This workshop proposes to provide a forum to discuss the structures and models of information resources in historical-comparative linguistic research outputs through the integration of informatic models from library science and archivy. We want to address pertinent issues impacting the indexing (for citation) and interoperability of datasets (for sustainability).

WS Title: First workshop on Data Models, Citation, Access, and Re-usability impacting Historical Linguistic Datasets Workshop at ICHL27, Santiago de Chile, 18-22 August 2025 Workshop Type: in-person Organizers: Hugh Paterson III & Oksana Zavalina Abstract Deadline: October 18th EXTENDED NEW DEADLINE NOV 7th. Abstract Details: up to 800 words excluding references. Submission: Email PDF of abstracts to both i@hp3.me and oksana.zavalina@unt.edu with [ICHL27 w8] in the subject line. Note: Workshops are in most cases restricted to 6 papers; all other papers, if accepted, will be given as part of the ICHL general sessions. Should there be sufficient interest for an extended workshop (up to 12 papers), we will lobby the local organizers to permit this format. Workshop Website: https://hughandbecky.us/Hugh-CV/project/2025-ichl27-historical-linguistic-da... Conference Website: https://ichl27santiago.cl PDF of Workshop abstract: https://hughandbecky.us/Hugh-CV/project/2025-ichl27-historical-linguistic-da... Publication: We are pursuing publication via edited volume post-workshop.

==Goal & Questions== The role of library models (e.g., IFLA-LRM: Riva, Le Bœuf, and Žumer 2017) and archival practice (e.g., lifecycle management: Higgins 2012) is under-explored in relation to the construction and reuse of Historical Linguistic Information Sources. This workshop proposes to provide a forum to discuss the structures and models of information resources in historical-comparative linguistic research outputs through the integration of informatic models from library science and archivy.

We invite papers describing the information models used for assembling large corpora (including wordlists) used in historical linguistics, highlighting assumptions for citation, referencing, segmentation, and reusability of the assembled collection of texts and their digital surrogates. We encourage papers which present typologies of use cases, categories of tracked information, provenance of data content, citability of aggregate content, and the identifiers-for and permanence-of user-generated datasets on research platforms.

What are the design patterns within datasets? What are the categories used? and what are their scopes? What are the kinds of objects subsumed into datasets?

==Background== Significant advances have been made in historical linguistics through the use of large compiled datasets (e.g., Kamholz et al. 2024; Tresoldi 2023; Arora et al. 2023; Dellert et al. 2020; Greenhill 2015; Segerer and Flavier 2013; Mielke 2008; Greenhill, Blust, and Gray 2008). While not precluding the contributions of single historical manuscripts and traditional manuscript consultation methods, the use of and creation of datasets (including corpora) has become the defacto way of generating new hypotheses (Wichmann and Saunders 2007; Steiner, Cysouw, and Stadler 2011; Segerer 2015). Datasets in historical linguistics generally do two things: (1) record critical researcher-created information such as reconstructed forms, cognacy judgments, confidence levels, along with contextual notes; and (2) contain foundational content from sources not created by the dataset compiler. Such source material often include historically published and unpublished resources including: maps (Hessle and Kirk 2020), language specific lexicons and published reconstructions (Kamholz et al. 2024), wordlists (Forkel et al. 2024; Segerer and Flavier 2013), transcriptions of manuscripts and texts (Weber et al. 2023; Genee and Junker 2018; Kytö 2011), and even reconstructions by other scholars, etc.

Interactional platform-tools such as RefLex (Segerer and Flavier 2013) or OUTOFPAPUA (Kamholz et al. 2024) allow users to create custom datasets based on specific selected resources available to the platform. They do this without requiring users to interact with the complete set of underlying resources and/or the platforms allow users to create new derivative aggregate collections (reconstructed forms and cognacy relations) independent of other platform users. Citing, referencing, and redistributing these custom datasets is challenging and impacts the verifiability of claims.

It is broadly accepted across linguistic research that scholarly work—including evidence— should be citable, accessable, and reusable (Bird and Simons 2003). Together these issues impact reproducibility, an important tenet in scholarship often overlooked in linguistics (Berez-Kroeker et al. 2018). However, it is also well acknowledged that the citation and reference of original source material for linguistic evidence is lacking across the field (Gawne et al. 2017). More specifically in historical-comparative linguistics, the context of citation and referencing of the evidentiary record along with current dataset assemblage and distribution practices generally do not support fine-grained or Work-oriented citation and referencing. This often means that specific and necessary details in comparative linguistics are not retrievable. Therefore, the data models embedded within historical comparative datasets become all the more important for the reproducibility of work and the testing, verification, and refinement of hypotheses (Bakro-Nagy 2010).

With the exception of leading work around Cross-Linguistic Data Formats (CLDF) use with historical-comparative data (Forkel et al. 2018; Forkel, Swanson, and Moran 2024) and approaches using linked data in linguistics (Kesäniemi et al. 2018; Tittel, Gillis-Webber, and Nannini 2020), the literature has been silent about the storage formats for historical-comparative data. Undiscussed are the information categories represented in historical comparative linguistic datasets. The informatic arrangement and description of compiled datasets has generally been ad-hoc and served the needs of individually-funded projects. This has resulted in a proliferation of divergent data categories mitigating against ease-of-reuse.

We set out to ignite discussion around compilations of manuscripts, wordlists, and other derivative resources which have become mainstream tools in hypothesis generation related to the language evolution. We explore the heretofore unapproached contribution that models such as Work-Expression-Manifestation-Item (WEMI), illustrated in figure 1, from library and information science (Coyle 2023; Riva, Le Bœuf, and Žumer 2017; IFLA, 1998) can offer those who compile, and cite/reference aggregate linguistic resources. Specifically, clarifying linking relationships between the literature and datasets, including dataset portions.

We invite papers describing the information models used for assembling large corpora (including wordlists) used in historical linguistics, highlighting assumptions for citation, referencing, segmentation, and reusability of the assembled collection of texts and their digital surrogates. We encourage papers which present typologies of use cases, categories of tracked information, provenance of data content, citability of aggregate content, and the identifiers-for and permanence-of user-generated datasets on research platforms.

Figure 1. Is available at the workshop website and the abstract in PDF form.

==References== Arora, Aryaman, Adam Farris, Samopriya Basu, and Suresh Kolichala. 2023. “Jambu: A Historical Linguistic Database for South Asian Languages.” arXiv. https://doi.org/10.48550/arXiv.2306.02514.

Bakro-Nagy, Marianne. 2010. “Data in Historical Linguistics: On Utterances, Sources, and Reliability.” Sprachtheorie Und Germanistische Linguistik 20.2: 133-195., January. https://www.academia.edu/3629841/Data_in_historical_linguistics_On_utterance....

Berez-Kroeker, Andrea L., Lauren Gawne, Susan Smythe Kung, Barbara F. Kelly, Tyler Heston, Gary Holton, Peter Pulsifer, et al. 2018. “Reproducible Research in Linguistics: A Position Statement on Data Citation and Attribution in Our Field.” Linguistics 56 (1): 1–18. https://doi.org/10.1515/ling-2017-0032.

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Forkel, Robert, Johann-Mattis List, Christoph Rzymski, and Guillaume Segerer. 2024. “Linguistic Survey of India and Polyglotta Africana: Two Retrostandardized Digital Editions of Large Historical Collections of Multilingual Wordlists.” In _Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), edited by

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Hessle, Christian, and John Kirk. 2020. “Digitising Collections of Historical Linguistic Data: The Example of The Linguistic Atlas of Scotland.” Journal of Data Mining & Digital Humanities Special issue on Visualisations in Historical Linguistics. https://doi.org/10.46298/jdmdh.5611.

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