## Metadata

Metadata is essential for managing and understanding research data, but it can take different forms depending on how it is organised and used.

Structured metadata refers to metadata that is organized in a defined, consistent format, often within a specific schema or database. This allows for easy searchability, categorization, and integration across different platforms, such as data repositories.

Unstructured metadata consists of more flexible, free-form information that doesn’t follow a specific format. This could include descriptions, notes, or other contextual information that provides valuable insights but may not fit neatly into a standardized framework. While unstructured metadata may require more effort to manage and analyse, it often captures nuances and details that structured metadata cannot.

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### Project and file level Metadata

Metadata can be created at different levels to describe and provide context for your data. At a minimum, you will have project-level metadata, which offers an overview of the entire research project. This may include information such as the project title, creator(s), institutional affiliation, funding details, project location, research objectives, and any ethical approvals. Project-level metadata helps others (and your future self) understand the broader context of your research.

In addition to project-level metadata, granular or file-level metadata can also be created. This refers to metadata associated with specific datasets, files, or pieces of analysis. For example, codebooks provide valuable contextual information for variables while comments within your code can describe the purpose of functions or variables, making your scripts easier to understand and reuse. Similarly, annotations or notes on interview transcripts can help explain themes, coding decisions, or analytical choices made during qualitative analysis.

Regardless of the level, metadata should be rich, consistent, and meaningful. Well-documented metadata improves the transparency, reusability, and long-term value of your data by ensuring that both you and others can accurately interpret and work with the data, even months or years after the original project has ended.

Metadata refers to information that describes your data, essentially, it's 'data about data'. In many cases, terms like metadata and documentation can be used interchangeably. for instance, materials such as ReadMe files or codebooks are often considered unstructured metadata but may be included within your documentation folder. There's no strict tile about whether to label such files as metadata or documentation. What's important is that you provide enough contextual information to help others, and yourself understand and interpret you data.