An empirical study on the performance evaluation of scientific data sharing platforms in China

Si, Li; Li, Yueting; Zhuang, Xiaozhe; Xing, Wenming; Hua, Xiaoqin; Li, Xin; Juan-juan, Xin

doi:10.1108/lht-09-2014-0093

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…Tenopir et al (2015) examined change over time of data sharing and reuse practices, and perceptions of organizational support for data sharing and reuse. Si et al (2015) evaluated scientific data sharing platforms for performance evaluations, focusing on operation management, data resource, platform function, and efficiency; however, these evaluations were not conducted by scientists testing the system. Fecher et al (2015) developed a conceptual framework of data sharing from the researcher's perspective.…”

Section: Literature Reviewmentioning

confidence: 99%

Data matters: how earth and environmental scientists determine data relevance and reusability

Murillo

2019

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Purpose The purpose of this study is to examine the information needs of earth and environmental scientists regarding how they determine data reusability and relevance. Additionally, this study provides strategies for the development of data collections and recommendations for data management and curation for information professionals working alongside researchers. Design/methodology/approach This study uses a multi-phase mixed-method approach. The test environment is the DataONE data repository. Phase 1 includes a qualitative and quantitative content analysis of deposited data. Phase 2 consists of a quasi-experiment think-aloud study. This paper reports mainly on Phase 2. Findings This study identifies earth and environmental scientists’ information needs to determine data reusability. The findings include a need for information regarding research methods, instruments and data descriptions when determining data reusability, as well as a restructuring of data abstracts. Additional findings include reorganizing of the data record layout and data citation information. Research limitations/implications While this study was limited to earth and environmental science data, the findings provide feedback for scientists in other disciplines, as earth and environmental science is a highly interdisciplinary scientific domain that pulls from many disciplines, including biology, ecology and geology, and additionally there has been a significant increase in interdisciplinary research in many scientific fields. Practical implications The practical implications include concrete feedback to data librarians, data curators and repository managers, as well as other information professionals as to the information needs of scientists reusing data. The suggestions could be implemented to improve consultative practices when working alongside scientists regarding data deposition and data creation. These suggestions could improve policies for data repositories through direct feedback from scientists. These suggestions could be implemented to improve how data repositories are created and what should be considered mandatory information and secondary information to improve the reusability of data. Social implications By examining the information needs of earth and environmental scientists reusing data, this study provides feedback that could change current practices in data deposition, which ultimately could improve the potentiality of data reuse. Originality/value While there has been research conducted on data sharing and reuse, this study provides more detailed granularity regarding what information is needed to determine reusability. This study sets itself apart by not focusing on social motivators and demotivators, but by focusing on information provided in a data record.

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Section: Literature Reviewmentioning

confidence: 99%

Data matters: how earth and environmental scientists determine data relevance and reusability

Murillo

2019

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“…Most of the literature in the first phase concerns practical cases of research data sharing. It includes four aspects: research data sharing platform construction, heterogeneous data resource integration and access (Liu and Zhu, 2007), the exploration of the influencing factors of sharing platform construction (Huang et al, 2008;Si et al, 2014), and the strategy of promoting research data sharing (Huang et al, 2014). The second stage is that scholars began to pay attention to the management and service of research data.…”

Section: Discussionmentioning

confidence: 99%

“…It includes four aspects: research data sharing platform construction, heterogeneous data resource integration and access (Liu and Zhu, 2007), the exploration of the influencing factors of sharing platform construction (Huang et al. , 2008; Si et al. , 2014), and the strategy of promoting research data sharing (Huang et al.…”

Section: Discussionmentioning

confidence: 99%

Knowledge mapping of research data in China: a bibliometric study using visual analysis

Yan

et al. 2022

LHT

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PurposeThis study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.Design/methodology/approachThe authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.FindingsThe research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.Originality/valuePrevious literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

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“…With the close participation of government departments, it is a task oriented and systematic way to effectively organize the upstream and downstream advantageous enterprises, scientific research institutions and universities in the industrial chain to tackle key problems. To solidate the alliance, it needs to construct resource foundation of sharing service of platform to boost the data exchange and sharing, and accelerate the management of resource interconnection[ [27]]. Meanwhile, on the regional level, cooperation mechanism can be established to promote the innovation factors flow between regions, and strengthen the connection between government, industry and universities.…”

Section: Practical Implicationsmentioning

confidence: 99%

Research on Evaluation of Scientific and Technological Innovation Efficiency in Chinese Universities-An Analysis Based on Sample Data of 31 Provinces

Chen

Long

et al. 2023

IEEE Access

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2011~2020 is a golden decade of innovation-driven development for China. During this period, scientific and technological innovation (STI) of Chinese universities has achieved historic development. This paper uses DEA-BCC model and Malmquist index to calculate the STI efficiency of Chinese universities in 31 provinces and municipalities from 2011 to 2020. The findings illustrate that: (1) the barrier effect of pure technical efficiency is greater than that of scale efficiency; (2)although the STI efficiency of Chinese universities increased by 0.6% on average, the contribution rate of technological progress growth was high, and the scale efficiency showed negative growth; (3) in terms of regional distribution, DEA efficient provinces and cities are concentrated in northern China, while non DEA efficient provinces and cities are mostly concentrated in southern China. Based on the findings, countermeasures and suggestions are made to improve the STI efficiency for Chinese universities from the aspects of scientific innovation alliance, evaluation mechanism and innovation management ability. It provides a theoretical and practical reference for innovation-driven development of Chinese universities. INDEX TERMSChinese universities; Scientific and technological innovation efficiency; DEA-BCC model; Mamlquist index

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An empirical study on the performance evaluation of scientific data sharing platforms in China

Cited by 4 publications

References 2 publications

Data matters: how earth and environmental scientists determine data relevance and reusability

Data matters: how earth and environmental scientists determine data relevance and reusability

Knowledge mapping of research data in China: a bibliometric study using visual analysis

Research on Evaluation of Scientific and Technological Innovation Efficiency in Chinese Universities-An Analysis Based on Sample Data of 31 Provinces

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