Ever since I have known and been active in the field of Digital Humanities, the scholarly conversation has kept revolving around the gap between technical needs and research questions. How do I, as a researcher, make myself understood by the people who will equip me with digital competence?For almost fifteen years now, it seems that we – “we” being defined here as a growing community of research – have not been able to convey that this problem, which might seem like a time- and energy-consuming issue at first, is in fact an epistemological blessing, something that makes Digital Humanities more than the sum of digital methods in Humanities. And yet: “How do I get the IT guy to do what I want?” is the main mantra.

In 2025, it is somehow still complicated to get Humanities researchers understand that this is not the question they should be asking when envisioning their work with digital methods.

But then: which questions should they be asking? And how can they be guided to these questions? There is no simple, institutionalized way to get there, more so a yet poorly addressed need for a reflective approach in the use of digital methods. This is what motivated us to offer consulting and training services tailored for digital-based Humanities and Heritage research. In the following, I present the analysis we conducted and the approach we have adopted in the context of Stripes and Strings.

Humanities researchers still often expect a machine to compute in the traditional sense, e.g., to perform calculus-based operations. There is little to no awareness of data being structured, and of the methods to process them being structured as well. The traditional vision of applying a defined method to a defined corpus, resulting in the production of conference papers, research articles or books, is still dominant in Humanities research. An actionable understanding of digital literacy would rather require to define a corpus as structured data and a method as a manifold process. One key notion in this process is that of modelling.

Modelling concerns both data and processing. The term is used in either setting with a slightly different meaning. Overall, it is a central element in the digital research process, its conceptualization and implementation takes time and energy while being poorly valued by Academic reputation mechanisms. There is to this day no equivalent to the “big monograph” encompassing the “big modelling achievements” in Humanities careers. This lack of recognition is one of the mechanisms that lead Humanities researchers to outsource modelling work to technical staff. There being no training that would put them in a position to master its complexities is another of these mechanisms.

How do we escape a vicious circle where researchers feel all the less concerned with key elements in the digital research process as it is not valued in their career as researchers, while the quality of their research actually depends on it?

The leverages are threefold: one concerns the reputation mechanisms themselves, the second the research standards recognized by the community of research, and finally the training opportunities that address the needs of the community at large.

Regarding reputation mechanisms, funders and institutions have a key role to play, but one that can be informed by representatives of the communities of research such as learned societies or scholarly associations. In that sense, the input provided by the communities of research themselves is essential, for instance in the form of white papers conceived in the context of working groups. The German DHd association

https://dig-hum.de/

has provided a platform for discussing such structuring papers, making it possible for them to establish standards in terms of publication practices or tooling for instance. This community-based validation process with its leveraging potential is also at the core of the Working Group Greening DH which promotes mindfulness of resources in the context of Digital Humanities research. It integrates epistemological and material challenges in an attempt to reflect and impact transformative practices, and strives to redefine sustainability in relation to the reality of planetary resources.

Humanities researchers find themselves in a situation where digital devices have become a part of their research process without being able to integrate a reflection on the transformative effects of this form of materialization/virtualization in the research process on the one hand, and on the other hand they are deliberately kept unaware of the significance of this (not yet formalized) transformation in their relationship to their overall environment, with significant actors denying systemic approaches to the ongoing polycrisis. The environmental impact of the production, use and end of life of the devices, algorithms and communication channels that underly research processes is never front and center. It is true that the extent of the crisis we find ourselves in is so scary that it questions research processes together with the whole North-Western civilization at large. Confronting this reality is, in many ways, uncomfortable, and looking away has been the main strategy developed to cope with it. But this strategy has amplified the digital divide, increased climate injustice, and favored, with the innovative mantra, the development of resource-intensive solutions.

This is the context in which we have conceived our consulting and training offer. The goal of Stripes and Strings

https://stripesandstrings.org/

is to empower Humanities and Heritage researchers in their use of digital methods. We want to make the dialogue between Humanities researchers and developers smoother. We focus on training Humanities researchers in data structuring and modelling. We provide them with access to reusable methods in order for project teams to tailor them to their needs and make their output, in turn, reusable. We provide guidance in assessing the usefulness of research protocols in relation to the expected research output and to the environmental impact they are likely to have. We foster Open Science good practices for research settings that will deliver relevant results today and remain viable and meaningful in the years ahead.