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Extending the value of a CRIS with Research Data Management
Abstract
In this paper we aim to analyse the adoption of Current Research Information Systems (CRIS) for Research Data Management (RDM). We show how CRISs hold a key role in facilitating the management and reporting of an
institution's research activities and outputs – not only do they offer extensive functionality for researchers and research administrators to effectively manage all aspects of their research information, but are also integrating more and more with specialized RDM tools, Institutional Repositories (IR), and other external systems.
This paper provides an overview of how CRISs have evolved and integrated to become a crucial part of the RDM chain, including the interoperability, registration, linking, and archiving of data.
... Masenya (2021) goes on to say that academic libraries are struggling to manage their research data due to a lack of established policies and standards, inadequate standardised storage infrastructure, time constraints on organising data, limited funding, insufficient resources, a lack of skills and training in managing research data, and a lack of incentives for researchers to share their data. Farinelli and Zigoni (2022), in their analysis of the adoption of current research information systems for RDM, found that current research information systems play a key role in facilitating the management and reporting of an institution's research activities and outputs. Not only do they offer extensive functionality for researchers and research administrators to manage all aspects of their research information effectively, but they are also integrating more and more with specialised RDM tools, institutional repositories and other external systems. ...
This article investigates the knowledge of research data management and services among library professionals in selected East African libraries. A survey research design was employed, and data was collected using a structured questionnaire from 180 respondents representing four East African countries: Malawi, Mozambique, Zambia and Zimbabwe. The findings reveal that only 31.1% of the selected East African librarians agreed that their institutional libraries provided research data management services. The standard research data management services offered by their libraries included data publishing, sharing and reuse, while collaboration with academic programmes was identified as an essential approach for research data management skill development. The study highlights the need for librarians to acquire legal, policy and advisory skills and knowledge of institutional and extra-institutional resources and the research life cycle for effective research data management service delivery.
... Another case study from The Netherlands confirms the role of the data quality and standardisation provided by CRIS for the interoperability of research data management, by the adoption of common standards for data exchange such as CERIF and Dublin Core for publications, or OpenAIRE for datasets [20]. ...
While the topics of CRIS and FAIR are not new, after decades of research on CRIS, CRIS and their FAIRness remain a relatively overlooked dimension of CRIS. To address this problem, we conduct a systematic literature review (SLR) that connects the fragmented knowledge accumulated through the observation of CRIS development/maturing dynamics and attempts to make them or their elements FAIR. Based on the SLR we assemble the overarching framework that expands the theoretical foundations of CRIS and their FAIRness based on the existing practices.
... Despite the opportunities it can provide, we find that it is rare and unaddressed in research management and especially in the empirical CRIS literature. Therefore, our future work will focus on what opportunities and challenges exist for institutions with the implementation of predictive analytics, especially regarding the optimization of decision-making in IT-enabled research management (such as CRIS, which is partly compliant with the call made by [17]). ...
The volume of data in companies and in the private sector is already gigantic today. Mobile devices such as smartphones constantly collect data on all possible environmental conditions. When surfing the Internet, everyone leaves an endless digital trail. The Internet of Things (IoT) promises comprehensive networking of all everyday devices and production tools that surround people. Nevertheless, the modern knowledge society has to face the question of whether we are really actively using all the data or whether useful knowledge has increased as a result. Answering this question is not trivial. It is true that today's opportunities for exploring data, for transforming data into information and thus for gaining knowledge from it, are greater than ever. But it is also true that this new knowledge, which consists of the hidden connections in data, does not appear in our mind's eye on its own. We must explore it to bring it to the surface which is related to recognizing patterns in the world of data. In the last step, these patterns must be correctly interpreted. Predictive analytics (PA) is going exactly in this direction. They are currently one of the most important application areas of big data and are seen as the most actionable form of business intelligence (BI). Predictive analytics can be used for a variety of purposes, from predicting customer behaviour in sales and marketing to determining risk profiles for financing. Another widely known application is credit reporting, used by financial institutions to determine the likelihood that customers will repay future loans on time. It can also be used when working with big data in predicting user behaviour and opinion. In this regard, the purpose of this paper is to develop a predictive analytics-driven decision framework based on machine learning and data mining methods and techniques. To test it, we conducted an experiment for predicting sentiments and emotions in social media posts, as well as discussed topics and extracted keywords.
Collecting, integrating, storing and analyzing data in a database system is nothing new in itself. To introduce a current research information system (CRIS) means that scientific institutions must provide the required information on their research activities and research results at a high quality. A one-time cleanup is not sufficient; data must be continuously curated and maintained. Some data errors (such as missing values, spelling errors, inaccurate data, incorrect formatting, inconsistencies, etc.) can be traced across different data sources and are difficult to find. Small mistakes can make data unusable, and corrupted data can have serious consequences. The sooner quality issues are identified and remedied, the better. For this reason, new techniques and methods of data cleansing and data monitoring are required to ensure data quality and its measurability in the long term. This paper examines data quality issues in current research information systems and introduces new techniques and methods of data cleansing and data monitoring with which organizations can guarantee the quality of their data.
The exponential growth of data in digital environments has highlighted the emergence of developing analytics processes for data visualization and evaluation. The same happens at the university research level. Therefore, universities need to have specific analytical processes for research evaluation.
The aim of our study is to find the way to apply general data analytical methods and technologies, specifically for university research environments, to help improve the results of their research.
We have found that universities that maintain a Current Research Information System, CRIS (CERIF Compliant), containing high quality data, are able to implement these analytical processes and improve their results in the assessment and evaluation of their research data.
This paper explains the process to implement such methodologies and techniques. Some results are also explained.
In 2015, Radboud University (Nijmegen, the Netherlands) started a project to extend its CRIS (Metis) with functionalities that allow researchers to register (metadata) and archive (uploading files) their research data, while at the same time making the data available for reuse in a FAIR way (via national Dutch data archive DANS). The new functionality was integrated with already existing functions in the CRIS, thus offering a one-stop-shop interface to researchers in which registration and archiving of data is combined with registration of publications, the uploading of full text to the university’s repository, the linking of datasets and publications and the creation of researcher’s profile (CV) pages. Next to the functional extension of the CRIS, the project also included an organizational element: the establishment of support and management structures and workflows, including data curation processes, in order to assure the quality of the data registration process and to foster the FAIRness of the research data.
In the period up to now, we continued to transform the university’s CRIS, by bringing it in line with the research life cycle perspective and policy changes in Research Data Management (RDM), including a Data Management Plan (DMP) module and FAIR data.
In this paper, it will be argued and demonstrated that both for researchers and research institutes, a CRIS oriented approach to RDM brings added value. We also point to future use cases that put a central role for CRIS’s even earlier within the research life cycle, e.g. at pre-registration of research questions and informed consent/ethics approval procedures. We further envision our CRIS to play a linking pin function between storage and service locations of data during research and at publication. The paper will use Radboud University as a good practice of past, present and future use of CRIS’s in the research life cycle that universities and research institutes as well as researchers and research support desks are currently dealing with in the FAIR data era.
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
OpenAIRE Guidelines for CRIS Managers 1
- J Dvořák
- A Bollini
- L Rémy
- J Schirrwagen