Sept. 4, 2025,
09:00 –
10:15
Room info:
Lecture Hall
Timetable
Sept. 4, 2025
To ensure FAIR data (Wilkinson et al., 2016: https://doi.org/10.1038/sdata.2016.18 ), well-described datasets with rich metadata are essential for interoperability and reusability. In Earth System Science, NetCDF is the quasi-standard for storing multidimensional data, supported by metadata conventions such as Climate and Forecast (CF, https://cfconventions.org/ ) and Attribute Convention for Data Discovery (ACDD, https://wiki.esipfed.org/Attribute_Convention_for_Data_Discovery_1-3 ).
While NetCDF can be self-describing, metadata often lacks compatibility and completeness for repositories and data portals. The Helmholtz Metadata Guideline for NetCDF (HMG NetCDF) Initiative addresses these issues by establishing a standardized NetCDF workflow. This ensures seamless metadata integration into downstream processes and enhances AI-readiness.
A consistent metadata schema benefits the entire processing chain. We demonstrate this by integrating enhanced NetCDF profiles into selected clients like the Earth Data Portal (EDP, https://earth-data.de ). Standardized metadata practices facilitate repositories such as PANGAEA ( https://www.pangaea.de/ ) and WDCC ( https://www.wdc-climate.de ), ensuring compliance with established norms.
The HMG NetCDF Initiative is a collaborative effort across German research centers, supported by the Helmholtz DataHub. It contributes to broader Helmholtz efforts (e.g., HMC) to improve research data management, discoverability, and interoperability.
Key milestones include:
- Aligning metadata fields across disciplines,
- Implementing guidelines,
- Developing machine-readable templates and validation tools,
- Supporting user-friendly metadata …
Meeting room
Lecture Hall
There is an increasing effort in scientific communities to create shared vocabularies and ontologies. These build the foundation of a semantically annotated knowledge graph which can surface all research data and enable holistic data analysis across various data sources and research domains.
Making machine-generated data available in such a knowledge graph is typically done by setting up scripts and data transformation pipelines which automatically add semantic annotations. Unfortunately, a good solution for capturing manually recorded (meta)data in such a knowledge graph is still lacking.
Herbie, the semantic electronic lab notebook and research database developed at Hereon, fills this gap. In Herbie, users can enter all (meta)data on their experiments in customized web forms. And once submitted, Herbie automatically adds semantic annotations and stores everything directly in the knowledge graph. So it is as easy to use as a spreadsheet but produces FAIR data without any additional post-processing work. Herbie is configured using the standardized SHACL Shapes Constraint Language and furthermore builds on well-established frameworks in the RDF ecosystem like RDFS, OWL, or RO-Crate.
We will showcase this approach through a typical example of a production and analysis chain as can be found in many scientific domains.
Meeting room
Lecture Hall
The collection and use of sensor data are vital for scientists monitoring the Earth's environment. It allows for the evaluation of natural phenomena over time and is essential for validating experiments and simulations. Assessing data quality requires understanding the sensor's state, including operation and maintenance, such as calibration parameters and maintenance schedules. In the HMC project MOIN4Herbie, digital recording of FAIR sensor maintenance metadata is developed using the electronic lab notebook Herbie.
In this talk, we will describe the process of configuring Herbie with ontology-based forms for sensor maintenance metadata in our two pilot cases, the Boknis Eck underwater observatory and the Tesperhude research platform. This includes the development of a sensor maintenance ontology and task-specific ontologies tailored for each use case. Ontologies, in information science, are a formalization of concepts, their relations, and properties. They allow for the collection of input that is immediately fit for purpose as findable, machine-readable, and interoperable metadata. By using ontologies, we can ensure the use of controlled vocabularies and organize the knowledge stored within for accessibility and reusability.
A further focus will be the translation of maintenance tasks into Shapes Constraint Language (SHACL) documents that can be rendered as forms to the users …