Technol Cult. 2026;67(3):973-7. doi: 10.1353/tech.2026.a996506.

ABSTRACT

This article offers the first comprehensive review of women's contributions and gender-focused research in Technology and Culture (T&C) from its early years through 2025. Using a mixed-methods approach that combines distant reading of digital publication datasets with qualitative mapping of milestones and turning points in the history of T&C, the article examines how the gender composition of authors evolved over time, how this has influenced the journal's content, and what broader role feminist theory has played in shaping new scholarly directions in the history of technology. Findings reveal that while women authors and gender-focused studies have steadily increased in number, engagement with feminist theory followed a less linear trajectory. Early historians advanced feminist approaches that influenced fields such as feminist science and technology studies and feminist digital humanities. Yet the normalization of gender analysis within the history of technology did not produce a similarly consolidated feminist historiographical tradition. The article concludes by outlining possible directions for recovering a more explicit feminist historiographical and methodological conversation within the field and across adjacent interdisciplinary domains.

PMID:42544481 | DOI:10.1353/tech.2026.a996506

Children (Basel). 2026 Jul 22;13(7):967. doi: 10.3390/children13070967.

ABSTRACT

BACKGROUND/OBJECTIVES: Artificial intelligence (AI) is increasingly used in youth mental health services, including clinical decision support, risk prediction, and digital therapeutics. However, existing frameworks provide limited guidance for integrating ethical, cultural, and relational considerations into the design, governance, and implementation of AI-enabled mental health technologies. This scoping review examined how digital humanities-informed approaches have been incorporated into AI-supported mental health interventions for children and adolescents.

METHODS: A scoping review was conducted following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. Peer-reviewed literature published between 2015 and 2025 was searched using PubMed and supplemented by semantic searches through Elicit. Systematic reviews, scoping reviews, and meta-analyses examining AI-enabled digital mental health interventions and digital humanities perspectives were included. Data were synthesized using inductive thematic analysis.

RESULTS: Seventeen review-level studies met the inclusion criteria. Six recurring themes were identified: engagement, participatory co-design, human oversight, equity, ethical governance, and implementation. Across the included reviews, humanities-informed approaches were associated with greater attention to relational engagement, stakeholder participation, transparency, contextual adaptation, and culturally responsive implementation. Evidence supporting intervention effectiveness was strongest in systematic reviews and meta-analyses, whereas findings related to ethics, governance, equity, and implementation were derived primarily from scoping reviews and conceptual syntheses.

CONCLUSIONS: This review suggests that digital humanities provides a valuable interdisciplinary perspective for informing the design, governance, and implementation of AI-enabled youth mental health interventions. Although the current evidence base remains heterogeneous, integrating humanities-informed approaches may support the development of AI systems that are more ethical, equitable, and developmentally responsive. Future research should evaluate these approaches through empirical implementation studies and emerging generative AI applications.

PMID:42509992 | PMC:PMC13406150 | DOI:10.3390/children13070967

Sci Rep. 2026 Jul 8;16(1):21193. doi: 10.1038/s41598-026-54438-8.

ABSTRACT

Arabic poet classification presents distinctive challenges stemming from the morphological richness and stylistic diversity inherent in both classical and modern Arabic verse. This study conducts an extensive comparative evaluation of several neural language models to assess their ability to represent poetic expression and capture authorial characteristics. Two carefully curated datasets are utilised: FrequentPoets, representing prolific authors with extensive verse collections, and CrossEraPoets, encompassing poets from distinct historical periods to examine temporal stylistic variation. A comparative evaluation framework is introduced to contrast domain-specific and general-purpose language models across prolific authorship and cross-era stylistic variation. The domain-adapted AraPoemBERT consistently achieves superior performance, attaining 73.11% accuracy (73.00% F1) on FrequentPoets and 77.06% accuracy (77.04% F1) on CrossEraPoets, whereas the general-purpose GPT-4o demonstrates considerably lower performance under zero-shot and few-shot evaluation settings. The results highlight the significance of domain-adapted pretraining for morphologically complex languages like Arabic and suggest the potential advantage of transformer-based architectures in modelling stylistic and linguistic nuances unique to Arabic verse. These findings also suggest that temporal diversity may play an important role in model generalisation across different poetic styles. The study contributes to Arabic Natural Language Processing (NLP) and digital humanities by enabling computational authorship attribution, stylistic analysis, and cross-historical exploration of Arabic literary heritage. Overall, the proposed framework provides a robust foundation for future research in Arabic poetry analytics and domain-specific language modelling.

PMID:42420346 | PMC:PMC13346815 | DOI:10.1038/s41598-026-54438-8

Data Brief. 2026 Jun 19;67:112983. doi: 10.1016/j.dib.2026.112983. eCollection 2026 Aug.

ABSTRACT

This article presents KannadaLit4NLP, a large-scale, machine-readable corpus of Kannada literary texts designed to support natural language processing (NLP) research for a low-resource language. The dataset comprises 24,746 literary verses from three major Kannada literary traditions-Vachanas (11th-19th century), Tripadis (16th century), and Kagga (20th century)-along with 22,369 corresponding interpretations curated from scholarly sources. The corpus captures linguistic, stylistic, and semantic variations across historical periods and literary forms. The dataset was developed through a systematic pipeline that included source identification, digitisation via optical character recognition (OCR), manual verification, and structured annotation. Each entry is organised in a structured format that includes the original verse, metadata (literary form, author, and source), and associated interpretation(s), enabling its use in tasks such as semantic textual similarity, textual entailment, information retrieval, and generative modelling. KannadaLit4NLP addresses the limited availability of culturally grounded Kannada datasets by providing a resource that integrates classical and modern literary content with interpretative annotations. The dataset can facilitate the development and evaluation of NLP models in areas such as semantic understanding, translation, and knowledge representation, while also supporting computational studies of literary and cultural texts. The dataset is made publicly available to encourage further research and reproducibility in Kannada NLP.

PMID:42389175 | PMC:PMC13320459 | DOI:10.1016/j.dib.2026.112983

Ambix. 2026 Jun 25:1-30. doi: 10.1080/00026980.2026.2668735. Online ahead of print.

ABSTRACT

This article documents the creation of EMLAP (Early Modern Latin Alchemical Prints), a machine-readable corpus of one hundred Latin alchemical printed works produced within the TOME (The Origins of Modern Encyclopaedism, 2023-2025) project. It first situates the corpus within both the digital humanities landscape and the historiography of alchemy, where the availability of reliable machine-readable texts remains limited. It then addresses the challenges of converting early modern Latin printed text into machine-readable format with a high standard of quality. The article argues that producing a high-quality transcribed corpus at scale still requires human scholarly intervention, and that a transcription project must balance the ideal of digital edition standards against the practical constraints of time and resources. The article describes the practical experience of building EMLAP: the selection of the Transkribus platform for AI-powered automatic text recognition, the choice of transcription models, the development of human quality standards to complement automated metrics, and the construction of a computational pipeline to process and enrich the transcriptions. The EMLAP corpus has been made publicly available in open access (Zenodo repository) as well as in the form of a website that offers different search opportunities for researchers.

PMID:42345208 | DOI:10.1080/00026980.2026.2668735

Sci Data. 2026 May 30. doi: 10.1038/s41597-026-07527-2. Online ahead of print.

ABSTRACT

The automatic extraction of geographical entities and spatial relationships from historical texts is a fundamental task for Named Entity Recognition (NER) and relation extraction (RE), with important implications for historical geography and digital humanities. Classical Chinese documents describing ancient cities pose particular challenges due to archaic language, implicit spatial expressions, and complex entity hierarchies. In this study, we present a manually annotated dataset designed for joint geographical entity and spatial relationship extraction from texts related to Lin'an, the capital of the Southern Song Dynasty. The dataset consists of 18 in-domain and 1 out-of-distribution historical documents comprising approximately one million Chinese characters, annotated with 24 categories of geographical entities and 34 types of spatial relationships. This dataset provides a valuable resource for advancing NER and spatial relation extraction in historical texts and supports future research in historical Geographic Information Systems (GIS), cultural geography, and digital heritage reconstruction.

PMID:42225712 | DOI:10.1038/s41597-026-07527-2

Data Brief. 2026 May 10;66:112835. doi: 10.1016/j.dib.2026.112835. eCollection 2026 Jun.

ABSTRACT

This dataset was collected during on-site fieldwork conducted in the district of Chinchero, located in the province of Urubamba, Cusco, Peru, a region internationally recognized for its rich Andean textile tradition rooted in Inca Culture heritage. The dataset comprises high-quality Photographic images of traditional handwoven Andean textile iconographies produced by local artisan communities. These images were captured directly at textile centers where the fabrics are woven, dyed and finished using ancestral techniques measuring authentic representation of colors, textures, and symbolic patterns under natural and controlled conditions. The dataset consists of 1358 images organized into 28 distinct classes, each corresponding to a specific textile iconography characteristic of the Chinchero tradition. The images are provided in a processed and curated format, facilitating organization enables systematic analysis of visual motifs that are often challenging to distinguish due to their intricate geometric patterns and cultural symbolism. The primary reuse potential of this dataset lies in its application to Artificial Intelligence (AI) and Machine Learning (ML) research focused on image classification, pattern recognition, and cultural heritage preservation. Researchers can leverage the dataset to develop and evaluate models capable of identifying and differentiating traditional Andean textile iconographies, addressing the growing difficulty faced by younger generations, local communities, and visitors in recognizing the cultural expressions. Additionally, the dataset supports interdisciplinary research in digital humanities, ethnography, textile studies, and cultural informatics contributing to the documentation and preservation of intangible cultural heritage. By making this dataset publicly available, this work aims to support the development of AI-driven tools for cultural preservation, educational applications, and heritage awareness, while fostering collaboration between researchers, technologists, and local artisan communities to safeguard ancestral knowledge for future generations.

PMID:42220648 | PMC:PMC13217882 | DOI:10.1016/j.dib.2026.112835

Sci Rep. 2026 May 21;16(1):23240. doi: 10.1038/s41598-026-52522-7.

ABSTRACT

Traditional Chinese paintings pose unique challenges for computational emotion analysis due to culturally-specific aesthetic principles that differ fundamentally from Western art paradigms. This study proposes an attention-guided CNN-Transformer hybrid model that integrates local feature extraction with global contextual modeling. The architecture employs spatial, channel, and cross-attention modules to fuse CNN-extracted brushwork details with Transformer-captured compositional relationships. Evaluated on a dataset of 7842 traditional Chinese paintings across seven emotion categories-tranquility, melancholy, vigor, elegance, desolation, joy, and solemnity-the model achieves 91.4% classification accuracy. Comparative experiments demonstrate superior performance over ResNet-101, DeiT-B, and ConvNeXt-T baselines. Ablation studies confirm the critical role of the attention-guidance module, while visualization analysis reveals alignment with traditional art theory principles. These results provide empirical support for domain-specific architectural designs in culturally-sensitive visual analysis within Han Chinese literati painting traditions, with generalizability to broader artistic domains constituting an important direction for subsequent investigation.

PMID:42168395 | PMC:PMC13402820 | DOI:10.1038/s41598-026-52522-7

Ann Sci. 2026 May 6:1-27. doi: 10.1080/00033790.2026.2638942. Online ahead of print.

ABSTRACT

This essay investigates the reception of William Gilbert's foundational work on magnetism, De Magnete, through a comprehensive analysis of extant copies of its early modern printed editions (1600, 1628, 1629, 1633). By employing a hybrid methodology combining quantitative and qualitative approaches to readers' annotations, this study charts patterns of engagement with Gilbert's text across diverse contexts and intellectual traditions. While celebrated for its experimental innovations and practical applications in navigation, it also elicited cosmological and humanist interests. Statistical analyses of readers' marks demonstrate a skewed distribution of engagement, with the majority of annotations concentrated in a small fraction of extant copies. This study moreover contributes to the historiography of early modern science by illustrating the methodological potential of combining large-scale digital datasets with close textual analysis, advocating for more systematic, collaborative approaches to the history of reading and book culture. In addition, a near-complete census of copies of De magnete is provided.

PMID:42091227 | DOI:10.1080/00033790.2026.2638942

Int J Archit Comput. 2024 Jan 13;22(3):277-294. doi: 10.1177/14780771231220903. eCollection 2024 Sep.

ABSTRACT

This article elaborates a computationally enabled approach to the study of design methods in 1960s North America. This entails the construction, visualization, and analysis of a digital database built from entries of the Design Methods Group Newsletter, a periodical published monthly between 1966-71. The article proposes a workflow that combines methods such as topic modeling and network visualization to activate the Newsletter as a source of anecdotal and informal knowledge, and to enable histories of connectivity and transaction that may elude archival investigations on singular actors or institutions. In doing so, the article contributes arguments and techniques for the study of design methods as a complex social, technical, and intellectual meshwork. The meshwork brings discursive themes, techniques, actors, and institutions at the same level of investigation and allows for layered cartographies of the field that advanced the systematic study of design and ushered in the development of early computer applications.

PMID:42038932 | PMC:PMC13104777 | DOI:10.1177/14780771231220903

PLoS One. 2026 Apr 21;21(4):e0347253. doi: 10.1371/journal.pone.0347253. eCollection 2026.

ABSTRACT

The overseas reception of classical literature through online platforms presents a critical lens for understanding cross-cultural dynamics in the digital age. This study investigates the overseas reception of English translations of Journey to the West by analyzing a corpus of 1,795 reviews from Amazon and Goodreads to examine temporal dynamics, cross-platform sentiment patterns, and topic modeling. The analysis covers four celebrated translators: Arthur Waley, Anthony C. Yu, Julia Lovell, and W.J.F. Jenner. Methodologically, we developed a hybrid sentiment lexicon by integrating a domain sentiment lexicon with AFINN, NRC, and VADER through weighted fusion, addressing the limited adaptability of general sentiment lexicons in translated literature analysis. LDA modeling was further applied to enable data-driven theme extraction. Key findings reveal a consistent year-on-year increase in review counts across all translations. Notably, despite an overall positive sentiment, significant cross-platform divergences emerge, reflecting the distinct evaluative mechanisms of digital platforms. Thematic analysis identifies three central reader concerns: translation quality, plot acceptance, and character portrayal, with plot acceptance exhibiting markedly higher negativity. Furthermore, translator-level analysis reveals performance variations across these themes. This study demonstrates how digital platforms reconfigure the valuation of literary translation, and pioneers a methodological framework for capturing the dynamic interplay between reader perception, media infrastructure, and textual mobility, offering new pathways for digital humanities research in translation studies.

PMID:42013105 | PMC:PMC13098970 | DOI:10.1371/journal.pone.0347253

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 Jun;117:102151. doi: 10.1016/j.shpsa.2026.102151. Epub 2026 Mar 30.

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

Int J Transgend Health. 2025 Mar 15;27(2):902-916. doi: 10.1080/26895269.2025.2478092. eCollection 2026.

ABSTRACT

BACKGROUND: Research on the challenges, marginalization, and identity of trans women in India has sparked important discussions, contributing to progressive changes in society. While the visibility and recognition of trans women is steadily growing, beneficial schemes tailored to their unique challenges are often overlooked, underscoring the need for greater attention and action.

AIM: The article aims to identify the unique healthcare and migration challenges faced by South Indian trans women and the reach and utilization of government-provided facilities by their community.

METHOD: A survey of 53 and interviews with 4 South Indian trans women focused on the utilization of state and central government schemes. Data from public and private healthcare facilities in Madurai were collected and visualized using Airtable, with results disseminated in Tamil and English to ensure accessibility for the trans community.

RESULTS: A relationship was identified between the effectiveness of state government welfare schemes and the well-being of the trans women's community. However, central government schemes often fail to reach their entire population. Furthermore, state government transportation schemes do not sufficiently support their healthcare access and economic development.

DISCUSSION: To enhance the socio-economic development of trans women, policymakers can ensure that beneficial schemes comprehensively reach all segments of society. Increased promotion, awareness, and advancements in these schemes are necessary to meet the needs of the trans women community. Additionally, extending free bus fare facilities specifically to trans women is recommended to improve their healthcare access, mobility, economic opportunities, and integration into mainstream society.

PMID:41891076 | PMC:PMC13015021 | DOI:10.1080/26895269.2025.2478092

Med Humanit. 2026 Feb 12:medhum-2025-013580. doi: 10.1136/medhum-2025-013580. Online ahead of print.

ABSTRACT

Within the context of the eastward spread of Western learning during the late Ming and early Qing dynasties, the cross-cultural dissemination of medical knowledge exhibited complex characteristics of visual transformation. This study selects Taixi Renshen Shuogai (1623) and Zhenjiu Dacheng (1601) as research samples, employing a research method combining iconographic analysis and digital humanities to explore the different body concepts and their cultural connotations carried by Chinese and Western medical illustrations. The study constructs a three-dimensional analytical framework covering visual vocabulary, content expression and quantitative statistics to conduct an in-depth analysis of the cognitive differences in human body representation between the two medical traditions. The results show that the mechanistic view of the body advocated by Western anatomy and the organic view of the body upheld by traditional Chinese medicine form a sharp contrast at the image level; the former is characterised by precise analysis and structural reduction, while the latter centres on holistic grasp and functional correlation. This difference is not only reflected in visual elements such as composition patterns and expressive techniques, but more profoundly reflects the knowledge construction logic of different epistemological systems. The coexistence of two body cognition models during the Ming and Qing dynasties reveals the selective mechanism and creative transformation ability of Chinese culture in the process of knowledge acceptance, providing a new interpretive path for understanding the interaction model between traditional culture and foreign civilisations. This study expands the methodological boundaries of medical history research and provides a historical mirror for contemporary cross-cultural medical exchanges.

PMID:41679973 | DOI:10.1136/medhum-2025-013580

Data Brief. 2026 Jan 20;65:112495. doi: 10.1016/j.dib.2026.112495. eCollection 2026 Apr.

ABSTRACT

This dataset presents a valuable compilation of question-answer (QA) pairs derived from cultural texts and sources related to Durga mythology. A total of 21,395 QA pairs, encompassing textual materials such as scriptures, ritual narratives, temple inscriptions, and traditional storytelling records. Each entry includes the source reference, question, and corresponding answer, provided in a structured format compatible with Excel for seamless integration into downstream natural language processing (NLP) tasks. Data collection involved manual curation and annotation by domain experts, followed by preprocessing steps including text normalization, duplication removal, and verification of factual and contextual accuracy. The dataset is designed to support generative QA models, culturally aware chatbots, and digital preservation of heritage knowledge. It is particularly valuable for research in AI-driven cultural applications, educational tools, and digital humanities initiatives aiming to bridge traditional knowledge with computational methods. Researchers and practitioners may utilize the dataset for training generative models, creating interactive educational platforms, developing culturally sensitive AI agents, and supporting comparative studies in cross-cultural heritage. This openly accessible resource adheres to ethical standards, with proper attribution to source materials, and provides a foundational asset for both academic research and applied development in culturally informed artificial intelligence.

PMID:41657412 | PMC:PMC12874138 | DOI:10.1016/j.dib.2026.112495

IEEE Comput Graph Appl. 2026 Feb 2;PP. doi: 10.1109/MCG.2026.3660122. Online ahead of print.

ABSTRACT

This paper presents a case study focusing on the exploratory visual analysis of a unique historical dataset consisting of approximately 4000 visual sketches and associated captions from an encyclopedic book published in 1909-1910. The book, which offers insight into Vietnamese crafts and social practices, poses the challenge of extracting cultural meaning and narrative structure from thousands of drawings and multilingual captions. Our research aims to explore and evaluate the effectiveness of multiple visualization techniques in uncovering meaningful relationships within the dataset while working closely with professional historians. The main contributions of this study include refining historical research questions through task and data abstraction, combining and validating visualization techniques for historical data interpretation, and involving a focus group of historians for further evaluation. These contributions offer generalizable insights for the development of domain-specific visualization tools and support interdisciplinary engagement in historical data visualization and critical digital humanities research.

PMID:41628052 | DOI:10.1109/MCG.2026.3660122

Data Brief. 2026 Jan 7;64:112443. doi: 10.1016/j.dib.2025.112443. eCollection 2026 Feb.

ABSTRACT

Here we present a coherent, georeferenced and chronologically qualified corpus of fossil plant remains compiled from published archaeobotanical records from archaeological sites from Central Italy, focused on Olea europaea (olive) and Vitis vinifera (grape). The dataset is entirely based on secondary data and does not include newly generated primary archaeobotanical analyses. The dataset integrates site, context and all relevant archaeobotanical occurrences within a coherent relational and spatial model. The corpus was initiated through a structured bibliographic survey aided by the BRAIN database. Exclusively published literature was consulted, allowing to model archaeological sites and link them to excavation contexts and individual archaeobotanical occurrences (defined as the combination of a taxon and the specific plant part recovered, e.g., fruit, seed, rachis). The geodatabase was implemented using QGIS, with a local backend in GeoPackage, then migrated to PostgreSQL/PostGIS to support complex spatial/relational queries and future online outputs. All entities have a defined spatial placement accompanied by explicit quality-control parameters documenting positional uncertainty, source type and authority, as derived from the original published sources, ensuring transparent assessment of locational reliability. To enrich taxonomic information, an automated open thesaurus was built from CC BY/CC BY-SA resources (Floritaly, Acta Plantarum, and Wikimedia projects). The workflow employs REST-style access (or form-equivalent submissions), conservative rate-limiting, randomized waits, retries, and checkpoints; provenance and attribution (including noted transformations) are preserved. A standardized chronological table harmonizes relative cultural phases using ICCD nomenclature, with controlled fallbacks to Perio.do or peer-reviewed literature; a self-referential hierarchy (parent_id) ensures inheritance from sub-phase to broader period. Crucially, the use of open licenses, stable identifiers and cross-references makes the dataset interoperable and interlinked with the source ecosystems from which the secondary archaeobotanical data were extracted: records can resolve back to Floritaly and Acta Plantarum, and our forthcoming web portal can expose these connections for bidirectional navigation, automated updating and external reuse. The result is an interoperable, verifiable resource suitable for spatial and temporal analyses of plant remains based on aggregated and standardized published archaeobotanical data, while remaining legally reusable under the original licenses.

PMID:41624435 | PMC:PMC12855569 | DOI:10.1016/j.dib.2025.112443

Sci Rep. 2025 Dec 24;16(1):2505. doi: 10.1038/s41598-025-32302-5.

ABSTRACT

This study aims to achieve controllable generation of Chinese traditional cultural narrative content and enhance cultural identity. First, it constructs a tri-modal generation framework of text-image-style based on Stable Diffusion v2.1 and Contrastive Language-Image Pretraining (CLIP) models, realizing the joint modeling of traditional cultural semantics and visual imagery. Second, the study introduces the Low-Rank Adaptation (LoRA) mechanism to embed traditional cultural style features in a lightweight manner, improving the model's style adaptability under small sample conditions. Finally, a three-level evaluation system of "generation quality-semantic consistency-cultural identity" is built, covering both objective indicators and user feedback, to systematically verify the model's performance. Results show that the proposed model significantly outperforms existing methods in multiple dimensions: in terms of image quality, the Fréchet Inception Distance (FID) is 22.85, the Learned Perceptual Image Patch Similarity (LPIPS) is 0.298, and the style recognition accuracy reaches 86.4%. Regarding narrative consistency, the Bilingual Evaluation Understudy (BLEU) score is 0.325, the CLIP text-image similarity is 0.793, and the Narrative Style Match is 82.3%. On the cultural perception level, the average user narrative resonance is 4.32 points, the imagery accuracy score is 0.748, and the question-answer task pass rate is 82.6%. The comparative results indicate that the proposed method has significant advantages in expressive diversity and depth of cultural communication. When properly designed, Artificial Intelligence Generated Content (AIGC) technology can be effectively used for the generation and identity reconstruction of Chinese traditional cultural narrative content. This study provides a scalable technical path for the integration of AI and traditional culture, and expands the boundaries of digital humanities in content generation and reception research.

PMID:41444384 | PMC:PMC12820238 | DOI:10.1038/s41598-025-32302-5

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