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 Is Democracy the Right System? Collaborative Approaches to Building an Engaged RDM Community

2017 ◽  
Author(s):  
Marta Teperek ◽  
Rosie Higman ◽  
Danny Kingsley
Keyword(s):  
Data Management ◽  
Community Engagement ◽  
Support Services ◽  
Cost Benefit Analysis ◽  
End Users ◽  
Research Data ◽  
Management Support ◽  
Initial Development ◽  
Positive Effects ◽  
Research Data Management

AbstractWhen developing new products, tools or services, one always need to think about the end users to ensure a wide-spread adoption. While this applies equally to services developed at higher education institutions, sometimes these services are driven by policies and not by needs of end users. This policy-driven approach can prove challenging for building effective community engagement. The initial development of Research Data Management support services at the University of Cambridge was policy-driven and subsequently failed in the first instance to engage the community of researchers for whom these services were created.In this practice paper we will describe the initial approach undertaken at Cambridge when developing RDM services, the results of this approach and lessons learnt. We will then provide an overview of alternative, democratic strategies employed and their positive effects on community engagement. We will summarise by performing a cost-benefit analysis of the two approaches. This paper might be a useful case study for any institutions aiming to develop central support services for researchers, with conclusions applicable to the wide sector, and extending beyond Research Data Management services.

scholarly journals Is Democracy the Right System? Collaborative Approaches to Building an Engaged RDM Community

2018 ◽  
Vol 12 (2) ◽  
pp. 86-95 ◽  
Author(s):  
Marta Teperek ◽  
Rosie Higman ◽  
Danny Kingsley
Keyword(s):  
Data Management ◽  
Community Engagement ◽  
Support Services ◽  
Cost Benefit Analysis ◽  
End Users ◽  
Research Data ◽  
Management Support ◽  
Initial Development ◽  
Positive Effects ◽  
Research Data Management

When developing new products, tools or services, one always need to think about the end users to ensure a wide-spread adoption. While this applies equally to services developed at higher education institutions, sometimes these services are driven by policies and not by the needs of end users. This policy-driven approach can prove challenging for building effective community engagement. The initial development of Research Data Management support services at the University of Cambridge was policy-driven and subsequently failed in the first instance to engage the community of researchers for whom these services were created. In this practice paper, we describe the initial approach undertaken at Cambridge when developing RDM services, the results of this approach and lessons learnt. We then provide an overview of alternative, democratic strategies employed and their positive effects on community engagement. We summarise by performing a cost-benefit analysis of the two approaches. This paper might be a useful case study for any institutions aiming to develop central support services for researchers, with conclusions applicable to the wider sector, and extending beyond Research Data Management services.

scholarly journals From Project to Strategic Vision: Taking the Lead in Research Data Management Support at the University of Sydney Library

2014 ◽  
Vol 9 (1) ◽  
pp. 253-262 ◽  
Author(s):  
Belinda Norman ◽  
Kate Valentine Stanton
Keyword(s):  
Data Management ◽  
Research Data ◽  
Management Support ◽  
Policy And Practice ◽  
Roles And Responsibilities ◽  
Strategic Vision ◽  
Research Data Management ◽  
The Commons ◽  
The University ◽  
Project Partner

This paper explores three stories, each occurring a year apart, illustrating an evolution toward a strategic vision for Library leadership in supporting research data management at the University of Sydney. The three stories describe activities undertaken throughout the Seeding the Commons project and beyond, as the establishment of ongoing roles and responsibilities transition the Library from project partner to strategic leader in the delivery of research data management support. Each story exposes key ingredients that characterise research data management support: researcher engagement; partnerships; and the complementary roles of policy and practice.

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 Teaching Open Science

2020 ◽  
Author(s):  
Helene N. Andreassen ◽  
Erik Lieungh
Keyword(s):  
Open Access ◽  
Data Management ◽  
Teaching And Learning ◽  
Open Science ◽  
Research Data ◽  
Open Access Publishing ◽  
The Arctic ◽  
Learning Support ◽  
Research Data Management ◽  
The University

In this episode, we are discussing how to teach open science to PhD students. Helene N. Andreassen, head of Library Teaching and Learning Support at the University Library of UiT the Arctic University of Norway shares her experiences with the integration of open science in a special, tailor-made course for PhD's that have just started their project. An interdisciplinary, discussion-based course, "Take Control of Your PhD Journey: From (P)reflection to Publishing" consists of a series of seminars on research data management, open access publishing and other subject matters pertaining to open science. First published online February 26, 2020.

Knowledge management, research data management, and university scholarship

VINE ◽  
2015 ◽  
Vol 45 (3) ◽  
pp. 344-359 ◽  
Author(s):  
Joyline Makani
Keyword(s):  
Knowledge Management ◽  
Data Management ◽  
Research Data ◽  
Management Support ◽  
Data Discovery ◽  
Content Type ◽  
Research On Research ◽  
Research Data Management ◽  
Academic Scholarship ◽  
Theoretical Perspectives

Purpose – The purpose of this paper is to synthesize existing research on research data management (RDM), academic scholarship and knowledge management and provide a conceptual framework for an institutional research data management support-system (RDMSS) for systems development, managerial and academic use. Design/methodology/approach – Viewing RDMSS from multiple theoretical perspectives, including data management, knowledge management, academic scholarship and the practice-based perspectives of knowledge and knowing, this paper conceptually explores the systems’ elements needed in the development of an institutional RDM service by considering the underlying data discovery and application issues, as well as the nature of academic scholarship and knowledge creation, discovery, application and sharing motivations in a university environment. Findings – The paper provides general criteria for an institutional RDMSS framework. It suggests that RDM in universities is at the very heart of the knowledge life cycle and is a central ingredient to the academic scholarships of discovery, integration, teaching, engagement and application. Research limitations/implications – This is a conceptual exploration and as a result, the research findings may lack generalisability. Researchers are therefore encouraged to further empirically examine the proposed propositions. Originality/value – The broad RDMSS framework presented in this paper can be compared with the actual situation at universities and eventually guide recommendations for adaptations and (re)design of the institutional RDM infrastructure and knowledge discovery services environment. Moreover, this paper will help to address some of the identified underlying scholarship and RDM disciplinary divides and confusion constraining the effective functioning of the modern day university’s RDM and data discovery environment.

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 The Informatics Transform: Re-Engineering Libraries for the Data Decade

2012 ◽  
Vol 7 (1) ◽  
pp. 126-138 ◽  
Author(s):  
Liz Lyon
Keyword(s):  
Data Management ◽  
Support Services ◽  
Research Data ◽  
Institutional Research ◽  
Roles And Responsibilities ◽  
Research Data Management ◽  
Data Support

In this paper, Liz Lyon explores how libraries can re-shape to better reflect the requirements and challenges of today’s data-centric research landscape. The Informatics Transform presents five assertions as potential pathways to change, which will help libraries to re-position, re-profile, and re-structure to better address research data management challenges. The paper deconstructs the institutional research lifecycle and describes a portfolio of ten data support services which libraries can deliver to support the research lifecycle phases. Institutional roles and responsibilities for research data management are also unpacked, building on the framework from the earlier Dealing with Data Report. Finally, the paper examines critical capacity and capability challenges and proposes some innovative steps to addressing the significant skills gaps.

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