Requirements for Urban Dataset Metadata
<|reference_start|>Requirements for Urban Dataset Metadata: 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 the Urban Dataset Metadata Requirements. 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 requirements encompass seven pivotal dimensions, spanning content to quality information, each stratified across six levels to guide the optimal documentation of datasets, ensuring ease of discovery, relevance assessment, and comprehensive dataset understanding. This paper also incorporates the metadata properties into a data cataloguing tool called CKAN through a custom plugin, CKANext-udc. The plugin introduces custom fields based on different metadata levels, allows for user interface customisation, and integrates with a graph database, converting catalogue data into a knowledge graph based on the Urban Data Metadata Requirements ontology.<|reference_end|>
@article{fox2024a,
    title={A Capability Maturity Model for Urban Dataset Meta-data},
    author={Mark S. Fox, Bart Gajderowicz, Dishu Lyu},
    journal={arXiv preprint arXiv:2402.05211},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05211},
    primaryClass={cs.DL}
}