scholarly journals Skilling Up to Do Data: Whose Role, Whose Responsibility, Whose Career?

2009 ◽  
Vol 4 (2) ◽  
pp. 158-170 ◽  
Author(s):  
Graham Pryor ◽  
Martin Donnelly
Keyword(s):  
Data Management ◽  
Training Needs ◽  
Research Data ◽  
Roles And Responsibilities ◽  
Research Data Management ◽  
Management Forum ◽  
Career Opportunities ◽  
Informed Opinion

What are the roles necessary to effective data management and what kinds of expertise are needed by the researchers and data specialists who are filling those roles?  These questions were posed at a workshop of data creators and curators whose delegates challenged the DCC and RIN to identify the training needs and career opportunities for the broad cohort that finds itself working in data management – sometimes by design but more often by accident.  This paper revisits previous investigations into the roles and responsibilities required by a “data workforce”, presents a representative spectrum of informed opinion from the DCC Research Data Management Forum, and makes some recommendations for raising capability, capacity and status.

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.

Generic concepts for organising data management in research projects

2021 ◽  
Author(s):  
Ivonne Anders ◽  
Swati Gehlot ◽  
Andrea Lammert ◽  
Karsten Peters-von Gehlen
Keyword(s):  
Project Management ◽  
Data Management ◽  
Best Practice ◽  
Research Data ◽  
Data Organization ◽  
Research Projects ◽  
General Application ◽  
Roles And Responsibilities ◽  
Research Data Management ◽  
Project Size

<p>Since few years Research Data Management is becoming an increasingly important part of scientific projects regardless of the number of topics or subjects, researchers or institutions involved. The bigger the project, the more are the data organization and data management requirements in order to assure the best outcome of the project. Despite this, projects rarely have clear structures or responsibilities for data management. The importance of clearly defining data management and also budgeting for it is often underestimated and/or neglected. A rather scarce number of reports and documentations explaining the research data management in certain projects and detailing best practice examples can be found in the current literature.  Additionally, these are often mixed up with topics of the general project management. Furthermore, these examples are very focused on the certain issues of the described projects and thus, a transferability (or general application) of provided methods is very difficult.</p><p>This contribution presents generic concepts of research data management with an effort to separate them from general project management tasks. Project size, details among the diversity of topics and the involved researcher, play an important role in shaping data management and determining which methods of data management can add value to the outcome of a project. We especially focus on different organisation types, including roles and responsibilities for data management in projects of different sizes. Additionally, we show how and when also education should be included, but also how important agreements in a project are.</p>

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.

scholarly journals PROVIDING RESEARCH DATA MANAGEMENT SERVICES AND PRACTICES AT PDDI-LIPI: PREPAREDNESS, ROLES, CHALLENGES, AND TRAINING

2020 ◽  
Vol 41 (2) ◽  
pp. 169
Author(s):  
Hermin Triasih ◽  
Rahmi Rahmi ◽  
Katrin Setio Devi
Keyword(s):  
Human Resources ◽  
Data Management ◽  
Online Survey ◽  
Training Needs ◽  
Research Data ◽  
Management Planning ◽  
Data Archiving ◽  
Research Data Management ◽  
And Training ◽  
Management Services

This study aims to analyse the implementation of RDM at PDDI-LIPI and to assess its staff’s understanding about RDM services. This article also discusses the challenges and obstacles PDDI faces in providing RDM services. The data was collected via an online survey from 28 July to 7 August 2020. The survey consisted of 35 questions and was shared with 36 respondents via social media. The results identified categories such as research data management services, data management planning services, data archiving services, funding, and staff competency and training needs. In addition, this article also discusses the approach and assessment of RDM services, challenges in providing RDM services, and plans for further developing RDM services at PDDI-LIPI. The results showed that the PDDI staff's understanding of RDM services is adequate. As a new service, the implementation of RDM at PDDI-LIPI continues to develop toward optimisation. RIN is a platform used by PDDI to support this goal. The three biggest obstacles faced by PDDI-LIPI in developing RDM services are limited human resources, competence and budget.  Various trainings related to RDM, both sending staff off campus and inviting trainers to campus, were carried out by PDDI to overcome these obstacles. It is recommended to conduct further research on the mapping and upskilling of staff in charge of RDM services.

scholarly journals The Third DCC-RIN Research Data Management Forum

2009 ◽  
Vol 4 (1) ◽  
pp. 152-158
Author(s):  
Martin Donnelly
Keyword(s):  
Data Management ◽  
Research Data ◽  
Information Network ◽  
Research Information ◽  
Digital Curation ◽  
The Third ◽  
Research Data Management ◽  
Management Forum

A report on the third meeting of the Research Data Management Forum which was held in Manchester, UK on April 30 and May 1, 2009, with an overarching  theme entitled "Value and Benefits". The event was co-sponsored by the Digital Curation Centre (DCC) and the Research Information Network (RIN).

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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