AgroLD: a knowledge graph for the plant sciences
The demand for food is expected to grow substantially in the coming years. To address this challenge, especially in the context of climate change, a deeper understanding of genotype-phenotype relationships is crucial for improving crop yields. Recent advances in high-throughput technologies have tra...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
BioMed Central
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/177034 |
| _version_ | 1855533829077336064 |
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| author | Pierre, Larmande Bertrand, Pittolat Ndomassi, Tando Yann, Pomie Bill Gates, Happi Happi Guignon, Valentin Manuel, Ruiz |
| author_browse | Bertrand, Pittolat Bill Gates, Happi Happi Guignon, Valentin Manuel, Ruiz Ndomassi, Tando Pierre, Larmande Yann, Pomie |
| author_facet | Pierre, Larmande Bertrand, Pittolat Ndomassi, Tando Yann, Pomie Bill Gates, Happi Happi Guignon, Valentin Manuel, Ruiz |
| author_sort | Pierre, Larmande |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The demand for food is expected to grow substantially in the coming years. To address this challenge, especially in the context of climate change, a deeper understanding of genotype-phenotype relationships is crucial for improving crop yields. Recent advances in high-throughput technologies have transformed the landscape of plant science research. However, there is an urgent need to integrate and consolidate complementary data to understand the biological system. Results We introduce AgroLD, a knowledge graph that uses Semantic Web technologies to seamlessly integrate plant science data. AgroLD is designed to facilitate hypothesis formulation and validation within the scientific community. With approximately 1.08 billion triples, it integrates and annotates data from more than 151 datasets across 19 distinct sources. Conclusion The overarching goal is to provide a specialized knowledge platform addressing complex biological questions in the plant sciences, including gene participation in plant disease resistance and adaptive responses to climate change. |
| format | Journal Article |
| id | CGSpace177034 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | BioMed Central |
| publisherStr | BioMed Central |
| record_format | dspace |
| spelling | CGSpace1770342025-12-08T09:54:28Z AgroLD: a knowledge graph for the plant sciences Pierre, Larmande Bertrand, Pittolat Ndomassi, Tando Yann, Pomie Bill Gates, Happi Happi Guignon, Valentin Manuel, Ruiz bioinformatics open data linked data plant sciences The demand for food is expected to grow substantially in the coming years. To address this challenge, especially in the context of climate change, a deeper understanding of genotype-phenotype relationships is crucial for improving crop yields. Recent advances in high-throughput technologies have transformed the landscape of plant science research. However, there is an urgent need to integrate and consolidate complementary data to understand the biological system. Results We introduce AgroLD, a knowledge graph that uses Semantic Web technologies to seamlessly integrate plant science data. AgroLD is designed to facilitate hypothesis formulation and validation within the scientific community. With approximately 1.08 billion triples, it integrates and annotates data from more than 151 datasets across 19 distinct sources. Conclusion The overarching goal is to provide a specialized knowledge platform addressing complex biological questions in the plant sciences, including gene participation in plant disease resistance and adaptive responses to climate change. 2025-10-03 2025-10-13T14:25:26Z 2025-10-13T14:25:26Z Journal Article https://hdl.handle.net/10568/177034 en Open Access application/pdf BioMed Central Pierre, L.; Bertrand, P.; Ndomassi, T.; Yann, P.; Bill Gates, H.H.; Valentin, G.; Manuel, R. (2025) AgroLD: a knowledge graph for the plant sciences. BMC Genomic Data 26(S1): 73. ISSN: 2730-6844 |
| spellingShingle | bioinformatics open data linked data plant sciences Pierre, Larmande Bertrand, Pittolat Ndomassi, Tando Yann, Pomie Bill Gates, Happi Happi Guignon, Valentin Manuel, Ruiz AgroLD: a knowledge graph for the plant sciences |
| title | AgroLD: a knowledge graph for the plant sciences |
| title_full | AgroLD: a knowledge graph for the plant sciences |
| title_fullStr | AgroLD: a knowledge graph for the plant sciences |
| title_full_unstemmed | AgroLD: a knowledge graph for the plant sciences |
| title_short | AgroLD: a knowledge graph for the plant sciences |
| title_sort | agrold a knowledge graph for the plant sciences |
| topic | bioinformatics open data linked data plant sciences |
| url | https://hdl.handle.net/10568/177034 |
| work_keys_str_mv | AT pierrelarmande agroldaknowledgegraphfortheplantsciences AT bertrandpittolat agroldaknowledgegraphfortheplantsciences AT ndomassitando agroldaknowledgegraphfortheplantsciences AT yannpomie agroldaknowledgegraphfortheplantsciences AT billgateshappihappi agroldaknowledgegraphfortheplantsciences AT guignonvalentin agroldaknowledgegraphfortheplantsciences AT manuelruiz agroldaknowledgegraphfortheplantsciences |