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Leveraging Ontologies within the National Microbiome Data Collaborative
- Duncan, WD;
- Ahmed, F;
- Anubhav, F;
- Baumes, J;
- Beezley, J;
- Borkum, M;
- Bramer, L;
- Canon, S;
- Chain, P;
- Christianson, D;
- Corilo, Y;
- Davenport, K;
- Davis, B;
- Drake, M;
- Fagnan, K;
- Flynn, M;
- Hays, D;
- Hu, B;
- Huntemann, M;
- Kelliher, J;
- Lebedeva, S;
- Li, PE;
- Lipton, M;
- Lo, CC;
- Mans, D;
- Martin, S;
- McCue, LA;
- Millard, D;
- Miller, K;
- Mouncey, N;
- Piehowski, P;
- Jackson, EP;
- Prymolenna, A;
- Purvine, S;
- Reddy, TBK;
- Richardson, R;
- Shakya, M;
- Smith, M;
- Sundaramurthi, JC;
- Miller, MA;
- Unni, D;
- Vangay, P;
- Wilson, B;
- Winston, D;
- Wood-Charlson, E;
- Xu, Y;
- Eloe-Fadrosh, E;
- Mungall, CJ
- et al.
Published Web Location
https://ceur-ws.org/Vol-3073/paper22.pdfNo data is associated with this publication.
Abstract
The National Microbiome Data Collaborative (NMDC) is a multi-organizational effort to integrate microbiome data across diverse areas in environmental science. Data provided by the NMDC can then undergo advanced analysis and provide new insights into metagenomics, metatranscriptomics, metaproteomics, and metabolomics. To address these challenges, we have developed our schema using the Linked data Modeling Language (LinkML). This allows us to easily map data to existing standards and ontologies.