인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Amidst numerous global crises, decision‐makers have recognized the critical need for fact‐based advice, driving unprecedented data collection. However, a significant gap persists between data availability and knowledge generation, primarily due to time and resource constraints. To bridge this gap, we propose involving a novel group of citizen scientists: volunteer code developers. Utilizing the modular, open‐source analysis platform MoveApps, we were able to engage 12 volunteer coders in a challenge to create tools for movement ecology, aimed at animal conservation. These volunteers developed functioning applications capable of analysing animal tracking data to identify stationary behaviour, estimate ranges and movement corridors and assess human–wildlife conflicts using data sets from human infrastructure, such as OpenStreetMap. Engaging citizen scientists in developing code has surfaced three primary challenges: (i) Community Building—attracting the right participants; (ii) Community Involvement—maintaining quality standards and directing tasks effectively; and (iii) Community Retention—ensuring long‐term engagement. We explore strategies to overcome these challenges and share lessons learnt from our coding challenge experience. Our approaches include engaging the community through their own preferred channels, providing an accessible open‐source tool, defining specific use cases in detail, ensuring quality through feedback, fostering self‐organized community exchanges and prominently illustrating the impact of contributions. We also advocate for other disciplines to consider leveraging volunteer involvement, alongside artificial intelligence, for data analysis and generating state‐of‐the‐art, fact‐based insight to address critical issues such as the global decline in biodiversity.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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