In microbiology, numerous databases provide valuable information about microorganisms. However, these resources often exist in isolation, requiring researchers to navigate from one database to another to gather comprehensive information about a specific microbial strain. This process frequently leads to inconsistencies and complicates research efforts.
The Digital Diversity Hub (https://hub.dsmz.de) addresses this challenge by offering central access to the integrated databases of the Leibniz Institute DSMZ. By consolidating resources such as BacDive, BRENDA, StrainInfo, MediaDive, SILVA, and others, the Hub provides a unified overview of both physical and digital resources. The platform already supports researchers by making information on taxonomy, phenotypes, genomes, enzymes, and rRNA sequences searchable. The presentation will demonstrate how the various resources of the Hub can be used to obtain a complete picture of a microorganism, using an example strain to illustrate the process. Additionally, it will offer a preview of how the Hub will further integrate and intelligently link this information in the future, enabling consistent and comprehensive data analysis.
Koblitz J. and Reimer L.C. The DSMZ Digital Diversity Hub - Central Access to Integrated Life Science Databases. Waterman Seminar, IPK Germany. 24.09.2024. (Invited lecture: long)
@misc{Koblitz2024,
Title = {The DSMZ Digital Diversity Hub - Central Access to Integrated Life Science Databases},
Author = {Koblitz, Julia and Reimer, Lorenz Christian},
Editor = {},
Year = {2024},
Abstract = {In microbiology, numerous databases provide valuable information about microorganisms. However, these resources often exist in isolation, requiring researchers to navigate from one database to another to gather comprehensive information about a specific microbial strain. This process frequently leads to inconsistencies and complicates research efforts.
The Digital Diversity Hub (https://hub.dsmz.de) addresses this challenge by offering central access to the integrated databases of the Leibniz Institute DSMZ. By consolidating resources such as BacDive, BRENDA, StrainInfo, MediaDive, SILVA, and others, the Hub provides a unified overview of both physical and digital resources. The platform already supports researchers by making information on taxonomy, phenotypes, genomes, enzymes, and rRNA sequences searchable. The presentation will demonstrate how the various resources of the Hub can be used to obtain a complete picture of a microorganism, using an example strain to illustrate the process. Additionally, it will offer a preview of how the Hub will further integrate and intelligently link this information in the future, enabling consistent and comprehensive data analysis.},
}
TY - SLIDE
AU - Koblitz, Julia
AU - Reimer, Lorenz Christian
TI - The DSMZ Digital Diversity Hub - Central Access to Integrated Life Science Databases
PY - 2024
AB - In microbiology, numerous databases provide valuable information about microorganisms. However, these resources often exist in isolation, requiring researchers to navigate from one database to another to gather comprehensive information about a specific microbial strain. This process frequently leads to inconsistencies and complicates research efforts.
The Digital Diversity Hub (https://hub.dsmz.de) addresses this challenge by offering central access to the integrated databases of the Leibniz Institute DSMZ. By consolidating resources such as BacDive, BRENDA, StrainInfo, MediaDive, SILVA, and others, the Hub provides a unified overview of both physical and digital resources. The platform already supports researchers by making information on taxonomy, phenotypes, genomes, enzymes, and rRNA sequences searchable. The presentation will demonstrate how the various resources of the Hub can be used to obtain a complete picture of a microorganism, using an example strain to illustrate the process. Additionally, it will offer a preview of how the Hub will further integrate and intelligently link this information in the future, enabling consistent and comprehensive data analysis.
CY - IPK Germany
ER -
