With continuous development of data-intensive research and open science, along with remarkable progress of data acquisition technology, there is an increasing demand of scientific data sharing. Hence, finding relevant data within massive scientific data repositories is an urgent need of scientific data users. Such need calls for a scientific data-specific retrieval technology designed and developed based on understanding of how people make judgments on the relevance of scientific data. Data sharing and discovery depend on the development of infrastructure, support systems and data supplies (Borgman, 2013), “it is equally important to understand the behaviours involved in data retrieval. But a user-focused analysis of data retrieval practices is lacking” as Gregory et al. (2017: 1) pointed out. Until now, however, such understanding and the underlying study are still out of the main research interests of the fields of information retrieval (IR) and data sciences.
User relevance has been acknowledged as a basic concept in information science because it contributes to the explanation of ubiquitous behaviours involving information selection and utilization (Borlund, 2003; Ingwersen & Järvelin, 2011; Saracevic, 2016). Many researchers have utilised the concept by investigating various forms of information such as scientific papers or reports, web pages, pictures and images, multimedia, and scientific data (Barry, 1994; Xu and Chen, 2006; Crystal and Greenberg, 2006; Savolainen, 2013; Choi, 2002; Inskip, 2010; Liu et al., 2019). The goal of all these studies is to upgrade traditional IR to be more interactive, cognition-friendly and highly effective. For scientific data, such upgrade seems more challenging. For example, Google dataset search1 as a representative data search system follows the traditional IR mechanism and cannot respond to particular requirements of data searchers due to their intensive concerns of data quality and credibility.
The understanding of how and why people select one dataset rather than another, or specifically, how and why people judge the relevance of a certain dataset, should be an important prerequisite for data retrieval. The study aims at addressing the problem by answering the following three questions:
Scientific data is a subcategory of data, also known as research data, which is defined as “recorded factual material commonly retrained by and accepted in the scientific community as necessary to validate research findings…”2 by Engineering and Physical Sciences Research Council (EPSRC), which is the main funding body for engineering and physical sciences research in the UK. Borgman (2013: 29) defined scientific data as “entities used as evidences of phenomena for the purposes of research or scholarship”. These definitions reveal the essential characteristics of fact-bearing of scientific data and the function of scientific data as “evidences”.
Scientific data is generally considered as fact-bearing information, while documents, the traditional objects of relevance study in IR, are regarded as knowledge-bearing information. Such distinctive feature leads to many differences between scientific data and documents such as formats, forms and communication characteristics etc. Among those the most important is that they have different functions. Data is generally regarded as evidences for reasoning and deciding of scientific data users, while information in documents usually carries various knowledge potentially modifying the receivers’ state of cognition. Such differences lead to the necessity of research on how scientific data users make relevance judgment by using different relevance criteria (relevance criteria study will be discussed in section 2.3).
The essential feature of fact-bearing of scientific data determines the necessity of developing proprietary scientific data retrieval systems and algorithms. Although information retrieval (IR) has been developed for more than 60 years, data retrieval is still a nascent field (Sanderson & Croft, 2012; Gregory et al., 2017), especially in user-oriented data retrieval. Some progress has been made in scientific data sharing and discovery. Google released Google dataset search in 2018. This has a dataset search engine similar to Google search but was released almost 19 years later. The World Data System (WDS)3 integrated data from over 100 independent data centres to support dataset retrieval. Quandl4 focuses on the search of financial and social science data sets. China built 23 scientific data sharing systems aiming at curation and sharing of scientific data in various fields, such as National Earth System Science Data Sharing Infrastructure,5 China Earthquake Data Centre,6 National Agricultural Scientific Data Centre.7
However, at present, these systems are all developed based on system-oriented retrieval methods. System relevance, or algorithm relevance, is a typical objective relevance depended on a given procedure or algorithm without considering the principal position of end users. For example, the relevant items are ranked by calculating the level of term matching between information objects and queries based on a vector space model. If relevance is not objective, then, how do people make relevance of information objects? Therefore, researchers studied the relative concept of system relevance or objective relevance—user relevance, or subjective relevance.
The study on user relevance focuses on describing, interpreting and modelling user relevance judgment process of information objects (Schamber et al., 1990; Cosijn & Ingwersen, 2000; Harter, 1992; Saracevic, 1975), as well as providing new design requirements and directions for IR practice. From the perspective of measurement, user relevance study focuses on the identification and use of RC. Schamber, Eisenberg, and Nilan (1990: 771) stated that “an understanding of relevance criteria, or the reasons underlying relevance judgment, as observed from the user’s perspective, may contribute to a more complete and useful understanding of the dimensions of relevance”.
RC identification is the premise and foundation of user relevance judgment research. Judging by existing user RC identification studies, the main results are summed into three points. First, researchers have verified that user relevance judgment not only depends on topicality, but also considers other RC (quality, authority, novelty, accessibility, etc.) by using methods of situational experiments and user interviews (Barry, 1994; Wang and Soergel, 1998; Bateman, 1998). Second, researchers have expanded the research scenarios from relevance judgment of documents to other information types such as images, music, and web pages (Laplante, 2010; Markkula, 2000; Tombros, 2003; Savolainen, 2013; Choi, 2002; Inskip, 2010), which were all prominent information types at that time. Third, user’s RC for inferences are fairly stable, in which a cross-situational RC set exists (Barry and Schamber, 1998; Wang and Soergel, 1998). The differences among RC are manifested in the differences of RC usage according to varying situations and information objects (Saracevic, 2016).
The use structure of RC reflects specific information behaviour of users. Greisdorf (2000) proposed the conjunctive and disjunctive rules of RC use in documents relevance judgment. For example, for conjunctive rules, users make relevant decisions based on positive aspects of RC. Xu and Chen (2006) empirically concluded that topicality and novelty were the two most important RC for documents relevance judgment. On this basis, they suggested four types of document retrieval modes to IR practice.
Researchers also explored the RC use of other information types. Choi (2002) found that topicality still played the most fundamental role in image relevance judgment, but image quality and clarity were the most frequently used RC. Crystal and Greenberg (2006) studied RC use modes of health-related web pages of web browsing users in different IR stages. Laplante (2010) and Inskip et al. (2010) studied RC use modes in music relevance judgment. They found that topicality is the most important criterion, but music users use criteria like personal hobbies, personal needs and novelty frequently.
Based on the perspective of user relevance research and the essential characteristics of fact-bearing of scientific data, this study carried out a two-phase study. In the first phase (see details in Section 4), by using the methods of situational interviews and content analysis, we conducted exploratory study focusing on what RC do scientific data users use and how they use RC to make relevance judgment? In the second phase of empirical study (see details in Section 5), seven research hypotheses were proposed based on the results of the first phase and the hierarchical structure of user information need provided by Taylor (see details in 5.1). The hypotheses were verified by using PLS-SEM (see details about this method in Section 5.3). The purpose of this study is to explore the proprietary relevance judgment patterns of scientific data users, and then to provide guidance for the development of user-oriented scientific data retrieval systems and algorithms.
The subjects in this study are participants from a national competition (Innovation Competition of Science and Technology Resources Sharing Service for College Student, “Sharing Cup” for short) of scientific data in China. The competition is a national science and technology activity aimed at promoting the reuse and efficiency of scientific data. Competitors are presumed to submit works in the form of research papers, multimedia presentation, website systems design, business plans, etc., all should base on given scientific data and topics provided by the 23 scientific data sharing platforms.
This study investigated the competitors from the fifth “Sharing Cup” (May 2017–December 2017). By sending emails to the competitors, 23 volunteer competitors were selected as subjects for the interview experiment in the exploratory study. The 23 subjects all used the data from the scientific data sharing platform and completed the competition works before the interview. There were 5 undergraduate students and 18 postgraduate students among these 23 subjects.
Twenty-three subjects were interviewed face-to-face in laboratory or video-meeting. The interviewers were two doctoral students who participated in the whole interview process cooperatively. Semi-structured situational interview method was used to collect data. Subjects answered questions (see Appendix A part one) related to long-term memory by recalling and questions (see Appendix A part two) related to real retrieval scenarios by showing how they judge one dataset relevant or not relevant. The whole interview process was recorded and videotaped. It generally took 30–60 minutes to complete each interview conversation and subjects were given about 50 Ren Min Bi (RMB) as reward for their participation.
The data collected by interview were transcribed into texts. Content analysis (CA) method was applied for this purpose. CA is a method for analyzing written, oral or visual communication messages, to construct a conceptual model to describe the research phenomenon (Downe-Wamboldt, 1992). The core process of CA was divided into three steps: the determination of analysis units, the development of categories and the construction of the relationships of categories. In this study, the coding units are the sentences from scientific data user’s descriptions in interviews, while the categories are the RC and corresponding clues mentioned by scientific data users, and the relationships of categories are the different combination usages of RC in data relevance judgment context. The transcribed texts were coded by three coders by using Nvivo11,8 and the coding coincidence rate (C.R.)9 reached 82%, which was greater than the minimum threshold of 60% (Perreault & Leigh, 1989).
Table 1 shows the main process of extracting key concepts from transcribed texts based on content analysis. As shown in Table 1, RC and corresponding stimulus clues were the main concepts extracted in this study. Clues were information features or attributes perceived by users, reflecting the connotation of RC. RC is the “cognitive tool” on which users rely for relevance judgment, and it also represents a certain level of judgment made by users (e.g. to judge the relevance of scientific data, user may judge the authority of the data by taking the producers and affiliation of the authors as the stimulus clues).
|Q1: What is your basis for judging the relevance of data in completing this task?|
|A1: Mainly focusing on data keyword||Data keywords|
|Q2: So what is the role of data keywords?|
|A2: I often use the keywords to determine whether it is the topic I want||Topicality|
According to the interviews, users use a combination of multiple RC, instead of using one RC, to judge the relevance of scientific data. The combination of RC reflects the Scientific data user’s relevance judgment patterns. The coding process is shown in Table 2.
|Interview process||Paths of criteria use|
|Coding based on direct answers|
|Q1: How do you judge the relevance of scientific data?|
|A1: First, I judge the topic based on the data keywords, and then check the quality of the data. If the data quality is satisfying, it will be relevant.||Topicality–>quality|
|Coding based on different answers in context|
|Q1: How do you judge the relevance of scientific data?|
|A1: It is based on data keywords|
|Q2: Do you rely solely on data keywords?|
|A2: No, I still need to see the data production organization and whether the data can solve my current task.”||Topicality–>authority–>usefulness|
Table 3 summarizes the coding results of RC and corresponding clues. Five RC (topicality, accessibility, quality, authority and usefulness) and 18 corresponding clues were coded. As shown in Table 4, each criterion was given a clear definition to fit scientific data research context as one of the important results of exploratory study.
|Data Title (DT)||107||19|
|Data keywords (DK)||123||20|
|Data description (DD)||60||14|
|Data time scope (DTS)||35||12|
|Data acquisition channel (DAC)||88||19|
|Data sharing level (DSL)||74||19|
|Support download? (DSD)||95||19|
|Data size (DS)||11||8|
|Data producer (DP)||44||13|
|Organization of data producer (DODP)||35||13|
|Data supply platform (DSP)||56||15|
|Data quality illustration (DQI)||54||16|
|Data producing and processing methods (DPPM)||67||16|
|Data Searching ranking order (DSRO)||3||2|
|Data Visiting volume (DVV)||2||2|
|US1: Scientific data as research evidences||44||15|
|US2: Scientific data can verify research theories||52||19|
|US3: Scientific data is the basis of my research||68||20|
|Topicality||The consistency between the topic perceived by users and the topic expressed by the data themselves.|
|Accessibility||The external restriction of the data.|
|Authority||The source of the data is reliable.|
|Quality||The data meet the requirements in terms of precision, accuracy, verifiability, etc.|
|Usefulness||Users perceive the utility of scientific data to solve problems in situations.|
Table 5 summarizes the coding results of the RC usage paths. Seven types of RC use paths were coded. The seven usage paths were all started with topicality and combined with other RC. Usefulness, as the user’s overall perception of data relevance, was influenced by other RC.
|RC use paths||Mentions||Percent||Respondents|
|TO → AC||96||20.3||19|
|TO → QU||65||13.8||19|
|TO → AU||64||13.6||17|
|TO → US||58||12.3||17|
|TO → AC → US||31||6.6||12|
|TO → QU → US||23||4.9||9|
|TO → AU → US||39||8.3||16|
The results of phase 1 showed that user’s judgment on scientific data relevance do not depend on just one CR, nor can the final decision be made for the first time. It is an interactive process, in which users form different levels of questions and make differential levels of relevance judgment before reaching the final decision.
Firstly, it involves user information question formation process. As Taylor (1968: 182) stated that: “There are four levels of question formation that shade into one another along the question spectrum in user information retrieval”. Different levels of questions reflect different needs for information. The original definitions of four questions proposed by Taylor (1968: 182) are as follows:
“Q1—the actual, but unexpressed need for information (the visceral need);
Q2—the conscious, within-brain description of the need (the conscious need);
Q3—the formal statement of the need (the formalized need);
Q4—the question as presented to the information system (the compromised need)”
Secondly, it involves different levels of user information relevance judgment. Corresponding to the question spectrum provided by Taylor, the results (Table 5 of phase1) showed that users combined different levels of RC to make the final judgment. For example, when a user’s information need state changes from Q1 to Q2, the user mainly determines the query and retrieval topics, in which he/she makes topic relevance judgment. When a user’s information need state changes from Q2 to Q3, the user understands and infers information based on various aspects of information content, in which he/she makes relevance judgment from the perspective of different aspects (such as quality and authority judgment of information). Finally, when a user’s information need state changes from Q3 to Q4, the user perceives relevance according to whether the information can solve the problem in the situation, in which he/she makes situation relevance judgment (judge the usefulness of information).
Considering the above two findings, the empirical study proposed the research model to be verified as shown in Figure 1. The model expresses two types of relations to be verified. First, the relationship between clues and corresponding RC need to be verified. Clues are information attributes or characteristics that reflect the connotations of RC (for example, as shown in Table 3, DT, DK, DD, DTS are the clues that reflect the connotation of topicality). Second, the relationships among scientific data of RC need to be verified. Based on the results of the exploratory study (as shown in Table 5), it is assumed that topicality, as a prerequisite RC, has positive effects on data quality, authority, accessibility and usefulness judgment (H1–H4), while data quality, accessibility and authority judgment have positive effects on the final judgment of data usefulness (H5–H7). The specific hypotheses are described in H1–H7:
H1–H4: DT, DK, DD, and DTS reflect the connotation of topicality which has a positive effect on data quality, authority, accessibility and usefulness judgment
H5: DQI, DPPM, DSRO, and DVV reflect the connotation of data quality which has a positive effect on data usefulness judgment
H6: DP, DODP, and DSP reflect the connotation of data authority which has a positive effect on data usefulness judgment
H7: DAC, DSL, DS, DSD and reflect the connotation of data accessibility which has a positive effect on data usefulness judgment
The subjects of the empirical study also came from the Fifth “Sharing Cup”. The subjects were presented with the same competition task. In the empirical study, 564 subjects participated in the questionnaire survey, and 544 valid questionnaires were finally used (see Section 5.4.1 for detailed demographic information).
Based on the results of the exploratory research, the corresponding questionnaire was designed in the empirical research (see Appendix B). The subjects scored each measurement variable according to its importance using a six-level scale – the importance increases continuously from zero (never pay attention) to five (very important).
A strict psychological measurement method, structural equation model (SEM), was used in this study. Anderson and Gerbing (1988) proposed this method to develop and verify theoretical assumptions. As an effective psychometric analysis method, SEM has been widely used in behavioural science, marketing, education and other fields (Garson, 2016). The analysis process of SEM was divided into two steps: measurement model and structural model analysis. The measurement model was used to verify the structural stability between the measurement index and the latent variable. For example, whether DT can be a measurement index of topicality needs to be verified. The structural model was used to verify the stability of the relationship between latent variables. For example, whether data quality judgment has impact on the data usefulness judgment needs to be verified.
There are two types of SEM, covariance-based (CB-SEM) and variance-based partial least squares (PLS-SEM). CB-SEM follows a maximum likelihood (ML) estimation procedure and aims at reproducing the covariance matrix without focusing on explained variance (Hair et al., 2011). Whereas PLS-SEM uses a regression-based partial least squares estimation method with the goal of explaining the latent constructs’ variance by minimizing the error terms (Hair et al., 2014). The two methods are complementary with each other. The most important reason to select CB-SEM or PLS-SEM is the research goal or research context. Hair et at (2011: 144) recommended:
Accordingly, this study aims at verifying the RC using structure: topicality as the driver construct, quality/authority/accessibility as intermediary constructs, and usefulness as the target construct. And this study is also an exploratory study that first adopts PLS-SEM in RC using structure. Therefore, we finally chose PLS-SEM and employed the SmartPLS310 as analysis tools.
This study received 544 valid questionnaires (excluding 20 invalid questionnaires), with the recovery rate of 96%. The gender ration of the subjects was balanced (M = 49.5%, F = 50.5%), the majority of subjects were postgraduate students (postgraduate = 95.6%, other = 4.4%), and the age range was mainly 18–30 (18–30 = 91.4%, other = 8.6%). In the aspect of user’s familiarity with scientific data, 84% of the subjects participated in at least one data-related research project, and for 92% of the subjects, their scientific data retrieval time accounted for more than 20% of the time of IR.
The measurement model verifies the structural validity of the construction. Structural validity tests the internal consistency, convergence validity and discrimination validity of construction. In this study, SmartPLS3 was used to evaluate the structural validity of the measurement model (Ringle, Wende & Becker, 2015). Cronbach’s alpha (α) and composite reliability (C.R) are important indicators to measure internal consistency. In confirmatory research, the threshold of C.R, Cronbach’s alpha (α) and standardized loading (SL) are required to be greater than 0.7 (Hair, Ringle & Sarstedt, 2011). Convergence validity is verified by average variance extracted (AVE). AVE should be greater than 0.5 in the confirmatory study. As shown in Table 6, C.R., SL, α and AVE all meet the above requirements.
The discrimination validity is verified by Fornel-Larcker-Criterium (Fornell & Larcker, 1981). Table 7 shows that if the top value (square root of AVE) in each column is greater than other values in that column, the discrimination validity is positive. As shown in Tables 6 and 7, the measurement model in this study meets all requirements. The clues (data attributes) are effective as measurement index of corresponding RC (as shown is Table 6, all are significant at the level of p < 0.001).
|Latent Variable Correlations(LVC)||Discriminant Validity met? (Square root of AVE>LVC?)|
The structural model tests the research hypotheses to interpret the prediction ability of the model. As shown in Figure 2 of the structural equation model verified in this study, the path coefficients are all normalized coefficients. The validity of latent variable relation is tested by bootstrap resampling technique (5000 bootstrap samples; no sign changes), which provides p-values and CLs to evaluate the significance of paths (Nevitt & Hancock, 2001). The results show that H1, H2, H3, H4, H5 and H7 are significant at the level of p < 0.001, and H6 is significant at the level of p < 0.05. The research hypotheses were all valid.
The interpretation and prediction capabilities of the PLS-SEM model were verified by the following indicators: R2 and composite-based standardized root mean square residual (SRMR). R2 is an important indicator to explain the predictive ability of the model, and the bigger the value of the R2 is, the stronger of the model’s prediction for the variance explanation of the endogenous variable will be. The R-square values of 0.25, 0.40 and 0.75 respectively indicate the weak, medium and strong level of the prediction ability of the model (Latan & Ramli, 2013; Hair et al., 2011). As show in Figure 2, the results of the coefficients are all great than the minimum threshold of 0.25. Furthermore, three of them are greater than 0.4, which means the model has a medium level of the prediction ability. In addition, from a review of similar studies (e.g., Lew and Sinkovics, 2013; Sarkar et al., 2001; Ralf and Siegfried, 2015) some authors used PLS-SEM. We concluded that the cut-off criteria of R2 of the model in this study were acceptable.
SRMR is an index to evaluate the overall fitting degree of the model. It measures the discrepancy between the observed correlation matrix and the model-implied correlation matrix. The smaller the SRMR value is, the better the model will fit. In CB-SEM, the model has a good fit when SRMR is less than 0.08 (Hu & Bentler, 1998, 1999). Whereas in terms of research goal and context (see details of the difference between CB-SEM and PLS-SEM in Section 5.3), the recommended minimum threshold of SRMR recommended might be 0.1 in PLS-SEM (Henseler et al., 2014; Cangur & Ercan, 2015; Garson, 2016).
Therefore, as shown in Figure 2, R2 of latent variables in this study are all greater than 0.25. Meanwhile, SRMR = 0.088 is less than the lenient threshold of 0.1 in PLS-SEM. Because this research belongs to exploratory research, the values of R2 and SRMR show that the model has moderate ability for interpretation and prediction.
The model verified and revealed the basic structure of RC use, which was characterized by the following three aspects. First, topicality (cause variable) is taken as the starting point of the user’s relevance judgment of scientific data, affecting other levels of relevance judgment (as shown in Figure 2, H1–H4 are all significant at the level of p < 0.001). Second, quality, authority, and accessibility (intermediate variables) judgment are important processes of the user’s relevance judgment of scientific data, which ultimately affect users’ judgment on the usefulness of scientific data (as shown in Figure 2, H5, H7 are significant at the level of p < 0.001, and H6 is significant at the level of p < 0.05). Third, usefulness (result variable) expresses the user’s comprehensive perception of the utility of scientific data in solving problems as the result of user relevance judgment. Based on the scientific data RC use path, user’s behaviour patterns of relevance judgment of scientific data were discussed (see Section 6.2 for specific discussion).
This study identified 5 RC (topicality, accessibility, quality, authority and usefulness) and 18 corresponding clues of scientific data as shown in Table 3. Dozens of RC are used in the user relevance judgment for documents and images (Barry, 1994; Barry & Schamber, 1998; Choi, 2002). It is difficult to consider every RJ in the practice of IR. While the number of RC used by scientific data users is relatively small, and there is a path structure for RC usage as shown in Figure 2. This study made new definitions to fit the context of scientific data research as shown in Table 4, though the concept of these 5 criteria are not proposed for the first time in this study. More importantly, the RC usage path reflects the patterns of scientific data user’s relevance judgment behaviours.
The two-phase study verified that topicality plays a fundamental role and functions as a prerequisite in user relevance judgment on scientific data. Table 3 showed that topicality is the most frequently used criterion with 325 coding nodes. Table 5 illustrated that all 7 RC usage paths take topicality as the starting point and the PLS-SEM also verify that topicality has a positive effect on data quality, authority, usefulness and accessibility (as shown in Figure 2, H1–H4 are significant at the level of p < 0.001).
The prerequisite function of topicality has also been confirmed in user relevance judgement on texts/documents, images, and audio as information carriers (Barry, 1994; Wang and Soergel, 1998; Choi, 2002; Crystal and Greenberg, 2006; Laplante, 2010). However, as shown in Table 3, clues (data attributes) that stimulate scientific data users to make topicality judgment included not only general textual information such as data title, data description, data keywords, but also data time scope information, which are the clues often used to judge the novelty or recency of documents (Xu and Chen, 2006). This difference originates from the essential feature of scientific data, which is expected to be “evidences” rather than “novel viewpoints or new discoveries” from documents.
As shown in Figure 2, H5 and H7 are significant at the level of p < 0.001, and H6 is significant at the level of p < 0.05. The results revealed that scientific data users pay “special” attention to the RC of data quality, authority and accessibility in scientific data relevance judgment. It can be summarised as the pattern of “data reliability judgment as the necessary process”. That is embodied in the following aspects. Firstly, quality and authority represent scientific data user’s judgment on the validity of data as “evidences”, because data without quality and authority are useless in solving practical problems. Secondly, accessibility is used as a conditional criterion for users to judge the “evidences” of data, because users cannot make sufficient judgment without the whole data or adequate information.
The results of the two-phase study verified that usefulness is the target and result variable for scientific data relevance judgment. Scientific data user’s judgment of topicality, quality, authority and accessibility all have positive effects on usefulness judgment (as shown in Figure 2, H3, H5 and H7 are significant at the level of p < 0.001, H6 is significant at the level of p < 0.05, Rus2 = 0.573). The research results verified that scientific data relevance judgment is a typical situational relevance judgment, which takes a pragmatic and measurable perspective and is operated as the utility/usefulness of the information objects to the user’s situational task at hand (Borlund, 2003; Cosjin & Ingwersen, 2000; Xu & Chen, 2006).
The nature of fact-bearing of scientific data leads to the scientific data relevance judgment as a result of users’ judgment of the utility of data as “evidences” to solve practical problems. This is also one of the most essential differences between data and document user. This difference also suggests that it is necessary to develop proprietary scientific data retrieval systems and algorithms.
Before drawing any implications, some limitations should be mentioned. First, this research took academic search as the research situation, the results may not explain the non-academic search situation well. Second, the study took student groups as samples, and the results of the study should be carefully extended to other user groups. Third, as it is the first study that adopts PLS-SEM in RC use structure, this research is a typical exploratory one with the moderate ability of the research model. Therefore, the results should be interpreted with caution. The research will be developed in the future in a dual path: the first direction is high level of generalization, which means the test of more situations and groups of users; the other way is to apply its findings to design and develop an interactive and cognition-friendly retrieval system specific to scientific data.
Except for enlarging the understanding of how human make relevance judgment on scientific data (or in a more general sense, information), the research seeks to upgrade or even trigger off a sort of data-specific and cognition-friendly retrieval technique based on the understanding of the relevance judgment pattern of scientific data users. As to current findings of the research, it can contribute to achieving the ultimate goal at least in the following aspects.
The essential features and attributes of scientific data and the RC employed by users suggest a more cognitive data description schema for informed decision of how to select a dataset. Traditionally, people describe datasets with various metadata schema. Comparing the user relevance judgement, including criteria and its underlying attributes of datasets, as well as the usage pattern of criteria, it is clear that traditional metadata schema cannot provide sufficient information for relevance decision. Given the importance role of representation and description of information to IR, it is imperative for researchers to provide suitable dataset description schema for a cognition-friendly data retrieval system.
The combination use structure of RC reveals the defect of a system-oriented relevance algorithm which can only partially capture topicality. Meanwhile, it also calls for the user-oriented relevance algorithm that comprehensively considers the paths and strength of different RC on relevance judgment. Previous researchers have done some exploratory studies in multi-criteria decision. Xu and Chen (2006) proposed multiple criteria (topicality/reliability/understandability/novelty/scope) use model by using the algorithm of multiple regression. However, Xu and Chen’s model only considered the influence strength of RC but did not consider the path among RC. Célia (2010, 2012) developed a multiple-criteria (aboutness/coverage/appropriateness/reliability) relevance evaluation model that considers the prerequisite role of aboutness by using the algorithm of priority aggregation. Specific to the findings of three RC use patterns in scientific data user’s relevance judgment, it will be a challenge and direction for future research to comprehensively understand these patterns in the form of algorithms.
The usage patterns and their underlying cognitive mechanism throw light on an interactive mechanism of data retrieval systems. The results show that user relevance judgment on scientific data does not just depend on one CR, nor make the final decision at beginning stage. It is an interactive process, in which user make differential levels of relevance judgment before reaching the final decision. However, traditional term-matching technologies, which just partially captures user’s information need in the aspect of topicality rather than considering usage patterns of other RC. It calls for an interactive scientific data retrieval system with more cognition-friendly.
The research carries out a two-phase study to explore how users judge the relevance of scientific datasets. Five RC and three patterns of the RC usage are identified in the context of data retrieval within an academic situation relating to data use. The findings will contribute to deepening the understanding of user relevance judgment, and will give suggestions and instructions for designing a novel interactive, cognition-friendly and hence more effective data-specific retrieval system.
The study was approved by the Logistics Department for Civilian Ethics Committee of Agricultural Information Institute, Chinese Academy of Agricultural Sciences.
All subjects who participated in the experiment were provided with and signed an informed consent form.
All relevant ethical safeguards have been met with regard to subject protection.
This work was supported by grants from National Scientific and Technical Infrastructure Center of China project titled “The Research on Quality Assessment of Scientific Data (2018DDJ1ZZ16)”, and Agricultural Science, Technology Innovation Project of CAAS (Project No.CAAS-ASTIP-2016-AII), and the project of Fundamental Research Funds for CAAS, “The Construction of Platform and Data Mining for Crop Breeding (Y2016XK08)” and “The Establishment of Data Collection Standard and System Development for Agricultural Experimental Stations (Y2019PT27)”.
The authors also acknowledge the data collection support from “National Science & Technology Infrastructure of China” (http://www.escience.net.cn).
The authors have no competing interests to declare.
Jianping Liu contributed to experimental design, data analysis, manuscript writing and revision. Jian Wang and Guomin Zhou contributed to guiding the experimental design and the revision of the manuscript. Mo Wang and Lei Shi contributed to the revision of the manuscript.
Jianping Liu is a research associate at the Agricultural Information Institute, Chinese Academy of Agricultural Sciences. He is a PhD student in information retrieval technology in Chinese Academy of Agricultural Sciences. His research has been published in Library & Information Science Research (2019) and The 3rd International Conference on Computer Science and Application Engineering(2019), and Man-Machine-Environment system engineering: proceedings of the 19th international conference on MMESE(2019).
Jian Wang is a professor in information sciences at the Cognitive Computing Research Laboratory of Agricultural Information Institute, Chinese Academy of Agricultural Sciences. He holds a PhD in geoinformatics from the Chinese Academy of Sciences. His research has been published in Sensor Letters (2010) and Library & Information Science Research (2019).
Guomin Zhou is a chancellor’s professor in information sciences, deputy director of Department of Science and Technology Management, Chinese Academy of Agricultural Sciences. He holds a PhD in information sciences from the Chinese Academy of Sciences. He is the executive editor of the Journal of Agricultural Big Data in China. His research has been published in Intelligent Automation and Soft Computing (2010) and Library & Information Science Research (2019).
Mo Wang is an assistant professor at Cognitive Computing Research Laboratory of Agricultural Information Institute, Chinese Academy of Agricultural Sciences. He holds a PhD in Geographical Information Systems from the Chinese Academy of Sciences.
Lei Shi is a professor in information resources management at the National Science and Technology Infrastructure Center in China.
Anderson, JC and Gerbing, DW. 1988. Structural Equation Modeling in Practice: A Review of the Two-Step Approach. Psychological Bulletin, 103(3): 411–423. DOI: https://doi.org/10.1037/0033-2909.103.3.411
Barry, CL. 1994. User-defined relevance criteria: an exploratory study. Journal of the American Society for Information Science, 45(3): 149–159. DOI: https://doi.org/10.1002/(SICI)1097-4571(199404)45:3<149::AID-ASI5>3.0.CO;2-J
Barry, CL and Schamber, L. 1998. Users’ criteria for relevance evaluation: A cross-situational comparison. Information Processing & Management, 34: 219–236. DOI: https://doi.org/10.1016/S0306-4573(97)00078-2
Borlund, P. 2003. The concept of relevance in IR. Journal of the American Society for Information Science & Technology, 54(10): 913–925. DOI: https://doi.org/10.1002/asi.10286
Cangur, S and Ercan, I. 2015. Comparison of Model Fit Indices Used in Structural Equation Modeling Under Multivariate Normality. Journal of Modern Applied Statistical Methods, 14(1). DOI: https://doi.org/10.22237/jmasm/1430453580
Choi, Y and Rasmussen, EM. 2002. User’s relevance criteria in image retrieval in American history. Information Processing & Management, 38(5): 695–726. DOI: https://doi.org/10.1016/S0306-4573(01)00059-0
Cosijn, E and Ingwersen, P. 2000. Dimensions of relevance. Information Processing & Management, 36(4): 533–550. DOI: https://doi.org/10.1016/S0306-4573(99)00072-2
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. DOI: https://doi.org/10.1002/asi.20436
da Costa Pereira, C, Dragoni, M and Pasi, G. 2010. User Evaluation of Multidimensional Relevance Assessment. IIR 2010 – Proceedings of the First Italian Information Retrieval Workshop, Padua, Italy, January 27–28, 2010.
da Costa Pereira, C, Dragoni, M and Pasi, G. 2012. Multidimensional relevance: prioritized aggregation in a personalized information retrieval setting. Information Processing & Management, 48(2): 340–357. DOI: https://doi.org/10.1016/j.ipm.2011.07.001
Downe-Wamboldt, B. 1992. Content analysis: method, applications, and issues. Issues in Health Care of Women, 13(3): 313–321. DOI: https://doi.org/10.1080/07399339209516006
Fornell, C and Larcker, DF. 1981. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1): 39–50. DOI: https://doi.org/10.1177/002224378101800104
Hair, JF, Ringle, CM and Sarstedt, M. 2011. PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory & Practice, 19(2): 139–152. DOI: https://doi.org/10.2753/MTP1069-6679190202
Harter, SP. 1992. Psychological relevance and information science. Journal of the Association for Information Science & Technology, 43(9): 602–615. DOI: https://doi.org/10.1002/(SICI)1097-4571(199210)43:9<602::AID-ASI3>3.0.CO;2-Q
Henseler, J, Dijkstra, TK, Sarstedt, M, Ringle, CM, Diamantopoulos, A, Straub, D, Ketchen, D, Hair, JF, Hult, GTM and Calantone, RJ. 2014. Common Beliefs and Reality About Partial Least Squares: Comments on Rönkkö & Evermann (2013). Social Science Electronic Publishing, 17(2): 182–209. DOI: https://doi.org/10.1177/1094428114526928
Hu, L and Bentler, PM. 1998. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4): 424–453. DOI: https://doi.org/10.1037/1082-989X.3.4.424
Hu, L and Bentler, PM. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1): 1–55. DOI: https://doi.org/10.1080/10705519909540118
Inskip, C, Macfarlane, A and Rafferty, P. 2010. Creative professional users’ musical relevance criteria. Sage Publications, Inc. DOI: https://doi.org/10.1177/0165551510374006
Laplante, A. 2010. Users’ Relevance Criteria in Music Retrieval in Everyday Life: An Exploratory Study. In: International Society for Music Information Retrieval Conference, Ismir 2010, Utrecht, Netherlands, August, 2010, 601–606.
Latan, H and Ramli, NA. 2013. The Results of Partial Least Squares-Structural Equation Modelling Analyses (PLS-SEM). Social Science Electronic Publishing. DOI: https://doi.org/10.2139/ssrn.2364191
Lew, YK and Sinkovics, RR. 2013. Crossing borders and industry sectors: behavioral governance in strategic alliances and product innovation for competitive advantage. Long Range Planning, 46(s1–2): 13–38. DOI: https://doi.org/10.1016/j.lrp.2012.09.006
Liu, JP, Wang, J and Zhou, GM. 2019. Understanding relevance judgment in the view of perceived value. Library & Information Science Research, 41(4). DOI: https://doi.org/10.1016/j.lisr.2019.100982
Markkula, M and Sormunen, E. 2000. End-User Searching Challenges Indexing Practices in the Digital Newspaper Photo Archive. Information Retrieval, 1(4): 259–285. DOI: https://doi.org/10.1023/A:1009995816485
Nevitt, J and Hancock, GR. 2001. Performance of Bootstrapping Approaches to Model Test Statistics and Parameter Standard Error Estimation in Structural Equation Modeling. Structural Equation Modeling A Multidisciplinary Journal, 8(3): 353–377. DOI: https://doi.org/10.1207/S15328007SEM0803_2
Perreault, WD and Leigh, LE. 1989. Reliability of nominal data based on qualitative judgments. Journal of Marketing Research, 26(2): 135–148. DOI: https://doi.org/10.1177/002224378902600201
Ralf, W and Siegfried, PG. 2015. The impact of dynamic capabilities on operational marketing and technological capabilities: investigating the role of environmental turbulence. Journal of the Academy of Marketing Science, 43(2): 181–199. DOI: https://doi.org/10.1007/s11747-014-0380-y
Ringle, CM, Wende, S and Becker, JM. 2015. Smart PLS 3. Bönningstedt: SmartPLS. Retrieved from http://www.smartpls.com.
Sanderson, M and Croft, WB. 2012. The History of Information Retrieval Research. Proceedings of the IEEE, 100(6): 1444–1451. DOI: https://doi.org/10.1109/JPROC.2012.2189916
Saracevic, T. 1975. RELEVANCE: A Review of and a Framework for the Thinking on the Notion in Information Science. Journal of the Association for Information Science & Technology, 26: 1915–1933. DOI: https://doi.org/10.1002/asi.20682
Saracevic, T. 2016. The Notion of Relevance in Information Science: Everybody knows what relevance is. But, what is it really? Synthesis Lectures on Information Concepts Retrieval & Services, 8(3): 1–109. DOI: https://doi.org/10.2200/S00723ED1V01Y201607ICR050
Savolainen, R and Kari, J. 2013. User-defined relevance criteria in web searching. Journal of Documentation, 62(6): 685–707. DOI: https://doi.org/10.1108/00220410610714921
Taylor, RS. 1968. Question-negotiation and information seeking in libraries. College & Research Libraries, 29. DOI: https://doi.org/10.5860/crl_29_03_178
Tombros, A, Ruthven, I and Jose, JM. 2003. Searchers’ criteria for assessing web pages. In: International ACM SIGIR Conference on Research and Development in Informaion Retrieval, Toronto, Canada, 28 July–01 Aug 2003. 385–386.
Wang, PL and Soergel, D. 1998. A cognitive model of document use during a research project. Study I. Document selection. Journal of the American Society for Information Science, 49(2): 115–133. DOI: https://doi.org/10.1002/(SICI)1097-4571(199802)49:2<115::AID-ASI3>3.0.CO;2-T
Xu, Y and Chen, Z. 2006. Relevance judgment: What do information users consider beyond topicality? Journal of the American Society for Information Science & Technology, 57(7): 961–973. DOI: https://doi.org/10.1002/asi.20361