High-quality environmental time series data require transparent, reproducible, and well-documented quality control (QC) workflows that integrate automated procedures and expert judgment. While many QC frameworks offer algorithmic methods, the processing information explaining how data quality decisions are made — including parameterization, flag semantics, and manual interventions — is often not formalized enough to be easily reused, reproduced, or exchanged across infrastructures.
\nIn this talk, we present a metadata schema for time series data that enables FAIR and reproducible data quality processing. The schema is designed to describe QC methods, execution contexts, and resulting quality flags in a machine-actionable and interoperable manner. It employs the OGC SensorThings API data model enhanced by the STAMPLATE schema and the concepts established in the SaQC framework. The schema follows the linked-data approach and aligns with standards such as the W3C Data Quality Vocabulary.
\nThe design of the proposed schema is motivated by concrete use cases for QC of time series data from the TERENO and ACTRIS observation networks. These use cases include detailed analyses of existing automated and manual QC workflows. By comparing and abstracting these practices, we derive common requirements and design patterns for representing QC processing information in a FAIR and reproducible manner. The resulting schema can be used straightforwardly with SensorThings API services and mapped into NetCDF files that align with the Helmholtz Metadata Guidelines for NetCDF. It can also be used with RO-Crates, embedding files in CSV format, for example.
\nOur metadata schema lays the foundation for a community-driven, FAIR, and reproducible quality control solution. Our goal is to integrate the requirements of other communities and develop a web application that allows users to visually inspect and flag time series data in a manner consistently with our schema.
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", "organizations": [ { "id": "04v4h0v24" } ], "page": "How does better metadata drive better science?
\nThe Helmholtz Metadata Collaboration (HMC) warmly invites you to the HMC Conference 2026, taking place 28–30 April 2026 at the Deutsches Krebsforschungszentrum (German Cancer Research Center, DKFZ) in Heidelberg, Germany. Under the theme “Metadata in Action,” the conference will explore how metadata generates real scientific impact across disciplines and infrastructures.
\nInfrastructure providers, data stewards, researchers, and developers come together to exchange ideas, showcase innovative solutions, and discuss how metadata actively contributes to data quality, interoperability, and research excellence.
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Why Should You Attend?
The HMC Conference 2026 offers a dynamic platform for interdisciplinary collaboration and inspiration. Whether you manage data, develop tools, or conduct research, this is your opportunity to see how metadata practices make science better.
\nBy attending, you will:
\nGain Insights: Learn about the latest developments, tools, and best practices in metadata management.
\nEngage in Hands-On Learning: Participate in interactive sessions and workshops designed for practical exchange.
\nExpand Your Network: Connect with experts, thought leaders, and peers from the Helmholtz Association and beyond.
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What’s on the Agenda?
The conference program includes:
\nTalks: Present your research or practical experiences to a broad audience.
\nPosters & Pitches: Share your ideas in concise and interactive formats.
\nDemos: Demonstrate tools, services, or prototypes live.
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Conference Themes
Key topics include:
\nSoftware Interoperability for (Meta)data Acquisition
\nFrom Minimum Requirements to FAIR and AI-Ready Metadata
\nEmpowering Research Communities: Turning Metadata into Action
\nSemantics in Practice: Domain & Application Ontologies
\nAdvancing FAIR Metadata with AI
\nJoin us in Heidelberg to explore how metadata is not just managed – it’s making a difference.
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