# Improving Findability

Metadata, keywords, and tags are essential components that significantly improve the findability of open data.&#x20;

#### Metadata

&#x20;High-quality metadata is crucial for several reasons:

1. **Contextual Information**: Metadata provides context about the data, such as its creator, creation date, format, and purpose. This context helps users understand the relevance and applicability of the data.
2. **Standardisation**: Using standardized metadata schemas (e.g., Dublin Core, DCAT) ensures that metadata is consistent and interoperable across different platforms and systems.
3. **Search Engine Indexing**: Search engines use metadata to index and rank content. Well-structured metadata improves the chances that your data will appear in search results.
4. **Filtering and Sorting**: Metadata enables users to filter and sort data based on various criteria, making it easier to find specific datasets within large repositories.

#### Keywords

Keywords are specific terms that describe the main topics or themes of the data. They play a critical role in improving findability:

1. **Relevance**: Keywords help users quickly determine the relevance of a dataset. By including relevant keywords, you increase the likelihood that your data will be discovered by users searching for specific topics.
2. **Search Engine Optimization (SEO)**: Keywords are a fundamental aspect of SEO. Including relevant keywords in your metadata and data descriptions can improve your data's ranking in search engine results.
3. **User Queries**: Keywords align with the terms users are likely to enter into search engines or data portals. By anticipating and including these terms, you make your data more discoverable.

#### Tags

Tags are similar to keywords but are often more flexible and user-generated. They can be added by both data providers and users to categorize and describe data:

1. **Flexibility**: Tags allow for a more dynamic and community-driven approach to categorization. Users can add tags that reflect their own understanding and use of the data.
2. **Crowdsourcing**: Tags can be crowdsourced, enabling a broader range of perspectives and increasing the likelihood that the data will be found by diverse users.
3. **Faceted Search**: Tags support faceted search, allowing users to filter and refine their searches based on multiple criteria. This makes it easier to find specific datasets within large collections.

#### Best Practices for Metadata, Keywords, and Tags

1. **Consistency**: Use consistent terminology and follow established standards for metadata, keywords, and tags.
2. **Relevance**: Ensure that keywords and tags are relevant to the content of the data. Avoid using generic or overly broad terms.
3. **Comprehensiveness**: Provide comprehensive metadata that includes all essential details about the data. Include a variety of keywords and tags to cover different aspects of the data.
4. **User-Friendly**: Make metadata, keywords, and tags user-friendly and easy to understand. Avoid jargon unless it is necessary and well-defined.
5. **Updates**: Regularly update metadata, keywords, and tags to reflect changes in the data or new insights from users.

AI Update: If you only have a few (or no) user queries to guide you, try asking conversational generative AI such as ChatGPT to suggest more. A good prompting strategy here is to use a 'persona', such as, 'I'm interested in finding data on water quality. What are some of the key terms I might use in a Google Search?'

AI Update: Frequently, keywords in metadata are missing or inconsistent. ChatGPT and other generative AIs can be used to suggest more.

