Data (work) shapes results, yet we don't document or even understand our datasets well enough. I argue for standardized documentation and auditing pracitces as well as properly valuing data work.
Through my engagement with critical digital humanities, I'm increasingly interested in the study of data work and its influence on processes of knowledge production (Gengnagel & Lang, 2026). After preliminary work on dataset documentation practices (Lang, 2025), I have articulated dataset audit as a practical strategy for identifying and addressing data gaps (missing reference). This is closely connected to fields such as critical AI studies, which increasingly recognise data and data work as central sources of bias and unethical outcomes in computational systems. Recognizing the sheer amount of hidden labour and data work required for computational humanities research, I have examined what it means to _do_ computational history (Lang, 2026). I have also investigated invisibilised labour in Computational Humanities contexts through the lenses of data work and the invisible technician discourse (Lang, 2027).
References
2027
Two-tier Computational Humanities: A Labour History of Undervalued Contributions in DH
Sarah Lang
In The De Gruyter Handbook of Feminist Digital Scholarship, Berlin/Boston, 2027
Knowledge work is shaped by hierarchies of visibility and recognition. Digital Humanities (DH) and the recent emergence of Computational Humanities (CH) as a prominent subfield are no exception. This article takes a labour history approach to computational humanities to examine how unequal forms of recognition have shaped the field. Building on earlier reflections on toxic masculinity and disciplinary identity in computational humanities (Lang 2020), it situates contemporary tensions surrounding CH within a longer history of the devaluation of particular forms of knowledge work. By positioning and historicising current debates within broader discussions ranging from the history of women in computing to invisible labour, care work, and AI data work and the invisible technician discourse, the article argues that what may appear as a recent disciplinary development is in fact the latest chapter in a much longer history of unequal valuation.
@incollection{Lang2027_TwoTierCH_Feminisms,author={Lang, Sarah},title={Two-tier Computational Humanities: A Labour History of Undervalued Contributions in DH},booktitle={The De Gruyter Handbook of Feminist Digital Scholarship},editor={Cong-Huyen, Anne and Knight, Kim},year={2027},publisher={De Gruyter},note={Forthcoming},location={Berlin/Boston},}
2026
A Discipline, Divided: On the Digital Humanities and Ideologies of Knowledge Work
Debates about the digital humanities (DH) have long centred on a perceived "tension at the heart" of the field. Whether framed in terms of neoliberalism ("the dark side of DH"), the hack vs. yack debate, or more recent concerns about generative AI, corporate influence, sustainability, and global inequalities, these debates share an underlying political and socio-economic dimension. This paper argues that such tensions are rooted in implicit ideological assumptions that shape the forms of knowledge produced within DH. We examine this through two historical micro-studies spanning the emergence of DH in the 1960s to its most recent transformations in the 2020s. The first investigates precursors of DH in socialist East Germany during the 1960s and 1970s. The second situates the rise of Computational Humanities Research (CHR) in the late 2010s and early 2020s through the lens of feminized labour. Combining perspectives from the history of knowledge with feminist scholarship on DH, the paper offers a comparative perspective on how ideological commitments have shaped both the institutional development of DH and the knowledge practices it continues to produce.
@inproceedings{GengnagelLang2026,author={Gengnagel, Tessa and Lang, Sarah},title={A Discipline, Divided: On the Digital Humanities and Ideologies of Knowledge Work},booktitle={DH2026 Book of Abstracts},year={2026},}
(Doing) Computational History: On the Role of Data Work in Computational Approaches
Computational methods have become increasingly prominent within the historical sciences, generating significant enthusiasm among some scholars. Yet their practical demands, epistemic limits, and ethical implications are less often critically examined than praised. This article explores what it means to do computational history today, arguing that it is not primarily defined by algorithms but by datasets. It is methodologically specific, resource-intensive, selective in scope, labour-heavy, and dependent on pre-digitised sources, specialised infrastructure, and interdisciplinary collaboration. These dependencies limit the scope of research questions and can produce narrow outcomes despite substantial effort, lending some validity to the concern over whether the field yields sufficient historiographical return for the labour invested. Corpus construction and data work lie at the epistemic core of computational history. These often undervalued tasks are not merely technical precursors to analysis, but interpretive and epistemic acts. Data are shaped by digitisation politics, historical bias, and institutional power. They shape the questions asked, the answers produced, and the legitimacy of findings. Recognising and valuing data work is essential, both to embed critical perspectives into computational humanities and to counteract the privileging of certain forms of labour over others. Due to the association of quantification with rigour and scholarly prowess, algorithmic work receives more credit, creating a two-tier system in this division of labour in which those who develop algorithms are elevated above those who curate data, despite their symbiotic interdependence. Computational history, when done well, requires deep engagement with our sources, be they historical or data. For computational history to stabilise as a meaningful discipline, it must prioritise building better datasets over pursuing increasingly complex algorithms on an unstable basis of data.
@article{Lang2026DoingComputationalHistory,author={Lang, Sarah},title={(Doing) Computational History: On the Role of Data Work in Computational Approaches},journal={Histories},volume={6},number={2},pages={26},year={2026},url={https://doi.org/10.3390/histories6020026},doi={10.3390/histories6020026},keywords={computational history; digital humanities; computational humanities; data work; knowledge work; corpus criticism; data ethics},}
2025
Documenting Datasets as a Tool for Change
Sarah Lang
In Digital Humanities 2025: Book of Abstracts, 2025
This paper argues that comprehensive dataset documentation could be a foundational yet undervalued practice in the Digital Humanities, linking technical standards with critical concerns such as data ethics, decolonisation, inclusivity, explainability, and reproducibility. Building on initiatives such as FAIR, CARE, Data Feminism, and Datasheets for Datasets, it proposes documentation as a practical means of improving transparency, enabling responsible data reuse, and strengthening both the ethical and scholarly quality of digital humanities research.
@inproceedings{Lang2025DocumentingDatasets,author={Lang, Sarah},title={Documenting Datasets as a Tool for Change},booktitle={Digital Humanities 2025: Book of Abstracts},editor={del Rio Riande, Gimena and Portela, Manuel and Alves, Daniel and Vieira Paulino, Joana},pages={885--887},address={Lisbon},year={2025},url={https://zenodo.org/records/18393402},doi={10.5281/zenodo.18393402},}