Reflectance imaging spectroscopy (RIS) is invaluable in mapping and identifying artists’ materials in paintings. The analysis of the RIS image cube first involves classifying the cube into spatial regions, each having a unique reflectance spectrum (endmember). Second, endmember spectra are analyzed for spectral features useful to identify the pigments present to create labeled classes. The analysis process for paintings remains semi-automated because of the complex diffuse reflectance spectra due to the use of intimate pigment mixtures and optically thin paint layers by the artist. As a result, even when a group of related paintings are analyzed, each RIS cube is analyzed individually, which is time consuming. There is a need for new approaches to more efficiently analyze RIS cubes of related paintings to address the growing interest in the study of related paintings within a group of artists or artistic schools. This work builds upon prior investigations of 1-D spectral convolutional neural networks (CNNs) to address this need in two ways. First, an expanded training set was used—ten illuminated manuscripts created by artists stylistically grouped under the notname “Master of the Cypresses” (15th century Seville, Spain). Second, two 1-D CNN models were trained from the RIS cubes: reflectance and the first derivative. The results showed that the first derivative-trained CNN generally performed better than the reflectance-trained CNN in creating accurate labeled material maps for these illuminated manuscripts.

The Index of American Design, a 1930s–40s federal work relief project, generated over 18,000 watercolors and extensive documentation now held at the National Gallery of Art. Reconciling thousands of associated constituents in the museum’s Collection Management System (CMS) with Wikidata using OpenRefine exposed both opportunities and challenges: incomplete or inconsistent identifiers, conflicts between local records and Wikidata, and the labor of aligning diverse roles beyond artists. This discussion paper examines those methodological hurdles, highlights strategies for improving reconciliation, and demonstrates how a linked Power BI tool enables ongoing monitoring of data quality, access, and representation across humanities research infrastructures.

Codex miscellanies are enigmatic witnesses to the intellectual and cultural exchanges of the Middle Ages, both within the Iberian Peninsula and across the Pyrenees. Individual texts plucked for reproduction from manuscript miscellanies are often encountered as fragments in secondary literature, isolated and compiled into subsequent gatherings of texts and images with their own respective functions. The practice of isolating these texts and images, as well as its intellectual motivations, has served to obscure our view of manuscript miscellanies and composites as material objects. This paper explores the possibilities of digital platforms for the investigation of miscellaneous codices through two brief case studies—Bibliothèque nationale de France (BnF) MS lat. 2858 and lat. 5132—to explore tools for collation visualization and annotation.