  In the current environment of data generation and publication, there is an
ever-growing number of datasets available for download. This growth
precipitates an existing challenge: sourcing and integrating relevant datasets
for analysis is becoming more complex. Despite efforts by open data platforms,
obstacles remain, predominantly rooted in inadequate metadata, unsuitable data
presentation, complications in pinpointing desired data, and data integration.
This paper delves into the intricacies of dataset retrieval, emphasizing the
pivotal role of metadata in aligning datasets with user queries. Through an
exploration of existing literature, it underscores prevailing issues such as
the identification of valuable metadata and the development of tools to
maintain and annotate them effectively. The central contribution of this
research is the proposition of a dataset metadata maturity model. Deriving
inspiration from software engineering maturity models, this framework
delineates a progression from rudimentary metadata documentation to advanced
levels, aiding dataset creators in their documentation efforts. The model
encompasses seven pivotal dimensions, spanning content to quality information,
each stratified across five maturity levels to guide the optimal documentation
of datasets, ensuring ease of discovery, relevance assessment, and
comprehensive dataset understanding. This paper also incorporates the maturity
model into a data cataloguing tool called CKAN through a custom plugin,
CKANext-udc. The plugin introduces custom fields based on different maturity
levels, allows for user interface customisation, and integrates with a graph
database, converting catalogue data into a knowledge graph based on the
Maturity Model ontology.
