SIGDIUS Seminar - online - 2pm

May 8, 2024, 2:00 p.m. (CEST)

Time: May 8, 2024, 2:00 p.m. (CEST)
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The Special Interest Group Data Infrastructure offers a forum to interested working groups that want to set up or further develop an RDM infrastructure at working group or institute level. We invite you to a monthly SIGDIUS seminar, to which we invite internal and external experts for presentations and discussions. SIGDIUS members will have the opportunity to exchange their experiences with concrete RDM infrastructures.

We cordially invite all interested parties to our next meeting on 8 May 2024 at 2 pm. This seminar will be held as an online seminar. For participation, please send an e-mail to

Steffen Neumann,
Leibniz Institute of Plant Biochemistry, Research group Computational Plant Biochemistry

(Bio)Schemas and SchemaOrg for Rich Metadata Integration (not only) in the Life-Sciences 

SchemaOrg is an approach that it easier for web pages to describe their actual content in a semantic, structured and machine-processable way. It is recognized by major search engines and data aggregators, making it easier for researchers to expose metadata describing their research outcomes. The Bioschemas community developed markup for many types relevant in the Natural and Life Sciences, including BioChemEntity, ChemicalSubstance, Gene, MolecularEntity, Protein, and others. The embedded markup is used by dedicated (metadata) search portals such as Google's Dataset Search, or domain-specific portals like the NFDI4Chem Search, the Omics Discovery Index (Omics-DI) or the ELIXIR Training Portal (TeSS). It is also possible to construct metadata Knowledge Graphs. Together with ontologies developed with the community, the SchemaOrg approach allows to represent, find and re-use scientific (meta)data.

Max Häußler,
University of Stuttgart, Institute of Biochemistry and Technical Biochemistry

In catalytic sciences, post-experimentation data processing predominantly leans on spreadsheet-based applications for the structuring, analyzing, and visualization of experimental data.

This approach necessitates manual intervention at various stages, including the handling and transformation of output data from analytical instruments. Moreover, it demands the repeated definition and integration of metadata throughout the data generation and analysis lifecycle. In our software-centric approach, a data model is defined as a Markdown document, which is translated into a Python object model. This Python object model is then supplemented with functions, managing data and metadata acquisition as well as data pre-processing. Ultimately, streamlines the data analysis process and makes the data accessible for analysis with tools of Python's scientific computing landscape.

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