Med Health Care Philos. 2026 Apr 16. doi: 10.1007/s11019-026-10347-1. Online ahead of print.

ABSTRACT

The uptake of social science methods by bioethics significantly expanded its methodological spectrum, raising new theoretical, methodological, and practical questions. Recently, we are witnessing another trend, adding advanced data science methods to bioethics' toolkit to aid, for example, in online data analysis, support scholarly writing, and inform clinical ethics. This article explores the emerging field of Digital Bioethics across its dimensions by analysing the tangled relationship between topics and methods, highlighting intersections between Digital Bioethics and Bioethics of the Digital, and advocating for a methods-based definition of the field. The use of advanced data science methods within bioethics must be interpreted in the context of the use of Artificial Intelligence (AI) in health care. At the same time, it presents unique opportunities and challenges. Defining, and thus demarcating, Digital Bioethics can create support for the new field but also requires navigating trade-offs. To do so, we take four kindred academic fields as points of comparison (Digital Humanities, Experimental Philosophical Bioethics, computational medicine and digitised biology) to analyse what each of them teaches for critically assessing and further developing Digital Bioethics. The article discusses potential pitfalls and concludes with recommendations on how the field can fully develop its potential to promote bioethical research and argument. Furthermore, the article discusses how a critical reflection of the use of AI methods within bioethics itself will also contribute to the ethical oversight of increasingly AI-driven branches of healthcare.

PMID:41989660 | DOI:10.1007/s11019-026-10347-1

Stud Hist Philos Sci. 2026 Mar 30;117:102151. doi: 10.1016/j.shpsa.2026.102151. Online ahead of print.

ABSTRACT

This paper examines large language models (LLMs) as research tools in the history, philosophy, and sociology of science (HPSS). Because LLMs can work directly with heterogeneous, unstructured texts and capture meaning-relevant associations from usage patterns, they offer new ways to bridge close reading and corpus-scale analysis, challenging the idea that computational scale and interpretive nuance must trade off. We provide a compact primer on LLMs, covering the main components of their neural network architecture, the differences between generative and full-context models, and adaptation strategies such as fine-tuning, prompt-based learning, and retrieval-augmented generation (RAG). Building on this foundation, we analyze how LLMs recast three classic methodological problems in HPSS: working with historically messy data, detecting and interpreting large-scale patterns, and modeling scientific change over time. Across these areas we synthesize recent work in HPSS and adjacent fields, and we clarify how LLM outputs can function as exploratory prompts, as inputs to more structured pipelines, or as evidence under stricter validation and documentation. We conclude with four lessons: 1) model choice embeds interpretive trade-offs, 2) responsible use requires LLM literacy, 3) HPSS should develop its own tasks and evaluation practices, and 4) LLMs should extend rather than replace established interpretive methods. We also situate these methodological questions within broader concerns about platform dependence, accountability, and the responsibilities attached to research infrastructures. Finally, we argue that HPSS is well positioned to both use LLMs and to interrogate what counts as explanation, evidence, and responsible use in interpretive research.

PMID:41916166 | DOI:10.1016/j.shpsa.2026.102151

Data Brief. 2025 Nov 8;63:112246. doi: 10.1016/j.dib.2025.112246. eCollection 2025 Dec.

ABSTRACT

Anime is an influential medium with global popularity, combining visual aesthetics with narrative depth and offering potential applications in content analysis, style transfer, and emotion recognition within computer vision research. Despite its widespread appeal, publicly available anime character datasets remain scarce. To address this gap, we propose the Attack on Titan: Anime Image Dataset, derived from the popular series Attack on Titan, to support anime-focused computer vision research. The dataset comprises 4041 high-quality images divided into 14 classes, each representing a prominent character from the series. These images are manually collected through high-resolution screenshots, capturing a wide range of character poses, expressions, costumes, and backgrounds. The dataset is suitable for various computer vision tasks, including character recognition, emotion detection, style classification, and domain adaptation.

PMID:41399437 | PMC:PMC12702017 | DOI:10.1016/j.dib.2025.112246

Data Brief. 2024 Dec 17;58:111238. doi: 10.1016/j.dib.2024.111238. eCollection 2025 Feb.

ABSTRACT

The MaschinenBauIndustrie Knowledge Graph (MBI-KG) is a structured and semantically enriched dataset extracted from the 1937 publication "Die Maschinen-Industrie im Deutschen Reich" (The Machinery Industry in the German Reich), published by the "Wirtschaftsgruppe Maschinenbau" and edited by Herbert Patschan. This historical source offers data on German companies within the mechanical engineering industry during the pre-World War II era. The book was digitized, and Optical Character Recognition (OCR) was applied to extract text. The unstructured extracted data was then structured and semantically enriched to enable data integration and reuse. The semantically enriched data was uploaded into an open-source knowledge-graph software. The resulting knowledge graph includes detailed information about companies, individuals, and administrative entities relevant to the German mechanical engineering industry. The data is accessible through various means, including a SPARQL endpoint, an API, advanced search functionalities, a reconciliation API, and bulk files. Each entity in the knowledge graph can be exported in multiple formats, such as CSV, RDF (ttl), JSON, and NDJSON, ensuring compatibility with diverse research tools and platforms. This dataset can be reused in various research domains, including economic history, data science, and digital humanities. By providing machine-readable, structured data from a crucial historical period, the MBI-KG facilitates novel analyses and insights into the economic and industrial landscape of early 20th-century Germany. The dataset's interoperability with other data sources and its alignment with FAIR principles further enhance its value for interdisciplinary research and long-term preservation.

PMID:39830614 | PMC:PMC11742587 | DOI:10.1016/j.dib.2024.111238

PeerJ Comput Sci. 2024 Oct 22;10:e2242. doi: 10.7717/peerj-cs.2242. eCollection 2024.

ABSTRACT

Narratives play a crucial role in human communication, serving as a means to convey experiences, perspectives, and meanings across various domains. They are particularly significant in scientific communities, where narratives are often utilized to explain complex phenomena and share knowledge. This article explores the possibility of integrating large language models (LLMs) into a workflow that, exploiting the Semantic Web technologies, transforms raw textual data gathered by scientific communities into narratives. In particular, we focus on using LLMs to automatically create narrative events, maintaining the reliability of the generated texts. The study provides a conceptual definition of narrative events and evaluates the performance of different smaller LLMs compared to the requirements we identified. A key aspect of the experiment is the emphasis on maintaining the integrity of the original narratives in the LLM outputs, as experts often review texts produced by scientific communities to ensure their accuracy and reliability. We first perform an evaluation on a corpus of five narratives and then on a larger dataset comprising 124 narratives. LLaMA 2 is identified as the most suitable model for generating narrative events that closely align with the input texts, demonstrating its ability to generate high-quality narrative events. Prompt engineering techniques are then employed to enhance the performance of the selected model, leading to further improvements in the quality of the generated texts.

PMID:39650368 | PMC:PMC11623210 | DOI:10.7717/peerj-cs.2242

Front Big Data. 2024 May 10;7:1188620. doi: 10.3389/fdata.2024.1188620. eCollection 2024.

ABSTRACT

Visualizations are ubiquitous in data-driven research, serving as both tools for knowledge production and genuine means of knowledge communication. Despite criticisms targeting the alleged objectivity of visualizations in the digital humanities (DH) and reflections on how they may serve as representations of both scholarly perspective and uncertainty within the data analysis pipeline, there remains a notable scarcity of in-depth theoretical grounding for these assumptions in DH discussions. It is our understanding that only through theoretical foundations such as basic semiotic principles and perspectives on media modality one can fully assess the use and potential of visualizations for innovation in scholarly interpretation. We argue that visualizations have the capacity to "productively irritate" existing scholarly knowledge in a given research field. This does not just mean that visualizations depict patterns in datasets that seem not in line with prior research and thus stimulate deeper examination. Complementarily, "irritation" here consists of visualizations producing uncertainty about their own meaning-yet it is precisely this uncertainty in which the potential for greater insight lies. It stimulates questions about what is depicted and what is not. This turns out to be a valuable resource for scholarly interpretation, and one could argue that visualizing big data is particularly prolific in this sense, because due to their complexity researchers cannot interpret the data without visual representations. However, we argue that "productive irritation" can also happen below the level of big data. We see this potential rooted in the genuinely semiotic and semantic properties of visual media, which studies in multimodality and specifically in the field of Bildlinguistik have carved out: a visualization's holistic overview of data patterns is juxtaposed to its semantic vagueness, which gives way to deep interpretations and multiple perspectives on that data. We elucidate this potential using examples from medieval English legal history. Visualizations of data relating to legal functions and social constellations of various people in court offer surprising insights that can lead to new knowledge through "productive irritation."

PMID:38798306 | PMC:PMC11116627 | DOI:10.3389/fdata.2024.1188620

Open Res Eur. 2024 Jan 8;4:11. doi: 10.12688/openreseurope.16712.1. eCollection 2024.

ABSTRACT

The Liber Insularum by Cristoforo Buondelmonti can be considered the first guide to the Greek islands, each of them described by a textual paragraph and illustrated by color maps, in a format which gave rise to the new literary genre of Isolaria." Mapping the Aegean: Cristoforo Buondelmonti's Liber Insularum" is a Marie Skłodowska-Curie project aimed at the study of this book. This paper illustrates the application of Cadmus, a structured content management tool, to the creation of a digital edition of the Liber and to do this, we focus on the text and map of Corfu as a case study. After a historical introduction on the author and his work and the presentation of the project, we explain why we chose to use this tool and its main characteristics, and we offer a concrete example of its application to the material pertaining to the description of Corfu by showing its frontend output.

PMID:38779340 | PMC:PMC11109684 | DOI:10.12688/openreseurope.16712.1

Int J Digit Humanit. 2021;2(1-3):107-128. doi: 10.1007/s42803-021-00036-1. Epub 2021 Jun 22.

ABSTRACT

The archived web provides an important footprint of the past, documenting online social behaviour through social media, and news through media outlets websites and government sites. Consequently, web archiving is increasingly gaining attention of heritage institutions, academics and policy makers. The importance of web archives as data resources for (digital) scholars has been acknowledged for investigating the past. Still, heritage institutions and academics struggle to 'keep up to pace' with the fast evolving changes of the World Wide Web and with the changing habits and practices of internet users. While a number of national institutions have set up a national framework to archive 'regular' web pages, social media archiving (SMA) is still in its infancy with various countries starting up pilot archiving projects. SMA is not without challenges; the sheer volume of social media content, the lack of technical standards for capturing or storing social media data and social media's ephemeral character can be impeding factors. The goal of this article is three-fold. First, we aim to extend the most recent descriptive state-of-the-art of national web archiving, published in the first issue of International Journal of Digital Humanities (March 2019) with information on SMA. Secondly, we outline the current legal, technical and operational (such as the selection and preservation policy) aspects of archiving social media content. This is complemented with results from an online survey to which 15 institutions responded. Finally, we discuss and reflect on important challenges in SMA that should be considered in future archiving projects.

PMID:38624884 | PMC:PMC8218557 | DOI:10.1007/s42803-021-00036-1

Public Underst Sci. 2024 Oct;33(7):918-934. doi: 10.1177/09636625241229923. Epub 2024 Feb 28.

ABSTRACT

Wikipedia's influence in shaping public perceptions of science underscores the significance of scientists being recognized on the platform, as it can impact their careers. Although Wikipedia offers guidelines for determining when a scientist qualifies for their own article, it currently lacks guidance regarding whether a scientist should be acknowledged in articles related to the innovation processes to which they have contributed. To explore how Wikipedia addresses this issue of scientific "micro-notability," we introduce a digital method called Name Edit Analysis, enabling us to quantitatively and qualitatively trace mentions of scientists within Wikipedia's articles. We study two CRISPR-related Wikipedia articles and find dynamic negotiations of micro-notability as well as a surprising tension between Wikipedia's principle of safeguarding against self-promotion and the scholarly norm of "due credit." To reconcile this tension, we propose that Wikipedians and scientists collaborate to establish specific micro-notability guidelines that acknowledge scientific contributions while preventing excessive self-promotion.

PMID:38419208 | PMC:PMC11504141 | DOI:10.1177/09636625241229923

Appl Corpus Linguistics. 2023 Apr;3(1):100037. doi: 10.1016/j.acorp.2022.100037. Epub 2022 Nov 3.

ABSTRACT

Understanding the reception of public health messages in public-facing communications is of key importance to health agencies in managing crises, pandemics, and other health threats. Established public health communications strategies including self-efficacy messaging, fear appeals, and moralising messaging were all used during the Coronavirus pandemic. We explore the reception of public health messages to understand the efficacy of these established messaging strategies in the COVID-19 context. Taking a community-focussed approach, we combine a corpus linguistic analysis with methods of wider engagement, namely, a public survey and interactions with a Public Involvement Panel to analyse this type of real-world public health discourse. Our findings indicate that effective health messaging content provides manageable instructions, which inspire public confidence that following the guidance is worthwhile. Messaging that appeals to the audience's morals or fears in order to provide a rationale for compliance can be polarising and divisive, producing a strongly negative emotional response from the public and potentially undermining social cohesion. Provenance of the messaging alongside text-external political factors also have an influence on messaging uptake. In addition, our findings highlight key differences in messaging uptake by audience age, which demonstrates the importance of tailored communications and the need to seek public feedback to test the efficacy of messaging with the relevant demographics. Our study illustrates the value of corpus linguistics to public health agencies and health communications professionals, and we share our recommendations for improving the public health messaging both in the context of the ongoing pandemic and for future novel and re-emerging infectious disease outbreaks.

PMID:37521321 | PMC:PMC9630298 | DOI:10.1016/j.acorp.2022.100037

JMIR Infodemiology. 2022 Apr 20;2(1):e30885. doi: 10.2196/30885. eCollection 2022 Jan-Jun.

ABSTRACT

BACKGROUND: Black women in the United States disproportionately suffer adverse pregnancy and birth outcomes compared to White women. Economic adversity and implicit bias during clinical encounters may lead to physiological responses that place Black women at higher risk for adverse birth outcomes. The novel coronavirus disease of 2019 (COVID-19) further exacerbated this risk, as safety protocols increased social isolation in clinical settings, thereby limiting opportunities to advocate for unbiased care. Twitter, 1 of the most popular social networking sites, has been used to study a variety of issues of public interest, including health care. This study considers whether posts on Twitter accurately reflect public discourse during the COVID-19 pandemic and are being used in infodemiology studies by public health experts.

OBJECTIVE: This study aims to assess the feasibility of Twitter for identifying public discourse related to social determinants of health and advocacy that influence maternal health among Black women across the United States and to examine trends in sentiment between 2019 and 2020 in the context of the COVID-19 pandemic.

METHODS: Tweets were collected from March 1 to July 13, 2020, from 21 organizations and influencers and from 4 hashtags that focused on Black maternal health. Additionally, tweets from the same organizations and hashtags were collected from the year prior, from March 1 to July 13, 2019. Twint, a Python programming library, was used for data collection and analysis. We gathered the text of approximately 17,000 tweets, as well as all publicly available metadata. Topic modeling and k-means clustering were used to analyze the tweets.

RESULTS: A variety of trends were observed when comparing the 2020 data set to the 2019 data set from the same period. The percentages listed for each topic are probabilities of that topic occurring in our corpus. In our topic models, tweets on reproductive justice, maternal mortality crises, and patient care increased by 67.46% in 2020 versus 2019. Topics on community, advocacy, and health equity increased by over 30% in 2020 versus 2019. In contrast, tweet topics that decreased in 2020 versus 2019 were as follows: tweets on Medicaid and medical coverage decreased by 27.73%, and discussions about creating space for Black women decreased by just under 30%.

CONCLUSIONS: The results indicate that the COVID-19 pandemic may have spurred an increased focus on advocating for improved reproductive health and maternal health outcomes among Black women in the United States. Further analyses are needed to capture a longer time frame that encompasses more of the pandemic, as well as more diverse voices to confirm the robustness of the findings. We also concluded that Twitter is an effective source for providing a snapshot of relevant topics to guide Black maternal health advocacy efforts.

PMID:35578642 | PMC:PMC9092478 | DOI:10.2196/30885

Int J Digit Humanit. 2021;2(1-3):43-63. doi: 10.1007/s42803-021-00032-5. Epub 2021 Apr 27.

ABSTRACT

Our work considers the sociotechnical and organisational constraints of web archiving in order to understand how these factors and contingencies influence research engagement with national web collections. In this article, we compare and contrast our experiences of undertaking web archival research at two national web archives: the UK Web Archive located at the British Library and the Netarchive at the Royal Danish Library. Based on personal interactions with the collections, interviews with library staff and observations of web archiving activities, we invoke three conceptual devices (orientating, auditing and constructing) to describe common research practices and associated challenges in the context of each national web archive. Through this framework we centre the early stages of the research process that are often only given cursory attention in methodological descriptions of web archival research, to discuss the epistemological entanglements of researcher practices, instruments, tools and methods that create the conditions of possibility for new knowledge and scholarship in this space. In this analysis, we highlight the significant time and energy required on the part of researchers to begin using national web archives, as well as the value of engaging with the curatorial infrastructure that enables web archiving in practice. Focusing an analysis on these research infrastructures facilitates a discussion of how these web archival interfaces both enable and foreclose on particular forms of researcher engagement with the past Web and in turn contributes to critical ongoing debates surrounding the opportunities and constraints of digital sources, methodologies and claims within the Digital Humanities.

PMID:34966889 | PMC:PMC8700321 | DOI:10.1007/s42803-021-00032-5

Front Artif Intell. 2020 Aug 25;3:62. doi: 10.3389/frai.2020.00062. eCollection 2020.

ABSTRACT

In this article we describe our experiences with computational text analysis involving rich social and cultural concepts. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of key questions that can guide work in this area. Our guidance is based on our own experiences and is therefore inherently imperfect. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that resonate for many. This leads to our final goal: to help promote interdisciplinary collaborations. Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis involving social and cultural concepts, and the more we bridge these divides, the more fruitful we believe our work will be.

PMID:33733179 | PMC:PMC7861331 | DOI:10.3389/frai.2020.00062

Curr Pharm Biotechnol. 2019;20(10):895-908. doi: 10.2174/1389201020666190211125550.

ABSTRACT

BACKGROUND & OBJECTIVE: Nutritional culturomics (NCs) is a specific focus area of culturomics epistemology developing digital humanities and computational linguistics approaches to search for macro-patterns of public interest in food, nutrition and diet choice as a major component of cultural evolution. Cultural evolution is considered as a driver at the interface of environmental and food science, economy and policy.

METHODS: The paper presents an epistemic programme that builds on the use of big data from webbased services such as Google Trends, Google Adwords or Google Books Ngram Viewer.

RESULTS: A comparison of clearly defined NCs in terms of geography, culture, linguistics, literacy, technological setups or time period might be used to reveal variations and singularities in public's behavior in terms of adaptation and mitigation policies in the agri-food and public health sectors.

CONCLUSION: The proposed NC programme is developed along major axes: (1) the definition of an NC; (2) the reconstruction of food and diet histories; (3) the nutrition related epidemiology; (4) the understanding of variability of NCs; (5) the methodological diversification of NCs; (6) the quantifiable limitations and flaws of NCs. A series of indicative examples are presented regarding these NC epistemology components.

PMID:30747060 | DOI:10.2174/1389201020666190211125550

Med Hist. 2016 Jan;60(1):129-31. doi: 10.1017/mdh.2015.84.

NO ABSTRACT

PMID:26651204 | PMC:PMC4847397 | DOI:10.1017/mdh.2015.84

Am J Clin Hypn. 2013 Apr;55(4):343-59. doi: 10.1080/00029157.2012.696078.

ABSTRACT

Culturomics is a new scientific discipline of the digital humanities-the use of computer algorithms to search for meaning in large databases of text and media. This new digital discipline is used to explore 200 years of the history of hypnosis and psychotherapy in over five million digitized books from more than 40 university libraries around the world. It graphically compares the frequencies of English words about hypnosis, hypnotherapy, psychoanalysis, psychotherapy, and their founders from 1800 to 2008. This new perspective explore issues such as: Who were the major innovators in the history of therapeutic hypnosis, psychoanalysis, and psychotherapy? How well does this new digital approach to the humanities correspond to traditional histories of hypnosis and psychotherapy?

PMID:23724569 | DOI:10.1080/00029157.2012.696078