[Purpose/significance] From the perspective of peer review on data journals, this paper identifies and puts forward scientific data quality evaluation indicators to improve the understanding of scientific data quality evaluation and provide a reference for the practice of peer review of data papers.[Method/process] Data review guidelines for 20 data journals were selected as source material for qualitative research. This paper used grounded theory and qualitative analysis software NVivo to openly encode, correlate and selectively encode the data, and finally tested the encoding results through the theoretical saturation test.[Result/conclusion] Finally, in the context of peer review of data papers, a scientific data quality evaluation index system was established, including four categories of data internal quality, data expression quality, data access quality, and data utility quality and 14 evaluation indicators. Then this article analyzed the specific meaning and classification of the indicators in detail to help the authors and reviewers of data papers understand the internal structure of scientific data quality.
Sa Xu
,
Wang Jian
,
Fan Zhixuan
,
Liu Jianpin
,
Zhang Guilan
,
Xu Bo
. Identification of Scientific Data Quality Evaluation Indicators from the Perspective of Data Journals Peer Review[J]. Library and Information Service, 2020
, 64(17)
: 123
-130
.
DOI: 10.13266/j.issn.0252-3116.2020.17.013
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