arXane › Computer Science › Computer Science
Wednesday, 4 November 2026: 2 new papers, 1 cross-list and 1 replacement in Digital Libraries (cs.DL), in arXiv's order, with full abstracts. The same papers as arXiv's own listing, set as a newspaper; every title links to arXiv.
JSON-LD is getting increasing attention for publishing Linked Data, especially within cultural heritage. We present LODox Data Viewer, a reusable web component library for data presentation and visualization in semantic portals. It renders JSON-LD as human-readable, themeable cards, embeddable in any semantic portal page with a single script tag. Package URL: https://www.npmjs.com/package/lodox-data-viewer
Data Carpentry (DC) has been developed to instruct participants on essential concepts, skills, and tools for more effective and reproducible data management. DC emphasizes data literacy, aiming to equip researchers with the skills necessary to retrieve, view, manipulate, analyze, and store their own and other's data in an open and reproducible manner to derive insights from data. The objective of this study was to assess the perceptions and attitudes towards DC among research scholars of University of Kerala and to find out the purpose for which DC tools is used by the research scholars in the University of Kerala. This study requires a sample of 167 and 179 research scholars completed a questionnaire from a total population of 294 using the Krejcie and Morgan formula, with a 95% confidence interval and a 5% margin of error. SPSS version 22 was used to perform descriptive and inferential statistics on the data. 65.9% of research researchers have experience with DC tools. 48.6% of research researchers at the University of Kerala use DC tools primarily for data analysis. 40.2% of research academics identified insufficient access to training as the most significant obstacle they experience while utilizing DC tools in the study. DC emphasizes data literacy, aiming to equip researchers with the skills necessary to access, view, modify, analyze, and manage their own and others' data in an open and repeatable manner to derive insights from data.
The reconstruction of fragmented historical textiles presents a significant challenge in computational archaeology. The Oseberg ship burial, dating to 834 CE, yielded hundreds of degraded textile fragments that have resisted systematic computational analysis. While deep learning has revolutionized image-based puzzle solving, progress on archaeological textiles has been limited by the lack of domain-specific datasets and systematic evaluation of modern architectures. To address this gap, we present TEXMET, a curated dataset of 18,644 historical textile images from the Metropolitan Museum of Art's Open Access collection, representing the first textile-specific benchmark for puzzle-solving research. Using TEXMET, we conduct a systematic evaluation of three state-of-the-art puzzle-solving models: JPDVT, FCViT, and DiffAssemble. Our experiments reveal that the transformer-based models pre-trained on standard datasets fail to generalize to textiles, but domain-specific fine-tuning proves highly effective. The graph-based DiffAssemble model, when fine-tuned on TEXMET, achieves 85.89% puzzle accuracy on textile data. Most significantly, applying this fine-tuned model to 45 highly degraded Oseberg fragments yields a performance increase from 26.67% to 64.44% puzzle accuracy. These results establish TEXMET as a new benchmark for textile reconstruction and demonstrate that domain adaptation is essential for applying puzzle-solving methods to imagery of real archaeological materials.