• 2019-2023close
• Closed Access close
• Publications close
• Other research products close
• DK close
• Digital Humanities and Cultural Her... close

During the 1960s and 1970s, Denmark experienced a significant shift in arts and crafts, driven in particular by younger women. This paper examines how such upheavals took place across different platforms, such as the 1969 student rebellion at the Copenhagen School of Arts and Crafts, the initiation of a collective crafts shop, and the staging of an international feminist art exhibition. Despite challenges posed by patriarchal hierarchies in art and craft, female practitioners, especially in textiles, leveraged these platforms to foster collaborative practices, reshape their status and secure livelihoods. Drawing on archives, interviews and feminist theory, the paper explores tensions between tradition and innovation, focusing on how craft evolved from decorative to critical practice, gaining institutional recognition. These struggles highlight the dynamic intersections of gender, art/craft hierarchies and societal change, offering insights into how Danish studio craft challenged conventions and inspired new frameworks for education, collaboration and feminist expression.

This chapter explores the challenges faced by multilingual digital humanities (DH) researchers when using natural language processing (NLP) techniques in their work. While recent developments in neural language models have improved the usefulness of NLP tools for DH experts working with complex datasets, there is a bias towards languages in the Global North, particularly English. This bias arises from data imbalance, unequal script, and vocabulary coverage, and differences in linguistic typology, leading to poor performance for most of the world’s languages. To address these issues, the chapter proposes collaboration between NLP and DH research efforts: Data-efficient fine-tuning and open data creation, for instance by following the FAIR principles. Custom model vocabularies where DH datasets and expertise might prove useful. Careful consideration of the semantic and morphosyntactic properties of the languages at hand. By integrating NLP techniques and DH expertise more successfully, the chapter argues that linguistic diversity can be better addressed, and fairness can be promoted in the application of NLP and multilingual DH across linguistic communities.

Abstract In the Arab countries, restrictions on events and gatherings due to covid-19 have as elsewhere affected religious practices. Mosques and churches have been closed temporarily and religious authorities have taken part in public discussions about the meaning and way of dealing with covid-19. In this context, the article explores the interaction between state and religion by analysing the arguments used by Muslim authorities to legitimize or challenge the Arab states’ politics towards covid-19 in the spring 2020. The scientific and secular arguments of the states are met by three positionings among Muslim authorities: Those who based on religious dogma agree with and confirm the states’ approach to covid-19; those who argue that religion gives all the answers; and those who discard and distrust the states’ motives and dealings. Accordingly, handling the covid-19 in the Arab countries repeats frequent positions in the continuing discussion on religion in Arab societies.

AbstractThe history of Danish neuroscience starts with an account of impressive contributions made at the 17th century. Thomas Bartholin was the first Danish neuroscientist, and his disciple Nicolaus Steno became internationally one of the most prominent neuroscientists in this period. From the start, Danish neuroscience was linked to clinical disciplines. This continued in the 19th and first half of the 20th centuries with new initiatives linking basic neuroscience to clinical neurology and psychiatry in the same scientific environment. Subsequently, from the middle of the 20th century, basic neuroscience was developing rapidly within the preclinical university sector. Clinical neuroscience continued and was even reinforced during this period with important translational research and a close co‐operation between basic and clinical neuroscience. To distinguish ‘history’ from ‘present time’ is not easy, as many historical events continue in present time. Therefore, we decided to consider ‘History’ as new major scientific developments in Denmark, which were launched before the end of the 20th century. With this aim, scientists mentioned will have been born, with a few exceptions, no later than the early 1960s. However, we often refer to more recent publications in documenting the developments of initiatives launched before the end of the last century. In addition, several scientists have moved to Denmark after the beginning of the present century, and they certainly are contributing to the present status of Danish neuroscience—but, again, this is not the History of Danish neuroscience.

We present the results of our investigations in karst caves on northern Vancouver Island. This work focuses on late Pleistocene faunal remains and potential archaeological deposits. Our analyses of faunal remains from these caves reveal that a variety of animals have inhabited the region since the Last Glacial Maximum. Pleistocene taxa recovered include black-tailed deer (Odocoileus hemionus), black bear (Ursus americanus), mountain goat (Oreamnos americanus), Vancouver Island marmot (Marmota vancouverensis), wolf/dog (Canis spp.), weasel (Mustela), and frog (Anura). We also report on a previously undescribed diminutive red fox (Vulpes vulpes) which no longer inhabits Vancouver Island. We present the results of sedaDNA analysis of cave sediments which demonstrates promise in supporting the morphological identification of animal remains. The results of this study, combined with previous research, help to constrain the timing of the Last Glacial Maximum and when land became viable to support land mammals on northern Vancouver Island.

Developed in the context of the Horizon 2020 project SPICE, this paper explores methods and tools for citizen curation, with a focus on designing activities that can effectively elicit and motivate citizens to produce meaningful interpretations and reflections on cultural heritage. Additionally, the paper aims to evaluate the potential of such methods and activities in supporting the analysis and reflection processes within the SPICE Interpretation-Reflection loop (IRL). We explore a selection of narrative-based methods for citizen curation to propose an approach for eliciting meaningful stories related to cultural heritage artifacts from citizens. The proposed approach is subsequently applied in a digital co-design workshop, conducted with members of the SPICE consortium, including the five SPICE museum partners. The collected story contributions are thereafter analysed using qualitative methods and tools adapted from narrative inquiry and the study of narrative identity. The findings provide valuable insights into the potential of narrative-based approaches for citizen engagement in the cultural heritage domain and support the development of the SPICE digital tools for promoting reflection, i.e. sentiment analysis, personalization, recommendation systems, and user- and community modeling tools. In SPICE, such tools play a central role in enabling citizens to create representations of themselves, while also cultivating an understanding and appreciation of similarities and differences across citizen groups. In this direction, the paper offers ideas and insights for designing engaging participatory approaches to elicit citizen input. At the same time, it aims to support the development of digital systems and tools in the cultural heritage domain, with the goal of fostering more inclusive and dynamic representations of citizens and citizen groups.

This evidence based practice paper presents preliminary results in using an artificial intelligence classifier to mark student assignments in a large class setting. The assessment task consists of an approximately 2000 word reflective essay that is produced under examination conditions and submitted electronically. The marking is a simple pass/fail determination, and no explicit feedback beyond the pass/fail grade is provided to the students. Each year around 1500 students complete this assignment, which places a significant and time-constrained marking load upon the teaching faculty.This paper presents a Natural Language Process (NLP) framework/tool for developing a machine learning based binary classifier for automated assessment of these assignments. The classifier allocates each assignment a score representing the probability that the assignment would receive a passing grade from a human marker. The effectiveness and performance of the classifier is measured by investigating the accuracy of those predictions.Several iterations and statistical analyses were carried out to determine operational thresholds that balance the risks of false positives and false negatives with the required quantity of human marking to assess the assignment.The resulting classifier was able to provide accuracy levels that are potentially feasible in an operational context, and the potential for significant overall reductions in the human marking load for this assignment.

What are the emotional effects of infertility on patients, partners, or both, and how can qualitative thematic analyses and natural language processing (NLP) help evaluate textual data?A cross-sectional, multi-country survey conducted between March 2019 and May 2019. A total of 1944 patients, partners, or both, from nine countries responded to the open-ended question asking about their initial feelings related to an infertility diagnosis. A mixed-method approach that integrated NLP topic modelling and thematic analyses was used to analyse responses. Sentiment polarity was quantified for each response. Linear regression evaluated the association between patient characteristics and sentiment negativity.Common emotional reactions to infertility diagnoses were sadness, depression, stress, disappointment, anxiety, frustration, confusion and loss of self-confidence. NLP topic modelling found additional reactions, i.e. shared feelings with partners, recollections about causes of infertility and treatment experience. Responses to the open-ended question were brief (median: three words) with 71.8% conveying negative sentiments. Some respondent characteristics showed small but significant associations with sentiment negativity, i.e. country (Spain, China and France were more negative than the USA, P < 0.001, P < 0.003 and P < 0.009 respectively), treatment engagement (no treatment was more negative than one or more treatment, P = 0.027) and marital status (missing/other was more negative than divorced, P = 0.003).Infertility diagnoses create an emotional burden for patients and partners. The mixed-method approach provides a compelling synergy in support of the validity of these findings and shows potential for these techniques in future research.