The Experts below are selected from a list of 3592227 Experts worldwide ranked by ideXlab platform
Otmane Azeroual - One of the best experts on this subject based on the ideXlab platform.
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Influence of Information Quality via Implemented German RCD Standard in Research Information Systems
Data, 2020Co-Authors: Otmane Azeroual, Joachim Schöpfel, Dragan IvanovicAbstract:With the steady increase in the number of data sources to be stored and processed by higher education and Research institutions, it has become necessary to develop Research Information Systems, which will store this Research Information in the long term and make it accessible for further use, such as reporting and evaluation processes, institutional decision making and the presentation of Research performance. In order to retain control while integrating Research Information from heterogeneous internal and external data sources and disparate interfaces into RIS and to maximize the benefits of the Research Information, ensuring data quality in RIS is critical. To facilitate a common understanding of the Research Information collected and to harmonize data collection processes, various standardization initiatives have emerged in recent decades. These standards support the use of Research Information in RIS and enable compatibility and interoperability between different Information systems. This paper examines the process of securing data quality in RIS and the impact of Research Information standards on data quality in RIS. We focus on the recently developed German Research Core Dataset standard as a case of application.
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A Text and Data Analytics Approach to Enrich the Quality of Unstructured Research Information
Computer and Information Science, 2019Co-Authors: Otmane AzeroualAbstract:With the increased accessibility of Research Information, the demands on Research Information systems (RIS) that are expected to automatically generate and process knowledge are increasing. Furthermore, the quality of the RIS data entries of the individual sources of Information causes problems. If the data is structured in RIS, users can read and filter out their Information and knowledge needs without any problems. This technique, which nevertheless allows text databases and text sources to be analyzed and knowledge extracted from unknown texts, is referred to as text mining or text data mining based on the principles of data mining. Text mining allows automatically classifying large heterogeneous sources of Research Information and assigning them to specific topics. Research Information has always played a major role in higher education and academic institutions, although they were usually available in unstructured form in RIS and grow faster than structured data. This can be a waste of time searching for RIS staff in universities and can lead to bad decision-making. For this reason, the present paper proposes a new approach to obtaining structured Research Information from heterogeneous Information systems. It is a subset of an approach to the semantic integration of unstructured data using the example of a RIS. The purpose of this paper is to investigate text and data mining methods in the context of RIS and to develop an improvement quality model as an aid to RIS using universities and academic institutions to enrich unstructured Research Information.
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Quality of Research Information in RIS Databases: A Multidimensional Approach
2019Co-Authors: Otmane Azeroual, Mohammad Abuosba, Gunter Saake, Joachim SchöpfelAbstract:For the permanent establishment and use of a RIS in universities and academic institutions, it is absolutely necessary to ensure the quality of the Research Information, so that the stakeholders of the science system can make an adequate and reliable basis for decision-making. However, to assess and improve data quality in RIS, it must be possible to measure them and effectively distinguish between valid and invalid Research Information. Because Research Information is very diverse and occurs in a variety of formats and contexts, it is often difficult to define what data quality is. In the context of this present paper, the data quality of RIS or rather their influence on user acceptance will be examined as well as objective quality dimensions (correctness, completeness, consistency and timeliness) to identify possible data quality deficits in RIS. Based on a quantitative survey of RIS users, a reliable and valid framework for the four relevant quality dimensions will be developed in the context of RIS to allow for the enhancement of Research Information driven decision support.
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BIS (1) - Quality of Research Information in RIS Databases: A Multidimensional Approach
Business Information Systems, 2019Co-Authors: Otmane Azeroual, Mohammad Abuosba, Gunter Saake, Joachim SchöpfelAbstract:For the permanent establishment and use of a RIS in universities and academic institutions, it is absolutely necessary to ensure the quality of the Research Information, so that the stakeholders of the science system can make an adequate and reliable basis for decision-making. However, to assess and improve data quality in RIS, it must be possible to measure them and effectively distinguish between valid and invalid Research Information. Because Research Information is very diverse and occurs in a variety of formats and contexts, it is often difficult to define what data quality is. In the context of this present paper, the data quality of RIS or rather their influence on user acceptance will be examined as well as objective quality dimensions (correctness, completeness, consistency and timeliness) to identify possible data quality deficits in RIS. Based on a quantitative survey of RIS users, a reliable and valid framework for the four relevant quality dimensions will be developed in the context of RIS to allow for the enhancement of Research Information driven decision support.
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Data measurement in Research Information systems: metrics for the evaluation of data quality
Scientometrics, 2018Co-Authors: Otmane Azeroual, Gunter Saake, Jürgen WastlAbstract: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.
Jennifer Ransom - One of the best experts on this subject based on the ideXlab platform.
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LibGuides: Research & Information Literacy (RIL Hybrid) - Fall 2015: CANVAS
2015Co-Authors: Jennifer RansomAbstract:ENG216A OHB Research & Information Literacy (RIL Hybrid) - Fall 2015 CNR's new Learning Management System
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LibGuides: Research & Information Literacy (RIL Hybrid) - Fall 2015: Glossary
2015Co-Authors: Jennifer RansomAbstract:ENG216A OHB Research & Information Literacy (RIL Hybrid) - Fall 2015 An alphabetical list of terms used in this course.
Jürgen Wastl - One of the best experts on this subject based on the ideXlab platform.
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Data measurement in Research Information systems: metrics for the evaluation of data quality
Scientometrics, 2018Co-Authors: Otmane Azeroual, Gunter Saake, Jürgen WastlAbstract: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.
Gunter Saake - One of the best experts on this subject based on the ideXlab platform.
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BIS (1) - Quality of Research Information in RIS Databases: A Multidimensional Approach
Business Information Systems, 2019Co-Authors: Otmane Azeroual, Mohammad Abuosba, Gunter Saake, Joachim SchöpfelAbstract:For the permanent establishment and use of a RIS in universities and academic institutions, it is absolutely necessary to ensure the quality of the Research Information, so that the stakeholders of the science system can make an adequate and reliable basis for decision-making. However, to assess and improve data quality in RIS, it must be possible to measure them and effectively distinguish between valid and invalid Research Information. Because Research Information is very diverse and occurs in a variety of formats and contexts, it is often difficult to define what data quality is. In the context of this present paper, the data quality of RIS or rather their influence on user acceptance will be examined as well as objective quality dimensions (correctness, completeness, consistency and timeliness) to identify possible data quality deficits in RIS. Based on a quantitative survey of RIS users, a reliable and valid framework for the four relevant quality dimensions will be developed in the context of RIS to allow for the enhancement of Research Information driven decision support.
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Quality of Research Information in RIS Databases: A Multidimensional Approach
2019Co-Authors: Otmane Azeroual, Mohammad Abuosba, Gunter Saake, Joachim SchöpfelAbstract:For the permanent establishment and use of a RIS in universities and academic institutions, it is absolutely necessary to ensure the quality of the Research Information, so that the stakeholders of the science system can make an adequate and reliable basis for decision-making. However, to assess and improve data quality in RIS, it must be possible to measure them and effectively distinguish between valid and invalid Research Information. Because Research Information is very diverse and occurs in a variety of formats and contexts, it is often difficult to define what data quality is. In the context of this present paper, the data quality of RIS or rather their influence on user acceptance will be examined as well as objective quality dimensions (correctness, completeness, consistency and timeliness) to identify possible data quality deficits in RIS. Based on a quantitative survey of RIS users, a reliable and valid framework for the four relevant quality dimensions will be developed in the context of RIS to allow for the enhancement of Research Information driven decision support.
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Data measurement in Research Information systems: metrics for the evaluation of data quality
Scientometrics, 2018Co-Authors: Otmane Azeroual, Gunter Saake, Jürgen WastlAbstract: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.
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Data Quality Measures and Data Cleansing for Research Information Systems
Journal of Digital Information Management, 2018Co-Authors: Otmane Azeroual, Gunter Saake, Mohammad AbuosbaAbstract: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.
Pablo De Castro - One of the best experts on this subject based on the ideXlab platform.
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The Rise of Current Research Information Systems (CRIS): The Case of the Indian Research Information Network System (IRINS)
2020Co-Authors: Pablo De Castro, Siva Shankar Kimidi, Kannan PalavesmAbstract:The paper describes the rapid arising of a national-level Research Information management infrastructure (RIM) in India as a case study for a bottom-up Current Research Information System (CRIS) implementation strategy. Less than a year and a half after its first launch, the Indian Research Information Network System (IRINS) has become a widespread institutional RIM asset with over 180 instances at Indian Research-performing organisations. As a result, India is currently leading the classification by number of CRIS per country in the euroCRIS Directory of Research Information Systems (DRIS), followed by Norway and the United Kingdom.As a background to the case study, the broad international CRIS context is also analysed. The causes for the quick rise of such systems are examined, together with their national-level implementation models in various countries and the differences between CRIS and expert finder systems.
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The Role of Research Information Management in Capacity Building
2019Co-Authors: Pablo De CastroAbstract:34 slides.-- Guest talk delivered at the full-day Aug 20th, 2019 workshop for the Indian Research Information Network System (IRINS) at the INFLIBNET Centre in Gandhinagar.
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Practices and patterns in Research Information management : findings from a global survey
2018Co-Authors: Rebecca Bryant, Pablo De Castro, Anna Clements, Joanne Cantrell, Annette Dortmund, Jan Fransen, Peggy Gallagher, Michele MennielliAbstract:Practices and Patterns in Research Information Management: Findings from a Global Survey represents an effort to better understand how Research institutions are applying Research Information management (RIM) practices. This survey was conducted as part of a strategic partnership between OCLC Research and euroCRIS, and contributes to shared goals to collect quantitative and qualitative data about Research Information management practices worldwide, to build upon previous Research by both organizations, and to provide a baseline of observations for future Research. A web-based survey was administered from 25 October 2017 through 8 February 2018 and yielded 381 survey responses from 44 countries, demonstrating the global nature of Research Information management activities. This survey employed a convenience sample and the subsequent report is intended to be exploratory and descriptive in nature. A working group comprised of subject matter experts in RIM practices representing both OCLC Research and euroCRIS worked collaboratively to synthesize the data and to write this report. Research Information management practices are complex, and institutions frequently report using several systems to support Research Information workflows that increasingly demand greater interoperability—with both internal and external systems. Increasingly consolidated commercial and open-source platforms are becoming widely implemented across regions, coexisting with a large number of region-specific solutions as well as locally developed systems. Interoperability is regularly considered a key feature valued or desired in a RIM system, something expected to improve in future systems or configurations, and the use of identifiers, standards, and protocols are perceived as most valuable when they can also facilitate interoperability. The growing need for improved interoperability between managing open access workflows and the curation of institutional Research outputs metadata is giving rise to the increasing functional merging of RIM systems and institutional repositories. This change is being driven in some locales by regional, national, and funder requests to make publicly sponsored Research findings openly available—and for institutions to track their progress toward open access goals. Complex, cross-stakeholder teams are necessary for providing the best possible Research support services. Research offices remain leading stakeholders in RIM practices, and the library is also shown to have significant responsibilities, particularly related to support for open access, metadata validation, training, and Research data management. Libraries are particularly involved in cases where RIM practices intersect with library responsibility for one or more scholarly communications repositories, reinforcing the increasing overlap of practice and workflows between previously siloed RIM systems and repository systems. This report frequently emphasizes the analysis of regional differences in order to provide insights on variations in practices and their level of consolidation. By examining Research Information practices from a global perspective, we are better able to understand the importance and breadth of national Research assessment frameworks and open science policies as a key driver strongly shaping priorities of RIM activities in those countries and regions where they exist. In addition, we can also observe an emerging set of additional objectives—such as the desire to improve services for Researchers or the need to support institutional reputation and decision-making—that institutions operating in less demanding policy environments see as key incentives for their own RIM strategies. OCLC Research and euroCRIS plan to repeat this survey in the future, developing longitudinal data and knowledge about evolving RIM practices in order to help inform the global Research community.
