ArticleDatabase : the journal of biological databases and curation2026
Improving annotation, access, and comparison of nutritional composition data for underutilized crops.
Article in Database : the journal of biological databases and curation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Open and interoperable data infrastructures are essential to advancing food and nutritional security research, yet few data management systems are specifically designed for crop nutritional datasets that support seamless querying, visualization, and comparison. Existing data sources are often syntactically and semantically heterogeneous, creating substantial barriers to interoperability and data reuse, and ultimately limiting the translation of research into sustainable agricultural innovation. To address this gap, we developed an ontology-based data access and integration (OBDI) framework that harmonizes publicly available, heterogeneous plant nutritional datasets within a unified semantic structure. By annotating datasets using established plant science ontologies, we enhanced their Findability, Accessibility, Interoperability, and Reusability by providing a scalable mechanism for virtual data integration via a knowledge graph enriched with domain semantics. Our workflow enables consistent comparison and computational reasoning across crop nutritional composition data, supporting both machine and human interpretation. The integration of compositional datasets for underutilized crops (UCs) alongside major crops allows for the identification of genetic resources that provide enhanced nutritional outcomes, fostering evidence-based diversification strategies. We outline how this approach may facilitate data integration and assist in the wider adoption and utilization of UCs. More generally, this open, ontology-driven approach highlights how investment in standardized, FAIR-aligned data infrastructure has the potential to accelerate interdisciplinary collaboration across plant sciences, nutrition, and policy. We demonstrate the concepts and specific methodologies for establishing semantic relationship between diverse datasets useful in operationalizing heterogeneous crop trait phenotyping knowledge bases. Furthermore, we discuss how the integration of ontology-based data integration (OBDI) with machine learning enables intelligent, accessible trait data management, thereby enhancing the adoption and advancement of AI-driven decision-making.
Indexed as
Identifiers
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.