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View all- Kim JSuzuka KYakel E(2020)Reusing qualitative video data: matching reuse goals and criteria for selectionAslib Journal of Information Management10.1108/AJIM-08-2019-021572:3(395-419)Online publication date: 9-Jun-2020
Scientific Data Relevance Criteria Classification and Usage
Article No.: 30, Pages 1 - 7
Abstract
In1 the big data era, scientific data plays a crucial role in scientific research. Data sharing, retrieval and usage has become an inevitable trend. We study how the users of scientific data select relevant data from the data sharing platform. The study was conducted in two stages. In the first stage, a total of 14 subjects were selected to obtain their relevance criteria and usage of scientific data through semi-structured interviews. In the second stage, 671 questionnaires were collected in order to classify criteria. Finally, we determined 9 relevance criteria for scientific data: topicality, availability, comprehensiveness, currency, authority, quality, convenience, standardization, and usability, and divided them to 5 groups. In order to truly make a better data search engine and improve its search efficiency, moving beyond the criteria often used by users, we need to determine those criteria that are not often used, but still very important. What's more, a more convenient data search platform needs to be considered.
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Index Terms
- Scientific Data Relevance Criteria Classification and Usage
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Published: 22 October 2018
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CSAE '18
CSAE '18: The 2nd International Conference on Computer Science and Application Engineering
October 22 - 24, 2018
Hohhot, China
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CSAE '18 Paper Acceptance Rate 189 of 383 submissions, 49%;
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View all- Kim JSuzuka KYakel E(2020)Reusing qualitative video data: matching reuse goals and criteria for selectionAslib Journal of Information Management10.1108/AJIM-08-2019-021572:3(395-419)Online publication date: 9-Jun-2020
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