Metadata explained: meaning, examples for everyday use 

Metadata are everywhere. They are essential for describing and harmonizing information, particularly in multi-source and multi-operator environments. Without well-structured metadata, information becomes inconsistent, leading to inefficiencies, misinterpretations, and failures in critical processes.

Major processes often fail due to poorly annotated data. Metadata serve as the backbone of quality control, best practices in laboratories, and the optimization of productivity. They save time, enhance efficiency, and provide transparency in workflows. By ensuring accurate data annotation, metadata help organizations limit failures in critical operations, improve decision-making, and establish a reliable foundation for information management.

Benefits of Metadata

• Ensures data quality – Reduces errors and inconsistencies in data annotation.
• Optimizes productivity – Saves time by streamlining data management and retrieval.
• Improves efficiency – Enhances workflows by structuring and organizing information.
• Provides transparency – Ensures that data and processes are clear and traceable.
• Enhances decision-making – Facilitates better analysis and insights through well-annotated data.
• Reduces process failures – Minimizes risks of critical errors due to poorly managed data.
• Ensure best practices – Standardizes methodologies and ensures regulatory compliance.
• Facilitates collaboration – Enables seamless data sharing and integration across teams.
• Future-proofs data management – Establishes a scalable foundation for long-term data use.

By adopting strong metadata practices, businesses and research institutions can streamline operations, reduce errors, and create a structured, efficient, and interoperable data ecosystem.

1. Get started

The purpose of this document is to define a standardized approach for collecting and annotating multi-source data using metadata. Metadata are descriptive variables that provide context to data entries. The goal is to create a unified metadata Excel sheet that allows seamless integration of data from various sources while maintaining consistency and interpretability.

2. Metadata Definition and Guidelines

Metadata are variables that describe the data being collected. These metadata variables are case-insensitive and should be consistently applied across datasets.

2.1 Data Entry Recommendations

• na = Not applicable
• nan = Not a number
• undefined = Missing value / Undefined (usual form)
• none = Missing value / Undefined (Pythonic form)

These standardized terms should be used to handle missing or inapplicable values.

2.2 Structure of the Metadata Excel Sheet

Each row in the dataset corresponds to a unique observation, while each column represents a metadata variable. Some variables may not be applicable to all observations, leading to empty or explicitly marked fields (e.g., "na").

Example Structure:

3. Example Use Cases

3.1 Biological Data Example

3.2 Finance/Marketing Data Example

3.3 Mixed Data Example (Biological + Financial Data)

To demonstrate the generalizability of this metadata strategy, a combined dataset is provided below with a new data_type column to indicate whether the data pertains to biology, finance, or a mix of both.

This structure enables a unified and flexible approach to handling diverse data types within a single dataset.

4. Metadata Ontology Description File (metadata_ontology)

To ensure harmonized data annotation, a metadata description file (metadata_ontology) must be maintained at the company level. This file standardizes metadata definitions, ensuring consistency and interoperability across different datasets. It can also be automatically generated based on existing data and later harmonized.

4.1 Metadata Ontology Structure

For each metadata variable, the following attributes should be defined:

• unique_name: Text (lower case, unique in the company)
• human_name: Text (all characters authorized, human-readable name)
• description: Short text explaining the metadata
• value_type: Numeric, boolean, text, or generic
• allowed_values: List of predefined allowed values (if applicable)

Example Metadata Ontology Table

This metadata_ontology file must be maintained as a company-wide reference to ensure data consistency and interoperability.

5. Extension: Key-Value Modeling for Metadata

To enhance flexibility and interoperability, the metadata structure can be extended using a key-value system. This approach allows dynamic handling of metadata without predefined columns, making it suitable for diverse applications.

5.1 Key-Value Table Format

This format allows for easy expansion and adaptability across different data sources, ensuring a more unified and scalable metadata approach.

6. Gencovery's Role in Metadata Best Practices

Gencovery could provide best practices and recommendations for metadata ontology management. This would include:

• Standardized guidelines for metadata annotation
• Automated metadata extraction tools
• Harmonization techniques for diverse data sources

7. Conclusion

A unified metadata Excel sheet provides a structured approach to collecting and annotating data while allowing flexibility for multi-source integration. Implementing standardized recommendations, maintaining a metadata_ontology file, and adopting key-value modeling enhances consistency, scalability, and data interoperability.

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