Data metrics are essential to assess the impact of data repositories’ holdings and to understand the research practices of the community that they serve. These metrics are useful for reporting to funders, to inform community engagement strategies, and to direct and sustain repository services. In turn, communicating these metrics to the user community conveys transparency and elicits their trust in data sharing. However, because data metrics are time-sensitive and context-dependent, tracking, interpreting, and communicating them is challenging. In this work we introduce data usage analyses including benchmarking and grouping, developed to better assess the impact of the DesignSafe Data Depot, a natural hazards data repository. Make Data Count compliant metrics are analysed in relation to research methods, sub-disciplines, natural hazard types, and time, to learn what data is being used, what influences data usage, and to establish realistic usage expectations. Results are interpreted in relation to the research and publication practices of the community and to natural hazard events. In addition, we introduce strategies to clearly communicate dataset metrics to users.
https://doi.org/10.2218/ijdc.v18i1.929
| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |
