Search

Abstract Understanding the nature–culture entanglement by combining the methods of natural sciences and humanities is little approached in neither of the fields. With a specific combination of methods from both digital humanities and ecology, we aimed at identifying several of people's life circumstances that relate to their individual sensitivity towards biodiversity. The circumstances with a strong correlation could be considered and targeted by decision‐makers, for example by developing specific education programmes for making people more eco‐conscious or adjusting relevant regulations. We applied machine learning techniques onto a database including information about the frequency of biodiversity mentioned in creative literature (BiL) from 1705 to 1969 as response variable related to metadata about the corresponding works and their authors as predictors, including localisation, age, gender and literature genre. The algorithm determined the response's dependency on each predictor, which can be interpreted as the intensity of this particular sensitivity parameter for biodiversity, and which we also related to time. We recognised that gender, age, region and settlement size are predictors significantly correlated to BiL. Statistically, these predictors can be viewed as starting points of the eventual individual level of awareness for biodiversity. For example, authors from villages exhibit a higher BiL than those from cities, which we interpret as a signal for the dependence of awareness for biodiversity on spatial distance from nature, which in turn can be addressed in urban development. Our conclusion is that applying a machine learning technique on literary data yields meaningful results, thereby showing potential for further similar investigations and the combination of methods from natural sciences and humanities to achieve so far unattainable insights. With our study, these insights could contribute to ecologically based decision‐making processes. Read the free Plain Language Summary for this article on the Journal blog.

The status of theory in the Digital Humanities (DH) has been the subject of much debate. As a result, we find different theory narratives competing and entangled with each other. If at all, these narratives can only be grasped and examined from a somewhat detached perspective. Here, we attempt to investigate these elusive narratives by means of a conceptual history approach. In doing so, we define different theory dimensions, ranging from specific cultural and literary theory frameworks to more generic uses of the concept of _theory_. We examine the use and semantic changes of these theory notions in a large corpus of DH journals. Using a mixture of heuristic methods and approaches from the field of distributional semantics, we aim to create tellable conceptual stories of DH theory.

Abstract Nature's non‐material contributions to people are difficult to quantify and one aspect in particular, nature's contributions to communication (NCC), has so far been neglected. Recent advances in automated language processing tools enable us to quantify diversity patterns underlying the distribution of plant and animal taxon labels in creative literature, which we term BiL (biodiversity in literature). We assume BiL to provide a proxy for people's openness to nature's non‐material contributions enhancing our understanding of NCC. We assembled a comprehensive list of 240,000 English biological taxon labels. We pre‐processed and searched a subcorpus of digitised literature on Project Gutenberg for these labels. We quantified changes in biodiversity indices commonly used in ecological studies for 16,000 books, encompassing 4,000 authors, as proxies for BiL between 1705 and 1969. We observed hump‐shape patterns for taxon label richness, abundance and Shannon diversity indicating a peak of BiL in the middle of the 19th century. This is also true for the ratio of biological to general lexical richness. The variation in label use between different sections within books, quantified as β‐diversity, declined until the 1830s and recovered little, indicating a less specialised use of taxon labels over time. This pattern corroborates our hypothesis that before the onset of industrialisation BiL may have increased, reflecting several concomitant influences such as the general broadening of literary content, improved education and possibly an intensified awareness of the starting loss of biodiversity during the period of romanticism. Given that these positive trends continued and that we do not find support for alternative processes reducing BiL, such as language streamlining, we suggest that this pronounced trend reversal and subsequent decline of BiL over more than 100 years may be the consequence of humans’ increasing alienation from nature owing to major societal changes in the wake of industrialisation. We conclude that our computational approach of analysing literary communication using biodiversity indices has a high potential for understanding aspects of non‐material contributions of biodiversity to people. Our approach can be applied to other corpora and would benefit from additional metadata on taxa, works and authors. A free Plain Language Summary can be found within the Supporting Information of this article.

This special issue deals with existing theory narratives and conceptions in DH scholarship. Introducing the neologism “theorytellings”, this special issue invites DH scholars to narrate and discuss their own theoretical contributions to the field.

The Covid-19 pandemic demonstrates the relevance of virtual museums, which provide access to art and cultural heritage even in times when museums are closed. Besides their undisputable relevance, another important factor is the acceptance of virtual museums, which primarily depends on usability and user experience. So far, there are only generic guidelines for the design of user-friendly virtual environments, such as the twelve heuristics presented by Sutcliffe and Gault 2004. This paper presents an evaluation of those VE heuristics and their applicability to virtual museums. To do so, we select six exemplary virtual exhibitions and evaluate them with respect to the existing heuristics. Based on the results, we present best practices for the design of virtual museums. Furthermore, we discuss limitations of the existing heuristics. Two existing heuristics do not seem to be suitable for virtual museums. In addition, we complement two heuristics which could be derived from the examples and argue for a further development of domain-specific heuristics and best practices for virtual museums in order to systematically improve knowledge transfer in the sense of the public history approach..

The MuSe (Music Sentiment) dataset contains sentiment information for 90,001 songs. We computed scores for the affective dimensions of valence, dominance, and arousal, based on the user-generated tags that are available for each song via Last.fm. In addition, we provide artist, title and genre metadata, and a MusicBrainz ID and a Spotify ID, which allow researchers to extend the dataset with further metadata.

We present the Game Walkthrough Corpus (GWTC), which contains 12,295 unique walkthrough documents covering 6,117 games. For each game walkthrough, we provide frequencies of unigrams and bigrams, treating the walkthrough document as a Bag of Words. In addition, we provide word frequencies at the sentence level. Furthermore, the GWTC contains a number of game-related metadata, including title, publisher, developer, year, and genre. All the language statistics and metadata are stored in separate plain text files and can be referenced through uniform resource names (URN). These URNs can also be used to derive any combination of statistics and metadata. Researchers, for instance, can investigate the most frequent unigrams for games in the “Adventure” genre. This way, the GWTC can be reused for different kinds of research questions on gaming language.