The research data landscape of the arts and humanities is characterized by a high degree of heterogeneity. To improve interoperability, recent initiatives and research infrastructures are encouraging the use of standards and best practices. However, custom data models are often considered necessary to exactly reflect the requirements of a particular collection or research project. To address the needs of scholars in the arts and humanities for a composition of research data irrespective of the degree of structuredness and standardization, we propose a concept on the basis of formal languages, which facilitates declarative data modeling by respective domain experts. By identifying and defining grammatical patterns and deriving transformation functions, the structure of data is generated or extended in accordance with the particular context and needs of the domain.