Expanding data on cultivation media and microbial traits [Preprint]

• Julia Koblitz
• Shuichi Kawashima
• Kohei Hidaka
• Yoko Okabeppu
• Satoshi Onoda
• Risa Otsuka
• Natsuko Ichikawa

Departments

Bioinformatics, IT & Databases

Abstract

The standardization and integration of cultivation media data are essential for advancing microbial research and enabling AI-based predictions of optimal growth conditions. This study addresses the challenges of data fragmentation by aligning terminologies and mapping ingredients between two prominent databases: MediaDive (DSMZ) and TogoMedium (DBCLS). We successfully linked 870 ingredients, expanded the Growth Media Ontology (GMO), and prepared data for media similarity calculations, thereby enhancing the interoperability of these resources. Additionally, we developed the first version of a BacDive RDF knowledge graph, incorporating mapping rules for 24 key entities and materializing the data in turtle format to facilitate integration into broader knowledge networks. We also propose a novel process for the standardized registration of media recipes by depositors, ensuring that these recipes can be cited and shared consistently. Together, these efforts contribute to the creation of a more cohesive and accessible microbial data ecosystem, supporting future research and innovation.

• DOI: 10.37044/osf.io/qxpn8

Cite this activity

@misc{Koblitz2024,
  Title = {Expanding data on cultivation media and microbial traits},
  Author = {Koblitz, Julia and Kawashima, Shuichi and Hidaka, Kohei and Okabeppu, Yoko and Onoda, Satoshi and Otsuka, Risa and Ichikawa, Natsuko},
  Editor = {},
  Year = {2024},
  Doi = {10.37044/osf.io/qxpn8},
  Abstract = {The standardization and integration of cultivation media data are essential for advancing microbial research and enabling AI-based predictions of optimal growth conditions. This study addresses the challenges of data fragmentation by aligning terminologies and mapping ingredients between two prominent databases: MediaDive (DSMZ) and TogoMedium (DBCLS). We successfully linked 870 ingredients, expanded the Growth Media Ontology (GMO), and prepared data for media similarity calculations, thereby enhancing the interoperability of these resources. Additionally, we developed the first version of a BacDive RDF knowledge graph, incorporating mapping rules for 24 key entities and materializing the data in turtle format to facilitate integration into broader knowledge networks. We also propose a novel process for the standardized registration of media recipes by depositors, ensuring that these recipes can be cited and shared consistently. Together, these efforts contribute to the creation of a more cohesive and accessible microbial data ecosystem, supporting future research and innovation.},
}

TY  - INPR
AU  - Koblitz, Julia
AU  - Kawashima, Shuichi
AU  - Hidaka, Kohei
AU  - Okabeppu, Yoko
AU  - Onoda, Satoshi
AU  - Otsuka, Risa
AU  - Ichikawa, Natsuko
TI  - Expanding data on cultivation media and microbial traits
PY  - 2024
DO  - 10.37044/osf.io/qxpn8
AB  - The standardization and integration of cultivation media data are essential for advancing microbial research and enabling AI-based predictions of optimal growth conditions. This study addresses the challenges of data fragmentation by aligning terminologies and mapping ingredients between two prominent databases: MediaDive (DSMZ) and TogoMedium (DBCLS). We successfully linked 870 ingredients, expanded the Growth Media Ontology (GMO), and prepared data for media similarity calculations, thereby enhancing the interoperability of these resources. Additionally, we developed the first version of a BacDive RDF knowledge graph, incorporating mapping rules for 24 key entities and materializing the data in turtle format to facilitate integration into broader knowledge networks. We also propose a novel process for the standardized registration of media recipes by depositors, ensuring that these recipes can be cited and shared consistently. Together, these efforts contribute to the creation of a more cohesive and accessible microbial data ecosystem, supporting future research and innovation.
ER  - 

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