Suppr超能文献

关于人类与人工智能合作以加强生物样本管理和研究的愿景。

A vision of human-AI collaboration for enhanced biological collection curation and research.

作者信息

Stenhouse Alan, Fisher Nicole, Lepschi Brendan, Schmidt-Lebuhn Alexander, Rodriguez Juanita, Turco Federica, Reeson Andrew, Paris Cécile, Thrall Peter H

机构信息

National Collections and Marine Infrastructure Research Unit, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, Australia.

Parks Australia, Canberra, Australia.

出版信息

Bioscience. 2025 Mar 28;75(6):457-471. doi: 10.1093/biosci/biaf021. eCollection 2025 Jun.

Abstract

Natural history collections play a crucial role in our understanding of biodiversity, informing research, management, and policy in areas such as biosecurity, conservation, climate change, and food security. However, the growing volume of specimens and associated data presents significant challenges for curation and management. By leveraging human-AI collaborations, we aim to transform the way biological collections are curated and managed, realizing their full potential in addressing global challenges. In this article, we discuss our vision for improving biological collections curation and management using human-AI collaboration. We explore the rationale behind this approach, the challenges faced in data management, general curation problems, and the potential benefits that could be derived from incorporating AI-based assistants in collection teams. Finally, we examine future possibilities for collaborations between human and digital curators and collection-based research.

摘要

自然历史藏品在我们理解生物多样性方面发挥着至关重要的作用,为生物安全、保护、气候变化和粮食安全等领域的研究、管理及政策提供信息。然而,标本及相关数据量的不断增加给整理和管理带来了重大挑战。通过利用人机协作,我们旨在改变生物藏品的整理和管理方式,充分发挥其在应对全球挑战方面的潜力。在本文中,我们讨论了利用人机协作改进生物藏品整理和管理的愿景。我们探讨了这种方法背后的基本原理、数据管理面临的挑战、一般整理问题,以及在藏品团队中引入基于人工智能的助手可能带来的潜在益处。最后,我们研究了人类策展人和数字策展人之间合作以及基于藏品的研究的未来可能性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验