How can data analysts identify spatio-temporal datasets that are suitable for their task? Answering this question is not only dependent on the aim of the analysis and the semantic contents of the data, but also on knowing whether the required data combinations and transformations, spatio-temporal analysis methods, charts and map visualizations are meaningfully applicable to the data. Operators need to assess whether they can meaningfully apply analytical operations to data to derive the information required. Answering this question in a general and computationally executable way is a crucial step on our way towards supporting data analysts and their research practice in e-Science. We propose an ontology design pattern for spatio-temporal information that enables to reason about the applicability of a number of fundamental classes of analyses in relation to given data, i.e., whether data sets can be compared, transformed, combined, and whether summary statistics can be applied to them. We demonstrate this ontology implemented in OWL through a set of corresponding SPARQL queries applied to meta-data of datasets from the AURIN portal.