Prior work proposed "Data Near Here" (DNH), a data search engine for scientific archives that is modeled on Internet search engines. DNH performs a periodic, asynchronous scan of each dataset in an archive, extracting lightweight features that are combined to form a dataset summary. During a search, DNH assesses the similarity of the search terms to the summary features and returns to the user, at interactive timescales, a ranked list of datasets for further exploration and analysis. We will demonstrate the search capabilities and ancillary metadata-browsing features for an archive of observational oceanographic data. While comparing search terms to complete datasets might seem ideal, interactive search speed would be impossible with archives of realistic size. We include an analysis showing that our summary-based approach gives a reasonable approximation of such a "complete dataset" similarity measure.

The past decade has seen a dramatic increase in the amount of data captured and made available to scientists for research. This increase amplifies the difficulty scientists face in finding the data most relevant to their information needs. In prior work, we hypothesized that Information Retrieval-style ranked search can be applied to data sets to help a scientist discover the most relevant data amongst the thousands of data sets in many formats, much like text-based ranked search helps users make sense of the vast number of Internet documents. To test this hypothesis, we explored the use of ranked search for scientific data using an existing multi-terabyte observational archive as our test-bed. In this paper, we investigate whether the concept of varying relevance, and therefore ranked search, applies to numeric data-that is, are data sets are enough like documents for Information Retrieval techniques and evaluation measures to apply? We present a user study that demonstrates that data set similarity resonates with users as a basis for relevance and, therefore, for ranked search. We evaluate a prototype implementation of ranked search over data sets with a second user study and demonstrate that ranked search improves a scientist's ability to find needed data.