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通过全球生物多样性信息设施(GBIF)共享昆虫数据:新的监测方法、机会和标准。

Sharing insect data through GBIF: novel monitoring methods, opportunities and standards.

机构信息

Global Biodiversity Information Facility, Universitetsparken 15, 2100 København Ø, Denmark.

出版信息

Philos Trans R Soc Lond B Biol Sci. 2024 Jun 24;379(1904):20230104. doi: 10.1098/rstb.2023.0104. Epub 2024 May 6.

DOI:10.1098/rstb.2023.0104
PMID:38705176
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11070266/
Abstract

Technological advancements in biological monitoring have facilitated the study of insect communities at unprecedented spatial scales. The progress allows more comprehensive coverage of the diversity within a given area while minimizing disturbance and reducing the need for extensive human labour. Compared with traditional methods, these novel technologies offer the opportunity to examine biological patterns that were previously beyond our reach. However, to address the pressing scientific inquiries of the future, data must be easily accessible, interoperable and reusable for the global research community. Biodiversity information standards and platforms provide the necessary infrastructure to standardize and share biodiversity data. This paper explores the possibilities and prerequisites of publishing insect data obtained through novel monitoring methods through GBIF, the most comprehensive global biodiversity data infrastructure. We describe the essential components of metadata standards and existing data standards for occurrence data on insects, including data extensions. By addressing the current opportunities, limitations, and future development of GBIF's publishing framework, we hope to encourage researchers to both share data and contribute to the further development of biodiversity data standards and publishing models. Wider commitments to open data initiatives will promote data interoperability and support cross-disciplinary scientific research and key policy indicators. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

摘要

生物监测技术的进步使我们能够以前所未有的空间尺度研究昆虫群落。这些进步使我们能够在最小化干扰和减少大量人力劳动的前提下,更全面地覆盖特定区域内的多样性。与传统方法相比,这些新技术使我们有机会研究以前无法触及的生物模式。然而,为了满足未来紧迫的科学研究需求,数据必须易于获取、可互操作和可重复使用,以便全球研究界能够共享。生物多样性信息标准和平台为标准化和共享生物多样性数据提供了必要的基础设施。本文通过全球生物多样性信息设施(GBIF)这一最全面的全球生物多样性数据基础设施,探讨了通过新型监测方法获得的昆虫数据发表的可能性和前提条件。我们描述了昆虫出现数据的元数据标准和现有数据标准的基本组成部分,包括数据扩展。通过探讨 GBIF 出版框架的当前机遇、限制因素和未来发展,我们希望鼓励研究人员共享数据,并为生物多样性数据标准和出版模式的进一步发展做出贡献。更广泛地承诺开放数据计划将促进数据互操作性,并支持跨学科科学研究和关键政策指标。本文是主题为“迈向全球昆虫生物多样性监测工具包”的一部分。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1cd/11070266/3df6e2080040/rstb20230104f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1cd/11070266/4144b90dbe6b/rstb20230104f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1cd/11070266/3df6e2080040/rstb20230104f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1cd/11070266/4144b90dbe6b/rstb20230104f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1cd/11070266/3df6e2080040/rstb20230104f02.jpg

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本文引用的文献

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2
Towards a toolkit for global insect biodiversity monitoring.迈向全球昆虫生物多样性监测工具包。
Philos Trans R Soc Lond B Biol Sci. 2024 Jun 24;379(1904):20230101. doi: 10.1098/rstb.2023.0101. Epub 2024 May 6.
3
Towards global insect biomonitoring with frugal methods.
Philos Trans R Soc Lond B Biol Sci. 2024 Jun 24;379(1904):20230101. doi: 10.1098/rstb.2023.0101. Epub 2024 May 6.
采用节约方法进行全球昆虫生物监测。
Philos Trans R Soc Lond B Biol Sci. 2024 Jun 24;379(1904):20230103. doi: 10.1098/rstb.2023.0103. Epub 2024 May 6.
4
The future of tropical insect diversity: strategies to fill data and knowledge gaps.热带昆虫多样性的未来:填补数据与知识空白的策略
Curr Opin Insect Sci. 2023 Aug;58:101063. doi: 10.1016/j.cois.2023.101063. Epub 2023 May 27.
5
Protected areas and the future of insect conservation.保护区与昆虫保护的未来。
Trends Ecol Evol. 2023 Jan;38(1):85-95. doi: 10.1016/j.tree.2022.09.004. Epub 2022 Oct 5.
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Emerging technologies revolutionise insect ecology and monitoring.新兴技术正在彻底改变昆虫生态学和监测。
Trends Ecol Evol. 2022 Oct;37(10):872-885. doi: 10.1016/j.tree.2022.06.001. Epub 2022 Jul 8.
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Metabarcoding reveals massive species diversity of Diptera in a subtropical ecosystem.宏条形码技术揭示了亚热带生态系统中双翅目丰富的物种多样性。
Ecol Evol. 2022 Jan 23;12(1):e8535. doi: 10.1002/ece3.8535. eCollection 2022 Jan.
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Deep learning and computer vision will transform entomology.深度学习和计算机视觉将改变昆虫学。
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