scholarly journals Research Data Management: A proposed framework to boost research in Higher Educational Institutes.

10.29173/iq12 ◽  
2017 ◽  
Vol 41 (1-4) ◽  
pp. 12
Author(s):  
Bhojaraju Gunjal ◽  
Panorea Gaitanou

This paper attempts to present a brief overview of several Research Data Management (RDM) issues and a detailed literature review regarding the RDM aspects adopted in libraries globally. Furthermore, it will describe several tendencies concerning the management of repository tools for research data, as well as the challenges in implementing the RDM. The proper planned training and skill development for all stakeholders by mentors to train both staff and users are some of the issues that need to be considered to enhance the RDM process. An effort will be also made to present the suitable policies and workflows along with the adoption of best practices in RDM, so as to boost the research process in an organisation. This study will showcase the implementation of RDM processes in the Higher Educational Institute of India, referring particularly to the Central Library @ NIT Rourkela in Odisha, India with a proposed framework. Finally, this study will also propose an area of opportunities that can boost research activities in the Institute.

2021 ◽  
Vol 16 (1) ◽  
pp. 78-90
Author(s):  
Felicity Tayler ◽  
Maziar Jafary

Objective – In consideration of emerging national Research Data Management (RDM) policy and infrastructure, this literature review seeks answers to the following questions: 1) What is the most effective way for a Canadian research university to build capacity among library and campus-wide research support staff, with a view towards providing coordinated RDM support services for our researcher community?2) What international training models and course offerings are available and appropriate for a local context?3) What national guidelines and best practices for pedagogical design and delivery can be adapted for a local context? Methods – This literature review synthesizes a total of 13 sources: 9 articles, 2 book chapters, and 2 whitepapers. The whitepapers were selected for a narrative literature review because of their focus on case studies detailing train-the-trainer models. Within the 13 sources we found 14 key case studies. This review serves as a supplement to the 2017 CARL Portage Training Expert Group white paper, “Research Data Management Training Landscape in Canada,” the focus of which was to identify RDM training gaps in order to recommend a coordinated approach to RDM training in a national environment. Results – The narrative review of case studies revealed three thematic areas. Firstly, pedagogical challenges were identified, including the need to target training to RDM support staff such as librarians and researchers, as they comprise distinct groups of trainees with divergent disciplinary vocabularies and incentives for training. Secondly, the case studies cover a broad range of pedagogical models including single or multiple sessions, self-directed or instructor-led, in-person or online instruction, and a hybrid of the two. Finally, RDM training also emerged as a key factor in community building within library staff units, among service units on campus, and with campus research communities. Conclusion – RDM training programs at local institutions should be guided by a set of principles aligned with the training methods, modes of assessment, and infrastructure development timeline outlined in a national training strategy. When adapting principles and training strategies to a local context, the following trends in the literature should be considered: librarians and researchers must have meaningful incentives to undertake training in RDM or to join a community of practice; disciplinary-specific instruction is preferable to general instruction; a librarian’s own training opportunities will influence their ability to provide discipline-specific RDM instruction to researchers; in-person training opportunities improve learning retention and produce beneficial secondary effects, whereas online instruction is most effective when paired with an in-person component; generalized third-party RDM training should be adapted to local context to be meaningful. Future directions for RDM training will integrate into open access and digital scholarship training, and into cross-disciplinary, open science communities of practice.


2019 ◽  
Vol 52 (2) ◽  
pp. 592-600
Author(s):  
Katarina Blask ◽  
André Förster

Although research institutions take on increased responsibility for providing infrastructures and services around the proper handling of research data, there is no comprehensive framework addressing the ideal conditions of this implementation process. To overcome this gap, we present the DIAMANT model, a reference model aimed at providing an orientation framework for the implementation of research data management guided by the research process itself. It builds upon a central research data management information unit controlling the information flow between all other organizational units involved in research data management. Due to the possibility of outsourcing organizational units, the implementation process is maximally flexible and efficient.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Murtaza Ashiq ◽  
Muhammad Haroon Usmani ◽  
Muhammad Naeem

Purpose Research data management (RDM) has been called a “ground-breaking” area for research libraries and it is among the top future trends for academic libraries. Hence, this study aims to systematically review RDM practices and services primarily focusing on the challenges, services and skills along with motivational factors associated with it. Design/methodology/approach A systematic literature review method was used focusing on literature produced between 2016–2020 to understand the latest trends. An extensive research strategy was framed and 15,206 results appeared. Finally, 19 studies have fulfilled the criteria to be included in the study following preferred reporting items for systematic reviews and meta-analysis. Findings RDM is gradually gaining importance among researchers and academic libraries; however, it is still poorly practiced by researchers and academic libraries. Albeit, it is better observed in developed countries over developing countries, however, there are lots of challenges associated with RDM practices by researchers and services by libraries. These challenges demand certain sets of skills to be developed for better practices and services. An active collaboration is required among stakeholders and university services departments to figure out the challenges and issues. Research limitations/implications The implications of policy and practical point-of-view present how research data can be better managed in the future by researchers and library professionals. The expected/desired role of key stockholders in this regard is also highlighted. Originality/value RDM is an important and emerging area. Researchers and Library and Information Science professionals are not comprehensively managing research data as it involves complex cooperation among various stakeholders. A combination of measures is required to better manage research data that would ultimately move forward for open access publishing.


2021 ◽  
Author(s):  
Kai Fay ◽  
Julie Goldman

The Harvard Medical School Countway Library’s Massive Open Online Course (MOOC) Best Practices for Biomedical Research Data Management launched on Canvas in January 2018. This report analyzes student reported data and course generated analytics from January 2018, through July 8, 2020, for the course Best Practices for Biomedical Research Data Management. By comparing the findings from the enrollment period through March 8, 2020 (pre-pandemic) to the period through July 8, 2020 (during-pandemic), the main goal is to investigate potential shifts due to the COVID-19 pandemic.


2021 ◽  
Vol 45 (3-4) ◽  
Author(s):  
Tom Kwanya

Research data management is an umbrella term used to describe activities related to the creation, organisation, structuring, naming, backing up, storage, conservation, and sharing of research data as well as all actions that guarantee security of research data. As is often the case, researchers from Sub-Saharan Africa are lagging behind their counterparts in developed countries in embracing the best practices of research data management. One of the factors to which this slow pace of adoption of research data management could be attributed, is inadequate research on the subject. The purpose of this paper is to analyse the quantity, quality, visibility and authorship of publications on research data management in Sub-Saharan Africa. Bibliometrics approaches were used to analyse publications on research data management from, and on, Sub-Saharan Africa which are currently indexed in Google Scholar. The index was chosen because it is free and is reputed to have liberal selection criteria which do not favour, or discriminate, any discipline or geographic regions. Data was retrieved from Google Scholar using Harzing’s “Publish or Perish” software and analysed using VOSviewer. The findings of the study revealed that the quantity, quality, visibility and authorship collaboration of scholarly publications on research data management in Sub-Saharan Africa is low. The findings may be used by libraries and research institutions in Sub-Saharan Africa to develop and promote best practices in research data management as a means of enhancing their research output and impact.


2020 ◽  
Vol 6 ◽  
Author(s):  
Kristin Briney ◽  
Heather Coates ◽  
Abigail Goben

The importance of research data has grown as researchers across disciplines seek to ensure reproducibility, facilitate data reuse, and acknowledge data as a valuable scholarly commodity. Researchers are under increasing pressure to share their data for validation and reuse. Adopting good data management practices allows researchers to efficiently locate their data, understand it, and use it throughout all of the stages of a project and in the future. Additionally, good data management can streamline data analysis, visualization, and reporting, thus making publication less stressful and time-consuming. By implementing foundational practices of data management, researchers set themselves up for success by formalizing processes and reducing common errors in data handling, which can free up more time for research. This paper provides an introduction to best practices for managing all types of data.


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