Characterising Dataset Search – An Analysis of Search Logs and Data Requests

Abstract

Large amounts of data are becoming increasingly available online. In order to benefit from it we need tools to retrieve the most relevant datasets that match ones data needs. Several vocabularies have been developed to describe datasets in order to increase their discoverability, but for data publishers is costly to cumbersome to annotate them using all, leading to the question of what properties are more important. In this work we contribute with a systematic study of the patterns and specific attributes that data consumers use to search for data and how it compares with general web search. We performed a query log analysis based on logs from four national open data portals and conducted a qualitative analysis of user data requests for requests issued to one of them. Search queries issued on data portals differ from those issued to web search engines in their length, topic, and structure. Based on our findings we hypothesise that portals search functionalities are currently used in an exploratory manner, rather than to retrieve a specific resource. In our study of data requests we found that geospatial and temporal attributes, as well as information on the required granularity of the data are the most common features. The findings of both analyses suggest that these features are of higher importance in dataset retrieval in contrast to general web search, suggesting that efforts of dataset publishers should focus on generating dataset descriptions including them.

Understanding users' accessing behaviors to local Open Government Data via transaction log analysis

“…The study of user data accessing behaviors is still in its early stage. Kacprzak et al (2019) mentioned that there are no systematic studies that investigate what properties of data users' needs are crucial for them to effectively search and discover datasets. Also, understanding users' data accessing behaviors is essential to the findability of datasets, yet “a user‐focused, cross‐disciplinary analysis of data retrieval practices is lacking” (Gregory et al, 2019; Ohno‐Machado et al, 2017).…”

“…Both studies used qualitative research methods, which is helpful to obtain insights on OGD users, but the accuracy and comprehensiveness of their results could be limited by the low numbers of participants. The third study on OGD users' search behaviors employed the search log analysis method (Kacprzak et al, 2019). It examined the patterns and specific attributes of search queries and compared the OGD search with a general web search.…”

“…Analyzing TL is to understand the interactions among searchers, content, and the system, including searching behaviors and navigation behaviors (Borges & Levene, 1999; Jansen, 2006). It also enables researchers to identify users searching characteristics, such as query analysis (Jansen, 2000; Kacprzak et al, 2019; Moukdad & Large, 2001) and to examine searchers' performance (Mat‐Hassan & Levene, 2001). Based on the understanding of TL analysis, the suggestions or guidelines of system design, searching assistance, and training strategies can be devised.…”

A Framework for Evaluating Snippet Generation for Dataset Search

“…Real Queries. We used crowdsourced natural language queries 9 that were originally submitted to data.gov.uk for datasets [20]. They were transformed into keyword queries by removing stop words using Apache Lucene.…”

Supporting open research data practice through data curation and discovery: A global perspective

“…Some recent studies have explored the issues of research data curation and discovery services from the perspectives of researchers and data curation and discovery service providers (Faniel, Frank, & Yakel, 2019; Kacprzak et al, 2019; Walsh et al, 2019). Yet a relatively small part of the research data has been shared outside the research community; the roles played by the different stakeholders and how to facilitate the practices of data sharing are still emerging (Cox, Kennan, Lyon, Pinfield, & Sbaffi, 2019; Khalsa, Cotroneo, & Wu, 2018; Polona, 2019; Wu, Psomopoulos, Khalsa, & de Waard, 2019).…”