A dataset can be well documented and still be the wrong fit for your task. Before asking whether it is “high quality,” define the decision or analysis it needs to support.
A climate risk study, a country comparison, and a model training pipeline need different kinds of evidence. These five questions provide a practical starting point.
1. Where did it come from?
Identify the original publisher and the process that produced the data. Distinguish measurements, survey responses, administrative records, and model estimates. An accessible download is useful, but it does not explain the underlying collection process.
2. What does it actually cover?
Check the population, geography, observation period, and resolution. Look for missing groups or gaps in time. Coverage is part of the answer, not a detail to sort out after analysis.
For example, ERA5 combines observations and physical models to estimate weather and climate conditions. That makes it useful for many applications, while also making it different from a direct measurement at every location.
3. Are the definitions compatible?
Read the units, denominators, and aggregation rules. Two columns with the same label can represent different populations or measurement methods. Keep a record of the definitions used in your analysis.
4. Which version are you using?
Separate the observation date from the release date and your download date. Historical figures may be revised. Preserve a version or snapshot when reproducibility matters, and explain how you handle updates.
5. Can you use it for this purpose?
Check licensing, attribution, access conditions, and restrictions that apply to your intended use. Publicly accessible documentation does not automatically make every associated file unrestricted.
A useful dataset has a clear relationship to your question. Start there, and treat documentation as part of the data rather than an optional extra.