通过有效的开放研究数据管理推进植物物候学研究的路线图

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2025-03-16 DOI:10.1016/j.ecoinf.2025.103109

Barbara Templ

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引用次数: 0

摘要

物候研究对于理解生态动态对环境变化的响应至关重要，越来越依赖于开放研究数据管理（ORDM）来提高科学成果。基于对物候学专家进行的一项结构化调查，本文探讨了采用FAIR原则（可查找性、可访问性、互操作性和可重用性）如何直接解决物候学数据的独特挑战，如元数据不一致、数据收集方法的可变性和数据集成的困难。这种综合不仅突出了物候学家面临的障碍，而且提出了战略解决方案，突出了明确的行动呼吁，引导物候研究朝着更加协作和开放的科学未来发展。

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A roadmap for advancing plant phenological studies through effective open research data management

Phenological research, critical for understanding ecological dynamics in response to environmental changes, increasingly relies on Open Research Data Management (ORDM) to enhance scientific outcomes. Based on insights from a structured survey conducted among phenology experts, this paper explores how the adoption of FAIR principles - Findability, Accessibility, Interoperability, and Reusability - directly addresses the unique challenges of phenological data, such as inconsistent metadata, variability in data collection methods, and difficulties in data integration. This synthesis not only highlights the obstacles faced by phenologists but also proposes strategic solutions highlighting a clear call to action steering phenological research toward a more collaborative and open science future.

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.