<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Today, many efforts have been made to implement information systems for supporting research evaluation activities. To produce a good framework for research evaluation, the selection of appropriate measures is important. Quality aspects of the systems’ implementation should also not be overlooked. Incomplete or faulty data should not be used and metric computation formulas should be discussed and valid. Correctly integrated data from different information sources provide a complete picture of the scientific activity of an institution. Knowledge from the data integration field can be adapted in research information management. In this paper, we propose a research information system for bibliometric indicator analysis that is incorporated into the adaptive integration architecture based on ideas from the data warehousing framework for change support. A data model of the integrated dataset is also presented. This paper also provides a change management solution as a part of the data integration framework to keep the data integration process up to date. This framework is applied for the implementation of a publication data integration system for excellence-based research analysis at the University of Latvia.</p></abstract>

Today, many efforts have been made to implement information systems for supporting research evaluation activities. To produce a good framework for research evaluation, the selection of appropriate measures is important. Quality aspects of the systems’ implementation should also not be overlooked. Incomplete or faulty data should not be used and metric computation formulas should be discussed and valid. Correctly integrated data from different information sources provide a complete picture of the scientific activity of an institution. Knowledge from the data integration field can be adapted in research information management. In this paper, we propose a research information system for bibliometric indicator analysis that is incorporated into the adaptive integration architecture based on ideas from the data warehousing framework for change support. A data model of the integrated dataset is also presented. This paper also provides a change management solution as a part of the data integration framework to keep the data integration process up to date. This framework is applied for the implementation of a publication data integration system for excellence-based research analysis at the University of Latvia.

Architecture Enabling Adaptation of Data Integration Processes for a Research Information System

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Today, many efforts have been made to implement information systems for supporting research evaluation activities. To produce a good framework for research...

Architecture Enabling Adaptation of Data Integration Processes for a Research Information System

Online veröffentlicht: 22. Juni 2018

Seitenbereich: 129 - 149

Eingereicht: 11. Jan. 2018

Akzeptiert: 21. März 2018

DOI: https://doi.org/10.1515/fcds-2018-0008

Schlüsselwörter
integration architecture, adaptation, research evaluation, research metrics, data quality, data model

© 2018 Darja Solodovnikova, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Architecture Enabling Adaptation of Data Integration Processes for a Research Information System

Online veröffentlicht: 22. Juni 2018

Seitenbereich: 129 - 149

Eingereicht: 11. Jan. 2018

Akzeptiert: 21. März 2018

DOI: https://doi.org/10.1515/fcds-2018-0008

Schlüsselwörterintegration architecture, adaptation, research evaluation, research metrics, data quality, data model

© 2018 Darja Solodovnikova, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
integration architecture, adaptation, research evaluation, research metrics, data quality, data model