Conference Agenda

The presentation aims to give a comprehensive overview of the experience gained when attempting to model the workflow of a philologist working with medieval runic inscriptions as entities in a relational database – in order to develop a kind of all-purpose basic model for a runological research database, containing the basic information every runologist requires for their work, but offering enough flexibility to conduct research from the point of view of different disciplines. It outlines the process of comparing the models of already existing runic databases, the distillation of the basic “core” features these share and how they are modelled for the specific purposes they were built for, all of which contributed to the proposed new, extendable model. Lastly, it will show examples of analyses that can now be conducted using this new model.
The database created based on this model was the main research tool of the interdisciplinary PhD project “Runes, runic inscriptions and runic writing as primary sources for town development”, begun in 2015 with the explicit aim to collect, compare and analyse previous attempts by runologists to use databases (relational and other) as tools to support their research. An analysis of this kind was, in the author's opinion, sorely needed; the first runic database was brought into existence already in the 1990s, with other researchers and their databases soon following. However, only a few preliminary work reports were published concerning the underlying data models, structures and technologies these databases were using. Within the runologist community, there seemed to be a general consensus that, as long as these databases worked (somewhat), understanding how and why they worked (or did not work, where specific research was concerned) was not a requirement.
The 2015 PhD project, finished in 2021 and published in 2023 as “Data-based Runes: Macrostudies on the Bryggen Runic inscriptions” (DOI: 10.15845/bryggen.v100), set out to question that approach. Instead of using a relational database solely as the tool to curate and gather data on runic inscriptions, it asked questions like: “Should we even be using relational databases for this purpose? Is this the right tool for what we want to do? If it is, how can we ensure the broadest possible range of use of the data (and the model) by other scholars?”
All of these are questions that, in the author’s opinion, are not asked often enough at the start of a digital humanities project, especially PhD projects. Instead, PhD students often rely on the software their supervisors use or on software that they are already familiar with, even when their project would be better served by using a different digital tool. The presentation aims to provide some guidelines for future digital humanities projects, not just in terms of which questions might be beneficial to ask at the start of a new DH project, but also on how to explain to supervisors not particularly familiar with DH projects why these questions should be asked, and why the most obvious or traditional tool may not be the best for the task at hand.
As mentioned above, the project’s ultimate goal was to design a basic model for a runological research database. The model was required to be able to store the basic information every runologist needs, but also to be flexible enough to include research from different disciplines. This flexibility was a key focus, since runic inscriptions are of interest to a broad variety of scholars from different disciplines, amongst them archaeology, philology, history and sociology. The question was therefore whether relational databases were up to the task. The goal was informed by more recent approaches to creating large, reusable collections of structured data, but also by the lack of explanation for data modelling choices where the already existing databases were concerned, which made understanding and replicating query results from these databases difficult. At its most basic, a relational database should attempt to model the world it is representing (as, for example, Ramsay explains in his 2016 contribution “Databases”, DOI: 10.1002/9780470999875); in this case, the reading, interpretation and contextualisation of runic inscriptions within the broader context of the world they originate in. A specific point of interest was thus the translation of a runologist’s workflow when deciphering a runic inscription into appropriate data models and structures, as well as the limitations of the relational technology when modelling this reality. The presentation looks at a range of hurdles and pitfalls when trying to model different stages of interpretation while working with texts written in non-Roman scripts and how to work with “texts” when the field itself doesn't quite agree what makes a “text”.
It also provides a short overview of how already existing databases such as Samnordisk runtextdatabas and the database of the Kieler Runenprojekt solved the same issues in order to understand their data modelling approaches. During the project, it soon became obvious that several lessons could be learnt from these older models, most of which had clearly been tailored to answer very specific research questions, thus limiting the reuse of the existing data for other projects. Further issues arose from a lack of documentation, both where the data model and the data itself were concerned, limiting reproducibility of results and even research into the underlying assumptions. Some ideas and tips on how to get around a lack of documentation are presented as well.
After the stages of analysis and comparison of already existing databases, the PhD project continued to develop its own data model, based on runological methodology and taking into account what previous databases had done. This “core” model database was then filled with data concerning the almost 700 medieval runic inscriptions from Bergen, Norway. This specific corpus of runic inscriptions was particularly suited to serve as a test corpus, because the inscriptions are carved into a wide variety of objects (such as wood, bone, ceramics, leather) and cover a wide range of different topics (from prayers to personal correspondence to vulgarities). Having been discovered in the course of archaeological excavations in the old town quarter Bryggen in Bergen, the objects themselves are of interest to archaeologists and tied to archaeological excavation data, while their textual contents have drawn the attention of runologists, Old Norse scholars, literature scholars and onomasticians. They were therefore well-suited to the task, since examining them in the context of their origins required using approaches from archaeology, runology, traditional text analysis, history and name studies.
Due to the broad range of potential example studies that can be conducted using the material, this particular project, however, only took into account the archaeology, onomastics and traditional text analysis. The presentation finishes by giving a short introduction into how the core database model as well as the various modular “research databases” modelling the archaeological, onomastic and textual aspects were developed to allow connecting and analysing different aspects of the runic inscriptions in relation to each other. From the perspective of three years on and in a follow-up project, it looks at where the final PhD database model “got it wrong” and how different factors such as unfamiliarity with the technology and difficult access to appropriate support contributed to these shortcomings. It concludes with a checklist of tips for how future projects, especially PhD projects, can avoid some of the issues this project ran into along the way. And, not least, how to convince your supervisors that yes, talking about the tool you are using should be part of your thesis.

The paper examines the contribution of the Pishu Tebe project to digitization of historical postcards. Pishu Tebe contains 45000 marked-up postcards and is thus one of the largest projects involved in postcard digitization. The principal innovation proposed and successfully implemented by Pishu Tebe is a multidimensional approach to digital preservation of postcards as cultural entities. In contrast to other initiatives, Pishu Tebe builds a digital archive opening the way for analyzing all kinds of postcard-related data: visual, textual, chronological, geographical and personal (sender/recipient's names). The paper starts with a discussion of the place of Pishu Tebe in contemporary postcards studies, its conceptual methodology and IT background. It describes then the key phases of the digitization process and identifies major challenges faced by a voluntary digitization project. In the end, the paper presents quantitative and qualitative results of Pishu Tebe and outlines further plans.

Sarpur is the Icelandic collective cultural history collection database and associated management system. Rekstrarfélag Sarps is responsible for the operation of Sarpur, which serves the majority of accredited museums in Iceland, with the order of 300 staff users which increases steadily. In total more than 60 different museums and memory institutions in Iceland currently use Sarpur.

The varied museums span from The National Museum of Iceland, The National Gallery of Iceland and Icelandic Museum of Natural History, to urban city-, folk- and art museums and small regional museums run by the municipalities as well as collections managed by non-profit organizations and private foundations. In year 2023 there were over 1,6 million registered artifacts, photographs, art works, historic sites, houses, drawings, documents, archaeological material, books, coins and intangible cultural material like site names and material from ethnological collections registered in Sarpur. Around 1,1 million of the registrations are displayed on the external web sarpur.is.

Sarpur facilitates both management and overview of Icelandic cultural heritage shared across member museums in the whole country. It enables museums to register their collections and to further process those. That means for instance managing object locations, conservation, exhibitions and outgoing loans. It also facilitates online exhibitions, crowdsourcing with the local communities as well as orders for images from the public.

Icelandic museums are often quite small compared to similar institutions abroad. This smallness has facilitated this collaborative effort of registering and managing cultural heritage in this one shared data well Sarpur. It’s opportunities are obvious when it comes to having an overview over cultural heritage in Iceland for i.e. the benefit of research, education and communication.

Currently, Rekstrarfélag Sarps is working on replacing the technical infrastructure for Sarpur including both the database and software. Present software from year 2012 will be replaced with products from the company Zetcom. The first version of Sarpur came about in 1998. All configurations and daily operations of the system will continue to be carried out at the central office of Rekstrarfélag Sarps but the affiliated institutions maintain certain ground-level functions and manage their “part” of the database.

In this presentation Rekstrarfélag Sarps will briefly introduce Sarpur and explain why and how it came about. It will iterate the significance of Sarpur for research activities in the field of digital humanities and art in the Icelandic society using examples of past- and current research projects. Opportunities in the field of education will be addressed. Pros and cons of Sarpur as a collaborative effort that started out as an experiment which resulted in a nationwide practice will be discussed. Similar examples in the field of libraries in Iceland will be mentioned. The presentation will touch on the societal benefit that may result from establishing cultural heritage databases on a national level in a small society with limited resources. If time allows future plans for Sarpur will be discussed.

The Uralic Trove (UraLaari)

– The digital datainfrastructure of Uralic language speaker area

Outi Vesakoski, Jenni Santaharju, Timo Rantanen & Meeli Roose

Keywords: Interdisciplinary studies, Human past, Finland, Spatial data, Language data

This paper presents a diverse data collection related to human past in the Uralic language speaker area expanding from Scandinavia to Siberia. The publication promotes F of the FAIR principles: The whole collection is shortly introduced here for further use.

Integrative approaches to building holistic human histories are little by little covering the globe, and the work by BEDLAN team (Biological Evolution and Diversification of Languages, www.bedlan.net) and Human Diversity consortium at the University of Turku have integrated the North-West Eurasian area into this emerging network of global integrative studies of the human past. We are framing a new infrastructure combining our current and forthcoming datasets of human diversity in the Uralic language speaker area. With the Uralic Trove we aim advantage the studies and development of methods to conduct truly integrative studies.

Currently, the Uralic Trove includes four datasets related to Uralic language speaker areas and four related especially to the area of Finland. The dataset are available in repositories and user interfaces. We have also build an interactive web app for easy access to static maps and to provide a possibility for lay audience to create their own maps – Uralic Historical Atlas (URHIA) is presented in Roose et al, this conference.

Uralic language speaker area

UraTyp is a language typological dataset (Norvik et al. 2022) consisting of 360 linguistic traits that are in form of questions with binary answers. The data is being build in GitHub, offered in Zenodo and is in easily approachable mode in web app Uralic Areal Typology Online built by MPI-EVA (Robert Forkel, uralic.clld.org). The features represent actually two typological lists:

1) Grambank data (GB) is a list of 195 grammatical features collected today for 2700 word languages with the aim of studying global typological diversity. The list was produced by the Grambank initiative by Max Planck Institute (Skirgård et al. 2023, grambank.clld.org). BEDLAN contributed the Uralic languages to GB.

2) Uralic specific traits (UT), 165 extra traits that we developed to resolve variation within Uralic languages (Norvik et al. 2022).

UraLex is a basic vocabulary data with cognate assessments indicating etymological connections between words for a meaning in different languages. This is a commonly used data type to construct quantitative language phylogenies (e.g. Gray & Atkinson 2003, Grollemund et al. 2015). Basic vocabulary consists of core items of the lexicon existing in all languages, such as lower numerals, pronouns and body parts. The data is published in Lexibank, which is a data repository organised by the Max Planck Institute for Science of Human History (https://github.com/lexibank/uralex; Syrjänen et al. 2018). UraLex 2.0 covers 26 languages and a reconstruction of Proto-Uralic. We have renewed the UraLex dataset totally (definitions of meanings, selection of words, updates to cognate assessments), and we will soon publish the version 3.0 in Zenodo. The data will be also made available and visible through Uralic Areal Typology Online.

Geographical database of the Uralic languages instead is a spatial data with polygons of Uralic languages speaker areas (Rantanen et al 2022). It consist of multiple versions of each speaker area digitalized first from literature and corrected later by experts. The data and maps are available also in URHIA web app.

Interdisciplinary spatial database of human history in the NW Euraasia is a collection of GIS-files and map vizualisations that are done for different publications. We now offer the GIS-files for further interdisciplinary use.

Finnish area

The Uralic Trove also includes data covering only Finnish language area. A new profile area of University of Turku, the Human Diversity consortium (www.humandiversity.fi) focusses especially to Finland for there are multidisciplinary data sets available. At the moment we have published or are publishing the following:

Preindustrial dialectal landscape of Finland is based on spatial data of linguistic variation in Finland collected by Lauri Kettunen. Santaharju et al. (submitted abstract to this conference) will discuss this data.

Archaeological Artefact Database of Finland is a spatial data offering location and typological classification of 49 000 artefacts located in Finnish museums. The database was collected 2013-2020 and is freshly published and will be described in Pesonen at al. (submitted ms.) Pesonen et al. has submitted a poster abstract about AADA to this conference.

Historical travel environment model over Finland is a spatial data with terrain and landscape attributes and combining information from historical sources that characterize the landscape in terms of travel effort given the environmental and human-related factors current up until the late 19^th century. The data is described in Rantanen et al. (2021) but will be OA during the spring 2024.

The Uralic Trove will include also spatial data of environmental variation in Finland (used in Honkola et al. 2018) and cultural data used in Honkola et al. (2018) and folk-culture data from 1600-1800 used in Rantanen, Santaharju et al. (in preparation). Publishing these data is an on-going process, and will be also explained in the paper.

This paper collects together the data sets produced within and around BEDLAN project (Biological Evolution and Diversification of Languages). Part of them were Open Access already earlier, part of them a published along with this article. We did not promote Open Access in the beginning of the project, but learned to appreciate FAIR principles during the journey: Now we see that that digital humanities using OA data could induce international interest to study the human history in the NW Eurasia. We encourage Uralistics and other researchers of the area to make their data OA, for more researcher will yell more voices and cumulative insights, and eventually – hopefully - lead to holistic understanding of human history in Scandinavia and NW Eurasia.

References

Honkola, T., Ruokolainen, K., Syrjänen, K. J. J., Leino U, Tammi, I, Wahlberg, N. & Vesakoski, O. (2018). Evolution within a language: Environmental differences contribute to divergence of dialect groups. BMC Evolutionary Biology. 18:132.

Norvik, M., Jing, Y., Dunn, M. Forkel, R., Honkola, T., Klumpp, G., Kowalik, R., Metslang, H., Pajusalu, K., Piha, Saar, E., Saarinen, S. & Vesakoski, O. Uralic typology in the light of new comprehensive data set. Journal of Uralic Linguistics 1: 4-41.

Rantanen, T., Tolvanen, H., Honkola, T., Vesakoski, O. 2021: A comprehensive spatial model for historical travel effort – a case study in Finland. Fennia 199 (1) 61-88.

Rantanen, Timo, Harri Tolvanen, Meeli Roose, et al. (2022). ‘Best Practices for Spatial Language Data Harmonization, Sharing and Map Creation—A Case Study of Uralic’. PLOS ONE 17 (6): e0269648.

Roose, Meeli, Timo Rantanen, Dmitri Kuznesov, et al. (2023). ‘Collection of Spatial Information and Maps of Human Past and Environment in the Uralic Languages Speaker Area’. https://doi.org/10.5281/zenodo.10081902 [data set]

Skirgård et al. (>100 authors) 2023. Grambank reveals the importance of genealogical constraints on linguistic diversity and highlights the impact of language loss. Science Advances. http://dx.doi.org/10.1126/sciadv.adg6175

Vesakoski, O, Salmela E ja Piezonka, H. (2024). Uralic archaeolinguistics. In Oxford Handbook of Archaeology and Languages, ed. by Martine Robbeets ja Mark Hudson. In press.