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Digital Humanities, Open Science, and Research Data Management: Exploring their intersections in Computational Literary Studies in Germany

Note that this post is the original English version of an editorial first published as《巻頭言》「デジタル人文学、オープンサイエンス、研究データ管理:ドイツの計算文学研究における交点を探る」in: Digital Humanities Monthly 167-1, June 2025 (International Institute for Digital Humanities, Tokyo, Japan). URL: https://w.bme.jp/bm/p/bn/htmlpreview.php?i=dhm&no=all&m=124.)

Digital Humanities, Open Science, and Research Data Management – three terms that have become something of a trinity in the contemporary academic landscape. They are all vibrants fields in their own right, promising new forms of knowledge production, collaboration, and dissemination. And they are closely aligned with each other and richly interconnected. Nonetheless, the ways in which their relations play out in practice may appear somewhat opaque. To develop an understanding of how these areas intersect in practice, it might be instructive to examine a concrete case: the current state of Computational Literary Studies (CLS) in Germany. Before we can do this, however, it is probably useful to provide some context on where Digital Humanities, Open Science and Research Data Management stand today, in Germany.

Digital Humanities (DH) has developed dynamically in many places, in the last decade or two, but has absolutely boomed in Germany. This rapid growth has become especially visible through a combination of sustained funding initiatives, expanding academic institutionalization, and the emergence of a strong scholarly community. One of the clearest signs is the proliferation of new professorships dedicated to DH across the country: It depends a bit on how you count, but there are now at least 40 such permanent positions, with 20 added in the last 5 years alone and 10 more currently in the process of being filled, reflecting a structural commitment to integrating digital methods into traditional humanities disciplines.1 This institutional growth has gone hand in hand with the creation of dedicated degree programs – particularly at the master’s level – at a wide range of universities from Bamberg and Cologne to Trier and Würzburg, many of which also offer cross-disciplinary modules to students from other fields. Funding bodies such as the German Research Foundation (DFG) and the Federal Ministry of Education and Research (BMBF) have played a critical role in this development, not only by supporting individual projects but also by launching targeted programs aimed at building infrastructure and capacity. Equally significant is the emergence of the DHd association (Digital Humanities im deutschsprachigen Raum), which since its founding in 2012 has become the central hub for the German-speaking DH community.2 The association’s annual DHd conference has grown into a major academic event, regularly attracting well over 500 participants mostly from Germany, Austria, and Switzerland. Together, these developments reflect the extent to which Digital Humanities in Germany has evolved from a peripheral innovation into an institutionally embedded, vibrant and diverse field.

Even more so than the Digital Humanities, Open Science is of course a global and cross-domain megatrend. It argues for and supports transparency, reproducibility and sustainability of academic research as well as public access to scholarly outputs. This concerns openly available datasets, open source software, open access publications, open educational resources and, more generally-speaking, openness as a deeply-ingrained fundamental value in research. In Germany, Open Science principles are being pursued in most if not all academic fields, albeit at different speeds and with differing intensity. Open Science has become a central priority in German research policy, supported by major funders, institutions, and national initiatives. The German Research Foundation (DFG), for example, now routinely expects open access publishing, data sharing, and transparent research workflows in its funding guidelines. Open Science is no longer an optional add-on but a widely supported and increasingly standard aspect of publicly funded research across disciplines.

The third piece in the puzzle is research data management (RDM). Effective RDM ensures that data is not only stored securely and kept accessible over the long term, but also curated in standardized, well-understood data formats and with sufficient metadata to enable and foster future reuse. In this area, funders’ mandates have played an important role in fostering more professional RDM: any research proposal submitted to the DFG, for example, now needs to include a RDM plan and projects need to provide very good reasons if they plan on not making certain project outputs openly available. Research infrastructures have been immensely important players in supporting best practices in research RDM: In the Digital Humanities, specifically, TextGrid was an early RI initiative, funded 2006–2015.3 TextGrid paved the way for DARIAH-DE which, together with CLARIN-D (both funded ca. 2011–2019), facilitated the inclusion of consortia such as Text+ and 4Culture into the German government’s long-term initiative for RDM, the National Research Data Infrastructure (NFDI).4 Today, no less than four among the 30 NFDI consortia are catering to a wide range of disciplines in the humanities.

At an abstract level, these three areas and their recent development closely align: Digital Humanities is very much supportive of Open Science principles and openness in a broad sense is certainly a core part of its identity. Similarly, research data infrastructures such as Text+ and 4Culture have been important in both setting standards and enabling best practices with regard to Open Science, specifically through their strong support for the FAIR principles. And important players in Digital Humanities research, such as Fotis Jannidis (in literary studies), Elke Teich (in linguistics) or Manfred Thaller (in history) – have also been and are still playing important roles in designing and steering today’s research data infrastructures for the (Digital) Humanities. Last but not least, researchers in DH of course use the existing infrastructures, such as TextGrid, DARIAH-DE (and then DARIAH-EU) and the NFDI consortia. But how does this alignment and this collaboration play out in practice? Let’s have a closer look at Computational Literary Studies to gain some insight into this question.

Computational Literary Studies (CLS) can be understood as a subfield of the Digital Humanities that essentially employs computational methods to analyze literary texts at scale. Methods such as stylometric analysis, topic modeling, network analysis and machine learning are used to explore questions of authorship, genre, narrative structure and content, or literary history. Albeit under a range of different designations, CLS is among the most long-standing strands within the Digital Humanities. In Germany, based on this long history and strong integration into DH, CLS has been able to grow and institutionalize significantly, over the last few years. To mention just three relevant developments: the funding of more than 20 research projects within the framework of the German Research Foundation’s Priority Programme on Computational Literary Studies (2018–2026);5 the launch of the Journal for Computational Literary Studies (JCLS) in 2022;6 and the availability of multilingual corpora such as ELTeC (providing collections of novels), DraCor (for drama) and PoeTree (for, obviously, poetry).7

In CLS, the alignment between research, Open Science, and research infrastructures is not just theoretical – it is embedded in the everyday practices of scholars. The infrastructures support projects directly: the “Digitale Bibliothek”, for example, was acquired and enhanced early on by the TextGrid initiative and has been transformative in making very large amounts of literary texts accessible for analysis. But RIs offer not just data, but also frameworks for using it: metadata standards, APIs, documentation, and persistent identifiers.

In addition, through their emphasis on FAIR principles and open access, these infrastructures have helped normalize Open Science practices in CLS, making transparency and reusability not just ideals but established best practices. For example, the Journal for Computational Literary Studies firmly expects that data and code used in any article published in the journal be made openly available to others for inspection and/or reuse, unless very good reasons make this unfeasible. And authors follow suit, in the process making their research much more reproducible and sustainable as it would otherwise be.

More generally, one may in fact argue that CLS as a field has, in Germany emerged in close symbiosis with infrastructure initiatives such as TextGrid and DARIAH-DE, which provided early platforms for text annotation, collaborative editing, and corpus building. Many of the tools and services now seen as basic infrastructure actually grew out of research needs and were developed by DH scholars themselves – TextGrid being a prominent example, originally built to support digital editions following the guidelines of the Text Encoding Initiative and now a vital platform for long-term preservation and access of materials in XML-TEI. Today, Text+, as part of the National Research Data Infrastructure (NFDI), continues this trajectory by supporting CLS projects through curated corpora, documentation standards, and data hosting, with direct input from active researchers in the field. Beyond Germany alone, initiatives like the COST Action Distant Reading for European Literary History have led to EU-level projects such as Computational Literary Studies Infrastructure, further demontrating close links between the community of researchers and infrastructure development.

What is often overlooked is the role research infrastructures (RI) play in shaping academic careers. For many early-career scholars, involvement in RIs – especially as staff working on field surveys, providing training to others, studying researchers’ requirements, or performing dissemination activities – provides essential exposure, networking, and skill-building opportunities. These roles are not only formative but often become springboards to permanent academic positions or grant success. One could say that the turnover of talent within infrastructures has infrastructural consequences itself: platforms must adapt to the loss of expertise as researchers move on, yet this very movement is a measure of the RI’s success in training and professionalizing the DH community.

Research infrastructures also significantly lower the entry barrier for DH projects by offering training, services, and access to high-quality data. Workshops and other training opportunities are often organized by DARIAH-DE or Text+ as well as by affiliated initiatives such as the FIDs (‘Fachinformationsdienste’, i.e. services offered by research libraries that include, but go far beyond, media acquisition ) and the recently-launched Datenkompetenzzentren (among them HERMES or SoDA).8 They help researchers – from MA students to established scholars – acquire the digital competencies needed for CLS work, using large text collections and suitable computational methods. A major lesson from these activities is that communities and competencies are just as essential to research infrastructure as are hardware, services and datasets.

In fact, a great deal of research is also conducted within these infrastructures themselves – concerning things like web-based services, data models, corpus-building standards, or evaluation frameworks for NLP pipelines. My own current responsibility in the Text+ consortium concerns developing and evaluating the fitness for purpose of solutions that allow the open sharing of in-copyright textual resources, an area of work where scholarly requirements, the legal context, Open Science principles, and infrastructural concerns all interact in complex ways. What becomes very tangible here, but pervades research infrastructure development, is the paradox, and complexity, of research-oriented infrastructure development: it needs to be stable, reliable and invisible as infrastructure, yet at the same time it also needs to be highly flexible and constantly realigned with the latest developments in research as well.

 

1For details, see Patrick Sahle’s regularly-updated list on the DHd-blog: https://dhd-blog.org/?p=11018, and the overview by the portal “Kleine Fächer” (i.e., small disciplines): https://www.kleinefaecher.de/kartierung/kleine-faecher-von-a-z/digital-humanities.
2The DHd association’s website can be found here: https://digitalhumanities.de/.
8See https://hermes-hub.de/ and https://sammlungen.io/ for more information.

OpenEdition suggests that you cite this post as follows:
Christof Schöch (July 10, 2025). Digital Humanities, Open Science, and Research Data Management: Exploring their intersections in Computational Literary Studies in Germany. The Dragonfly's Gaze. Retrieved March 16, 2026 from https://doi.org/10.58079/14b76


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