This paper introduces a modular research data framework designed for geoscientific research across disciplinary boundaries. It is specifically designed to support small research projects, that need to adhere to strict data management requirements from funding bodies, but often lack the financial and human resources to do so. The framework supports the transformation of raw research data into scientific knowledge. It addresses critical challenges, such as the rapid increase in the volume, variety and complexity of geoscientific datasets, data heterogeneity, spatial complexity, and the need to comply to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. The approach optimises the research management process by enhancing scalability and enabling interdisciplinary integration. It is adaptable to evolving research requirements and it supports various data types and methodological approaches, such as machine learning and deep learning, that have high requirements on the used data and their formats. A case study in Western Romania presents the data framework's application in an interdisciplinary geoarchaeological research project by processing and storing heterogeneous datasets, demonstrating its potential to support geoscientific research data management by reducing data management efforts, improving replicability, findability and reproducibility and streamlining the integration of high-dimensional data.