科学数据用户相关性标准研究

User-defined Relevance Criteria in Scientific Data

[目的/意义]以透镜理论为依据,从认知加工的角度出发,研究科学数据用户如何在数据共享平台中选取合适的数据。[方法/过程]研究分两个阶段进行,第一阶段选取14位被试通过半结构化访谈初步获取科学数据相关性标准集合及其使用情况;第二阶段通过发放671份问卷获取相关性标准的重要性,并对第一阶段获取的相关性标准内涵进行验证。[结果/结论]最终得到9个科学数据相关性标准,分别为主题性、可获得性、全面性、时效性、权威性、质量、便利性、规范性和可用性,并对其内涵进行了界定和验证。研究结果发现,全面性和规范性是科学数据的新增标准;可获得性、可用性和便利性存在很强的关联性;质量和规范性存在很强的关联性;质量与权威性虽然不相关,却保持一致的判断趋势。在未来的研究中为真正提升检索效率,改进检索系统,除考虑用户经常使用的标准之外,还要发掘那些使用频率不高但是很重要的标准。

[Purpose/significance] Based on lens theory and from the perspective of cognitive processing, the paper studied how the users of scientific data select relevant data from the data sharing platform. [Method/process] 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. The level of importance of the relevance criteria was determined, and in the second stage, the concepts of the relevance criteria obtained in the first stage were further verified through 671 questionnaires. [Result/conclusion] Finally, 9 relevance criteria were determined for scientific data:topicality, availability, comprehensiveness, currency, authority, quality, convenience, standardization, and usability, and the defined these concepts. The results showed that comprehensiveness and standardization are unique criteria related to the nature of scientific data. The concepts of convenience, availability, and usability are highly associated. The concepts of quality and authority are irrelevant, but they are consistent in some descriptive phrases. Thus, the concepts that define them must be further clarified. In order to truly make a better data search engine and improve its search efficiency, moving beyond the criteria often used by users, it is necessary to determine those criteria that are not often used, but still very important.