Please use this identifier to cite or link to this item: http://hdl.handle.net/11366/632
Title: Data Quality Measures and Data Cleansing for Research Information Systems
Authors: Azeroual, Otmane 
Saake, Gunter 
Abuosba, Mohammad 
Keywords: research information systems
CRIS
RIS
data quality
research information
measures
data cleansing
science system
standardization
Issue Date: 1-Feb-2018
Publisher: Digital Information Research Foundation
Source: O Azeroual, G Saake, M Abuosba (2018), "Data Quality Measures and Data Cleansing for Research Information Systems". Journal of Digital Information Management 16(1): 12-21
Journal: Journal of Digital Information Management
Abstract: The collection, transfer and integration of research information into different research information systems can result in different data errors that can have a variety of negative effects on data quality. In order to detect errors at an early stage and treat them efficiently, it is necessary to determine the clean-up measures and the new techniques of data cleansing for quality improvement in research institutions. Thereby an adequate and reliable basis for decision-making using an RIS is provided, and confidence in a given dataset increased.
In this paper, possible measures and the new techniques of data cleansing for improving and increasing the data quality in research information systems will be presented and how these are to be applied to the research information.
Description: 10 pages.-- Published in the Journal of Digital Information Management, Vol 16 Iss 1 (Feb 2018)
URI: http://hdl.handle.net/11366/632
Appears in Collections:Outreach: Papers

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