資料密集視角下的臺灣公民科學計畫：整體實踐與品質維護策略

Data-intensive Citizen Science in Taiwan: Overall Practices and Strategies for Data Quality Maintenance

本研究採用混合研究方法，以內容分析法系統性檢視臺灣127件公民科學計畫，分析其基本特徵、資料徵集方式與資料類型，並依據FAIR原則評估資料開放程度。另以「臺灣動物路死觀察網（路殺社）」為案例，透過深度訪談分析其資料品質管理策略。結果顯示，多數計畫長期運作且與政策連結，資料徵集多透過Web應用程式或社群媒體推動，亦促成多元的資料品質維護方式。以路殺社為例，計畫由初期鼓勵民眾通報，隨資料量及社群成熟度提升，發展為系統化與標準化的調查模式。研究並以Wikidata建置開放後設資料，提升公民科學計畫之可發現性與再利用性。綜合而言，本研究提供臺灣公民科學的系統概覽，並提出兼顧資料品質與開放性的管理策略，以強化其對永續發展之貢獻。

This study adopts a mixed-methods approach to systematically examine 127 citizen science projects in Taiwan through content analysis. The analysis identifies each project’s core characteristics, data collection methods, and data types, and evaluates their levels of data openness based on the FAIR Principles (Findability, Accessibility, Interoperability, and Reusability). In addition, the Taiwan Roadkill Observation Network (TaiRON) is used as a representative case, with qualitative interviews conducted to explore its data quality management strategies and the gradual transition process undertaken in response to evolving challenges.
The findings reveal that most citizen science projects in Taiwan demonstrate long-term continuity and strong connections to policymaking. Data collection is primarily driven by web applications and social media platforms, fostering diverse mechanisms for maintaining data quality. In the TaiRON case, the project began by encouraging public reporting through social media to broaden participation, and progressively evolved into a more systematic and standardized survey model as data volume and community maturity increased. Furthermore, this study established open metadata for Taiwan’s citizen science projects on Wikidata to enhance their discoverability and reusability. Overall, this research provides a systematic overview of Taiwan’s citizen science landscape and proposes management strategies that balance data quality with openness, thereby supporting research teams and policy agencies in strengthening the contributions of citizen science to sustainable development.