Abstract
The purpose of this study is to explore the information retrieval process in scientific data and to better understand the concepts and internal relationships of metadata and relevance criteria. Qualitative and quantitative analyses were performed using interview and eye movement data from 36 subjects. The results show that users paid attention to 45 types of metadata and used nine relevance criteria to judge the relevance of scientific data. There was a complex relationship between the metadata and criteria, mainly manifesting as one stimulus–multiple responses and multiple stimuli–one response. Metadata associated with the relevance criterion of topicality is the most complex, which includes common metadata and subject-related metadata. Metadata associated with the other relevance criteria (such as quality and authority) has no obvious professional characteristics. What’s more, because of the essential difference between scientific data and documents, users use different criteria. When retrieving data, users pay more attention to the availability of data and whether they can be further analyzed and processed. This study clarifies the concepts of metadata and relevance criteria as well as their roles in relevance judgments. In addition, this study deepens the understanding of the scientific data relevance judgments and their cognitive process and provides a theoretical basis for improving scientific data-sharing platforms.
Highlights
ZhangData Science JournalScientific data are series of original data, processing data, and result data produced by scientists in the process of scientific research
To better understand relevance judgment, this study explores the relationship between metadata and relevance criteria
(1) Users mainly paid attention to 45 types of scientific metadata, and used a total of nine relevance criteria to make relevance judgments when searching for data
Summary
Scientific data are series of original data, processing data, and result data produced by scientists in the process of scientific research. Mass scientific data resources are the basis of scientific research. Scientific data sharing is the key to realizing information value and data reuse, and it is an important way to promote the flow of scientific data among researchers so that these data may be transformed into scientific conclusions (Deng Zhonghua, 2017). To better share and reuse scientific research results, which avoids wasting research funds, scientific communities in all fields are building scientific data sharing platforms, which provide mass data resources for researchers. They seek assurance that the data can be understood, and they must trust the data (IM Faniel, 2010). In contrast to other information carriers (like literature, images and videos), scientific data are highly purpose, targeted, subject-related, and technical
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