Data standardization and management to facilitate large-scale and interdisciplinary approaches access
Bringing data related to recreational fishers and fisheries together across large scales can provide tremendous insight. Methods for collecting, analysing, and storing data can vary dramatically, which can have significant implications for the use of these data. Efforts to standardise data within organisations often increase the ability to compare datasets from different areas, monitor changes over time, and increase the utility of the data for management and research. Though employing standardised methodology and data architecture results in the most straightforward and robust opportunities for data integration, doing so may not be possible due to variation in data collection objectives, continuity with historical programs, and resource limitations. Additionally, managing data according to FAIR principles (findability, accessibility, interoperability, and reusability) helps support data sharing and large-scale research efforts. Key elements of integrable data include appropriate data structures, adequate documentation of methodology, interpretable and complete metadata, and accessible storage formats. We document the potential benefits of standardising data and offer example approaches. We also explore best practices regarding the formatting, storage, and transmission of recreational fisher data. Efforts to increase the level of standardisation and integrability of recreational fisher data can create opportunities to better understand fisher behaviour, needs, and fulfilment.