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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.
References
[1]
Schamber L. and Bateman J. 1996. User criteria in relevance evaluation: Toward development of a measurement scale. In Proceedings of the ASIS Annual Meeting, Information Today Inc, 33, 218--225.
[2]
Saracevic T. 2007. Relevance: A review of the literature and a framework for thinking on the notion in information science. Part II: Nature and manifestations of relevance. Journal of The American Society For Information Science And Technology. 58(13), 1915--1932.
[3]
Schamber L. 1991. Users' Criteria for Evaluation in a Multimedia Environment. In Asis Meeting, 126--133.
[4]
Schamber L., Eisenberg M. B., and Nilan M. S. 1990. A re-examination of relevance: toward a dynamic, situational definition? Information Processing & Management. 26(6), 755--776.
[5]
Park T. 1993. The Nature of Relevance in Information Retrieval: An Empirical Study. Library Quarterly. 63(3), 318--351.
[6]
Barry C. 1994. User-defined relevance criteria: an exploratory study. Journal of the American Society for Information. 45(3),149--159.
[7]
Wang P. and Soergel D. 1998. A cognitive model of document use during a research project. Study I. Document selection. Journal of the Association for Information Science & Technology. 49(2), 115--133.
[8]
Westbrook L. 2001. Faculty Relevance Criteria: Internalized User Needs. Library Trends. 50(2), 197--206.
[9]
Choi Y. and Rasmussen E. M. 2002. Users' relevance criteria in image retrieval in American history. Information Processing & Management. 38(5), 695--726.
[10]
Crystal A. and Greenberg J. 2006. Relevance criteria identified by health information users during Web searches. Journal of The American Society For Information Science and Technology. 57(10), 1368--1382.
[11]
Savolainen R. and Kari J. 2006. User-defined relevance criteria in web searching. Journal of Documentation. 62(6), 685--707.
[12]
Balatsoukas P. and Ruthven I. 2012. An eye-tracking approach to the analysis of relevance judgments on the Web: The case of Google search engine. Journal of The American Society for Information Science and Technology. 63(9), 1728--1746.
[13]
Balatsoukas P. and Ruthven I. 2010. The use of relevance criteria during predictive judgment: an eye tracking approach. Proceedings of the American Society for Information Science & Technology. 47(1), 1--10.
[14]
Kim Y and Park S J. 2014. User-based Relevance and Irrelevance Criteria during the Task Pursuing of Middle School Students. Journal of the Korean Society for Library & Information. 48(3), 55--70.
[15]
Yang M. 2005, An exploration of users' video relevance criteria. Umi to Sora. 71, 97--112.
[16]
Albassam S A A and Ruthven I. 2017. Users' relevance criteria for video in leisure contexts. Journal of Documentation. 74(1).
[17]
Laplante A. 2010. Users' Relevance Criteria in Music Retrieval in Everyday Life: An Exploratory Study. In ISMIR. 601--606.
[18]
Inskip C., MacFarlane A., and Rafferty P. 2010. Creative professional users' musical relevance criteria. Journal of Information Science.36(4), 517--529.
[19]
Taylor A. 2013 Examination of work task and criteria choices for the relevance judgment process. Journal of Documentation. 69(4), 523--544.
[20]
Taylor A. 2012. User relevance criteria choices and the information search process. Information Processing & Management. 48(1), 136--153.
[21]
Xu Y. 2006. Relevance judgment: What do information users consider beyond topicality? Journal of the American Society for Information Science & Technology. 57(7), 961--973.
[22]
Hamid R. and Thom J. 2010. Criteria that have an effect on users while making image relevance judgments. In Fifteenth Australasian Document Computing Symposium, School of Computer Science and IT, RMIT University, 8.
[23]
Hamid R. 2016. Effects of relevance criteria and subjective factors on web image searching behavior. Journal of Information Science.43(6), 786--800.
[24]
Dam K K V, Walker A M, and James M. 2012. Integrating Data Management and Collaborative Sharing with Computational Science Processes. 506--538
[25]
Stillerman J, Greenwald M, and Wright J. 2018, Scientific data management with navigational metadata. Fusion Engineering & Design. 128, 113--116.
[26]
Wang Yong 2016. The development status and challenge in the big data era. Gansu Agriculture, 4, 21--24.(in Chinese)
[27]
Gao F. 2017. An Exploratory Research on the Relationship Between Agriculture Scientific Data User Relevance Clues and Criteria. Library and Information Service. 61(15),72--80.
[28]
Crystal A and Greenberg J. 2006. Relevance criteria identified by health information users during Web searches. Journal of the American Society for Information Science & Technology. 57(10), 1368--1382.
[29]
Tombros A, Ruthven I, and Jose J M. 2005. How users assess Web pages for information seeking. Journal of the American Society for Information Science & Technology. 56(4), 327--344.
[30]
Carata L, Akoush S, and Balakrishnan N et al. 2014. A Primer on Provenance. Queue. 12(3), 10.
[31]
Wallis J C, Rolando E, and Borgman C L. 2013. If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology. Plos One. 8(7), e67332.
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%;
Overall Acceptance Rate 368 of 770 submissions, 48%
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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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