基于研究数据评价的引证优化:高被引数据集特征视角

Citation Optimization Based on Evaluation of Research Data:Feature Perspective of Highly Cited Data Sets

【目的/意义】研究数据在科学研究中占据重要的基础性地位,高价值研究数据的引用对推动科学研究起着重要作用,因此如何评价出高价值研究数据并对此进行引证显得尤为关键。【方法/过程】本文从DCI近十年社会科学领域的数据集入手,确立研究数据评价指标和方案。【结果/结论】低被引数据集作者总被引频次与高被引数据集差距悬殊;高被引数据集具有数据作者篇均被引频次较高;基金资助数量较多;数据仓储机构的数据平均被引频次较高;关键词数量、操作方式较多;提供DOI号及元数据描述方式较详细等。为数据引证影响因素的分析带来一定启发。【创新/局限】得出数据引证行为的优化实施建议:促进评价体系多元化、培养数据伦理意识、规范数据引证形式、加强各个环节的数据治理。

【Purpose/significance】Research data occupies an important fundamental position in scientific research. The citation of high-value research data plays an important role in promoting scientific research, so how to evaluate and cite high-value research data is particularly critical.【Method/process】This paper starts with the data set of DCI in the field of Social Sciences in recent ten years, establishes evaluation indicators and programs for research data.【Result/conclusion】There is a large gap between the total citation frequency of authors in low citation dataset and high citation dataset. The data set with high citation frequency has characteristics. The cited frequency of single paper data of data authors was high. The total citation frequency of data authors and articles are higher;the number of fund support is large;the average cited frequency of data in data storage institutions is high;there are many keywords and operation modes;provide DOI number and metadata description in detail. It brings some inspiration to the analysis of influencing factors of data citation.【Innovation/limitation】Suggestions for optimal implementation of data citation behavior are obtained: promoting the diversification of evaluation system, cultivating the consciousness of data ethics, standardizing the form of data citation, and strengthening the data governance in each link.