Abstract

• A (Feminist) Toolkit for Data Ethics
• 1. Digital Hermeneutics and Source Criticism for Data Work in (Digital) Humanities
• 2. Digital Humanities Work is More Political Than it May Seem at First Glance
• 3. Data Feminism for the Digital Humanities
  • Principle 1: Examine Power
  • Principle 2: Challenge Power
  • Principle 3: Elevate Emotion and Embodiment
  • Principle 4: Rethink Binaries and Hierarchies
  • Principle 5: Embrace Pluralism
  • Principle 6: Consider Context
  • Principle 7: Make Labor Visible
• 4. A Data Feminist Approach to Historical Letter Network Data
• 5. Conclusion: Making Data Feminism Work in Practice
• Bibliography

A (Feminist) Toolkit for Data Ethics

[1]With the growing accessibility of machine learning tools, applications and approaches, the Digital and Computational Humanities face an urgent need to focus on ethical frameworks, research integrity and quality assurance in research outcomes.‍[1] While traditional source criticism is well established in the historical disciplines, the nature of digital projects necessitates a digital source criticism that evaluates not only historical sources but also the processes involved in creating and curating data. Data Feminism offers a compelling framework for addressing ethical challenges, but it remains underutilized within Digital Humanities (DH).‍[2] This raises the questions: Why is there a hesitation to adopt a Data Feminist approach in the Digital Humanities, and how can the principles of Data Feminism be applied within a Digital Humanities framework?

[2]We argue that the limited adoption of Data Feminism in Digital Humanities can be attributed to a combination of misconceptions and practical challenges. One possible cause may be the assumption that Data Feminism principles only concern issues about gender or women in particular.‍[3] This, however, is not the case: Data Feminism, as articulated by Catherine D’Ignazio and Lauren F. Klein, focuses on power structures and how marginalization shapes data. In providing guidelines which operationalize feminist thought as actionable principles, the authors of Data Feminism make clear that a project can be Data Feminist in content, form, or process.‍[4] Yet misconceptions about what Data Feminism means and who it is for may offer an explanation why Digital Humanities practitioners only sparingly engage with its principles, likely underestimating their universal applicability. Data Feminism applies to all data-related projects because every project is inherently political, whether its agenda is explicit or implicit. The Data Feminism manifesto provides tools for addressing power imbalances in data work by drawing on feminist theory’s longstanding critique of such structures. The principles build on established strategies that can be applied to (any) data work. As such, Data Feminism is a toolkit for dealing with ethical issues in data projects and thus, highly relevant for all of Digital Humanities. But despite its potential, Data Feminism has yet to achieve widespread recognition as a standard ethical framework for Digital Humanities. This likely limits its broader adoption, creating a barrier to entry that hinders the significant policy changes that could arise from widespread acceptance of Data Feminist principles.

[3]And, while the principles in Data Feminism are clearly articulated and memorable, they are broad guidelines that require adaptation to specific contexts and the operationalization of Data Feminism for a practical (Digital Humanities-)use.‍[5] Many practitioners lack the time or resources to engage deeply with feminist theory, creating a barrier to its adoption. Even those familiar with feminist principles may need to read and re-read the book to fully engage with its content, a time commitment that many Digital Humanities professionals cannot afford within their projects.‍[6]

[4]In this article, we aim to ›translate Data Feminism‹ for more traditionally minded (Digital) Humanists. We provide both theoretical context and concrete implementation examples to ease the burden on Digital Humanities professionals who might otherwise need to navigate this material independently. Thereby, we hope to encourage more practitioners to explore and apply these principles in their daily work, potentially motivating those who might otherwise be hesitant to engage with feminist methodologies. Ultimately, we hope to promote stronger ethical standards within the field, fostering a cultural shift in how data projects are approached. Regulatory bodies, such as grant agencies, could also benefit from these clearer guidelines, enabling them to set higher expectations for diversity, inclusivity and accountability in Digital Humanities work. This is especially crucial regarding the steady increase of machine learning and datafication, while ethics remain somewhat understudied and undertheorized in Digital Humanities.‍[7]

[5]This article aims to bridge the implementation gap between Data Feminism’s conceptual foundation and its practical application in Digital Humanities. By contextualising its principles and providing concrete examples, we seek to lower the barriers to entry and demonstrate its relevance for everyday Digital Humanities practice. Our discussion begins with an exploration of digital hermeneutics and source criticism for data work in (Digital) Humanities and emphasizes the inherently political nature of Digital Humanities work. We then provide an overview of Data Feminism, based on the original Data Feminism book, but tailored to the Digital Humanities context, offering strategies for its practical implementation. The article concludes with a case study from our own research on women’s roles in historical letter networks, illustrating how Data Feminist principles can inform project design and implementation. In doing so, this article not only clarifies potential misconceptions surrounding Data Feminism but also offers practical pathways for its adoption in Digital Humanities. By making these principles more accessible and actionable, we hope to promote stronger ethical standards in the field, addressing the ethical challenges posed by the increasing integration of machine learning technologies with problematic development histories and dubious ethical standards. This work represents a step towards institutionalizing the Data Feminism principles as a universal ethical framework and toolkit for Digital Humanities, so that its transformative potential can be fully realized.

1. Digital Hermeneutics and Source Criticism for Data Work in (Digital) Humanities

[6]Issues with bias in machine learning algorithms increasingly highlight how Humanities skills, such as historical hermeneutics and source criticism, will be central to Digital Humanities work of the future. In fact, far from rendering the more traditional Humanities – or the sub-field of Digital Humanities that Camille Roth refers to as the ›Digitized Humanities‹‍[8] – obsolete, the AI revolution makes it more crucial than ever for the Digital Humanities to engage in critical data work.‍[9] In such data work, Data Feminism can serve as a methodology that complements what is known in the Humanities as source criticism.‍[10]

[7]In historical disciplines, source criticism is an accepted technique to identify bias in data and ask how historical sources are ›situated‹.‍[11] However, in Digital Humanities it must be applied on multiple levels and often for an entire dataset or collection rather than a single document. We must also acknowledge that we cannot simply recreate a dataset from scratch; instead, we are forced to work with what has been transmitted through the historical record. This means grappling with datasets that are inherently rife with problems in their very makeup. Any reparative measures we implement can only do so much to mitigate their harmful impact and contextualize the potentially problematic worldviews that this data perpetuates or documents. Accordingly, employing data feminist approaches to data work involves considering the data’s history in two ways.

[8]First, the history of how the sources came into existence must be investigated, using historical-critical methods such as source criticism or hermeneutics, alongside questions about the sources’ provenance and archival history.‍[12] Second, the history of how those sources became data must be examined. Questions arise, such as who was responsible for the digitization, what were the needs behind the data model, and whether historical-critical methods were considered during digitization and reflected in the metadata. Third, it is important to consider who created the metadata, whose interests the project serves, and what their goals were. Factors such as their societal position within a hierarchy of power and privilege, as well as the academic discipline relevant to the digitization, may, too, have influenced how it was digitized and which aspects either received special attention or were neglected. These considerations influence the affordances‍[13] of the resulting data, i.e. what can, cannot or should not be done with it.‍[14]

[9]Only those in relatively powerful positions are typically able to create data on a larger scale, archive it, and decide how it is treated. Thus, as the authors of Data Feminism point out, data generally emerges from structures of power and reflects societal hierarchies inherently worth making explicit. Data demands even greater scrutiny when it originates from particularly problematic sources, such as those known to contain racist slurs. However, even sources that may not appear problematic, such as metadata which are commonly perceived as neutral, can be rife with issues that warrant investigation. Such issues with one’s sources may already be known or should be carefully examined during source criticism to uncover biases that may be present in unknown datasets.

[10]This involves not only analyzing the data itself but also consulting theoretical literature that addresses power structures from the relevant historical period. As the authors of Data Feminism and Noble 2018 have concluded, working with data does not require less theory or signal the end of theory – it requires more.‍[15] Without a strong theoretical foundation, those annotating data inevitably have to fall back on ›common sense‹, where stereotypes and biases reside, which can then become inscribed into the data, reproducing or even reinforcing those biases. If theoretical principles are not properly operationalized in the data, the results may fail to reflect the object of study accurately, and resulting algorithmic models may not actually engage with the information they claim to analyze in the way they are expected to. This issue is further compounded by the historical perception of tasks such as data entry, labeling, and metadata creation as menial work, unworthy of credit or attribution. Digital Humanities scholars have repeatedly demonstrated that this is, in fact, significant Humanities work, involving hermeneutic processes that significantly shape the final output.‍[16] However, due to this past perception, it is often impossible to trace who performed the work, preventing an investigation into the potential biases they may have introduced. Scholars reusing such data today cannot know past metadata creator’s motivations – whether they were simply completing a task, had specific agendas, or held political views that, while considered acceptable or even standard opinions at the time, are now recognized as harmful or problematic. However, it must be noted that even political views deemed unproblematic can influence a dataset and are worth acknowledging and making traceable.‍[17] For instance, imagine the major consequences on research possibilities in the seemingly minor case of manuscript metadata where scribes are always identified as men, despite there often being no evidence to support this assumption apart from received stereotypes about how the past works, possibly further shaped by the metadata creator’s personal worldview.‍[18] This highlights the importance of crediting all data work in modern projects to ensure transparency and traceability, which are essential to understanding the biases built into algorithms. While transparency alone does not guarantee ethical outcomes, it at least allows for explainability.‍[19] This is particularly important regarding the demands for using AI to do this seemingly ›less important‹ data work in order to save resources in Digital Humanities projects. When researchers do not know who created the data one reuses or where it originated, achieving transparency in AI ethics becomes nearly impossible.

2. Digital Humanities Work is More Political Than it May Seem at First Glance

[11]Digital Humanities practitioners may assume that research ethics are not relevant to their work, as their subjects may be long dead.‍[20] They may equally assume that because their work is not actively political or activist, they do not require activist methodologies like feminism. However, this is a misunderstanding. In fact, every project is inherently political: Simply by selecting certain topics, materials, or sources over others, one makes a political choice, prioritizing time and resources for a particular project while others, potentially more deserving or urgent, are overlooked. This is evident in many Digital Humanities projects, where, despite the discipline’s promise of innovation, much of the work focuses on the same canonical topics and sources. This is particularly true for projects that do not involve digitizing new sources, as they often rely on pre-digitized material selected through a politics of digitization that is neither fair nor equally representative of all historical materials.‍[21] This disparity is especially apparent in the digitization gap between the Global North and the Global South that clearly privileges data deemed relevant by Westerners who hold the power and resources to digitize and manage their digital cultural heritage.‍[22]

[12]As Mark Hall discusses in his 2019 contribution DH Is the Study of Dead Dudes, the Digital Humanities are at risk of perpetuating old canons when they keep analyzing data produced following former research agendas: The first data to get digitized in high quality was frequently chosen because of the availability of high-quality scholarly editions.‍[23] Former scholarly editing priorities focused on what was considered part of the exclusive canon of the supposedly best literature, including only what was deemed (Western and white) ›high culture‹ like, for instance, William Shakespeare or Johann Wolfgang von Goethe.‍[24] When Digital Humanities apply new methods to old sources – commonly presented as a strength, maybe even the unique sales proposal of the field – they may, in fact, reinforce old canons that many in traditional humanities have already moved beyond to look at a more diverse range of sources. In doing so, Digital Humanities research may further amplify research priorities of the past which are already overrepresented in available data.

[13]But, of course, the problem extends beyond the issue of research priorities: The traditions, sources and collections Digital Humanities build upon are inherently shaped by hegemonic norms and structures rooted in patriarchal, colonial, racist, capitalist, and other systems of power. Digital Humanities research engaging with the material, visual, and textual cultural heritage collected by dominant groups risks perpetuating these hegemonic patterns through their data-driven research if power dynamics are not critically examined and contextualized. This issue is not limited to the design of new epistemological frameworks aimed at preventing the digital reproduction of discriminatory structures; it also includes a critical interrogation of the processes by which data and sources are accumulated. Often, collection practices are based on colonialist, Eurocentric, and cis-male viewpoints, raising important questions about how the Digital Humanities will address archival gaps that hinder the exploration of narratives beyond those centered on cis-male, white experiences. This historically rooted imbalance is evident in the fact that data concerning marginalized groups is frequently absent from archives, and when preserved, they are often filtered through the perspectives of hegemonic groups. As a result, archives simultaneously produce and perpetuate both visibilities and invisibilities, as well as expressabilities and inexpressabilities.‍[25]

[14]This suggests that Digital Humanists need to be more self-critical and aware of our practices than we may initially think, questioning the self-image of rebellious innovators breaking away from the received confines and problematic traditional structures of academia. This sense of ›vocational awe‹ – a term originally coined by Fobazi Ettarh and applied to Digital Humanities by Melissa Terras – can obscure the field’s internal challenges.‍[26] These include domination by privileged white Westerners, precarious employment conditions, and gender imbalances. ›Vocational awe‹ describes a profession’s uncritical belief in its own inherent goodness, which can blind its practitioners to the field’s flaws – a phenomenon that D’Ignazio and Klein describe as a ›privilege hazard‹ in the context of Data Feminism.‍[27] While not all of Digital Humanities practitioners may be in positions of power, many Digital Humanities practitioners are in positions of privilege. This comes with the responsibility to use it well.

3. Data Feminism for the Digital Humanities

[15]But how can Data Feminist approaches be operationalized within Digital Humanities? What research areas could serve as practical entry points for those wishing to apply Data Feminism methods? Addressing the challenges posed by Data Feminism requires the Digital Humanities community to build structures that critically confront biases and imbalances. But we are not empty-handed in dealing with these challenges: Lessons can be drawn from various projects, which offer valuable insights for opening up spaces for dialogue within the field. The ›Full Stack‹ Feminism project, for example, aims to create a toolkit that integrates feminist methods and design principles to promote more socially conscious technologies and infrastructures in Digital Humanities: This involves applying feminist design principles, decentering traditional voices, and incorporating intersectional feminist methodologies into Digital Humanities projects.‍[28] In addition, there is an extensive body of research that can be drawn on for applying Data Feminist principles to the Digital and Computational Humanities. Beyond the foundational work on feminism and intersectionality in Digital Humanities‍[29], numerous studies directly engage with Data Feminism.‍[30] However, much of the existing Digital Humanities work focuses on recovering the contributions of historically overlooked women.‍[31] This suggests that Digital Humanities has not yet fully embraced the broader applicability of Data Feminism to diverse areas of data work beyond these specific, though important cases. Data Feminism is organized into principles that can be applied at various stages of the research process. The following overview is organized according to the chapters of the original book, Data Feminism, which are themselves structured around these principles.‍[32]

4. A Data Feminist Approach to Historical Letter Network Data

[39]To apply the information discussed previously, we present an example of Data Feminism in practice from our own research: Applying Data Feminism as a framework to women’s networks in the correspondence of Early Romanticism.‍[96] The study focuses on a quantitative analysis of women’s roles and functions within the letter network and, among other aims, explores whether women’s often overlooked creative potential can be formalized through historical network analysis. The letters as data are collected in context of the ongoing project ›Correspondence of Early Romanticism‹, which compiles and analyzes letters exchanged between Romantic authors from 1790 to 1802 by reusing existing editions, supplementing them with original manuscripts, and adding inferred letters to fill gaps. In the following, we discuss lessons learned from this work.

[40]Women and individuals beyond the cis-male category are particularly affected by the limitations of historical archival practices. In many collections of cultural heritage, gender is not documented at all or when it is, not as a self-identification but is instead externally imposed. But this gender data gap can distort both the interpretability and validity of historical research findings.‍[97] In the context of the Romantic period, dominant historical narratives, which are still prevalent in current scholarship, often reflect patriarchal assumptions. Women are frequently represented as muses, assistants, or passive observers, rather than as autonomous individuals with their own agency or as creators.‍[98] Our project takes a critical stance on these inherited perspectives, framing such female perspectives within the concepts of care work and mental load. Acknowledging these dimensions is essential for a more comprehensive understanding of women’s experiences and contextualizing their roles in historical networks of correspondence.

[41]In order to do so, our work explores how digital methodologies can be used to make visible the structural absences and biases in historical datasets. Transparency in data creation and annotation, as well as an active engagement with the epistemological implications of missing data, are essential to this approach. Gender-sensitive representation, though only one aspect of appropriate data modeling, requires careful attention. Retrofitting historical classification systems to accommodate current understandings of gender identity is not straightforward and involves ethical and methodological challenges. Widely used standards such as the PICA acquisition schema, TEI-XML, or gender modeling in Wikidata typically do not accommodate fluid gender identities, for a long time did not distinguish between sex and gender, and rarely account for temporality or uncertainty. In most cases, these models enforce a binary framework that contradicts both current theories of gender and, crucially, the complexities in the lived reality of historical individuals.‍[99] Gender is shaped by the cultural and institutional norms of a given time period and therefore inherently possesses a temporal dimension. However, digital encoding systems, if they allow for differentiated gender encoding at all, rarely account for this temporality. They often lack mechanisms to record the temporal context of the attribution, as well as the context in which the metadata was created, including underlying assumptions and the definitions of terms at that time. Since terms used to describe marginalized groups frequently shift in meaning, sometimes becoming slurs or being reclaimed, it is essential to include contextual information for both the historical period being described and the time when the metadata is added, along with the cultural situatedness of the metadata creator. Moreover, gender attributions are subject to change, which is why it has been proposed to model gender as an event to account for its temporality.‍[100] A further complicating factor is the difficulty of reconstructing gender identity from historical records, particularly where the documentation itself is sparse or biased. For example, in the case of Anastasius Lagrantinus Rosenstengel (1687–1721), there is scholarly debate as to whether this individual was a lesbian woman living as a man or a trans man, which is not easily resolved with available records.‍[101] In most historical contexts, individuals were assigned a binary gender in line with cultural expectations, which was reflected in their gendered first names, and only few historical sources document forms of non-binary identification.‍[102] Our work does not claim to resolve these complexities, but it seeks to identify patterns of gendered roles within the constraints of and social frameworks represented in our source data.

[42]However, such sophisticated forms of gender encoding remain rare and often elusive in the Digital Humanities. This also applies to the underlying data of our case study: The scholarly editions our project builds upon seldom annotate gender as a research category.‍[103] To make gender analyzable as part of a dataset audit examining the gender data gap, the dataset was semi-automatically enriched with gender information.‍[104] This was annotated as ›assigned_gender‹ to acknowledge the provisional and externally attributed nature of these classifications. This facilitates future reinterpretations or corrections and provides transparency for subsequent researchers. In this context, it must be noted that the act of classifying, as discussed in Data Feminism’s Principle 4 (›Rethink Binaries‹), is inherently contentious and thus, must be critically assessed in each instance to determine whether it causes more harm than benefit. However, stopping to classify is not an option either: Our data exists within pre-defined systems that we reuse more than we redefine them and it has already been classified in various ways. The digital ecosystem as a whole is built on classification and categorization. Therefore, it may be more constructive to leverage existing classifications and make them our objects of study, recognising that they do not represent reality accurately or holistically. Rather, they reflect a dominant view of reality that privileges a narrow range of perspectives over a plurality of voices. As the well-known saying goes, »All models are wrong, but some are useful« (George Box). Studying our models can help us learn about the world through the discrepancies between these representations and the realities they are meant to encode.‍[105]

[43]The initial enrichment process involved matching individuals with Gemeinsame Normdatei (GND) authority record identifiers and importing gender information using OpenRefine. For individuals lacking a GND-ID or gender data within the GND, the Python library ›gender-guesser‹ was used to infer gender from first names.‍[106] Because gender assignment is inherently problematic, this step was followed by manual verification.‍[107] The method is neither definitive nor neutral, but it serves as a starting point to interrogate what can be known within the limits of existing authority records and software. As Dominique Schirmer notes, there is an inherent tension between the ›requirements of reproduction‹ and ›dangers of reproduction‹ when gender must be inferred to enable analysis.‍[108] Without such annotation, gender cannot be operationalized as a research variable. Yet the act of assigning it introduces new uncertainties. To acknowledge the limitations and challenges of gender assignments made by third parties, automated or not, and to indicate their provisional nature, we recorded this as ›assigned gender‹. This facilitates future reinterpretations or corrections and provides transparency for subsequent researchers. While our database remains a work in progress and is not yet complete, these preliminary figures offer insights into the persistent challenges associated with historical research data, particularly in terms of gender representation.

[44]The enrichment of data with gender information and the using of gender as an analytical category enables more complex, diverse perspectives on datasets. With ›assigned_gender‹ added and embedded as information in data about persons in historical sources, we are able to address and filter marginalized groups like women in corpora in which they otherwise would be hidden from quantitative historical research. Thereby, we are able to ask new research questions and open the field of quantitative historical research for debates on gender aspects. In the presented use case, for example, we find a significant disparity in male and female gender data, which raises critical questions about the historical sources underpinning the database. The imbalance reflects both the archival loss of women’s writings and the biases of earlier collection practices: Research, such as Wernli 2022, indicates that many letters authored by women in the early 19th century were lost, destroyed, or only preserved through editorial frameworks that reduced women to gender roles defined by patriarchal norms. Whether as housewives admiring their spouses, assistants supporting their husbands, or muses serving as passive inspiration for male authors, these gender roles reflect persistent patterns of positioning women in relation to male figures, rather than as independent agents. This historical imbalance not only perpetuates gender disparities in datasets built upon these sources, it also shapes how datasets are constructed for data availability reasons. These figures exemplify the gender data gap within historical authority records and its implications for digital historical research.

[45]While our method of assigning gender based on first names is limited, its utility lies in enabling an initial audit of the dataset. This implements a key principle of Data Feminism: The value of making absences visible. Even a preliminary analysis can expose the effects of data gaps, prompting further investigation and opening up space for new research questions to be asked. For instance, which roles are systematically attributed to male versus female correspondents? How does gender distribution shape access to particular topics or social functions within the letter network?

[46]Embedding ›assigned_gender‹ into our dataset allows us to extract and examine the participation of historically marginalized groups, in this case women, in a corpus where they might otherwise remain invisible. This in turn facilitates the integration of gender history and feminist critique into quantitative historical research. While this approach does not yet account for non-binary identities or fluid gender expressions and may be considered crude in some respects, it lays a foundation for future work that explicitly addresses gender distributions in our corpus, a possibility the original state of the data did not afford. This serves as a good example of how projects can make important contributions by providing structured data on issues such as gender representation. Although the measure is relatively simple, it still requires reflective depth to be implemented responsibly within the limitations of digital systems that inherently classify. It also requires proper documentation, such as through the use of datasheets, to help future users understand how the dataset was created and what kinds of research questions it can or cannot support.

5. Conclusion: Making Data Feminism Work in Practice

[47]Data Feminism, as conceptualized by Catherine D’Ignazio and Lauren F. Klein, represents a strand of intersectional feminism that critically examines the predominance of white, cis-male perspectives in data science. In it, the authors have attempted to operationalize existing feminist strategies to be used in data science and AI contexts by defining seven key principles to guide such work. Data feminist critique extends beyond data analysis to encompass the modeling, collection, curation, and presentation of datasets, even the development of algorithms and digital infrastructures.

[48]Data Feminist approaches hold significant potential for advancing more ethical research within the Digital Humanities; however, they have not yet been fully integrated in the German-speaking Digital Humanities community. A notable implementation gap exists in the development of concrete guidelines and frameworks for embedding Data Feminist principles into everyday Digital Humanities project practices. Addressing the challenges posed by Data Feminism requires the Digital Humanities community to build structures and establish best practices that critically confront biases and imbalances. Fortunately, there is much work that the Digital Humanities community can draw on. We have developed this guide to provide essential information, offering an accessible entry point and outlining strategies specific to Digital Humanities for implementing ethically responsible scholarship. We hope that we have not only provided an accessible introduction to the Data Feminism principles for Digital Humanists through providing a Digital Humanities implementation example, but also offered an entry point into the available secondary literature, opening up the rich body of sources from the Digital Humanities and related fields for those interested in beginning to work with and explore these ideas further.

[49]To make Data Feminism effective in Digital Humanities, it is essential to engage seriously with its principles rather than using them as a form of pinkwashing.‍[109] Furthermore, the time and resources needed to enrich data and fully implement ethical approaches to data in practice should not be underestimated. Lastly, raising awareness of Data Feminism by incorporating it into teaching and academic curricula is crucial to ensuring its influence on future scholarship and practice.

[50]As we have argued and the authors themselves have pointed out, the term Data Feminism leads many to the misconception that the framework is relevant only to feminism and feminists. We hope to have dispelled the common misconception that Data Feminism is solely about women or activist issues. Data Feminism offers feminist methodologies to address problems that affect everyone working with (historical) data. We hope to have convinced the Digital Humanities community of its broad usability and applicability, particularly given its strong alignment with established traditional historical methodologies like source criticism, to which it can be seen as an extension for the digital sphere. In doing so, we have presented Data Feminism as a universally relevant framework for conducting ethically sound data work.

[51]In this context, one may wonder whether this truly fulfills the aspiration stated in the introduction: to illustrate how Data Feminist principles can inform project design and implementation. Are such seemingly simple steps enough to make data or projects feminist (irrespective of what people may typically imagine when they encounter the term)?‍[110] Is this ›genuinely Data Feminism‹ or simply quality assurance and good scientific practice? This is the false binary we have aimed to debunk in this article. Data Feminism is one method within the broader toolkit of quality assurance in research and should be viewed as a way to enhance good scientific practice. It is a framework that more scholars should integrate into their everyday research. Scholarship that does not account for a plurality of perspectives – even, and especially, when that plurality is lacking in the data – is not good scholarship. Research that fails to acknowledge the role of power structures in shaping datasets and historical records during analysis and interpretation must be considered significantly lacking in quality.

[52]Can what we have provided truly be considered a how-to guide to Data Feminism in the Digital Humanities? Perhaps not, as the presented case study is only one small example of how Data Feminism can be applied, inspired by the many other examples discussed in relation to the Data Feminism principles. Our implementation example is not a definitive solution but rather a call to action: an invitation for others to engage in similar work and contribute their own approaches. It represents a first step toward making a dataset ready to even begin asking critical questions about issues such as the gender data gap.

[53]Were we able to offer an easy how-to for fully applying the principles? Not exactly. Like the principles of research ethics and good scientific practice, applying Data Feminism requires interpretation and adaptation to one’s specific context. One researcher’s dataset may already include gender assignments but may lack other forms of representation. Data Feminism encourages us to scrutinize our data and tools holistically, making us more aware of potential pitfalls and inspiring us to take active steps to address them. In this spirit, we have aimed to bridge the implementation gap between the Data Feminism manifesto and the daily working practices of Digital Humanists and more traditional historians. We hope that with these guidelines in hand, others will find it easier to identify which steps, whether simple or more complex, can help make data work in the Digital Humanities more inclusive, ethical, and rigorous.