Which property best describes "good data" in an analysis?

"Good data" in an analysis is characterized by being objective and tracked over a time period. This means that the data is not influenced by personal feelings or opinions, making it more reliable for analysis. Objective data provides a clear, factual basis for drawing conclusions and making informed decisions.

Tracking data over time allows for the observation of trends, patterns, and changes, which can be critical for understanding the dynamics of the subject being analyzed. Longitudinal data can reveal seasonal variations, growth patterns, or the impact of interventions, providing deeper insights that one-time data (as suggested by other options) would not.

Longitudinal data collected objectively ensures that it maintains consistency and relevance in the context of the analysis, making it a foundational component of robust research and predictive modeling. This quality makes data inherently more valuable when assessing risks or making strategic decisions within the FAIR framework.