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This article investigates popular topics and topical fluctuations in a diachronic corpus of 43,772 Dutch songs, all written between 1550 and 1750, contained within the Dutch Song Database. Computational methods such as topic modelling are used to analyse the relationship between topical changes and cultural-historical developments. Two cultural trends are used as case studies: the role of Petrarchism, and the articulation of a patriotic identity in early modern Dutch song culture. Furthermore, this data-driven approach reveals how subcategories can be defined within the existing but incomplete genre classification in the song collection. The results obtained contribute to a better understanding of the richness of the Dutch Song Database, and will facilitate the use of the song collection by future users.

This essay examines a copy of the Qur’ān from India, now in the India Office Collections at the British Library. The manuscript, registered as IO Loth 4, belongs to the reasonably large group of early Qur’āns that date to the eighth and ninth centuries CE. While some of these manuscripts have charted histories, what is not widely known is that early Qur’āns also made their way to India. There they have their own special histories, meanings and associations. In attempt to address the long ‘after-life’ of these manuscripts, this paper will examine a single example that arrived in India in the Mughal period and was eventually presented to the Library of the East India House by Lord Dalhousie in 1853. While not the earliest of the Qur’āns brought to India, it nonetheless dates to the circa ninth century CE, making it older than any surviving manuscripts in Sanskrit or Prakrit in India proper.

This thesis investigates how artificial intelligence can support e-mental health for depression, i.e. the delivery of treatment and prevention interventions for depression using technology. E-mental health for depression is a promising means for bridging the treatment gap since it addresses many of the barriers that prevent people in need of help from seeking or obtaining it. Additionally, many systems have been found to be effective in controlled trials. However, as human support for e-health interventions decreases so do their effectiveness and users’ adherence. While one possible explanation is that human support is a necessary ingredient of a successful intervention, another is that the technology is not satisfying the needs of users to the best of its abilities. This finding inspired us to take a closer look at the technological implementation of the functionality of these systems. To this end, we developed a set of scales that assess the technological sophistication of the functional components of systems, the e-mental health degree of technological sophistication (eHDTS) scales. In a systematic literature review of the field, we then divided all systems developed between 2000 and 2017 for the prevention or treatment of depression reported in the scientific literature into their functional components and rated those components with the eHDTS scales. We found that most systems that had been developed until 2017 were low-tech implementations, consisting mostly of psychoeducation and having a one-way information stream from system to user. This clearly contrasts with face-to-face therapy in which the therapist closely attends to the patient and provides his or her knowledge and insight strategically to signal understanding and empathy, foster self-reflection, teach, or obtain more information. Based on this consideration, we set out to develop a conversational agent capable of signaling to the user that it had processed the content of what it had been told when completing a thought record together with a user in dialog with the hypothesis that this would be able to motivate the user to complete more thought records and feel more engaged. Thought recording is a core technique of cognitive therapy in which patients are asked to systematically monitor their thinking in situations that caused a maladaptive response. Cognitive theory posits that the negative, cognitive appraisals that are responsible for the low mood experienced in patients with depression stem from maladaptive schemas, i.e., beliefs that we hold as truths about the world, ourselves, and the future. To get the conversational agent to “understand” the thoughts provided by the user from this cognitive theory perspective, we collected a corpus of thought records from Amazon Mechanical Turk workers, manually coded the thoughts with respect to the underlying schema, and trained various machine learning models to do the same labeling. A set of deep neural networks outperformed the other algorithms and was then deployed in the conversational agent. We used a between-subjects design to expose 308 participants recruited from Prolific to the conversational agent. The three conditions differed with respect to the feedback-giving capabilities of the conversational agent in response to a thought record: low feedback richness entailed an acknowledgment of the completion of the thought record (thanking the user), medium feedback richness entailed the acknowledgment plus feedback on the process (how many steps the user did in relation to his or her previous thought records), and rich feedback richness entailed medium feedback richness combined with feedback on the content (an interpretation of the thought record with respect to the underlying schema). While all users were able to complete the thought records with the conversational agent, we did not find supportive evidence that the agent’s feedback strategy could increase users’ motivation to complete more thought records or their self-reported engagement in self-reflection. Future research may investigate why we observed these null results by studying whether the feedback is processed correctly, whether a population with depression that is motivated by a wish to get healthy might behave or experience the system differently from our sample that was recruited online and did not meet diagnostic criteria for depression, or whether more advanced social and interaction capabilities need to accompany the complex feedback for it to be believable.

Abstract This article examines the representation of Jerusalemite identity in William of Tyre’s Historia Ierosolymitana (c.1184). William laments that his contemporaries in Jerusalem did not live up to the standards of their forefathers anymore: they were not wise, virtuous men, but put their own needs before those of the community. In doing so, William makes use of a narrative strategy that is found in the Roman historians Livy and Sallust as well. In the histories of Livy and Sallust, it was contact with the Near East that prompted societal decline. The riches and dolce far niente of the East had, in their eyes, corrupted Roman morals. In William’s work, by contrast, the Eastern Other often functions as a mirror for the Self. This, in combination with William’s emphasis on former generations as reference point for the current generation allows for a much more dynamic interplay of identities than an orientalist binary East-West division.

The Dutch Drankwet (hereinafter: Liquor Act) of 1881, the result of decades of temperance activism, was met with much criticism — little had come of the national legislation’s aim to reduce the consumption of alcohol. Even so, did this also mean that little changed in the sale of alcohol? This article examines how the Liquor Act was implemented locally in Leiden and what impact this had on the sale of alcohol there. To this end, both city council minutes and patent registers are analyzed. Patent registers served as compulsory patent taxes and as licenses for liquor stores and drinking establishments. They provide valuable insight into the variation within the sector for alcohol sales in Leiden throughout the nineteenth century. Our examination shows that, contrary to the criticism of the law, the Liquor Act had both short- and long-term effects on Leiden’s pubscape. It led to a limited decrease in the number of public houses and primarily affected the smallest public houses, often owned by women.

This final report is the third and final deliverable of Work Package 3. It builds on the first report D3.1, in which the outcomes of the initial ARIADNE project as well as the PARTHENOS project, which were used as starting points for the current ARIADNEplus project, were taken into account [Hollander 2020]. It also builds on the second, interim report D3.2, in which the activities of the partners to support the creation of FAIR data in the archaeological sector were described [Hollander 2022]. This current report summarises the activities carried out by the different partners during the four-year project duration (January 2019 – December 2022), as well as the results achieved through the work package. The following partners have been involved: DANS-KNAW, PIN, UoY-ADS, CNR, CONICET, BUP, NIAM-BAS, AMZ, ARUP, AU, UH, CNRS, INRAP, RGK, ATHENA-RC, PP, HNM, FI, IAA, MIBACT-ICCU, NARA, DGCP, SND, and ASU. The objectives of Work Package 3 “Policies and Good Practices for FAIR Data Management” are to: Support the creation of FAIR data in the archaeological sector. Define and spread guidelines to good practices in archaeological data management. Adapt standard quality criteria for datasets and data to the archaeological case, and support their implementation among users. Chapter 2 describes how to define and disseminate guidelines on good practices in archaeological data management. Commonly developed and widely applicable guides ensure that archaeological data will be FAIR and available in the long-term. Chapter 3 presents an overview of the activities to develop and implement a portfolio of tools to support users in their work with archaeological data. The ARIADNEplus partners developed and implemented a new Horizon Europe Data Management Plan Template for Archaeological Datasets, a Protocol for Archaeological Data Management, and a Guide for Archaeological Data Management Planning, accessible through the new ARIADNEplus DMP tool.1 The Policy Wizard Tool was updated. Chapter 4 shows the importance of sharing experiences from partners with already certified repositories to partners willing to set up an archaeological data repository. Providing guidelines and support on repository creation and management was the focus of activity here. Workshops, webinars, symposia and hackathons took place and scientific articles on data management policies and practices of digital archaeological repositories were published. Chapter 5 describes what partners willing to certify their repository need to be provided with: the explanation of and training on accreditation requirements when applied to repositories of archaeological data with a perspective on international initiatives, e.g. access restrictions for security and privacy reasons. Achieving a Trustworthy Digital Repository status, and making and keeping data FAIR is a joint journey. Chapter 6 highlights the application of the FAIR principles to archaeological data, taking into account different regulations throughout Europe and the potential sensitivities and IPR-related issues. The aim is to work towards solutions that harmonise the diverse approaches adopted. A major step forward has been an online survey conducted among repositories, with 60 respondents, giving essential insights into current policies and where there is room for improvement. Chapter 7 describes training activities on FAIR data management. Training and training materials have been produced and published.

Patients share valuable advice and experiences with their peers in online patient discussion groups. These uncensored experiences can provide a complementaryperspective to that of the health professional and thereby yield novel hypotheses which could be tested in further rigorous medical research. This thesis focuses on the development of automatic extraction methods to harvest these patient experiences from online patient forums using text mining techniques. We also examine the complementary value of these patient-reported outcomes to traditional sources of medical knowledge for scientific hypothesis generation. Specifically, we focus on the extraction of adverse drug events (i.e., side effects) and coping strategies for dealing with adverse drug events.