🔧 What is the Scientific Data Layer (SDL)?

The SDL is an end-to-end framework for managing scientific data using a system-of-systems architecture built on Linked Data Platform (LDP), RDF, and domain ontologies such as SSN, SOSA, DCAT, and PROV-O. It includes backend services, a modular frontend, and ontology-driven data modeling.

🧩 What types of entities can I manage in SDL?

You can manage a wide range of scientific entities including:

• Observations (sosa:Observation)
• Samples (sosa:Sample)
• Platforms and systems (sosa:Platform, ssn:System)
• Deployments (ssn:Deployment)
• Catalogs and datasets (dcat:Catalog, dcat:Dataset)
• Agents and activities (prov:Agent, prov:Activity)

📦 How are data organized?

SDL organizes data using LDP containers. These containers group related entities, such as all samples from a deployment or all datasets in a catalog. Each container corresponds to an endpoint where RDF resources can be created, listed, and queried.

🧪 How do I record an observation?

To create an observation:

1. Define the observed property (ssn:Property)
2. Link the feature of interest (e.g., a sample or environment)
3. Provide a result value (e.g., pH, temperature)
4. Optionally, associate the observation with the sensor and the time it occurred

SDL uses sosa:Observation entities for this purpose.

🧬 How do I represent samples?

Samples are represented using sosa:Sample. They can:

• Reference their origin using sosa:isSampleOf
• Be used as features of interest in observations
• Carry provenance information about collection (who, when, where)

🗃 How are datasets and files handled?

Datasets are modeled as dcat:Dataset entities. Files and downloadable content are modeled as dcat:Distribution, which link to URLs, checksums, media types, and related metadata.

🧭 How do I trace provenance?

SDL supports full provenance modeling via prov:Entity, prov:Activity, and prov:Agent. These classes allow you to:

• Attribute data to people, organizations, or software
• Document how data was generated or transformed
• Link observations and results to workflows or deployments

🌐 What ontologies are used?

SDL integrates:

• SOSA/SSN — for sensors, systems, observations, samples
• DCAT — for data catalogs and distributions
• PROV-O — for provenance and traceability
• DoCO — for documents and narrative content
• QUDT — for quantities, units, and scientific meanings

💬 Where can I get help or request features?

Please contact the SDL support team or open a ticket in the issue tracker of your SDL deployment. For ontology-specific questions, refer to the Entity Documentation or contact a data steward.

For advanced modeling questions or integration topics, visit the Architecture Overview or explore the service documentation.