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This book is available as open access through the Bloomsbury Open programme and is available on www.bloomsburycollections.com. It is funded by Trinity College Dublin, DARIAH-EU and the European Commission.This book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation.

This book is a collection of seventeen papers which describe the impact that the ARIADNE project and its successor, ARIADNEplus (2019-2022) have had on the archaeological community, both in Europe and further afield. Each case study has been contributed by organisations involved in the ARIADNE Infrastructure who cover many countries from across Europe as well as Argentina and Japan. These papers were originally presented at the CAA Conference in Krakow, April 2019 and cover aspects such as data management, application of standards and guidelines, the use of CIDOC-CRM and Open Data to name but a few.

The concept of “scholarly primitive” has been widely welcomed both by humanists and system designers in the humanities, due to the fact that it made it possible to have a solid conceptual basis for the operationalization of the essential functionalities required for advancing computer-mediated work in the humanities. It has also helped to prioritize, and to provide general frameworks for the analysis of system requirements, which otherwise would have remained vague, or too specifically tied to the particular projects. However, in the design of actual systems or digital infrastructures, most initiatives that have tried to apply the concept of “scholarly primitive” have acknowledged the importance of looking at the relationship between them (e.g., Palmer et al., 2009), but haven't framed these primitives or activities within the larger workflows in which researchers transition from one activity to the other, or perform multiple connected activities in tandem. One of the most fully-fledged conceptual models for scholarly research activity based on scholarly primitives was developed by DARIAH (note 1). Grounded in conceptual and empirical research, this conceptual model aims to fit the needs of the actual research life-cycle (Benardou, 2013), with the resulting NeDiMAH (note 2) Methods Ontology (NeMO) centered around a list of research activities, which are hierarchically connected by means of a taxonomical structure. We have developed a process perspective that can help to understand how these activities are interconnected. For example, the activity “gathering” is defined by the NeMO ontology as “aggregating discovered resources [...] for further analysis.” (note 3). While this normalization of the activity name and definition is useful for several purposes in system design, the NeMO ontology makes no connection to the activities that occur during “discovering” or “analysis”, which makes it difficult to understand how “gathering” occurs in practice, and how it works in real contexts where other activities precede, succeed, or occur simultaneously to the “gathering” activity. The lack of understanding of the scholarly primitives (or of the research activities) in a workflow perspective has negative implications in designing systems that support the research life-cycle. To overcome this limitation, this paper introduces the concept of “workflow transitions” and presents investigations of scholarly work using this concept. The method we propose consists of selecting a sample of representative “research projects” (Koolen et al., 2020) and use them as the unit of analysis for the study of workflows and workflow transitions. We developed our approach through analysing the research process of two digital humanities projects in great detail, by interviewing the project collaborators (using Critical Incident Technique), studying their papers and looking at datasets, tools and scripts. In this paper we will present the resulting workflow visualizations, the findings of our analysis and their implications for digital infrastructure support for humanities research.

Contains fulltext : 240932.pdf (Publisher’s version ) (Open Access) 20 december 2021 10 p.

This white paper primarily served as an internal working document for the DARIAH ERIC. We inspected current service policies and practices across ERIC’s with an emphasis on social sciences and humanities. We summarised earlier analysis of the DARIAH service portfolio. The ultimate purpose of the paper was to create a common ground of understanding what DARIAH services are and how to develop governance and management around them. Still, when writing this paper, we realised that others might encounter similar questions in their quest, and so could learn from our exploration.

In recent years, there has been a growing interest from the Digital Humanities in knowledge graphs as data modelling paradigm. Already, many data sets have been published as such and are available in the Linked Open Data cloud. With it, the nature of these data has shifted from unstructured to structured. This presents new opportunities for data mining. In this work, we investigate to what extend data mining can contribute to the understanding of archaeological knowledge, expressed as knowledge graph, and which form would best meet the communities' needs. A case study was held which involved the user-driven mining of generalized association rules. Experiments have shown that the approach yielded mostly plausible patterns, some of which were seen as highly relevant by domain experts.

Recent years have seen the emergence of graph-based Knowl-edge Bases build upon Semantic Web technologies, known as KnowledgeGraphs (KG). Popular examples are DBpedia and GeoNames. The formal system underlying these KGs provides inherent support for deductive reasoning. Growing popularity has exposed several limitations of thisability, amongst which are scalability and uncertainty issues, as well ascoping with heterogeneous, noisy, and inconsistent data. By supplementing this form of reasoning with Machine Learning algorithms, these hurdles are much more easily overcome. Of the existing research in this area,only a handful have been considering a Deep Neural Network. Moreover,only one of these studies has addressed the problem of hyper-parameteroptimization, albeit under specific conditions. To contribute to this areaof research, we propose a research design that will investigate a DeepNeural Network with optimized hyper-parameters for its effectiveness toperform link prediction on real-world KGs.

Poster