Comparative analysis of quality factors in public R&D data platforms: A multi-case study from the perspective of climate technology data utilization

This study analyzes domestic and international climate technology R&D data platforms to identify strategic directions for improving their practical utility. Using an extended Information Systems (IS) Success Model, adapted from DeLone and McLean (2003) to reflect trends in AI and Open APIs, the study conducts a cross-case analysis of six platforms: NASA Earthdata, IEA Data & Statistics, and Copernicus as international cases, and SROME, KREONET, and the NTIS Social Issue Platform as domestic cases. The analysis reveals distinct strategic differences across three quality dimensions. In terms of system quality, international platforms prioritize intuitive spatiotemporal visualization that reduces users' cognitive load, whereas domestic platforms show strengths in AI-driven interpretation tools. In terms of information quality, global platforms assign persistent DOIs and provide standardized metadata, thereby strengthening scientific credibility, whereas domestic platforms remain largely focused on administrative reliability through inter-agency linkage. In terms of service quality, leading international platforms have developed open knowledge ecosystems through Jupyter Notebook environments and Open APIs, whereas domestic platforms remain limited to conventional Q&A-based support. Based on these findings, this study proposes three advancement strategies: integrating intuitive user interfaces with intelligent analysis environments, establishing DOI-based data governance and Linked Open Data standards, and building an open ecosystem that combines specialized APIs with code-based training programs.