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

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

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

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

Sci Data. 2025 Dec 1;12(1):1804. doi: 10.1038/s41597-025-06241-9.

ABSTRACT

This dataset offers a comprehensive digital catalogue of 483 archaeological settlement sites in western Anatolia dating to the Middle and Late Bronze Age (c. 2000-1200 BCE). Compiled over a decade, it brings together evidence from excavation reports, systematic surveys, historical sources, and remote sensing. Each site is georeferenced and described through a standardized set of metadata, including chronological attribution, site function, material culture, bibliographic references, and associated ancient mineral resources. The dataset is published on Zenodo as a collection of openly accessible files, structured with consistent keys that ensure integration across records. To enhance semantic interoperability, settlement entries are linked to external reference datasets such as open knowledge bases, enabling opportunities for comparative, geospatial, and interdisciplinary research spanning archaeology, digital humanities, and historical geography. By combining standardized metadata with semantic linking, the resource facilitates reuse within broader digital infrastructures. It thereby provides a transparent, openly licensed foundation for analyzing regional settlement systems and encourages more comprehensive approaches to the study of Bronze Age Anatolia.

PMID:41326413 | PMC:PMC12669694 | DOI:10.1038/s41597-025-06241-9

F1000Res. 2025 Sep 9;13:1305. doi: 10.12688/f1000research.156917.2. eCollection 2024.

ABSTRACT

BACKGROUND: Reproducibility is a basis of scientific integrity, yet it remains a significant challenge across disciplines in computational science. This reproducibility crisis is now being met with an Open Science movement, which has risen to prominence within the scientific community and academic libraries especially. At the Carnegie Mellon University Libraries, the Open Science and Data Collaborations (OSDC) Program promotes Open Science practices with resources, services, and events. Hosting hackathons in academic libraries may show promise for furthering such efforts.

METHODS: To address the need for reproducible computational research and promote Open Science within the community, members of the OSDC Program organized a single-day hackathon centered around reproducibility. Partnering with a faculty researcher in English and Digital Humanities, we invited community members to reuse Python code and data from a research publication deposited to Harvard Dataverse. We also published these materials as a compute capsule in Code Ocean that participants could also access. Additionally, we investigated ways to use ChatGPT to troubleshoot errors from rerunning this code.

RESULTS: Three students from the School of Computer Science participated in this hackathon. Accessing materials from Harvard Dataverse, these students found success reproducing most of the data visualizations, but they required some manual setup and modifications to address depreciated libraries used in the code. Alternatively, we found Code Ocean to be a highly accessible option, free from depreciation risk. Last, ChatGPT also aided in finding and addressing the same roadblocks to successfully reproduce the same figures as the participating students.

CONCLUSIONS: This hackathon allowed several students an opportunity to interact with and evaluate real research outputs, testing the reproducibility of computational data analyses. Partnering with faculty opened opportunities to improve open research materials. This case study outlines one approach for other academic libraries to highlight challenges that face reproducibility in an interactive setting.

PMID:41064702 | PMC:PMC12501581 | DOI:10.12688/f1000research.156917.2

J Med Ethics. 2025 Sep 4:jme-2025-111310. doi: 10.1136/jme-2025-111310. Online ahead of print.

ABSTRACT

We respond to Gillon's critique of our data-driven analysis of the history of Journal of Medical Ethics (JME), in which we used a topic model to trace intellectual trends in the journal's first 50 years. Gillon, drawing on his personal memories as JME's second (and longest serving) editor, challenges several of our findings, particularly those concerning the prominence and classification of topics such as Ethics education In this reply, we clarify misunderstandings that led to part of his criticisms of our method. At the same time, we also briefly discuss some nuances of topic modelling, in particular, its reliance on simplified representations of text, sensitivity to modeling choices and topic interpretations. Rather than viewing computational models and editorial memory as competing sources of insight, we propose that they are complementary: each illuminates different dimensions of the journal's evolution. Gillon's engagement with our work ultimately highlights the importance of methodological transparency and the value of combining digital humanities tools with lived experience in the historiography of academic disciplines.

PMID:40908135 | DOI:10.1136/jme-2025-111310

Proc Natl Acad Sci U S A. 2025 May 20;122(20):e2503850122. doi: 10.1073/pnas.2503850122. Epub 2025 May 14.

ABSTRACT

The distribution of personal names provides unique, yet often overlooked, insight into modern and historical societies. This study employs diversity statistics-commonly used in ecology-to analyze onomastic data from Iron Age II archaeological excavations in the Southern Levant (950-586 BCE). Our findings reveal higher onomastic diversity in the Kingdom of Israel compared to Judah, suggesting a more cosmopolitan society. We also observe a decrease in name diversity in Judah over time, potentially reflecting sociopolitical changes. Center/periphery analysis shows contrasting patterns in Israel and Judah. These results provide insights into social dynamics, cultural interactions, and identity formation in these ancient societies. Our methodology, validated using supplementary archaeological data, as well as modern datasets, offers a robust framework for applying diversity statistics across various modern and historical contexts.

PMID:40366687 | PMC:PMC12107089 | DOI:10.1073/pnas.2503850122

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

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

Arch Sex Behav. 2024 Mar;53(3):901-915. doi: 10.1007/s10508-023-02759-4. Epub 2023 Dec 26.

ABSTRACT

In Western Europe, the Early Modern Period is characterized by the rise of tenderness in romantic relationships and the emergence of companionate marriage. Despite a long research tradition, the origins of these social changes remain elusive. In this paper, we build on recent advances in behavioral sciences, showing that romantic emotional investment, which is more culturally variable than sexual attraction, enhances the cohesion of long-term relationships and increases investment in children. Importantly, this long-term strategy is considered especially advantageous when living standards are high. Here, we investigate the relationship between living standards, the emotional components of love expressed in fiction work, and behavioral outcomes related to pair bonding, such as nuptial and fertility rates. We developed natural language processing measures of "emotional investment" (tenderness) and "attraction" (passion) and computed romantic love in English plays (N = 847) as a ratio between the two. We found that living standards generally predicted and temporally preceded variations of romantic love in the Early Modern Period. Furthermore, romantic love preceded an increase in nuptial rates and a decrease in births per marriage. This suggests that increasing living standards in the Early Modern Period may have contributed to the emergence of modern romantic culture.

PMID:38148451 | PMC:PMC10920442 | DOI:10.1007/s10508-023-02759-4

LREC Int Conf Lang Resour Eval. 2010 May;2010:1996-2001.

ABSTRACT

Propp's influential structural analysis of fairy tales created a powerful schema for representing storylines in terms of character functions, which is straightforward to exploit in computational semantic analysis and procedural generation of stories of this genre. We tackle two resources that draw on the Proppian model - one formalizes it as a semantic markup scheme and the other as an ontology - both lacking linguistic phenomena explicitly represented in them. The need for integrating linguistic information into structured semantic resources is motivated by the emergence of suitable standards that facilitate this, and the benefits such joint representation would create for transdisciplinary research across Digital Humanities, Computational Linguistics, and Artificial Intelligence.

PMID:37987027 | PMC:PMC10659064

Evol Hum Sci. 2023 Apr 13;5:e19. doi: 10.1017/ehs.2023.6. eCollection 2023.

ABSTRACT

The Scientific Revolution represents a turning point in the history of humanity. Yet it remains ill-understood, partly because of a lack of quantification. Here, we leverage large datasets of individual biographies (N = 22,943) and present the first estimates of scientific production during the late medieval and early modern period (1300-1850). Our data reveal striking differences across countries, with England and the United Provinces being much more creative than other countries, suggesting that economic development has been key in generating the Scientific Revolution. In line with recent results in behavioural sciences, we show that scientific creativity and economic development are associated with other kinds of creative activities in philosophy, literature, music and the arts, as well as with inclusive institutions and ascetic religiosity, suggesting a common underlying mindset associated with long-term orientation and exploration. Finally, we investigate the interplay between economic development and cultural transmission (the so-called 'Republic of Letters') using partially observed Markov models imported from population biology. Surprisingly, the role of horizontal transmission (from one country to another) seems to have been marginal. Beyond the case of science, our results suggest that economic development is an important factor in the evolution of aspects of human culture.

PMID:37587945 | PMC:PMC10426016 | DOI:10.1017/ehs.2023.6

Data Brief. 2023 Jun 3;48:109295. doi: 10.1016/j.dib.2023.109295. eCollection 2023 Jun.

ABSTRACT

Historical data on land cover/use and road networks are important not only for cultural heritage preservation in the context of digital humanities, but also for understanding the evolution of landscapes and human infrastructures aimed at efficient management of land systems. In this manuscript, we present a spatial database that contains basic background layers of the island of Cyprus in the 1960s. These data are derived from the topographic map of Cyprus produced in the 1960s and published in 1969. They were acquired after the digitization of the K715 map series (1:50,000) of the Corps of Engineers of the U.S. Army Map Service (hereafter "K715 map") [1]. The database consists of the following vector layers: a) land use/land cover, b) road network, c) coastline, d) settlements, and covers the entire area of the island (9,251 km2). The road network is divided into six categories and the land use/land cover into thirty-three different types according to the legend of the original map. In addition, the 1960 census was included in the database to assign population data to settlement entities (towns or villages). This census was the last census of the total population under the same authority and method, since Cyprus was divided into two parts five years after the map was published and as a result of the Turkish invasion. Therefore, the dataset can be used not only for cultural and historical preservation purposes, but also to measure the different development of the landscapes that fell under a different political status since 1974.

PMID:37383749 | PMC:PMC10294077 | DOI:10.1016/j.dib.2023.109295

J Imaging. 2023 Jun 15;9(6):120. doi: 10.3390/jimaging9060120.

ABSTRACT

Hands represent an important aspect of pictorial narration but have rarely been addressed as an object of study in art history and digital humanities. Although hand gestures play a significant role in conveying emotions, narratives, and cultural symbolism in the context of visual art, a comprehensive terminology for the classification of depicted hand poses is still lacking. In this article, we present the process of creating a new annotated dataset of pictorial hand poses. The dataset is based on a collection of European early modern paintings, from which hands are extracted using human pose estimation (HPE) methods. The hand images are then manually annotated based on art historical categorization schemes. From this categorization, we introduce a new classification task and perform a series of experiments using different types of features, including our newly introduced 2D hand keypoint features, as well as existing neural network-based features. This classification task represents a new and complex challenge due to the subtle and contextually dependent differences between depicted hands. The presented computational approach to hand pose recognition in paintings represents an initial attempt to tackle this challenge, which could potentially advance the use of HPE methods on paintings, as well as foster new research on the understanding of hand gestures in art.

PMID:37367468 | PMC:PMC10299537 | DOI:10.3390/jimaging9060120

J Imaging. 2022 Oct 15;8(10):285. doi: 10.3390/jimaging8100285.

ABSTRACT

Recent advances in object detection facilitated by deep learning have led to numerous solutions in a myriad of fields ranging from medical diagnosis to autonomous driving. However, historical research is yet to reap the benefits of such advances. This is generally due to the low number of large, coherent, and annotated datasets of historical documents, as well as the overwhelming focus on Optical Character Recognition to support the analysis of historical documents. In this paper, we highlight the importance of visual elements, in particular illustrations in historical documents, and offer a public multi-class historical visual element dataset based on the Sphaera corpus. Additionally, we train an image extraction model based on YOLO architecture and publish it through a publicly available web-service to detect and extract multi-class images from historical documents in an effort to bridge the gap between traditional and computational approaches in historical studies.

PMID:36286379 | PMC:PMC9605005 | DOI:10.3390/jimaging8100285

Arch Sci (Dordr). 2022;22(3):285-291. doi: 10.1007/s10502-022-09396-1. Epub 2022 May 26.

NO ABSTRACT

PMID:35637675 | PMC:PMC9134972 | DOI:10.1007/s10502-022-09396-1

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

PeerJ Comput Sci. 2019 Apr 18;5:e189. doi: 10.7717/peerj-cs.189. eCollection 2019.

ABSTRACT

The analysis of literary works has experienced a surge in computer-assisted processing. To obtain insights into the community structures and social interactions portrayed in novels, the creation of social networks from novels has gained popularity. Many methods rely on identifying named entities and relations for the construction of these networks, but many of these tools are not specifically created for the literary domain. Furthermore, many of the studies on information extraction from literature typically focus on 19th and early 20th century source material. Because of this, it is unclear if these techniques are as suitable to modern-day literature as they are to those older novels. We present a study in which we evaluate natural language processing tools for the automatic extraction of social networks from novels as well as their network structure. We find that there are no significant differences between old and modern novels but that both are subject to a large amount of variance. Furthermore, we identify several issues that complicate named entity recognition in our set of novels and we present methods to remedy these. We see this work as a step in creating more culturally-aware AI systems.

PMID:33816842 | PMC:PMC7924459 | DOI:10.7717/peerj-cs.189