAIR-compliant practices is systematically developed, and recommendations regarding the optimisation of workflows throughout the data life cycles are made. Particular attention is given to data transparency and traceability (e.g. data citation, licensing and persistence).

Measure 3: Development of a data-quality measurement systemPlease click button to expand or collapse content

M3 develops a measurement system for historical research data quality. An expert group representing various subcommunities assesses existing generic data-quality certification systems (whether project-based or institutionalised). An overview of existing quality schemes is systematised and synchronised through dialogue with the community. On this basis, a measurement system distinguishing various quality levels is developed, summarising the data-quality evaluation criteria and facilitating their application. The quality levels will enable the self-assessment of research data and facilitate a dialogue among data providers, data collecting institutions and data reusers. Existing peer-review standards and data-quality processes are evaluated in cooperation with TA5. Improving quality levels will enable interoperability (TA2 and TA3) and may qualify data for peer-review and scholarly publication (see TA5-M4).

Measure 4: Development of recommendations and guidelines Please click button to expand or collapse content

In close collaboration with international boards and committees, an expert group translates the findings of M1-M3 into recommendations, catalogues and guidelines for the creation, assessment and future certification of research data. The aim is to establish a permanent collaboration with existing international quality-assurance initiatives (e.g. GoFair, RDA). To that end, the expert group establishes a board for developing recommendations and identifies exemplary projects and work routines in which data quality is crucial for research and steps for developing suitable infrastructures for historical research. With regard to systematically implementing new project-management practices to improve workflows, this measure may serve as orientation for the DFG Review Boards and provide subject-specific information for the implementation of the DFG “Code” (Deutsche Forschungsgemeinschaft 2019), other third-party funding agencies and international (European) initiatives, committees and boards like CERI and RDA. Furthermore, the data-quality requirements for the 4Memory Data Space are defined. Tasks in this measure are carried out in collaboration with TA2, TA3, TA4 and TA5.

Measure 5: Development of editorial frameworks for data creationPlease click button to expand or collapse content

M5 aims to significantly lower the barrier of time-consuming data preparation through efficient workflows and a concept for dividing labour through editorial processes within an (organisational) framework applicable to existing institutional settings. Institutions in the consortium that engage in both RDM and traditional publication processes will jointly develop, implement and iterate editorial workflows for publishing research data. These data-editing processes – e.g. formal quality checks, format transformations, explicit documentation or metadata enrichment prior to publication – are developed with relevant actors in the key fields of research support, data management and publishing. Targeting formal data quality at an early production stage, the editorial procedures prime the peer-review process, focused on methodology and content, to be established by TA5. A curated network brings together actors from RDM and publishing departments. Sharing existing workflows and jointly identifying needs create prerequisites for a data editorial framework designed in a working group. The exploratory and preparatory work results in a service concept for data-editorial processes at the contributing institutions. M5 will allow the outcomes and concepts of M1-M4 to be reviewed in practice and, if necessary, adapted or extended. The collaboration of established editorial offices and newly installed RDM positions initiates institutional change.

Measure 6: Competence network: implementation of data qualityPlease click button to expand or collapse content

Crucial to all TA1’s activities is a constant dialogue with the community and all other TAs for the implementation and dissemination of data-quality measures. The guidelines will therefore be designed to meet different target groups. Particular attention will be given to smaller institutions, academic committees and learned societies as actors that structurally lack the capacities and infrastructure needed to develop and implement digital data-quality standards and practices at their institutions. Therefore, this measure provides RDM guidance and activities for data-quality training in coordination with TA4 (“train the trainer”).

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