Intelligent Information System for Academic Institutions

2022 ◽  
pp. 788-806
Author(s):  
Mamata Rath
Keyword(s):  
Data Management ◽  
Big Data Analytics ◽  
Research Data ◽  
Research Data Management ◽  
Innovative Work ◽  
Management Techniques ◽  
Scientific Results ◽  
Intelligent Information ◽  
Biomedical Publications ◽  
Work Done

Research and publication is considered an authenticated certificate of innovative work done by researchers in various fields. In research, new scientific results may be assessed, corrected, and further built up by the scientific neighborhood only if they are available in published form. Guidelines on accountable research and publication are currently set to encourage and promote high ethical standards in the conduct of research and in biomedical publications. They address various aspects of the research and publishing including duties of editors and authorship determination. The chapter presents research and publication system using big data analytics and research data management techniques with a background of information systems and need of information in research data management.

Intelligent Information System for Academic Institutions

2019 ◽  
pp. 207-232 ◽  
Author(s):  
Mamata Rath
Keyword(s):  
Data Management ◽  
Big Data Analytics ◽  
Research Data ◽  
Research Data Management ◽  
Innovative Work ◽  
Management Techniques ◽  
Scientific Results ◽  
Intelligent Information ◽  
Biomedical Publications ◽  
Work Done

Research and publication is considered an authenticated certificate of innovative work done by researchers in various fields. In research, new scientific results may be assessed, corrected, and further built up by the scientific neighborhood only if they are available in published form. Guidelines on accountable research and publication are currently set to encourage and promote high ethical standards in the conduct of research and in biomedical publications. They address various aspects of the research and publishing including duties of editors and authorship determination. The chapter presents research and publication system using big data analytics and research data management techniques with a background of information systems and need of information in research data management.

scholarly journals Research Data Management in a Developing Country: A Personal Journey

2015 ◽  
Vol 10 (2) ◽  
pp. 33-47 ◽  
Author(s):  
Martie Van Deventer ◽  
Heila Pienaar
Keyword(s):  
Data Management ◽  
Developing Country ◽  
Research Data ◽  
Important Lesson ◽  
Personal Journey ◽  
Research Data Management ◽  
Conducting Research ◽  
Work Done ◽  
The Way

This paper explores our own journey to get to grips with research data management (RDM). It also mentions the overlap between our own ‘journeys’ and that of the country. We share the lessons that we learnt along the way – the most important lesson being that you can learn many wonderful and valuable RDM lessons from the international trend setters, but in the end you need to get your hands dirty and get the work done yourself. You must, within the set parameters, implement the RDM practice that is both appropriate and acceptable for and to your own set of researchers – who may be conducting research in a context that may be very dissimilar to that of international peers.

scholarly journals The Notion of "Research Data": Types and Kinds of Research Data in the Context of Data Management Practice

2021 ◽  
pp. 128-138
Author(s):  
Svitlana Chukanova
Keyword(s):  
Data Management ◽  
Management Practice ◽  
Rapid Development ◽  
Computer Code ◽  
Open Science ◽  
Research Data ◽  
Data Types ◽  
Research Data Management ◽  
Scientific Results ◽  
Scientific Attention

With the rapid development of the concept of Open Science, the quantitative growth of data obtained during the research, scientific attention to the practice of research data management (research data management) increases, which actualizes the definition of “research data” and identifying types of research data within the practice of their management, justification and coverage of the specifics of such data. The methodological tools of the study are based on the terminological method, the use of which was due to the need to identify relevant interpretations of the concept of “research data”, as well as analysis of repositories for data from various fields of science, indexed by re3data.org., in the general areas presented in the register, namely: descriptions of repositories, including information on the types of data deposited by scientists and data curators. The analysis made it possible to define research data as materials obtained and collected to substantiate the scientific results of research in any field and in any form: numerical, textual, computer code, etc., as well as to identify types of data specific to different branches of science, which, in turn, allowed us to conclude the existing data formats, the most common among both natural and human sciences: text, numerical and graphic formats. As a result of the analysis, it was found that research data can be considered textual, numerical, software, archival, graphic and other objects (files) that serve as the basis of the study and the factual basis for scientific conclusions in a particular field of science. It was found that the type of data directly depends on the nature of the study and the characteristics of the discipline or field of research.

scholarly journals Data meets history: A research data management strategy for the historically oriented humanities

2021 ◽  
pp. 155-178
Author(s):  
Fabian Cremer ◽  
Silvia Daniel ◽  
Marina Lemaire ◽  
Katrin Moeller ◽  
Matthias Razum ◽  
...  
Keyword(s):  
Data Management ◽  
Management Strategy ◽  
Research Data ◽  
Research Data Management

In defense of decentralized research data management

Neuroforum ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michael Hanke ◽  
Franco Pestilli ◽  
Adina S. Wagner ◽  
Christopher J. Markiewicz ◽  
Jean-Baptiste Poline ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Management ◽  
Research Data ◽  
Data Archive ◽  
Individual Scientist ◽  
Computing Platform ◽  
Research Data Management ◽  
Cloud Computing Platform ◽  
Research Objects ◽  
Central Services

Abstract Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.

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