Characterising Dataset Search Queries

Kacprzak, Emilia; Koesten, Laura; Tennison, Jeni; Simperl, Elena

doi:10.1145/3184558.3191597

Cited by 13 publications

(28 citation statements)

References 16 publications

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“…Queries are significantly shorter than requests and contain different types of information. Similar to Kacprzak et al (2018), we found that some categories or meta information appear more often in requests than in queries. Socio‐demographic requirements, for example, are mentioned more frequently in requests than in queries, while data properties appear more often in queries than in requests.…”

Section: Discussionsupporting

confidence: 89%

“…Roughly two‐thirds of all queries of a data archive for the Social Science are known‐item queries (Kern & Mathiak, 2015), and the average query length in http://data.gov.uk is 2.44 words (Koesten et al, 2017). Additionally, Kacprzak et al (2018) examined search query log files and data requests submitted to the UK Government Open Data portal to gather more insights into how people search for data. In both logged search queries and data requests, they identified four prominent themes: geospatial information, temporal information, restrictions (like format, price, data types, license), and information about granularity.…”

Section: Related Workmentioning

confidence: 99%

“…Kacprzak et al (2018) identified non‐academics as the major user group of the UK Government Open Data Portal searching for data in many disciplines. We assume that the information needs of academics differ from non‐academics.…”

Section: Related Workmentioning

confidence: 99%

“…We assume that the information needs of academics differ from non‐academics. We therefore build on the work of Kacprzak et al (2018) but focus explicitly on data search in the context of academic research. Additionally, we narrow our research scope and analyze the specific information needs within a single discipline: the social sciences.…”

Section: Related Workmentioning

confidence: 99%

“…In a comprehensive survey of dataset search, Chapman et al (2020) identified a couple of open problems, including the desire to provide interfaces that go beyond simple keyword and faceted search. By comparing data requests and search query logs of the UK Government Open Data portal, Kacprzak et al (2018) confirmed that keyword search does not cover data seekers' information needs. In our research, we go a step further and analyze the genuine and detailed information needs of researchers in a specialized domain.…”

Section: Introductionmentioning

confidence: 99%

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Genuine Information Needs of Social Scientists Looking for Data

Papenmeier

Krämer

Friedrich

et al. 2021

Proceedings of the Association for Information Science and Tech

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Publishing research data is widely expected to increase its reuse and to inspire new research. In the social sciences, data from surveys, interviews, polls, and statistics are primary resources for research. There is a long tradition to collect and offer research data in data archives and online repositories. Researchers use these systems to identify data relevant to their research. However, especially in data search, users' complex information needs seem to collide with the capabilities of data search systems. The search capabilities, in turn, depend to a high degree upon the metadata schemes used to describe the data. In this research, we conducted an online survey with 72 social science researchers who expressed their individual information needs for research data like they would do when asking a colleague for help. We analyzed these information needs and attributed their different components to the categories: topic, metadata, and intention. We compared these categories and their content to existing metadata models of research data and the search and filter opportunities offered in existing data search systems. We found a mismatch between what users have as a requirement for their data and what is offered on metadata level and search system possibilities.

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Section: Discussionsupporting

confidence: 89%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genuine Information Needs of Social Scientists Looking for Data

Papenmeier

Krämer

Friedrich

et al. 2021

Proceedings of the Association for Information Science and Tech

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show abstract

Open Data Chatbot

Keyner

Savenkov

Vakulenko

2019

The Semantic Web: ESWC 2019 Satellite Events

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Recently, chatbots received an increased attention from industry and diverse research communities as a dialogue-based interface providing advanced human-computer interactions. On the other hand, Open Data continues to be an important trend and a potential enabler for government transparency and citizen participation. This paper shows how these two paradigms can be combined to help non-expert users find and discover open government datasets through dialogue.

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