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 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 Data literacy for researchers and data librarians

2016 ◽  
Vol 49 (1) ◽  
pp. 3-14 ◽  
Author(s):  
Tibor Koltay
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Literacy ◽  
Academic Library ◽  
Research Data ◽  
Review Of Literature ◽  
Data Literacy ◽  
Data Intensive ◽  
Research Data Management ◽  
Data Citation

This paper describes data literacy and emphasizes its importance. Data literacy is vital for researchers who need to become data literate science workers and also for (potential) data management professionals. Its important characteristic is a close connection and similarity to information literacy. To support this argument, a review of literature was undertaken on the importance of data, and the data-intensive paradigm of scientific research, researchers’ expected and real behaviour, the nature of research data management, the possible roles of the academic library, data quality and data citation, Besides describing the nature of data literacy and enumerating the related skills, the application of phenomenographic approaches to data literacy and its relationship to the digital humanities have been identified as subjects for further investigation.

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 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 Data governance, data literacy and the management of data quality

IFLA Journal ◽  
2016 ◽  
Vol 42 (4) ◽  
pp. 303-312 ◽  
Author(s):  
Tibor Koltay
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Building Blocks ◽  
Research Data ◽  
Data Governance ◽  
Data Literacy ◽  
Data Services ◽  
Data Intensive ◽  
Research Data Management ◽  
Address Data

Data governance and data literacy are two important building blocks in the knowledge base of information professionals involved in supporting data-intensive research, and both address data quality and research data management. Applying data governance to research data management processes and data literacy education helps in delineating decision domains and defining accountability for decision making. Adopting data governance is advantageous, because it is a service based on standardised, repeatable processes and is designed to enable the transparency of data-related processes and cost reduction. It is also useful, because it refers to rules, policies, standards; decision rights; accountabilities and methods of enforcement. Therefore, although it received more attention in corporate settings and some of the skills related to it are already possessed by librarians, knowledge on data governance is foundational for research data services, especially as it appears on all levels of research data services, and is applicable to big data.

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.

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

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
Sign in / Sign up

Export Citation Format

Share Document