Beyond Monkey Jobs. Leveraging a 'Data as Code' Approach for Efficient and FAIR Research Data Management

The growing complexity of research data infrastructures presents both opportunities and challenges for social science research. A persistent hurdle is the burden of repetitive, error-prone data management tasks that consume valuable time and resources. The ‘Data as Code’ (DaC) paradigm offers a transformative solution by applying software development methodologies to research data management. This approach enhances reproducibility, efficiency, and transparency, aligning with the Open Science and the FAIR (Findable, Accessible, Interoperable, and Reusable) principles for digital research infrastructures, thereby fostering methodological rigor and innovation. This presentation explores the integration of programmatic tools, artificial intelligence, and the DaC approach within social science data infrastructures, contributing to ongoing discussions on the future of research data management while providing practical insights for researchers and data archivists to improve data usability and long-term preservation. Through case studies from the Data Archive for Social Sciences in Italy (DASSI)—the Italian Service Provider of CESSDA ERIC—we demonstrate how these methodologies improve data quality, reproducibility, and long-term preservation. By reducing manual interventions and enhancing data curation processes, this approach minimizes human error and bolsters data archiving, accessibility, and interoperability, thereby allowing professionals to dedicate more time to critical, high-value tasks. By embracing the Data as Code paradigm, research infrastructures can move beyond traditional matrix-based data structures, enabling seamless integration and management of diverse data formats.This flexibility ensures that heterogeneous datasets—ranging from hierarchical and nested data to unstructured sources—can be seamlessly integrated, curated, and disseminated, expanding the scope and potential of social science research.