scholarly journals Methods to Evaluate Lifecycle Models for Research Data Management

2019 ◽  
Vol 43 (1) ◽  
pp. 75-81
Author(s):  
Tobias Weber ◽  
Dieter Kranzlmüller
Keyword(s):  
Data Management ◽  
Empirical Evaluation ◽  
Research Data ◽  
Research Data Management

AbstractLifecycle models for research data are often abstract and simple. This comes at the danger of oversimplifying the complex concepts of research data management. The analyses of 90 different lifecycle models lead to two approaches to assess the quality of these models. While terminological issues make direct comparisons of models hard, an empirical evaluation seems possible.

scholarly journals A collective challenge: Open Science from the perspective of Science Europe

2019 ◽  
Vol 72 (2) ◽  
pp. 424-430
Author(s):  
Marie Timmermann
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Open Science ◽  
Research Data ◽  
Major Research ◽  
Research Data Management ◽  
Management Policies ◽  
Good Research ◽  
Quality Of Research

Open Science aims to enhance the quality of research by making research and its outputs openly available, reproducible and accessible. Science Europe, the association of major Research Funding Organisations and Research Performing Organisations, advocates data sharing as one of the core aspects of Open Science and promotes a more harmonised approach to data sharing policies. Good research data management is a prerequisite for Open Science and data management policies should be aligned as much as possible, while taking into account discipline-specific differences. Research data management is a broad and complex field with many actors involved. It needs collective efforts by all actors to work towards aligned policies that foster Open Science.

scholarly journals Laboratory Forensics for Open Science Readiness: an Investigative Approach to Research Data Management

2021 ◽  
Author(s):  
Armel Lefebvre ◽  
Marco Spruit
Keyword(s):  
Data Management ◽  
Information Quality ◽  
Open Science ◽  
Research Data ◽  
Digital Evidence ◽  
Research Data Management ◽  
Analysis Laboratory ◽  
Near Future ◽  
Conceptual Overview

AbstractRecently, the topic of research data management has appeared at the forefront of Open Science as a prerequisite for preserving and disseminating research data efficiently. At the same time, scientific laboratories still rely upon digital files that are processed by experimenters to analyze and communicate laboratory results. In this study, we first apply a forensic process to investigate the information quality of digital evidence underlying published results. Furthermore, we use semiotics to describe the quality of information recovered from storage systems with laboratory forensics techniques. Next, we formulate laboratory analytics capabilities based on the results of the forensics analysis. Laboratory forensics and analytics form the basis of research data management. Finally, we propose a conceptual overview of open science readiness, which combines laboratory forensics techniques and laboratory analytics capabilities to help overcome research data management challenges in the near future.

scholarly journals Research Data Management Practices of Faculty Members

2018 ◽  
pp. 60-75
Author(s):  
Haseeb Ahmad Piracha ◽  
Kanwal Ameen
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Faculty Members ◽  
Research Data Management ◽  
Qualitative Research Design ◽  
The University ◽  
Depth Interviews ◽  
Significant Factors

The study aimed to explore the Research Data Management (RDM) practices of university faculty members through qualitative research design. The data was collected through semi-structured, in-depth interviews from purposely selected ten faculty members from the University of Punjab (PU). The study discovered some significant factors including RDM and curation practices, the amount of research data produced, the support needed for data curation and their willingness to share it. In addition, the study explored issues the researchers face with regards to RDM. The findings reveal that respondents need assistance regarding storage and security of data, improving the quality of backup, support for storage and preservation. They agreed with a need for a central repository of the University.

scholarly journals Treatment of Bad Big Data in Research Data Management (RDM) Systems

2020 ◽  
Vol 4 (4) ◽  
pp. 29 ◽  
Author(s):  
Otmane Azeroual
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Negative Impact ◽  
Research Data ◽  
Main Memory ◽  
Business Success ◽  
Research Data Management ◽  
Day To Day Operations ◽  
Operational Processes

Databases such as research data management systems (RDMS) provide the research data in which information is to be searched for. They provide techniques with which even large amounts of data can be evaluated efficiently. This includes the management of research data and the optimization of access to this data, especially if it cannot be fully loaded into the main memory. They also provide methods for grouping and sorting and optimize requests that are made to them so that they can be processed efficiently even when accessing large amounts of data. Research data offer one thing above all: the opportunity to generate valuable knowledge. The quality of research data is of primary importance for this. Only flawless research data can deliver reliable, beneficial results and enable sound decision-making. Correct, complete and up-to-date research data are therefore essential for successful operational processes. Wrong decisions and inefficiencies in day-to-day operations are only the tip of the iceberg, since the problems with poor data quality span various areas and weaken entire university processes. Therefore, this paper addresses the problems of data quality in the context of RDMS and tries to shed light on the solution for ensuring data quality and to show a way to fix the dirty research data that arise during its integration before it has a negative impact on business success.

scholarly journals Research data management support

2019 ◽  
Author(s):  
Abdurhman Kelil Ali
Keyword(s):  
Data Management ◽  
Support Services ◽  
Research Data ◽  
The Arctic ◽  
Management Support ◽  
Research Data Management ◽  
Road Map ◽  
Senior Research ◽  
Research Meeting

Good management and sharing of research data is a key principle for UiT The Arctic University of Norway, rooted in the value of increased transparency, reproducibility and reuse as well as increased quality of research. Meeting this aspiration requires operational support services, infrastructure, competence and a road map for different stakeholders. In line with these requirements, UiT has taken important steps to implement the ambition of FAIR research data management. These include the establishment of UiT Open Research Data archive in September 2016. Since then, more than 600 datasets with more than 5000 files have been uploaded, curated and made openly available. Moreover, UiT has been conducting a senior research data project that aims to preserve research data from senior researchers and make them available for future use. Additionally, UiT has adopted a policy for research data management that came into effect in September 2017. The poster outlines and reviews these and other efforts by UiT The Arctic University of Norway to provide support services for FAIR research data management.

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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