Current Trends in Research Data Management

Redkina, N. S.

doi:10.3103/s0147688219020035

Cited by 18 publications

(7 citation statements)

References 23 publications

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“…Research data management (RDM) can be understood as the organisation, storage and sharing of data from the start of a project through to the publication and archiving of research findings (Redkina, 2019). RDM is an evolving discourse in the research context and has significant implications for researchers (e.g.…”

Section: Introductionmentioning

confidence: 99%

An initial evaluation of research data management online training at the University of New South Wales

Chew

Wong

et al. 2021

IFLA Journal

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In response to low research data management engagement at the University of New South Wales, Australia, an introductory research data management online training was developed and rolled out to all newly enrolled Higher Degree Research candidates. This article outlines the development process of the research data management online training and provides an initial evaluation of the training from the perspectives of the candidates and the university. As such, this article joins up with existing literature on research data management training to assist institutions and research data management stakeholders with the development of research data management training to help researchers and research students enact research data management best practices. Overall, the majority of the candidates ( n = 643) were satisfied with the quality of the training and found it helpful. The benefits the training brought to the university are mapped out by linking institutional research data management problems, the research data management online training’s design and findings into a coherent narrative.

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Section: Introductionmentioning

confidence: 99%

An initial evaluation of research data management online training at the University of New South Wales

Chew

Wong

et al. 2021

IFLA Journal

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“…In addition to the FAIR criteria, the concept of a Data Management Plan (DMP) has a significant impact on the success of any RDM effort. The DMP is a comprehensive document that details the management of a research project’s data throughout its entire lifecycle [ 17 ]. A standard DMP in fact does not exist, as it must be individually tailored to the requirements of the respective research project.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to the benefits already mentioned, the use of a research data infrastructure facilitates the visibility of scientists’ research as well as identifying new collaboration partners in industry, research, or funding bodies [ 5 , 16 ]. In the meantime, funding bodies in particular have recognized the necessity of effective RDM, making it a prerequisite for the submission of research proposals [ 16 , 17 ].…”

Section: Related Workmentioning

confidence: 99%

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

Lehmann

Schorz

Rache

et al. 2023

Sensors

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One of the main topics within research activities is the management of research data. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally be managed by Findable, Accessible, Interoperable, and Reusable (FAIR) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and digital twin technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management. This paper contributes a novel architectural approach for gathering and managing research data aligned with the FAIR principles. A reference implementation as well as a subsequent proof of concept is given, leveraging the utilization of digital twins to overcome common data management issues at equipment-intense research institutes. To facilitate implementation, a top-level knowledge graph has been developed to convey metadata from research devices along with the produced data. In addition, a reactive digital twin implementation of a specific measurement device was devised to facilitate reconfigurability and minimized design effort.

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“…FAIR Principles (findable, accessible, interoperable, and re-usable) 1 and international initiatives in the field of open science are implemented in the recommendations of the publishing houses of scientific journals on providing access to primary data, on the openness of peer review, and in the requirements of scientific foundations on the openness of data as a prerequisite for obtaining international and national grants. Among these requirements is open access to published materials [ 2 ].…”

Section: Development Of the Ecosystem Of Open Sciencementioning

confidence: 99%