=Paper=
{{Paper
|id=Vol-3110/paper11
|storemode=property
|title=SPARQLing Ogham Stones: New Options for Analyzing Analog Editions by Digitization in
Wikidata
|pdfUrl=https://ceur-ws.org/Vol-3110/paper11.pdf
|volume=Vol-3110
|authors=Sophie C. Schmidt,Florian Thiery
|dblpUrl=https://dblp.org/rec/conf/graph/SchmidtT20
}}
==SPARQLing Ogham Stones: New Options for Analyzing Analog Editions by Digitization in
Wikidata==
https://ceur-ws.org/Vol-3110/paper11.pdf
SPARQLing Ogham Stones: New
Options for Analyzing Analog Editions
by Digitization in Wikidata
Sophie C. Schmidt1
Florian Thiery2
1 German Archaeological Institute (DAI)
Berlin, Germany
2 Römisch-Germanisches Zentralmuseum,
Leibniz Research Institute for Archaeology (RGZM),
Mainz, Germany
Abstract
Linked Open Data (LOD) should be the standard for the modeling and
publication of research data, as it greatly improves the research process
by facilitating access and reducing the likelihood of mistakes. To en-
courage our readers to create LOD, we have developed a workflow for
the enrichment of datasets taken from analog and digital sources by in-
terlinking and publishing them in Wikidata. This paper will discuss the
main principles of LOD, as well as current attitudes towards it in the
digital humanities. Our vision for LOD has lead us to create SPARQL
Unicorn, a series of tools designed to make Wikidata easier to use. As
an example of SPARQL Unicorn in action, the second half of our pa-
per will describe the Wikidata integration of the inscriptions found on
the early medieval Ogham stones. Sources, workflow, tools, and data
models will be explained in detail, as two examples of (geo-) statistical
analysis showcase the benefits of enriched LOD to researchers.
Creative Commons License Attribution 4.0 International (CC BY 4.0).
In: Tara Andrews, Franziska Diehr, Thomas Efer, Andreas Kuczera and Joris van Zun-
dert (eds.): Graph Technologies in the Humanities - Proceedings 2020, published at
http://ceur-ws.org.
211
1 Introduction
This paper introduces a workflow for digitizing analog editions in the open
access data storage facility Wikidata1 , and shows how the enriched data can
be extracted and used for further research. For this purpose, we draw upon
the Corpus Inscriptionum Insularum Celticarum (CIIC), the most com-
plete analog catalog of Ogham stones assembled by R. A. S. Macalister (1945,
1949), the CISP project,2 and the Ogham in 3D Project.3 As we will show,
the SPARQL Unicorn tool framework enables users unfamiliar with the
SPARQL language to work with data stored in Wikidata. Our analysis of
the created dataset by means of the SPARQLing Unicorn QGIS Plugin and
the statistical scripting language R demonstrates the viability and usefulness
of such an approach.
The SPARQL Unicorn tool framework and the ᚑᚌᚔ (Ogi) Ogham Pro-
ject are developed and maintained by the Research Squirrel Engineers4 re-
search network and specific working groups. The Research Squirrel En-
gineers are an open and international association of researchers with a
background in software engineering, geoinformatics, and cultural heritage,
which was founded by the authors of this paper, Sophie C. Schmidt5 and
Florian Thiery6 . Its members cooperate with other researchers and institu-
tions to implement research projects and research software based on Linked
Data and the Semantic Web, without institutional and funding dependen-
cies.
This chapter begins with some basic information concerning the in-
scriptions on the Ogham stones and the Ogham alphabet itself. We will
then introduce Linked Open Data (LOD) as a data modeling concept,
and Wikidata and Wikimedia Commons as examples of this Open Sci-
ence approach. Our vision of LOD lays the groundwork for the section
on SPARQL Unicorn, a tool framework designed to facilitate the use of
Wikidata. Last, but not least, we will describe the Ogi Ogham Project in
detail: our sources, the workflow of the project, the import of data into
Wikidata, and the modeling process.
1
https://wikidata.org
2
https://www.ucl.ac.uk/archaeology/cisp/database/manual/
3
https://ogham.celt.dias.ie
4
http://squirrel.link
5
Research Associate at the Deutsches Archäologisches Institut (NFDI4Objects)
6
Research Software Engineer at the Römisch-Germanisches Zentralmuseum, Depart-
ment of Scientific Information Technology
212
2 What is Ogham?
Ogham (Old Irish: Ogam) stones (Figure 1) are monoliths inscribed with
the Ogham script (Ferguson, 1864; Graves and Limerick, 1876), which is an
early medieval alphabet used predominantly in Ireland and the western parts
of Britain between the fourth and the ninth centuries CE. The so-called ‘or-
thodox’ inscriptions were employed from the fourth to the sixth centuries
to record the Primitive Irish language, of which the stones are the earliest
sources, whereas the ‘scholastic’ inscriptions of the sixth to the ninth cen-
turies were used to represent Old Irish. The script was probably inspired by
the Latin alphabet and contains 26 letters written from bottom to top, left
to right, and top to bottom, in the form of a boustrophedon. Each letter
is composed of strokes placed along a central line. Their relation to one an-
other and the angle towards this central line, for which the ridge or natural
arris of the stone is often used, encode their meaning (MacManus, 1997).
The texts found on Ogham stones mostly refer to persons, tribes, or fam-
ily affiliations (Macalister, 1945). Since graph databases rely on linking state-
ments between two entities to form a network, the formula used to record
kinship relations on the stones makes this corpus especially well-suited to be-
ing represented in a graph form along the lines of X MAQI Y → X son of
Y (e.g. Q69389090)7 or X MAQI MUCOI Y → X son of the tribe Y (e.g.
Q69388229)8 .
3 Introduction to Linked Open Data
The term ‘Linked Open Data’ describes the goal of interlinking data within
the World Wide Web with other semantically related data. Tim Berners-Lee
has proposed the Linked Data principles consisting of four simple rules (Fig-
ure 2):
1. Use URIs as names for things
2. Use HTTP URIs so that people can look up those names
3. When someone looks up a URI, provide useful information using the
standards RDF* and SPARQL
4. Include links to other URIs, so that users can discover more things
However, the shortcomings that Berners-Lee pointed out in 2006 remain
a challenge today: “a surprising amount of data isn't linked [...] because of
problems with one or more of the steps.” (Berners-Lee, 2006)
The concept of the Semantic Web as established by Berners-Lee (Berners-
Lee et al., 2001) draws on Open Data to describe the web resources, links,
7
https://www.wikidata.org/wiki/Q69389090
8
https://www.wikidata.org/wiki/Q69388229
213
and machine-readable interfaces that are required for creating a Giant Global
Graph (Berners-Lee, 2007). Applying LOD techniques helps in creating
FAIR (findable, accessible, interoperable, reusable) (Wilkinson et al., 2016)
data. Yet publishing data with unique identifiers on the internet (HTTP
URIs) and providing links to other resources is of limited utility unless the
data is both open and usable – only then does it qualify as LOD (Hausen-
blas and Boram Kim, 2015). In the best case scenario, the data in question
adheres to Linked Open Usable Data (LOUD) principles (Sanderson, 2018),
which are all combined in the seven-sphere data model proposed by Thiery
(2019).
The model's seven spheres (Figure 3) are defined as follows:
1. OL (Data is available in the Web under an open license)
2. RE (Data is available as structured machine-readable data)
3. OF (Data is available in an open, non-proprietary, structured, and
machine-readable data format)
4. RDF (Data is available as URIs and semantically modeled as RDF)
5. LOD (Data is available as a resource, semantically modeled as RDF, and
semantically linked to other resources)
6. LOUD (LOD is available as usable data, according to the LOUD prin-
ciples)
7. LOUD+FAIR (LOUD is available as findable, accessible, interoper-
able, and reusable data, according to the FAIR principles)
3.1 LOD in the Digital Humanities and Digital Archaeology
In 2011, Leif Isaksen described possible applications for Semantic Web (and
Linked Data) technology in the discipline of archaeology (Isaksen, 2011).
This was the starting point for Linked Data initiatives in the field of ancient
studies in general, including seminal projects like Pelagios Commons9 (Si-
mon et al., 2016). In the wake of this development, a number of community
projects and networks such as Linked Pasts10 have been established, and in-
creasing numbers of researchers are joining the scholarly LOD community.
The work being carried out in this field includes the modeling and reasoning
of vagueness and uncertainties in graph data with the help of the Academic
Meta Tool (Unold et al., 2019).
In popular culture, the phrase hic sunt dracones (‘here be dragons’) is often
used to designate areas on historical maps that are unknown to the authors.
It has also been applied to the harvesting and storage of data (Wuttke, 2019):
nowadays, researchers can share their research as LOD, enabling the com-
9
https://pelagios.org
10
http://linkedpasts.org/assets/lp_whitepaper.pdf
214
munity to participate in the scholarly discourse in order to create new know-
ledge; yet unfortunately, much of this data is neither findable nor accessible,
and is therefore not FAIR, resulting in modern “unknown data dragons.”
Often, these ‘data dragons’ are not interoperable and thus unusable for fur-
ther scholarly research. These shortcomings can be overcome with the stand-
ards and recommendations outlined above: Semantic Web and LOD, the
FAIR principles, and LOUD (Thiery et al., 2019). To reduce the number of
archaeological ‘data dragons,’ the Computer Applications and Quantitative
Methods in Archaeology (CAA) Special Interest Group (SIG) on Semantics
and LOUD in Archaeology (SIG-DataDragon)11 was founded in 2019.
3.2 Wikidata and the Wikimedia Universe
Established in 2012, Wikidata12 is a data hub for structured data that was not
developed as a primary database for a specific project, but rather to gather ex-
isting entries from other databases (Vrandečić and Krötzsch, 2014). It is a
free and open knowledge base where anybody can add and edit data, and
serves as the central storage for structured data of Wikimedia projects such
as Wikipedia and Wikimedia Commons. Data within Wikidata is available
under a free licence (CC0, Public Domain), is multilingual, is accessible to
both humans and machines (e.g. GUI, API, SPARQL), is exportable using
standard formats (e.g. JSON, RDF, SPARQL), and is interlinked to other
open datasets in the Linked Data Cloud. The data model used by Wikidata
contains items (e.g. label, description, alias, identifier) and statements (e.g.
property, value, qualifier, reference), and encourages attribution, links to
other entities, and the indication of provenance and sources as references
(Voß et al., 2014). It lends itself particularly well to the non-hierarchical rep-
resentation of divergent interpretations in cultural heritage research, as it is
capable of representing uncertainties and ongoing scholarly debates: doubts,
ambiguities, and uncertainties can be made explicit by means of qualifiers
such as P518 (‘applies to part’) and Q18122778 (‘presumably’), which in-
dicate that a part of a statement remains open to question.
Modeling decision-making processes is difficult in any kind of data base,
and Wikidata is no exception. To resolve this and other potential drawbacks
of Wikidata, it is possible to create a user-defined ontology based on, for
example, the CIDOC CRM CRMsci extension (Doerr et al., 2020) or the
PROV ontology (PROV-O).13
In Wikidata, versions of the entries are recorded, but while the entry his-
11
http://datadragon.link
12
https://wikidata.org
13
https://www.w3.org/TR/prov-o/
215
tory may be viewed, links to a specific version of an entry are impossible,
because the Semantic Web and URIs are not designed to allow this. As any-
body can edit the entries, the original author does not have data sovereignty,
and false information may be entered. What is more, information cannot
be flagged as debatable – it can only be deleted and discussed in the history
view, which, in the absence of structured moderation or editing, may lead to
duplicates and inconsistencies. However, despite all of these shortcomings,
Wikidata is nonetheless suitable for a number of Open Science applications,
as will be discussed in the following sections.
3.3 Our Vision
LOD should be the standard in digital heritage data management, but un-
fortunately this is not yet the case. Currently, we see two main obstacles
to achieving this goal: 1) data extraction requires SPARQL knowledge, and
data hosting calls for extensive technical training, which only few researchers
have the necessary time and resources to acquire; 2) researchers do not gain
any professional recognition for opening up their archives to share and link
their data, even though this would greatly enrich the research process in that
it enables data to be found faster, reduces errors in transcription and citation,
and leads to reproducible research practices. Wikidata allows for the direct
creation of LOD as part of the Linked Open Data Cloud.14 Yet despite a
number of existing applications,15 Wikidata lacks user-friendly tools for re-
searchers and archaeologists, which is where the SPARQL Unicorn comes
into play.
4 The SPARQL Unicorn
The SPARQL Unicorn was born at the Computer Applications and Quant-
itative Methods in Archaeology conference 2019 in Kraków, Poland.16 Over
the course of this conference, it became abundantly clear that data ana-
lysis and the creation and maintenance of databases play a central role in
archaeological and digital humanities-related research. However, very few
databases are freely and openly available and accessible, and even fewer are
connected to the Linked Open Data Cloud, which greatly hampers the
comparative analysis of records across multiple datasets. One data hub
that has recently picked up momentum is Wikidata, a repository based on
the SPARQL query language. Yet while supporters of free and open re-
search data have a strong affinity towards community-driven and volunteer-
14
https://lod-cloud.net
15
https://hay.toolforge.org/directory/#/search/wikidata
16
https://2019.caaconference.org
216
operated data collecting initiatives such as Wikidata, there is – in addition
to the various issues outlined above – a shortage of user-friendly, free, and
open tools for LOD technologies and repositories, including Wikidata it-
self. To help mitigate these problems, we developed the SPARQL Unicorn,
which we would describe as a friendly tool series for researchers working with
Wikidata and other related triple stores. More specifically, the SPARQL
Unicorn aims to help researchers from the field of ancient studies use the
community-driven data from Wikidata by making it more accessible to those
without expertise in LOD or SPARQL (Thiery et al., 2020b).
4.1 The SPARQLing Unicorn QGIS Plugin
For geospatial-related research, community-based LOD repositories,
their triple stores, as well as SPARQL endpoints (e.g. Wikidata,17
LinkedGeoData,18 and DBpedia19 ) have been established in the Linked
Open Data Cloud. Additionally, gazetteer repositories such as GeoNames20
or Pleiades,21 as well as administrative providers such as Ordnance Survey
UK22 and OS Ireland,23 also offer their geodata as LOD. Unfortunately,
these resources have gained little traction in the geo community thus far. We
believe that the reason for this is a lack of support for LOD-processing GIS
applications. As of yet, triple stores, i.e. semantic databases and SPARQL,
are not supported by GIS software, GeoServer implementations, or OGC
services. Also, the handling of spatial information is quite a challenge for
the Linked Data community: although there are vocabularies like Geo-
SPARQL,24 which at least allow the modeling of spatial 2D information,
even a simple conversion of a Linked Data geo dataset into another reference
system can currently only be accomplished with significant scripting effort.
The SPARQLing Unicorn QGIS Plugin25 (Thiery and Homburg, 2021)
addresses the problem of a lack of tools for geospatial data in the Semantic
Web. Our experimental plugin can be installed in QGIS26 from the cent-
ral QGIS repository27 (latest release v0.12.2, 2021-06-11).28 It allows for
17
https://query.wikidata.org/
18
http://linkedgeodata.org/sparql
19
https://dbpedia.org/sparql
20
http://factforge.net/repositories/ff-news
21
http://sandbox.mainzed.org/pleiades/sparql
22
http://data.ordnancesurvey.co.uk/datasets/os-linked-data/apis/sparql
23
http://sandbox.mainzed.org/osi/sparql
24
https://www.ogc.org/standards/geosparql
25
https://github.com/sparqlunicorn/sparqlunicornGoesGIS
26
https://www.qgis.org/en/site/
27
https://plugins.qgis.org/plugins/sparqlunicorn
28
https://plugins.qgis.org/plugins/sparqlunicorn/version/0.12.2/
217
the graphical user interface based execution of queries in (Geo)SPARQL to
selected (archaeology and humanities-related) triple stores and geo-enabled
SPARQL endpoints, and prepares the results of the queries as geospatial vec-
tor layers in QGIS (Thiery and Homburg, 2020b) for use by the geo com-
munity and other interested researchers.
As it stands, the plugin offers three main functions: a) the simplified
querying of LOD sources, b) the enrichment of geodata, and c) the trans-
formation of GeoJSON to RDF data.
Function A (Figure 4) permits assisted querying of predefined triple
stores, e.g. Wikidata, Nomisma,29 Linked Ogham Data,30 and Roman
Open Data.31 It also makes it possible to add another SPARQL endpoint.
The SPARQLing Unicorn QGIS Plugin will automatically search for se-
mantically described geospatial data in the widely used vocabularies Geo-
SPARQL32 and WGS84 Geo Positioning,33 e.g. geosparql:asWKT, geo-
sparql:asGeoJSON, geo:lat and geo:long.
To assist the user, the plugin provides query examples for the predefined
triple stores which can be used as query templates. The plugin will automat-
ically detect geospatial-related (inferred) classes/concepts (Figure 4, right).
As shown in Figure 4, the user can select one of the geospatial concepts by
clicking on it (e.g. Stone), and the plugin will automatically replace the class
in the query template. Additionally, the user can restrict the spatial area by
a bounding box (Figure 5) or a loaded geospatial layer. This results in a Geo-
SPARQL function34 query using geof:sfIntersects, as shown in Figure 6 (if
the triple store supports GeoSPARQL).
If a user employs the Linked Ogham Data SPARQL endpoint, the
Concept + Label query template, the geospatial concept Stone, or the bound-
ing box shown in Figure 5, this will result in a QGIS vector layer that can be
saved in common geo data file formats, e.g. GeoJSON, or a shapefile. By way
of example, Figure 7 shows the distribution of Ogham stones on the Dingle
Peninsula.
Function B allows the enrichment of geo data using LOD resources from
the Linked Open Data Cloud, especially Wikidata (e.g. Ogham site elevation
levels). Geospatial data, in particular, can always be seen in an application
context, which usually requires additional data from other knowledge do-
mains. Semantically interpreted Linked Data may represent such a resource
29
http://nomisma.org/sparql
30
https://digits.mainzed.org/ogham/
31
https://romanopendata.eu/sparql/epnet.html
32
http://www.opengis.net/ont/geosparql#
33
http://www.w3.org/2003/01/geo/wgs84_pos
34
http://www.opengis.net/def/function/geosparql/
218
for data enrichment.
Function C converts geospatial information from formats such as
GeoJSON into RDF formats like Turtle, which enables the publication of
archaeology-related geo data as Linked Data.
5 Ogi Ogham Project
The ᚑᚌᚔ (Ogi) Ogham Project was initially created in 2019 by members of
the Research Squirrel Engineers network in conjunction with the SPARQL
Unicorn project,35 and is now supported by the Wikimedia Foundation.
The project is a work in progress, whose aims include the digitization and
publication of the analog catalog compiled by Macalister (1945, 1949), as
well as the linking of existing online resources into Wikidata.36 The stand-
ards applied in the creation of this set of LOD mean that the data involved
will become FAIR. The Wikidata repository can be used to support repro-
ducible statistical and geostatistical analysis of the data. We will draw on
Macalister's corpus to illustrate this point and to demonstrate the value of
modeling analog catalogs as LOD.
5.1 Sources on Ogham Stones and Script
There are several sources, both analog and digital, for information on
Ogham stones. As it is our goal to link existing datasets to each other, we
will introduce them here in some detail.
5.1.1 Traditional Offline Sources
Our main source of information is the aforementioned Corpus Inscrip-
tionum Insularum Celticarum (CIIC) compiled by Macalister (1945, 1949),
in which a large number of Ogham stones in Ireland (CIIC 1-317), Wales
(CIIC 317-456), England (CIIC 457-499), the Isle of Man (CIIC 500-505),
and Scotland (CIIC 506-520) are recorded. In the first phase of the project,
we concentrated on the Irish37 Ogham stones (CIIC 1-317). The CIIC is the
most important reference work for Ogham inscriptions, and its numbering
system, CIIC, has also been used to reference stones in other sources.
Macalister describes two different word categories: formula words and
nomenclature words. Examples of formula words are MAQI ᚋᚐᚊᚔ (son,
e.g. CIIC 203, Q67978531) or MUCOI ᚋᚒᚉᚑᚔ (tribe/sept, e.g. CIIC
197, Q69388229). Examples of nomenclature words are CUNA ᚉᚒᚅᚐ
(wolf/hound, e.g. CIIC 154, Q68002826) and CATTU ᚉᚐᚈᚈᚒ(battle, e.g.
35
https://www.wikidata.org/wiki/Q73901970
36
https://www.wikidata.org/wiki/Q70873595
37
https://w.wiki/Wmz
219
CIIC 58, Q70892430). Other names refer to deities, such as the god Lugh
(LUC ᚂᚒᚌ), who appears in names such as LUGADDON ᚂᚒᚌᚌᚐᚇᚑᚅ
(CIIC 4, Q70899515).
As most of the stones are damaged, linguistic analysis often yields diver-
gent readings and reconstructions (MacManus, 1997). A graph-based ap-
proach makes it possible to model these differing interpretations as equally
significant readings of the same text. The encoded relations between inscrip-
tions and the spatial topology of the sites of their discovery also lend them-
selves to visualization and analysis within a graph data system, for example
in the form of historical network research, see e.g. Deicke (2017).
Of course, the archaeological and linguistic research conducted in the in-
tervening years means that the CIIC is not the only relevant source. It is,
however, the most complete, and since subsequent scholarship has contin-
ued to use Macalister's numbering system for the Ogham stones, the CIIC
is an expedient starting point for our data acquisition. Later sources, such as
MacManus (1997) and Forsyth (1996), can be added as a second step.
We used the spatial information given in Macalister's catalog – county,
barony, and townland – as well as the inscriptions themselves to identify the
stones in other resources. A townland (Irish: baile fearainn),38 the most pre-
cise category describing the location of the stones, is a spatial unit of Gaelic
origin, though it has been modified by later re-structuring processes such
as the Ordnance Survey. This information was used for linking the data
to OpenStreetMap with the help of Townlands,39 and in some cases Lo-
gainm.40 Most Ogham stones recovered so far have been re-used over the
centuries and can now be found as lintels in huts, in the walls of churches
etc. (MacManus, 1997). As a spatial category, the townland thus provides a
sufficient level of detail, while avoiding the pitfall of implying a higher level
of precision than is actually achievable.
5.1.2 Online Databases
Several online databases for Ogham-related research exist. The Thesaurus
Indogermanischer Text- und Sprachmaterialien (TITUS) database41 hosted
by Jost Gippert dates from 1996. It is online, but it is neither open nor in-
teroperable, and cannot be interlinked with other datasets, which prevented
us from using it in our case study.
Another rich source of information on Ogham stones is the database of
38
https://data-osi.opendata.arcgis.com/datasets/559dc2f725004823b51923e1d6ba5889_0/data
39
https://www.townlands.ie/
40
https://www.logainm.ie/en/
41
http://titus.uni-frankfurt.de/ogam/frame.htm
220
the Celtic Inscribed Stones Project (CISP)42 assembled by University Col-
lege London, which gathered numerous inscriptions (not only in Ogham)
and published them online in 2001. This database has no API, and one can
only click through the various entries. It is online, but neither linked nor
open, which is wholly understandable given the year of its publication. We
would like to thank Dr. Kris Lockyear, who generously made the project's
Access database available to us and gave us permission to use the data.
Some more recent open research projects offer online and open data,
including the Ogham in 3D Project,43 which creates 3D scans of known
Ogham stones in Ireland (as of January 2021, there were c. 160 scanned
stones44 ). An online platform45 offers 3D data (3D PDF and OBJ files), as
well as EPIDOC46 files with annotated stone inscriptions for download un-
der a Creative Commons License. An example is stone CIIC 180, Emlagh
East (Imleach Dhún Séann), Co. Kerry,47 which is shown in Figure 8.
Another important resource is the Historic Environment Viewer,48
which contains a WebGIS that provides access to the records of the Na-
tional Monuments Service's Sites and Monuments Record (SMR) and the
National Inventory of Architectural Heritage (NIAH). While this reposit-
ory is very helpful, there are no links to other datasets.
It is therefore safe to conclude that there is a lack of Ogham research
data in the Linked Open Data Cloud. This results in various challenges,
and makes comparative analysis of records across multiple datasets difficult.
However, some useful information can be found in the Wikimedia universe
with its variety of tools and projects under the umbrella of the Wikimedia
Foundation, as will become clear in the following section.
5.1.3 Ogham in the Wikimedia Universe
The Wikimedia project universe49 provides researchers with a large num-
ber of free and open access data hubs. This includes the free encyclopedia
Wikipedia, the free media repository Wikimedia Commons, the free know-
ledge base Wikidata, as well as the bibliographic extension to Wikidata, Scho-
lia. Wikipedia, for example, contains articles on the Ogham alphabet50 and
42
https://www.ucl.ac.uk/archaeology/cisp/database/
43
https://ogham.celt.dias.ie/menu.php?lang=en
44
https://ogham.celt.dias.ie/menu.php?lang=en&menuitem=81&overviewinfo=epidoc_stone_details
45
https://ogham.celt.dias.ie/menu.php?lang=en&menuitem=81&overviewinfo=file
46
https://epidoc.stoa.org/gl/latest/
47
https://t1p.de/o3d180
48
https://webgis.archaeology.ie/historicenvironment/
49
https://meta.wikimedia.org/wiki/Wikimedia_projects
50
https://en.wikipedia.org/wiki/Ogham
221
Ogham inscriptions.51 Some bibliographic information concerning Ogham
has already been assembled in Scholia, but the collection requires further at-
tention52 to expand the small number of existing items related to the topic.53
Wikidata Commons offers a set of free Ogham images,54 including a
chart of the Ogham alphabet,55 Macalister's drawing of CIIC 81 at Gar-
ranes, Barony of Kinalmeaky (Figure 9), and the photograph of CIIC 81
in University College Cork's Stone Corridor (Figure 1) with the inscription
C[A]SSITT[A]S MAQI MUCOI CALLITI (CASSITTAS son of the tribe
CALLITI, the hard ones).
5.2 Workflow
The first step of our Ogi Ogham Project workflow (Figure 10) is the extrac-
tion of spatial information on the Ogham stones and their inscriptions from
Macalister's offline catalog. This spatial information is then linked to Open
Street Map data, other Geo LOD resources, as well as further Ogham data,
which allows the mapping of databases according to location and informa-
tion on the inscription (e.g. the CISP database). As a next step, this data can
be imported into Wikidata, making it possible to extract the enriched inform-
ation with SPARQL Unicorn and to analyze it further with software such
as QGIS (using the SPARQLing Unicorn Plugin) or R (see Section 5.3) in a
process that can be applied to data from other sources as well. In the follow-
ing sections, we will first discuss Wikidata modeling and the import process
for spatial information, before presenting two concrete examples of how our
workflow can be applied.
5.2.1 Wikidata Import
The Association of Research Libraries (ARL, 2019) recommends us-
ing OpenRefine56 for inserting, publishing, and maintaining information
stored in Wikidata. Our basis for editing in OpenRefine are CSV files,
which contain all information along with links to other Wikidata entities.
We manually compiled these CSV files from sources such as Macalister,
Logainm, and Open Street Map. Our first import57 contained CSVs for
51
https://en.wikipedia.org/wiki/Ogham_inscription
52
https://ogham.celt.dias.ie/menu.php?lang=en&menuitem=80
53
https://scholia.toolforge.org/topic/Q184661
54
https://commons.wikimedia.org/wiki/Category:Ogham
55
https://commons.wikimedia.org/wiki/Ogham_alphabet
56
https://openrefine.org/
57
https://github.com/ogi-ogham/ogham-wikidata
222
‘Ogham sites/townlands,’58 ‘Ogham collections,‘59 and ‘Ogham stones.’60
OpenRefine offers the possibility of creating individual mapping schemes
for Wikidata, which we applied to, among other things, Ogham sites61 and
Ogham stones62 . The results of this mapping process can be transformed
and exported by OpenRefine to QuickStatements.63 The Quick Statements
2 Tool64 is capable of creating new Wikidata entities and updating existing
ones automatically based on the QuickStatements syntax.65 In light of our
own experience with this workflow, we can highly recommend using Open-
Refine and QuickStatements as Wikidata import tools.66
5.2.2 Wikidata Modeling: Words
Wikidata contains a number of Ogham-related entities67 which describe
Ogham as an early medieval writing system (Q184661), Ogham stones as
stones with Ogham inscriptions (Q2016147), Ogham sites as sites where
Ogham stones have been found (Q72617071), and Ogham letters as char-
acters in the Ogham alphabet (Q41812345).
As outlined above, a variety of formula and nomenclature words can be
found in Ogham inscriptions. A list of these words according to MacManus
(1997) accompanied by a translation and a list of references and variants has
been published by the Ogi Ogham Project members on GitHub.68 While
some of the listed words exist as a Wikidata entity, additional entries will be
published within the Ogi Ogham Project.
5.2.3 Wikidata Modeling: Sites
As we have already pointed out, Macalister's CIIC established the Ogham
stones' geospatial placement down to the level of individual townlands. Ini-
tial comparisons were made with Townlands (based on OSM) and Logainm,
but several problems arose during this process: some locations were un-
known to Macalister; he made mistakes (typographical errors, wrong place
names), or gave imprecise information (occasionally, townlands in one bar-
ony have the same name). In some cases, a shift in the structure of baron-
58
https://github.com/ogi-ogham/ogham-wikidata/tree/master/Townlands
59
https://github.com/ogi-ogham/ogham-wikidata/tree/master/OgamCollections
60
https://github.com/ogi-ogham/ogham-wikidata/tree/master/OgamStones
61
https://t1p.de/gh-ogi-1
62
https://t1p.de/gh-ogi-2
63
https://www.wikidata.org/wiki/Help:QuickStatements
64
https://quickstatements.toolforge.org/#/
65
https://www.wikidata.org/wiki/Help:QuickStatements#Command_sequence_syntax
66
https://github.com/ogi-ogham/ogham-wikidata/blob/master/HowTo/OpenRefine.md
67
for a short list, see https://github.com/ogi-ogham/ogham-wikidata
68
https://github.com/ogi-ogham/oghamextractor/blob/master/words/words.csv
223
ies, electoral divisions, and townlands had occurred between 1945 and 2020.
This created a situation where some of the townlands referred to by Macal-
ister could no longer be identified by us.
We used the database of archaeological finds uploaded by the Department
of Culture, Heritage and the Gaeltacht (the above-mentioned Historic En-
vironment Viewer), to check our information. Although the locations of
some items in this database are listed as unknown, attempts at clarification
of their precise whereabouts were undertaken by local experts, to whose find-
ings we adhered (this applies to a total of 16 cases; concerning CIIC 204, for
example, we followed the experts' evaluation that Macalister had erroneously
given its location as Curraghmore West instead of East).
The website Logainm also proved helpful, as it linked a monument about
which Macalister's catalogue yielded only imprecise information to a specific
townland. In instances where Macalister provided further information on
the stone's location by describing the area or certain landmarks, we were able
to use these details to improve the precision of the spatial data. For example,
Macalister's comment that the stone CIIC 48 was found south of the village
allowed us to tentatively identify the townland in question. In the rare cases
in which two townlands with the same name were located right next to each
other, we opted for the larger of the two, crosschecking this educated guess
with the Historic Environment Viewer.
In this way, 185 out of the 196 townlands mentioned by Macalister could
be identified and the corresponding information entered into Wikidata: the
townland's anglicized name; its Gaelic alias; the province, county, barony,
civil parish, and electoral division it belongs to; and a point coordinate in-
dicating the middle of the townland polygon. The dataset was linked by
providing the OSM, Logainm, and OSi GeoHive IDs, as well as the link to
the Townlands IDs from which we derived most of our data. In order to map
the eleven Ogham stones whose location remained unknown, we resorted to
the center of the barony as indicated by Macalister (Thiery et al., 2020a).
As stated above, decisions such as these are currently difficult to model
using Wikidata. A bespoke ontology with self-defined semantic modeling
rules could be a step towards more transparency in decision modeling.69
Table 1 shows the example of the Wikidata model for the Ogham sites in
Garranes Townland, Co. Cork. Each site is described with a label, a descrip-
tion, an alias (Irish name), the country, the province, and other administrat-
ive divisions, a coordinate location, as well as external identifiers linked to
OpenStreetMap and the Logainm website. Wikidata also offers the possib-
ility of setting statements for reference information, thereby linking inform-
69
more information at https://doi.org/10.5281/zenodo.4091743
224
ation to its source. This was done for several properties of an Ogham site
including administrative divisions, location, and external identifiers (Table
2), so as to provide a reference for each piece of controversial information
with regards to the stones' location.
5.2.4 Wikidata Modeling: Stones
The entries on Ogham stones in Wikidata follow a basic modeling struc-
ture: P31 (instance of) Ogham stone (Q2016147), P361 (part of) Ogham
stones (Q67978809)/Ogi-Ogham Project (Q70873595), P17 (country) Ire-
land (Q27), P625 (coordinate location) derived from the Ogham site, P189
(location of discovery) county and Ogham site, P1684 (inscription) and
P1545 (series ordinal). Some of the stones contain additional information
as shown in Table 3 for CIIC 81.70 Our goal is to model all known Ogham
stones. We would like to add details on P186 (material used), P195 (col-
lection), P2043/P2049/P2048 (length, width, height), P6568 (inscription
mentions), P5816 (state of conservation) and P18 (image). The sources of
this in-depth information are shown in Table 4. As discussed above, refer-
ences such as these are especially relevant in cases of conflicting information
regarding the texts and their interpretation.
5.3 Analysing Ogham
The information on the Ogham stone sites collected in Wikidata enables a
multitude of analytical approaches. By way of example, we will concentrate
on spatial analysis and the exploration of word counts.
To gather and import Wikidata data into a format that is usable in the stat-
istical programming language R, a package called WikidataQueryServiceR
(Popov, 2020) can be used. It is an API client for the Wikidata Query Ser-
vice71 and implements the function query_wikidata. query_wikidata trans-
forms the data retrieved from Wikidata by means of an input SPARQL query
into a dataframe, which is one of the most widely used classes for tabular data
in R. The scripts for the analytical approaches described in the following sec-
tions can be found on GitHub.72
In our investigation of the spatial relations between the Ogham sites and
word frequencies, several R packages were utilized.73 In addition, we also
70
https://www.wikidata.org/wiki/Q69385424
71
https://query.wikidata.org/
72
https://github.com/sparqlunicorn/oghamaps
73
R Core (R Core Team, 2020); for data cleaning and visualization: tidyr, reshape, gg-
plot2, viridis (Wickham and Henry, 2020; Wickham, 2007, 2016; Garnier, 2018); for spatial
analysis: ggspatial, rgdal, sp (Dunnington, 2018; Bivand et al., 2019, 2013).
225
employed QGIS and the SPARQLing Unicorn QGIS Plugin to conduct vari-
ous types of spatial analysis on the modeled Ogham stones.
5.3.1 Spatial Analysis
It is evident from the density and distribution analysis carried out by means
of the R project oghamaps74 (Schmidt, 2021) and QGIS (Figures 11 and 12)
that the majority of Ogham sites are located in the counties of Kerry, Cork,
and Waterford (Table 5), and the Dingle Peninsula (western hotspot in Fig-
ure 12). The south of Ireland is the main distribution area with decreasing
density of sites from west to east.
To further examine the various areas, percolation analysis was applied; an
explorative clustering algorithm (Maddison, 2020) which lends itself well to
the identification of clusters and the establishment of densities and distances
between archaeological sites (Maddison and Schmidt, 2020). Euclidean dis-
tance is used to create clusters from points that are within a certain radius
from each other. This radius is then increased in an iterative way, and maps
as well as summaries of the development of certain parameters with regard
to this radius are generated.75
In our example, the change in the mean cluster size is shown in relation to
the clustering radius (Figure 14). At the points where the curve flattens, only
a small number of sites appear to be integrated into the existing clusters. As a
result, we can see how dense the points inside small clusters are, and how far
away the next points lie. Many Ogham sites are about 10 km away from each
other, with the next steps in the distribution occurring at a radius of 22, 34,
and 44 km, after which almost all stones are incorporated into a single cluster.
At 22 km, all Ogham stones in the south of Ireland belong to a single cluster,
whereas several distinct clusters still exist in the north – a clear indicator of
the lower density of Ogham stones in this area.
5.3.2 Analysis of Word Frequency
The foregoing discussion begs the question: which words are most fre-
quently found on the 317 Ogham stones in Ireland? And do some of these
words regularly appear in combination with others?
To answer these questions, we used the Ogham Extractor Tool, available
on Github,76 and the table ciictowords2 (Thiery and Homburg, 2020a), in
which relevant words and the various readings of the Ogham inscriptions on
74
source code at https://github.com/ogi-ogham/oghamaps
75
see DOI 10.17605/OSF.IO/7EXTC for the R package, which has this algorithm im-
plemented.
76
https://github.com/ogi-ogham/oghamextractor/
226
the CIIC stones are recorded. As our focus was on their meaning and not on
variances in spelling, we resorted to the most common forms of the words in
question (for example, MAQI instead of MAQ).
The resulting word list was used to a) show how often the most common
words occur (Figure 13) and b) how often words co-occur (Figure 15). As dis-
cussed above, MAQI (son) and MUCOI (tribe) are most common, followed
by LUG (god), AVI (grandson, descendant), and ANM (name). This, com-
bined with the preponderance of names in the inscriptions, underscores the
importance of familial affiliation in the communities that used the Ogham
alphabet. The religious dimension also appears to have played a significant
role (MacManus, 1997).
The second word analysis looks at the co-occurrence of words on the
Ogham stones (Figure 15). In this case, we wished to determine whether
or not specific names or topics always appear in combination.
Our analysis shows a range of values between 0 and 21. The diagonal must
be ignored, as it reflects how often a word occurs all in all (co-occurrence of
words with themselves). The most frequent co-occurrence is of the words
MAQI and MUCOI (21 times) – once again, son and tribe. As MAQI is
the most common word overall, it also has the most co-occurrences with
other words. Given that the dataset yielded by the Ogham inscriptions is
rather small and consists of a limited number of words, the most common
co-occurrence frequency is 0, as some words do not co-occur with others
at all (e.g. CARI, loves).77 By contrast, two words that are seldom used –
VIR (man, 7x) and CATTU (battle, 3x) – do co-occur, even though the com-
pound word CATTUVIR (man of battle) also exists. This particular case of
co-occurrence gives us another brief glimpse at the workings of the society
behind these monuments and the importance it attached to warfare.
6 Conclusion and Outlook
In this paper, we discussed LOD and its role as the emerging standard for re-
search data management and semantic data modeling in the digital humanit-
ies, digital archaeology, and archaeoinformatics, as well as its practical imple-
mentation in Wikidata. Acknowledging that the SPARQL query language is
not yet widely known, we introduced the SPARQL Unicorn tool framework
developed by the community of Research Squirrel Engineers with the aim of
helping researchers use SPARQL. One of the cornerstones of this framework
is the SPARQLing Unicorn QGIS Plugin, which is already published and en-
ables researchers to query Wikidata for geospatial and other LOD within the
77
It should be noted, however, that the dataset has been reduced to the words that occur
at least three times in total.
227
QGIS environment.
A short overview of the Ogi Ogham Project demonstrated the practical
application of our digitization and linking workflow for the creation of LOD,
and showcased how OpenRefine and QuickStatements can be employed to
import that data into Wikidata. Two analytical approaches that have already
been implemented illustrated the potential of our toolkit to foster open and
reproducible research.
In the future, we will focus on the steps necessary for modeling our own
bespoke Ogham ontology and hosting it in a triple store, which will increase
our flexibility with regard to semantic modeling, and will allow us to use
the Wikidata entries as links to the Linked Open Data Cloud. This planned
development work is part of the project Irische ᚑᚌᚆᚐᚋSteine im Wikimedia
Universum and is funded by the Wikimedia Deutschland Fellow-Programm
Freies Wissen.78
As has become clear, the Ogham data is perfectly suitable for text analysis
(e.g. text as a graph using a graph database like Neo4J79 ), and will lend itself
equally well to network analysis of the relationships between persons and
tribes – all that is needed is the further enrichment of Ogham data through
the addition of sources and information (including provenance information)
by the community.80
Acknowledgements
Timo Homburg and Martina Trognitz, fellow members of the Research
Squirrel Engineers network, have been of great support, both in the devel-
opment of our project and its presentation in this paper. We would also like
to thank Dr. Kris Lockyear for his encouragement and for kindly making
the CISP database accessible to us, and Dr. David Wigg-Wolf for language
corrections.
78
http://ogham.squirrel.link
79
https://neo4j.com
80
The Research Squirrel Engineers welcome new squirrels interested in feeding the
Linked Data Cloud with Ogham nuts!
228
Figures and Tables
Figure 1: UCC Stone Corridor, Stone 4, CIIC 81 [Florian Thiery, CC BY 4.0 via
Wikimedia Commons]
229
Figure 2: Linked Data principles [Florian Thiery, CC BY 4.0 via Wikimedia Com-
mons]
Figure 3: 7-sphere data model [Florian Thiery, CC BY 4.0 via Wikimedia Com-
mons]
230
Figure 4: Query tab to search Ogham stones using the SPARQLing Unicorn QGIS
Plugin
Figure 5: Bounding box pop-up in the SPARQLing Unicorn QGIS Plugin
231
Figure 6: Query tab to search Ogham stones with the bounding box in Figure 5
using the SPARQLing Unicorn QGIS Plugin
Figure 7: top-left: Irish Ogham stones overview, red: stones filtered by the bound-
ing box; bottom-left: Ogham stones on the Dingle Peninsula filtered by the bound-
ing box represented in QGIS (over an OpenStreetMap H.O.T. map) queried by the
SPARQLing Unicorn QGIS Plugin; right: excerpt of the resulting attribute table
232
Figure 8: CIIC 180, Emlagh East (Imleach Dhún Séann), Co. Kerry based on a
3D scan by the Ogham in 3D Project [Florian Thiery, CC BY 4.0 via Wikimedia
Commons]
Figure 9: Example of an Ogham-related Wikimedia Commons entry: drawing of
CIIC 81 by Macalister (1945) [Florian Thiery, CC BY 4.0 via Wikimedia Com-
mons]
233
Figure 10: Workflow in the Ogi Ogham Project
Figure 11: CIIC Ogham Ireland cluster created with QGIS [Florian Thiery, CC BY
4.0]
234
Figure 12: CIIC Ogham Ireland density map created with R (oghamaps)
Figure 13: Count of CIIC Ogham words occurring more than three times created
with R (oghamaps) [Code in the oghamaps - project on github, Sophie C. Schmidt,
CC BY 4.0]
235
236
Figure 14: Percolation analysis. Graph showing the mean cluster size at differing radii. The maps represent particularly significant points in the
distribution [Sophie Schmidt, CC BY 4.0]
Figure 15: CIIC Ogham Ireland co-occurrence statistics created with R (oghamaps)
for words that occur more than three times [Code in the oghamaps - project on
github, Sophie C. Schmidt, CC BY 4.0]
237
property description example
label desc Garranes Townland
description description Garranes Townland, Co. Cork
alias add. information An Garrán
P31 (instance of) townland townland
geographic location (Q2151232)
county town
P361 (part of) project Ogi-Ogam Project (Q70873595)
P17 (country) Ireland Ireland (Q27)
Northern Ireland
P131 province Province Munster
territorial entity (Q131438)
county County Cork
(Q162475)
barony Barony Kinalmeaky
(Q20616069)
civilparish Templemartin
(Q60557037)
electoraldevision Templemartin
(Q59724324)
P625 lat 51.816389
(coordinate location) lon -8.761667
P402 (OSM) OpenStreetMapID 6168494
P5097 (Logainm) LogainmID 8299
Table 1: Wikidata townland modeling example (Q69385525)
Qualifier description example
Ireland P248 (stated in) Q70256237
County Cork P248 (stated in) Q85384744
Kinalmeaky P248 (stated in) Q85384744
Templemartin P248 (stated in) Q85384744
civil parish
Templemartin P248 (stated in) Q85384744
electoral district
Munster P248 (stated in) Q85384744
51.816389;-8.761667 P248 (stated in) Q85384744
LogainmID P248 (stated in) Q85384744
OpenStreetMapID P248 (stated in) Q85384744
Table 2: Wikidata townland modeling example (Q69385525 references)
238
property description example
label desc CIIC 81
description description Ogam Stone CIIC 81
alias add. information Ogam Stone 4 (Stone Cor-
ridor at UCC)
P31 (instance of) ogham stone Q2016147
P361 (part of) Ogi Ogam Project Q70873595
P361 (part of) Ogam Stones Q67978809
P18 (image) photo or drawing figures 1 and 9
P17 (country) modern country Ireland (Q27)
P625 lat 51.816389
(coordinate location) lon -8.761667
P2868 qualifier archaeological site
(subject has role) (Q839954)
P186 (material used) stone material stone (Q22731)
P195 (collection) collection of ogham stones Stone Corridor Uni-
versity College Cork
(Q69379477)
P189 findspot County Cork
(location of discovery) (Q162475)
Garranes Townland
(Q69385525)
P2043 (length) stone length 17.78 cm
P2049 (width) stone width 48.26 cm
P2048 (height) stone height 175.26 cm
P1684 (inscription) ogham inscription C[A]SSITT[A]S MAQI
MUCOI CALLITI
P518 qualifier presumably
(applies to part) (Q18122778)
P1545 (series ordinal) number 81,4
P6568 ogham words MAQI (Q67381254)
(insc. mentions) MUCOI
(Q67999759)
P5816 stone status preserved
(state of conservation) (Q56557591)
Table 3: Wikidata Ogham Stone modeling example (Q69385424)
239
qualifier description example
ogham stone P248 (stated in) Q70256237
Ogham Stones P248 (stated in) Q70873595
Ireland P248 (stated in) Q70256237
51.816389;-8.761667 P248 (stated in) Q70256237
stone P248 (stated in) Q70256237
Stone Corridor UCC P248 (stated in) Q70383873
County Cork P248 (stated in) Q70256237
Garranes Townland P248 (stated in) Q70256237
length/ width/ height P248 (stated in) Q70256237
inscription P248 (stated in) Q70256237
series ordinal (81) P248 (stated in) Q70256237
series ordinal (4) P248 (stated in) Q70256237
MAQI P248 (stated in) Q70256237
MUCOI P248 (stated in) Q70256237
preserved P248 (stated in) Q70256237
Table 4: Wikidata Ogham Stone modeling example (references of Ogam Stone
CIIC 81 = Q69385424)
County Number of Ogham stones
Kerry 127
Cork 81
Waterford 47
Kilkenny 12
Kildare 8
Mayo 8
Table 5: The counties with the largest number of Ogham stones in Ireland
References
ARL (2019). ARL White Paper on Wikidata Opportunities and Re-
commendations. https://scholarworks.iupui.edu/bitstream/handle/1805/18902/
ARL-white-paper-on-Wikidata.pdf.
Berners-Lee, T. (2006). Linked Data - Design Issues. https://www.w3.org/
DesignIssues/LinkedData.html.
Berners-Lee, T. (2007). Giant Global Graph. https://web.archive.org/web/
20160713021037/http://dig.csail.mit.edu/breadcrumbs/node/215.
Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The Semantic Web. Sci-
entific American, 284(5):34–43, DOI: 10.1038/scientificamerican0501-34.
240
Bivand, R., Keitt, T., and Rowlingson, B. (2019). rgdal: Bindings for the
'Geospatial' Data Abstraction Library, https://CRAN.R-project.org/package=
rgdal. R package version 1.4-8.
Bivand, R. S., Pebesma, E., and Gomez-Rubio, V. (2013). Applied spatial
data analysis with R, Second edition. Springer, New York, NY, https://
asdar-book.org/.
Deicke, A. (2017). Networks of Conflict: Analyzing the "Culture of
Controversy" of Polemical Pamphlets of Intra-Protestant Disputes (1548-
1580). Journal of Historical Network Research, page 71–105, DOI:
10.25517/jhnr.v1i1.8.
Doerr, M., Kritsotaki, A., Rousakis, Y., Hiebel, G., et al. (2020). Definition
of the CRMsci - An Extension of CIDOC-CRM to support scientific
observation. http://www.cidoc-crm.org/crmsci/sites/default/files/CRMsci%20v.1.
2.8.pdf.
Dunnington, D. (2018). ggspatial: Spatial Data Framework for ggplot2,
https://CRAN.R-project.org/package=ggspatial. R package version 1.0.3.
Ferguson, S. (1864). Account of Ogham Inscriptionsin the Cave at Rath-
croghan, County of Roscommon. In Proceedings of the Royal Irish
Academy, volume 9, page 160–170.
Forsyth, K. S. (1996). The Ogham Inscriptions of Scotland: An Edited Corpus.
PhD thesis, Harvard University.
Garnier, S. (2018). viridis: Default Color Maps from 'matplotlib', https://
CRAN.R-project.org/package=viridis. R package version 0.5.1.
Graves, C. and Limerick, C. (1876). The ogham alphabet. Hermathena,
2(4):443–472.
Hausenblas, M. and Boram Kim, J. G. (2015). 5-star Open Data. http://
5stardata.info/en/.
Isaksen, L. (2011). Archaeology and the Semantic Web. Thesis (Doctoral),
University of Southampton, School of Electronics and Computer Sci-
ence, https://eprints.soton.ac.uk/id/eprint/206421.
Macalister, R. A. S. (1945). Corpus inscriptionum insularum Celticarum.
Number Vol. I in Corpus inscriptionum insularum Celticarum. Station-
ery Office, Dublin.
241
Macalister, R. A. S. (1949). Corpus inscriptionum insularum Celticarum.
Number Vol II. in Corpus inscriptionum insularum Celticarum. Station-
ery Office, Dublin.
MacManus, D. (1997). A Guide to Ogam. Number 4 in Maynooth mono-
graphs. An Sagart, Maynooth, Ireland.
Maddison, M. S. (2020). Percolation analysis. In Gillings, M., Hacıgüzeller,
P., and Lock, G., editors, Archaeological Spatial Analysis: A Methodolo-
gical Guide, page 77–92. Routledge, DOI: 10.4324/9781351243858.
Maddison, M. S. and Schmidt, S. C. (2020). Percolation Analysis – Archae-
ological Applications at Widely Different Spatial Scales. Journal of Com-
puter Applications in Archaeology, 3(1):269–287, DOI: 10.5334/jcaa.54.
Popov, M. (2020). WikidataQueryServiceR: API Client Library
for 'Wikidata Query Service'. https://CRAN.R-project.org/package=
WikidataQueryServiceR.
R Core Team (2020). R: A Language and Environment for Statistical
Computing. R Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org/.
Sanderson, R. (2018). LOUD: Linked Open Usable Data. https://linked.
art/loud/.
Schmidt, S. C. (2021). oghamaps. DOI: 10.5281/zenodo.3969519.
Simon, R., Isaksen, L., Barker, E., and de Soto Cañamares, P. (2016). The
Pleiades Gazetteer and the Pelagios Project. In Berman, M. L., Mostern,
R., and Southall, H., editors, Placing names: enriching and integrat-
ing gazetteers, The spatial humanities, pages 97--109. Indiana University
Press, Bloomington, IN.
Thiery, F. (2019). Sphere 7 Data: LOUD and FAIR Data for the Research
Community. DOI: 10.5281/zenodo.2643469.
Thiery, F. and Homburg, T. (2020a). oghamextractor. DOI:
10.5281/zenodo.3969514.
Thiery, F. and Homburg, T. (2020b). QGIS - A SPARQLing Unicorn? Eine
Einführung in Linked Open Geodata zur Integration von RDF in QGIS
Plugins. DOI: 10.5281/ZENODO.3719127.
242
Thiery, F. and Homburg, T. (2021). SPARQLing Unicorn QGIS Plugin.
DOI: 10.5281/zenodo.3786814.
Thiery, F., Homburg, T., Schmidt, S. C., Trognitz, M., et al. (2020a).
SPARQLing Geodesy for Cultural Heritage – New Opportunities for
Publishing and Analysing Volunteered Linked (Geo-) Data. DOI:
10.5281/ZENODO.3751769.
Thiery, F., Schmidt, S. C., Homburg, T., and Trognitz, M. (2020b). The
SPARQL Unicorn: An introduction. DOI: 10.5281/ZENODO.3742185.
Thiery, F., Trognitz, M., Gruber, E., and Wigg-Wolf, D. (2019). Hic
Sunt Dracones! The Modern Unknown Data Dragons. DOI:
10.5281/zenodo.3345711.
Unold, M., Thiery, F., and Mees, A. (2019). Academic Meta Tool. Ein
Web-Tool zur Modellierung von Vagheit. In Kuczera, A., Wübbena, T.,
and Kollatz, T., editors, Die Modellierung des Zweifels – Schlüsselideen
und -konzepte zur graphbasierten Modellierung von Unsicherheiten, num-
ber 4 Special issue in Zeitschrift für digitale Geisteswissenschaften. DOI:
10.17175/SB004_004.
Voß, J., Bausch, S., Schmitt, J., Bogner, J., et al. (2014). Normdaten in
Wikidata Handbuch. Hochschule Hannover Fakultät III Abteilung
Information und Kommunikation, http://nbn-resolving.de/urn/resolver.pl?
urn:nbn:de:bsz:960-opus4-4381.
Vrandečić, D. and Krötzsch, M. (2014). Wikidata: A Free Collaborat-
ive Knowledgebase. Communications of the ACM, 57(10):78–85, DOI:
10.1145/2629489.
Wickham, H. (2007). Reshaping Data with the Reshape Package. Journal
of Statistical Software, 21(12), http://www.jstatsoft.org/v21/i12/paper.
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer,
New York, NY, https://ggplot2.tidyverse.org.
Wickham, H. and Henry, L. (2020). tidyr: Tidy Messy Data, https://CRAN.
R-project.org/package=tidyr. R package version 1.0.2.
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., et al.
(2016). The FAIR Guiding Principles for Scientific Data Management
and Stewardship. Scientific Data, 3:160018, DOI: 10.1038/sdata.2016.18.
243
Wuttke, U. (2019). “Here be dragons”: Open Access to Research
Data in the Humanities. https://ulrikewuttke.wordpress.com/2019/04/09/
open-data-humanities/.
244