Self-Describing Data Asset

A flexible data model not defined by the storage environment but by the enriched data and metadata

Enhanced Data
Interoperability

Self-describing data assets include metadata that provides context about the data, such as its structure, format, and meaning. This makes it easier to integrate and use data across different systems and applications

Flexibility & Scalability

These assets can adapt to various data formats and structures, making them highly flexible. This is particularly important in environments with diverse data sources, such as IoT devices, where data formats can vary widely

Improved Data Quality
and Consistency

By embedding metadata directly within the data, self-describing assets ensure that data is consistently described and understood. This reduces errors and inconsistencies, improving overall data quality

Support for
Advanced Analytics

With clear metadata, self-describing data assets are better suited for advanced analytics and AI/ML applications. The metadata helps these systems interpret and process the data more effectively

Enhance Data Reliability and Reutilization Making it Future Proof

Rigid or predefined database models are limiting the ability to interrogate data later on. This restricts the capability to flexibly query data assets, to share data with relevant parties and to maximize downstream data value.

SciY self-describing data asset represent a new data enrichment, standardization and storage paradigm, based on the concept of data models associated to individual items leveraging a common, extensive ontology, keeping full data model flexibility through data life cycle. Below there is an application example.

Example 1 illustrating the principle of self-describing data assets applied to medicinal chemistry. Data and metadata are enriched continuously enriched along the workflow.

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Features

Storage of metadata and files/documents, continuous metadata enrichment
Many different data entities and types supported (small and biomolecules, spectral/instrument data, alphanumerical data, etc.)
Data standardization – (translation ontologies)
Cloud data storage
FAIR data format, (vendor neutral, JSON based)
RESTful API, making all data available through services/microservices to sources and consumers

Example 2 illustrating the principle of self-describing data assets in a laboratory request-to-result workflow. Data and metadata are enriched continuously enriched along the workflow.

Articles

The Importance of Data Centricity in Achieving a Successful Digital Transformation for Modern Laboratories

Data: The Most Valuable Commodity of the Digital Age

Optimizing Pharmaceutical Operations: A Deep Dive into an E2E Request-to-Result Worfkow

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