Citation: Siebold, Anna, and Matteo
Valleriani. 2022. Digital Perspectives
in History. Histories 2: 170–177.
https://doi.org/10.3390/
histories2020013
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Received: 31 March 2022
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Article
Digital Perspectives in History
Anna Siebold 1,2 and Matteo Valleriani 2,3,4,*
1Fak. IV, University of Oldenburg Haarentor Campus, 26129 Oldenburg, Germany;
2Department I, Max Planck Institute for the History of Science, 14195 Berlin, Germany
3Fak. 1, Berlin Institute of Technology, 10623 Berlin, Germany
4Faculty of Humanities, Tel Aviv University, Tel Aviv 6997801, Israel
*Correspondence: [email protected]
Abstract:
This article outlines the state of digital perspectives in historical research, some of the
methods and tools in use by digital historians, and the possible or even necessary steps in the future
development of the digital approach. We begin by describing three main computational approaches:
digital databases and repositories, network analysis, and Machine Learning. We also address data
models and ontologies in the larger context of the demand for sustainability and linked research data.
The section is followed by a discussion of the (much needed) standards and policies concerning data
quality and transparency. We conclude with a consideration of future scenarios and challenges for
computational research.
Keywords:
computational history; digital humanities; digital database; network analysis; machine
learning; sustainable research data; transparency; digital hermeneutics
1. Introduction
The state of the art of digital perspectives in historical scholarship can only be con-
sidered within the larger framework of the Digital Humanities (DH). Although digital
humanists were engaged in definitional debates up until the early years of the 2010s, in
recent years DH scholarship has both proliferated and matured (Gold 2012). Not only is it
increasingly published in a number of established journals and represented institutionally
by university chairs, but DH scholarship is also characterized by a deepening and narrow-
ing of scholarly niches in the field. Digital or Computational History, encompassing the
range of digital perspectives in historical research, may be considered such a niche. Its
relevance, however, to DH as a whole lies not only in the historical content it produces,
but also in the methodological, self-reflexive debates it prompts (Zaagsma 2013). Recent
examples include the ongoing debates around digital source criticism, digital hermeneutics,
the demand for the development of algorithm and tool criticism, as well as discussions
about the potential effects of digital methods on the production of (historical) knowledge
(Fickers and Zaagsma 2022).
The digitization of historical sources, however, gained momentum about twenty years
ago and was driven primarily by two factors. First, the increasing availability of the Internet
offered new possibilities for the exchange of ideas between scholars. Email, electronic mail-
ing lists (distributed for instance by the online forum H-Net launched in 1993), electronic
publishing (disseminated initially via CDs, DVDs, and later the Internet), and the creation
of digital libraries and catalogs serve as early examples. From its very beginning, the spread
of the Internet was thus accompanied by the dream that the online world would create a
new form of global (academic) community. Second, memory institutions such as archives,
libraries, and heritage institutions saw the digitization process—one not necessarily tied to
Internet dissemination—as a new means of preserving historical sources, since a digitized
manuscript does not need to be physically browsed.
Histories 2022,2, 170–177. https://doi.org/10.3390/histories2020013 https://www.mdpi.com/journal/histories
Histories 2022,2171
The surge of research projects engaged in digitizing historical sources led to the
creation of large digital collections, which have since been integrated into research practices
and are now essential for the making of new historical knowledge. Against this background,
highly structured and painstakingly curated collections of data have emerged, and at the
same time, various digital tools and methods have been conceived, developed, and applied
in order to analyze these collections. The question of whether these changes give rise to
new research questions and approaches remains open, although recently it has been the
focus of increased scholarly attention.
Given that Computational History itself has already undergone various developments,
our aim in this article is to provide an introduction to a selection of computational tools
and technologies for historians interested in digital approaches. Our contribution is not
intended as an instructive introduction for practicing digital historians, but rather as an
overview and consolidation of some of the computational approaches relevant to historical
research developed over the last years, as well as some thoughts on how they might evolve
in the future. We focus on three main areas—databases, network analysis, and Machine
Learning (ML)—illustrated by examples from research projects in the history of science. The
subsequent sections address the related issues of sustainability as a necessary condition for
the outlined future development of Computational History, and the challenges of ensuring
transparency and data quality.
2. Computational Approaches and Their Future
Computational approaches used for historical research purposes are manifold, and the
three areas discussed here are not exhaustive. Their selection is informed by our scholarly
environment and experience in the field. Comprehensive introductions to a wider range of
technologies and tools can be found in what has become a large body of literature (Graham
et al. 2015;Drucker 2021).
2.1. Digital Databases
As mentioned in the introduction, access to the Internet as well as the digitization of
historical sources led to the creation of large digital collections. These collections, in turn,
required repositories in which data could be stored, described, and managed. Now an
essential part of humanities research, digital databases range from relatively simple digital
catalogs containing numerical or textual information to more advanced systems managing
a variety of multimedia content such as images, videos, audio files, and maps.
Initially, DH research projects used database technologies developed in the context
of other disciplines and applications, mostly around the intersection of computer science
and business. Among the most widely used technology is the relational database, which
was developed in the early 1970s and released as a product in 1978. Compared to earlier
database systems (which were based on hierarchical or network database models), the
relational database simplified the management, processing, and querying of data. This is
partly due to the introduction of the table as an organizing principle, which enables data
to be organized into rows and columns that can be related to each other. For historical
research, the relational database meant that historians could conduct qualitative research
digitally (Kemman 2021). The first system designed specifically for historical research
purposes, called CLIO and developed by historian Manfred Thaller in 1980, has in fact
been regarded as the beginning of “history and computing” (Boonstra et al. 2004). Today,
relational database systems in DH prevail, which is particularly evident from the fact that
general handbook entries about databases often exclusively cover relational databases
(Crompton et al. 2016).
More recently, however, against the background of the development of so-called
semantic technologies, a new type of database model has emerged: the graph database.
Graph-based structures are well suited for the modeling and storing of complex, highly
connected datasets and thus also for historical data. In comparison to the relational data
model, in the graph database, distinct entities (people, places, events, objects, or concepts)
Histories 2022,2172
are connected via relations that form a network. Such representations are also referred to
as knowledge graphs, and essentially represent the historian’s view of a particular research
field. In addition to enabling the modeling of complex structures, graph databases also
provide more flexibility with regard to the database schema (i.e., it is easier to make changes
to the data and structure), facilitate the querying of relational structures, and support the
visualization of networks. These qualities have made them increasingly promising for DH
research.
Examples of large collections managed in databases for research purposes are col-
lections management systems, i.e., cataloging databases used in libraries, museums, and
archives. Most of them also offer access to digitized versions of (some of) their sources
via additional database systems. The library of the Max Planck Institute for the History of
Science, for instance, began digitizing its collection of rare sources in 2002 as part of the
project ECHO–Cultural Heritage Online, which was recently replaced by Digital Libraries
Connected (Collections MPIWG—Digital Libraries Connected n.d.). Today, its library holds
more than 1500 volumes of digitized primary sources and texts, as well as maps, relevant
to all aspects of the history of science. Scholars engaged in Computational History and
beyond, however, do not only make use of already existing database collections. Some
contribute to them or build their own, participating in the database design process and the
numerous decisions that accompany it.
To conclude this brief overview, it is important to point out that while digital histor-
ical research initially integrated discipline-foreign database technology into its research
practices, it is now in the process of developing its own technologies and standards to
address the specific requirements of humanities research. Particularly, many efforts focus
on how information is best modeled, stored, and exchanged, questions discussed at greater
length in the section below-titled Sustainability: Data Models and Ontologies. Finally, it is
important to add that databases have evolved from being used mainly as repositories for
storing, manipulating, and searching data to serving as the backbone of research projects,
the site from which further software is applied to analyze and visualize the collected data.
Databases have thus become vehicles for further data exploration.
2.2. Network Analysis
To analyze data, humanities scholars, especially in the frame of Computational History,
have increasingly turned to network analysis. Examples can be found in the publications,
workshops, and conferences organized by the Historical Network Research Community
(launched in 2009), its open-access journal launched in 2017, and the many introductions
to historical network research (Düring et al. 2016;Kerschbaumer et al. 2020). Network
analyses (similar to graph databases) emphasize the relationships between people, places,
events, objects, or concepts. They aim to describe the character of a network, its density or
central orientation, the nature of relationships in the network, and who or what occupies a
central role. They allow, in contrast to a single document or biography, for the description of
complex behavior in a network of relationships over time (König 2019). Modeled networks
that emerge in this context, however, are not to be understood as representations of real-
world networks, but rather as a way to approach and view a particular field of research.
Once a researcher has determined that the application of network analysis is beneficial to
the overall research question, it involves several steps: the collection of data, its encoding,
algorithmic computation, and subsequent visualization.
An illustrative example is the research project The Sphere: Knowledge System Evolution
and the Shared Scientific Identity in Europe (The Sphere n.d.). It consists of several components:
at its core is a rich collection of bibliographic data from a corpus of more than 350 different
editions of textbooks used for teaching astronomy in European universities from the late
fifteenth century to the mid-seventeenth century. This data is stored together with digital
copies of the textbooks in a graph database. The corpus is centered around a specific text,
namely Johannes de Sacrobosco’s treatise Tractatus de sphaera, and consists of editions that
either contain this text or are closely related to it. Originally compiled in the thirteenth
Histories 2022,2173
century at the University of Paris, the text represents a qualitative introduction to geocentric
astronomy, which was read at nearly all European universities until ca. 1650. The text was
repeatedly reprinted, annotated, modified, and, in the printed editions enriched by other
texts that deepened particular aspects. Given such a long tradition, attention was directed
to the question of how the embedded knowledge changed over time and, in particular,
how it became increasingly homogenous. To investigate this question, different parts of
each edition were identified and semantically related to one another, creating a semantic
network on which network analysis, the second important building block of the project,
could then be applied. Among other things, this approach enabled the computational
identification of which “families of books” were successful, in as much as they (a) became
models imitated by others over a long period of time and (b) introduced new knowledge
that was included in subsequent editions (Kräutli and Valleriani 2017;Valleriani et al. 2019;
Zamani et al. 2020).
It is important to point out that there is no all-encompassing universal network theory.
What does exist, however, is a shared core of analytical concepts, such as density, centrality,
and community building. Many of these have been transformed into indicators, imple-
mented in software, and are presented in textbooks (Lemercier 2015). Although initially
scholars primarily invoked Social Network Analysis (SNA)—the approach that seemed
most appropriate to their subject matters—recent developments reveal the limitations of
SNA, which is why some historians are moving toward approaches that were originally
developed in the frame of physics of complex systems. The reason for this shift lies in
the nature of the datasets that the historical sources generate; they are usually somewhat
smaller than those of the natural or life sciences. Big Data, especially in the context of a
single research project, are still rare phenomena in the humanities, and using the term often
reflects the intent of using a buzzword rather than describing the scale of the dataset. What
matters, however, is that humanities datasets exhibit a degree of data heterogeneity and
dimensionality with which statistics and physics have little experience. In other words, the
widespread notion that the historian depends exclusively on the tools and approaches of
computer science or mathematics is increasingly proving false. Instead, the humanities are
both challenging these disciplines and encouraging fruitful collaborations—developments
that may well continue into the future.
2.3. Machine Learning
Machine Learning (ML) is based on the idea that once trained with given examples,
computers are able to recognize connections and patterns and thus ‘learn’ (Schöch 2017).
ML can be performed with two kinds of rationale. On the one hand, it is used to provide
new information about datasets and their structures by classifying, clustering, or inferring
new relations between various data points using supervised and unsupervised approaches.
On the other hand, it is possible to analyze which features or rules were crucial for a
particular classification or clustering, potentially resulting in a better understanding of the
object of study (Holzinger et al. 2022).
ML, and most recently Neural Networks, entered the toolbox of historians in the
context of computer-vision applications, for instance as part of further developing Optical
Character Recognition (OCR), the conversion of images of hand-written, typed, or printed
text into machine-readable text (Lyu et al. 2021). The poor quality of OCR techniques, which
persistently proved unsatisfactory when dealing with early modern historical sources or
working with handwritten material, led to the integration of ML, with the hope of turning
the existing corpus of manually cleaned texts and their corresponding images into a
training set for neural networks. In addition to applying ML technology to create machine-
readable texts—an achievement which, in turn, allows for the application of a plethora
of text analysis approaches—ML is increasingly applied with the intention of atomizing
(segmenting) historical sources in order to discover and capture specific elements, such
as illustrations, tables, and diagrams, or in fact any other text or shape that reappears
(Lee et al. 2020).
Histories 2022,2174
One example of a project that uses Deep Learning-based image recognition is Digital
Heraldry. Its research goal is to trace coats of arms, the most common visual medium of
the Middle Ages and Early Modern times, across time, space, and societal groups (Digital
Heraldry n.d.). The first step involved collecting digitized historical sources that contain
representations of coats of arms and building a database. A part of the dataset then served
as the basis for training image recognition algorithms to successfully predict areas in images
that depict coats of arms in additional historical sources (Hiltmann et al. 2020). In the future,
these trained algorithms may be applied to the digital collections of other institutions, thus
expanding the dataset. The project also aims to use ML and Semantic Web technologies to
enable semi-automatic descriptions of coats of arms, thereby generating metadata that can
be used for further analysis.
The possibility of generating this type of data represents a fundamental change in the
epistemic status of ‘error’. In comparison, the process of manually analyzing a large corpus
of historical sources, i.e., humans extracting data, is time-consuming and error-prone—
incorrect metadata, for instance, may be entered by mistake, and predefined keywords in
a database can be mismatched. Processes of data cleaning are therefore applied up to the
point where humans (scholars) decide that the data is of sufficient quality. In the case of
historians, this means confidence that the number of errors is small enough to not affect the
final research results. The advantage of using ML to replace the manual work of humans
previously responsible for data extraction is that it will become possible to move from
medium-sized corpora of sources to large ones—possibly a step towards “Big,” or at least,
bigger data. By applying ML, however, errors become unavoidable products of source
analysis. This is because ML technologies function in terms of probabilities and predictions.
Neural networks trained to identify illustrations in historical sources, for instance, are
unlikely to find all the illustrations in a given corpus. It can be argued, however, that the
resulting incompleteness of the dataset is compensated by the increased size of the dataset
and thus by the law of large numbers.
The development and proliferation of such algorithms in the frame of Computational
History has only just begun, but it is expected that their implementation will soon generate
larger datasets than previously possible. Not only will these datasets be available to hu-
manities scholars, creating new challenges and opportunities for the formation of historical
knowledge, but given the size of such datasets, ML will be required once again to analyze
them adequately, for example, to search for genealogies of influence between sources. To
achieve this, a new philology is being developed based on (graph-based) principles of
sameness and similarity. It will enable, for instance, to cluster corpora of illustrations
according to sameness, similarity, or the recurrence of specific elements among them.
3. Sustainability: Data Models and Ontologies
A close look at digital databases, network analysis, and ML shows that a very diverse
and highly specialized set of new research practices has evolved, many of which are still
being further developed. However, it must also be noted that the current situation is
marked by a certain heterogeneity as to how these new practices are carried out. During
the time that a project is active, the digital tools applied may, for instance, be based on a
particular data model, tied to certain software, or be bounded by (limited) access rights.
The need for standards and open or, even better, linked data policies, therefore, takes on
an important role. Clear standards and policies are urgently needed, particularly because
the heterogeneity described above not only prevents the sharing and linking of datasets,
but worse, causes them to disappear altogether. Once a project is completed, publications
may remain, but the datasets that were originally generated, together with their external
web presentations and possibly even the electronic copies of the analyzed material are
often no longer available online. This phenomenon is due to the lack of a sustained
perspective: Because projects are usually funded for a limited period of time, it is highly
uncommon for the institution that hosted the project to take over its maintenance and
preservation (
Kräutli et al. 2021
). A record of lost datasets has so far not been compiled,
Histories 2022,2175
but the problem is widely recognized. The discipline has responded to this situation by
developing DH-specific approaches with regard to data modeling and formal ontologies.
The idea behind these endeavors is to standardize the structure of data to the extent that it
becomes independent of the platform on which it was originally hosted, and that it can
circulate, i.e., be hosted by different platforms and integrated with different datasets. This
allows data to survive beyond the lifetime of the project in which it was created.
An increasingly widespread data model is the Resource Description Framework (RDF),
which provides a syntax for representing data and resources on the Web. RDF breaks
statements about resources into expressions of the form subject–predicate–object, also
known as triples. The subject determines the resource, and the predicate determines traits
or aspects of the resource, which can include relationships between the subject and another
object. In this way, data stored and linked according to RDF form a graph structure. Each
element of the triple can be expressed using re-usable Uniform Resource Identifiers (URI),
which are compact sequences of characters that identify abstract or physical resources. A
linked-data context thus means evolving from a document-based web to a web of linked
data, allowing data to be linked at a whole different level.
Although the use of a common data model is an important step toward standardizing
how data is structured, formal ontologies provide the possibility of expressing concepts in
the same way, thus integrating different datasets in coherent semantic systems. Particularly
relevant in this realm is the CIDOC Conceptual Reference Model (CRM), which is both a
theoretical and conceptual tool for information integration in the field of cultural heritage.
The use of a specific conceptual reference model presupposes a shared understanding of
cultural heritage information and can, if applied across research projects, enable their inte-
gration in a semantic environment that stores and connects their data. The implementation
of a shared data model and ontology thus allows for the realization of a vision that has
always been inherent to the DH, namely being able to share and link data as well as enable
inter-operability across projects and institutions.
4. Data Quality and Transparency
Another aspect that concerns the discipline as a whole is the need for new research
principles that are agreed upon and followed by the entire community. The first concerns the
transparency of data. More than often, research results are presented and published without
accounting for the above-mentioned decisions. Which data model was applied? Which
formal ontology forms the basis of the project’s ontology? How was the data collected?
Other factors might include more practical questions, such as how and to what extent was
the collection of data limited (for example due to image rights limitations or uncooperative
institutions)? Not only should these conditions, which ultimately shape the research
endeavor and thus its results, be made transparent and considered, but the actual data
should also be made available through so-called API access for others to use. In combination
with the demands formulated here, this practice would support interoperability between
projects and institutions, and hence fuel creative research endeavors. Just as importantly, it
would enable datasets to be verified by other researchers at any time, thereby channeling
expertise, making mutual reviewing more common, and finally guaranteeing a higher level
of data quality.
Another pressing matter is that of crediting; working with and on data needs to be
accounted for in academic crediting systems, since it is not only a time-consuming task,
but also one that takes knowledge and skill. A broader debate is needed to find solutions
for integrating crediting systems in the humanities that adequately reflect the nature of
this type of work. Should this matter remain unresolved, the motivation to follow research
questions that contain extensive data labor will diminish. This is especially the case for
young scholars who are highly dependent on credits at the beginning of their careers.
Another overdue discussion related to these issues concerns how to deal with the fact that
research groups are increasingly interdisciplinary and, correspondingly, their publications
multi-authored. Similar to what has been done in the natural sciences, it is necessary to
Histories 2022,2176
introduce standards that reflect the degrees of responsibility within the group so that they
are transparent to other researchers and crediting systems.
5. Outlook
Despite the fact that not all historical sources are available in digital form and any form
of digitization process involves the act of selecting, the availability of an unprecedented
number of sources in digital form is making it both increasingly appealing and necessary
for historians to turn to computational approaches. Although traditional approaches,
especially with regard to case studies and in-depth analyses of specific sources, remain
available to scholars and are equally essential for historical study, Computational History
can be regarded as an extension of the historical disciplines, allowing for a broadening of
research approaches and methods. In particular, it allows for longue duréeinvestigations
based on the close analysis of a large number of historical sources, which enables the
formulation of new kinds of research questions. As mentioned in the introduction, we
believe that one of the great potentials of Computational History for historical scholarship,
but also for the humanities as a whole, lies in the fact that it addresses its own methodology.
In recognizing the potential of Computational History to make tacit, hidden, or unconscious
assumptions explicit (Krämer 2018), it becomes possible to reflect on how digital historical
perspectives were produced in the first place. Which historical entities and relationships
were modeled and thus taken into account, and which were not? What does a knowledge
graph include and where do its limits lie? What tools or digital research approaches were
applied, and on what grounds? Do they have a different effect on the making of historical
knowledge than traditional approaches? The history of science in particular, with its long
tradition of investigating epistemological changes, can make relevant contributions to
answering these questions. Many of them will—if aptly considered and discussed—help
to further formulate a hermeneutics of the digital, necessary not only in order to achieve
digital literacy in academia but throughout the entire education system.
Author Contributions:
Conceptualization and Writing: A.S. and M.V. All authors have read and
agreed to the published version of the manuscript.
Funding:
This research was founded by the Max Planck Institute for the History of Science, by the
Carl von Ossietzky Universität Oldenburg, and by the Berlin Institute for the Foundations of Learning
and Data (BIFOLD): ref. 01IS18037A.
Conflicts of Interest: The authors declare no conflict of interest.
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