اندازه گیری داده ها در سیستم های اطلاعاتی پژوهشی
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اندازه گیری داده ها در سیستم های اطلاعاتی پژوهشی

عنوان فارسی مقاله: اندازه گیری داده ها در سیستم های اطلاعاتی پژوهشی: معیارهای ارزیابی کیفیت داده ها
عنوان انگلیسی مقاله: Data measurement in research information systems: metrics for the evaluation of data quality
مجله/کنفرانس: علم سنجی – Scientometrics
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات، مدیریت
گرایش های تحصیلی مرتبط: مدیریت سیستم های اطلاعاتی، مدیریت فناوری اطلاعات
کلمات کلیدی فارسی: سیستم های اطلاعاتی پژوهشی معاصر (CRIS)، سیستم های اطلاعاتی پژوهشی (RIS)، اطلاعات پژوهشی، کیفیت داده، ابعاد کیفیت داده، اندازه گیری داده ها، نظارت بر داده ها، سیستم علمی، استاندارد سازی
کلمات کلیدی انگلیسی: Current research information systems (CRIS), Research information systems (RIS), Research information, Data quality, Data quality dimensions, Data measurement, Data monitoring, Science system, Standardization
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1007/s11192-018-2735-5
دانشگاه: German Center for Higher Education Research and Science Studies – Germany
ناشر: اسپرینگر - Springer
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 3.246 در سال 2018
شاخص H_index: 95 در سال 2019
شاخص SJR: 1.113 در سال 2018
شناسه ISSN: 1588-2861
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: pdf
تعداد صفحات مقاله انگلیسی: 20
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E9166
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Research information system (RIS)


3- Data quality and data quality dimensions


4- Measurement of data quality of the RIS


5- Measurement of completeness


6- Measurement of timeliness


7- Measurement of correctness


8- Measurement of consistency


9- Discussion


10- Conclusion and outlook


References

نمونه متن انگلیسی مقاله

Abstract


In recent years, research information systems (RIS) have become an integral part of the university’s IT landscape. At the same time, many universities and research institutions are still working on the implementation of such information systems. Research information systems support institutions in the measurement, documentation, evaluation and communication of research activities. Implementing such integrative systems requires that institutions assure the quality of the information on research activities entered into them. Since many information and data sources are interwoven, these different data sources can have a negative impact on data quality in different research information systems. Because the topic is currently of interest to many institutions, the aim of the present paper is firstly to consider how data quality can be investigated in the context of RIS, and then to explain how various dimensions of data quality described in the literature can be measured in research information systems. Finally, a framework as a process flow according to UML activity diagram notation is developed for monitoring and improvement of the quality of these data; this framework can be implemented by technical personnel in universities and research institutions.


Introduction


The topic of ‘‘research databases and research information systems’’ is by no means new. In recent years, the introduction of research information systems at universities and research institutions has strongly increased in Germany and throughout Europe (DINI AG Research Information Systems 2015). Research information systems can provide universities and research institutions with a current overview of their research activities, collect information on their scientific activities, projects and publications and manage and integrate into their website. For researchers, they offer opportunities to collect, categorize and make use of research information, be that for publication lists, for the development of new projects, to reduce the effort required to produce reports, or in the external presentation of their research and scientific expertise. Data quality plays an important role in the usability and interpretation of institutionspecific data. The quality of data is however also a significant consideration for external data sources. University administrations and researchers have since the early 1990s begun to recognize the importance of quality of data that are electronically stored databases. A few years ago, almost all German universities and research institutes were interested in the topic of quality of data in their RIS—a development that has been since further progressed. The growing quantity of data and the increasing number of source systems are becoming serious problems for institutions

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