ArticleBioscience2025
A vision of human-AI collaboration for enhanced biological collection curation and research.
Article in Bioscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Scientific evolution and translational horizons of plant core germplasm: a global bibliometric synthesis and strategic insights.Frontiers in plant science · 2026Pooled it
- The utility of herbarium collections for genetic monitoring.Bioscience · 2026Review
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- Multi-angle, cross-domain fusion strategy enhances automated insect identification and hierarchical categorization: a case study on assassin bugs (Hemiptera: Reduviidae).Cladistics : the international journal of the Willi Hennig Society · 2026Article
- Unlocking biodiversity data with robotic imaging and AI-driven transcription of natural history collections.Science advances · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.