D3.2 Nemesis Update & Upgrade
Year: 2026
Type: article
Abstract: This TWINRD deliverable D3.2 provides a complete and reproductible framework for modeling technological innovation dynamics based on R&D, patent and citation data. It consists of three main components: Methodological Report (Word document)A detailed description of the technological innovation module, including the theoretical background, modeling choices, and the structure of the equations implemented. The report explains how innovation processes are captured and integrated into an economic modeling framework. Python Implementation (ZIP archive)The full source code of the module developed in Python. This implementation relies on the PyIODE / IODE environment and includes all necessary scripts to reproduce the modeling workflow, from data processing to equation generation and simulation. DatasetsAll data required to run the module are provided, including: Input datasets formatted for direct use with IODE / PyIODE Harmonized datasets structured according to the NACE Rev.2 classification, enabling reuse in alternative modeling environments beyond IODE Key Features: Integration of R&D, patent and citation data into a unified innovation modeling framework Explicit modeling of technological dimensions and cross-sectoral interactions Fully reproducible program from raw data to model-ready equations Compatibility with both IODE-based workflows and more general analytical environments Intended Use: TWINRD is designed for researchers, economists, and modelers interested in: Innovation and technological change R&D-driven growth modeling Sectoral and cross-country analysis based on NACE classifications The deliverable enables both direct use within the IODE ecosystem and adaptation to other tools or modeling frameworks. Main Output:This deliverable provides a comprehensive innovation module detailing 66 green technologies, 14 JTAG technologies, and 10 WIPO technologies, along with their interactions, designed to be integrated into broader macroeconomic simulation models.
Institution: Seureco (France)
Language: English
Subfield: Management of Technology and Innovation
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7154334288