Open Science
in Computational Literary Studies



Christof Schöch
Trier Center for Digital Humanities
Trier University, Germany


JADT 2026
Palermo, Italy

09 Jul 2026

Introduction

Overview

  1. Introduction
  2. Open Science and ‘Repetitive Research’
  3. Open Access Publications
  4. Open Research Data
  5. Open Research Software
  6. Conclusion




Slides: jadt.dh-trier.de


Open Science and ‘Repetitive Research’

Aspects of Open Science

What is Open Science?

Building on the essential principles of academic freedom, research integrity and scientific excellence, open science sets a new paradigm that integrates into the scientific enterprise practices for reproducibility, transparency, sharing and collaboration resulting from the increased opening of scientific contents, tools and processes.

– UNESCO, Recommendations on Open Science, 2021.

What is ‘Repetitive Research’?

  • A mode of research that repeats and/or is repeatable
  • A conceptual space with three dimensions: research question, dataset, and method/code
  • Several forms of repetitive research
    • Follow-up research: similar question, but new or similar data and method
    • Re-use & re-analysis (of data): same data
    • Revision (of method): same question and data
    • Replication (of research): same question, data and method

Bringing it all together: The Open Science Staircase

Open Access Publications

Publication Models: Digital Humanities Journals

Benefits of Open Access Publications

  • Readers: access research quickly and at no cost
  • Authors: make their research widely read and cited
  • Repetitive research
    • Facilitates follow-up research
  • Open licenses and file formats: accessibility and re-usability

The Open Access Citation Advantage

The case of the Journal of Computational Literary Studies

Dimensions of Openness in Open Access

  • Not just “free to read”
  • Text formats for articles
    • PDF: for reading
    • HTML: better accessibility
    • JATS: long-term archiving and reuse
    • BibTex: structured bibliographic data
  • Identifiers and standards for metadata
    • DOIs for articles (and in the references!)
    • ORCiD: to identify authors (and reviewers)
    • ROR: for institutional affiliations
    • CReDIT: to describe author contributions

Open Research Data

Benefits of Open Research Data

  • Transparency
  • Sustainability
  • Repetitive research
    • Data re-use
    • Data re-analysis

Challenges for Open Research Data

  • Additional effort required
  • Limited academic rewards (=> data papers)
  • Personal Data protection
  • Copyright limitations

Open Research Data at JCLS: policy

Submissions based on code or data must make their code and data available. Exceptions can be made if copyright or personal data concerns arise, and these should be discussed with the editors.

Open Research Data at JCLS: scenarios

  • Options in the latter case
    • Downstream research-data is shared
    • Metadata is shared

Problem: The ‘window of opportunity’ effect

  • Positive impulses
    • Open Science
    • Academic Freedom
  • Constraining factors
    • Copyright Law1
    • OCR Technology

Solution: Derived Text Formats

Derived Text Formats (DTFs) are the result of a strategic transformation of textual materials that are protected by copyright in their original form, such that the resulting data is useful for computational analyses and can be openly shared following best practices of Open Science without infringing copyright law.

Create a DTF in four simple steps

  • Legal access to textual data is required
    (TDM exception)
  • Information enrichment, e.g. POS, NER, parsing, etc.
  • Information reduction, e.g. randomisation, replacement or masking, etc.

Example of a simple DTF

original

Il était une fois, il y a bien longtemps, trois petites filles, la première Mi, la seconde Do, la troisième La. Elles avaient une marraine qui sentait bon, qui ne les grondait jamais lorsqu’elles n’étaient pas sages, et qu’on nommait marraine Midola. […]

enriched

Il_il_PRON 
était_être_VERB 
une_un_DET  
fois_fois_NOUN 
,_,_PUNCT
il_il_PRON 
y_y_PRON 
a_avoir_VERB 
bien_bien_ADV  
longtemps_longtemps_ADV  
,_,_PUNCT
trois_trois_NUM  
petites_petit_ADJ  
filles_fille_NOUN 
...

randomised

ne_ne_PART
trois_trois_NUM
pas_pas_ADV
une_un_DET
on_on_PRON
a_avoir_VERB
,_,_PUNCT
avaient_avoir_VERB
._._PUNCT
il_il_PRON
lorsqu'_lorsque_SCONJ
première_premier_ADJ
sentait_sentir_VERB
marraine_marraine_NOUN
était_être_VERB
Il_il_PRON
...

Some current research on DTFs in CLS

Open Research Software

Benefits of Open Research Software

  • High level of transparency
  • Re-use of data and software
  • Repetitive research
    • replication (of research)

Use of tools and technologies in CLS (2011-2021)

Research software in JCLS: practices and challenges

  • Practices
    • Script languages: Python (most) or R (some)
    • Open-source software with GUI: Gephi, stylo (2x)
    • Proprietary data analysis software: Matlab (1x)
  • Challenges
    • Code quality / completeness
    • Lack of detailed documentation
    • Versioning / dependencies
    • LLMs: access and resources

Conclusion

What are some of the lessons learned in CLS, so far?

  • Publications are research data, too.
  • Look at publications, data and code as a package.
  • Policies matter, not arguments.
  • A fresh start goes a long way.

Thank you!

References

Da, Nan Z. 2019. “The Computational Case Against Computational Literary Studies.” Critical Inquiry 45 (3): 601–39. https://doi.org/10.1086/702594.
Du, Keli. 2023. “Understanding the Impact of Three Derived Text Formats on Authorship Classification with Delta.” In Jahreskonferenz der Digital Humanities im deutschsprachigen Raum. DHd-Verband. https://doi.org/10.5281/zenodo.7715298.
Du, Keli, Sarah Ackerschewski, Uygar Navruz, Nazan Sinir, Julian Valline, and Christof Schöch. 2025. “Reconstructing Shuffled Text: Bad Results for NLP, but Good News for Using in-Copyright Texts.” Journal of Computational Literary Studies 4. https://doi.org/10.48694/jcls.4163.
Du, Keli, and Christof Schöch. 2024. “Shifting Sentiments. What Happens to BERT-Based Sentiment Classification When Derived Text Formats Are Used for Fine-Tuning.” In Digital Humanities 2024: Book of Abstracts. ADHO.
Fraser, Nicholas, Fakhri Momeni, Philipp Mayr, and Isabella Peters. 2020. “The Relationship Between bioRxiv Preprints, Citations and Altmetrics.” Quantitative Science Studies 1 (2): 618–38. https://doi.org/10.1162/qss_a_00043.
Fu, Darwin Y., and Jacob J. Hughey. 2019. “Releasing a Preprint Is Associated with More Attention and Citations for the Peer-Reviewed Article.” eLife 8: e52646. https://doi.org/10.7554/eLife.52646.
Hinzmann, Maria, Matthias Bremm, Tinghui Duan, Anne Klee, Johanna Konstanciak, Julia Röttgermann, Moritz Steffes, Christof Schöch, and Joëlle Weis. 2026. “Patterns in Modeling and Querying a Knowledge Graph for Literary History.” In Patterns in Language and Communication, edited by Sabine Arndt-Lappe, Sören Stumpf, Milena Belosevic, Peter Maurer, Claudine Moulin, and Achim Rettinger, 135–76. De Gruyter. https://doi.org/10.1515/9783111199740-006.
Huang, Chun-Kai, Cameron Neylon, Lucy Montgomery, Richard Hosking, James P. Diprose, Rebecca N. Handcock, and Katie Wilson. 2024. “Open Access Research Outputs Receive More Diverse Citations.” Scientometrics 129 (2): 825–45. https://doi.org/10.1007/s11192-023-04894-0.
Keith, Allison, Antonio Rojas Castro, Kerstin Jung, Hanno Ehrlicher, and Sebastian Padó. 2026. “A Computational Analysis of Character Archetypes in the Works of Calderón de La Barca.” Journal of Computational Literary Studies 5 (1). https://doi.org/10.48694/jcls.4167.
Kocula, Martin. 2021. “Volltext vs. abgeleitetes Textformat: Systematische Evaluation der Performanz von Topic Modeling bei unterschiedlichen Textformaten mit Python.” Trier University. https://doi.org/10.5281/zenodo.5552486.
Koolen, Marijn, Joris van Zundert, Eva Viviani, Carsten Schnober, Willem van Hage, and Katja Tereshko. 2024. “From Review to Genre to Novel and Back. An Attempt to Relate Reader Impact to Phenomena of Novel Text.” Journal of Computational Literary Studies 3 (1). https://doi.org/10.48694/jcls.3927.
Kugler, Kai, Simon Münker, Johannes Höhmann, and Achim Rettinger. 2024. “InvBERT: Reconstructing Text from Contextualized Word Embeddings by Inverting the BERT Pipeline.” Journal of Computational Literary Studies 2 (1). https://doi.org/10.48694/jcls.3572.
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Levchenko, Maria. 2025. “Computational Analysis of Literary Communities. Event-Based Social Network Study of St. Petersburg 1999-2019” 4 (1). https://doi.org/10.48694/JCLS.4217.
Neugarten, Julia. 2025. “A Powerful Hades Is an Unpopular Dude. Dynamics of Power and Agency in Hades/Persephone Fanfiction.” Journal of Computational Literary Studies 4 (1). https://doi.org/10.48694/jcls.4208.
Piper, Andrew. 2025. “Towards a Perspectival Moral History of the Novel Using LLMs.” Journal of Computational Literary Studies 4 (1). https://doi.org/10.48694/jcls.4168.
Schöch, Christof. 2023. “Repetitive Research: A Conceptual Space and Terminology of Replication, Reproduction, Revision, Reanalysis, Reinvestigation and Reuse in Digital Humanities.” International Journal of Digital Humanities. https://doi.org/10.1007/s42803-023-00073-y.
Schöch, Christof. 2026. “Derived Text Formats as Strategic Transformations of in-Copyright Materials to Support Open Science: A Survey.” In Leveraging Derived Text Formats to Unlock Copyrighted Collections for Open Science: Book of Abstracts, edited by Trippel, Thorsten, Barth, Florian, Tello, Jose Calvo, Genêt, Phillipe, Lendvai, Piroska, and Schöch, Christof. https://api.zotero.org/users/228821/publications/items/3RZJJCIK/file/view.
Schöch, Christof, Frédéric Döhl, Achim Rettinger, Evelyn Gius, Peer Trilcke, Peter Leinen, Fotis Jannidis, Maria Hinzmann, and Jörg Röpke. 2020. “Abgeleitete Textformate: Text und Data Mining mit urheberrechtlich geschützten Textbeständen.” Zeitschrift für digitale Geisteswissenschaften 5. https://doi.org/10.17175/2020_006.
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Schöch, Christof, Maria Hinzmann, Julia Röttgermann, Katharina Dietz, and Anne Klee. 2022. “Smart Modelling for Literary History.” International Journal of Humanities and Arts Computing 16 (1): 78–93. https://doi.org/10.3366/ijhac.2022.0278.
Trippel, Thorsten, Florian Barth, José Calvo Tello, Phillipe Genêt, Lendvai, Piroska, and Christof Schöch, eds. 2026. Leveraging Derived Text Formats to Unlock Copyrighted Collections for Open Science. LREC.
Trippel, Thorsten, Florian Barth, Jose Calvo Tello, Keli Du, Phillipe Genêt, Daniel Kurzawe, Peter Leinen, Piroska Lendvai, Christof Schöch, Andreas Witt, and Arden Zimmermann. 2026. “DIN 19461: A National Standard for Derived Text Formats.” In Leveraging Derived Text Formats to Unlock Copyrighted Collections for Open Science (Workshop at LREC 2026), edited by Trippel, Thorsten, Barth, Florian, Tello, Jose Calvo, Genêt, Phillipe, Lendvai, Piroska, and Schöch, Christof.
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Bonus slides

What is Computational Literary Studies?

CLS concerns “the development, the application, and the critique of computational approaches to Literary Studies. […] [CLS concerns] issues such as building literary corpora, identifying peculiarities of literary texts, domain adaptation of methods, operationalization of concepts, annotation of texts, evaluation of measures, interpretability and transparency of results, and reproducibility of research.” – JCLS, Mission Statement, 2021.


As a research field, computational literary studies introduce the use of computational techniques within literary scholarship. They can be defined as a distinctly interdisciplinary endeavor, which relies heavily on computational linguistics and natural language processing, thus bridging computer science and the humanities. For this reason, they can also be considered as a subfield of the Digital Humanities, a term used to refer to a broader research community, more generally interested in the integration between humanistic disciplines and digital technologies […]. – Simone Rebora, Computational Literary Studies, forthcoming.

An open project: Mining and Modeling Text (TCDH, 2018–2023)