scholarly journals Essentials for Data Support: Training the Front Office

2015 ◽  
Vol 10 (1) ◽  
pp. 240-248 ◽  
Author(s):  
Marjan Grootveld ◽  
Ellen Verbakel
Keyword(s):  
Data Management ◽  
Digital Preservation ◽  
Research Data ◽  
Lessons Learned ◽  
Online Resources ◽  
Research Data Management ◽  
Data Support ◽  
Front Office ◽  
Library Staff ◽  
And Training

At the end of 2011 a Data Intelligence 4 Librarians course was developed to provide online resources and training for digital preservation practitioners, specifically library staff. Lessons learned during the first rounds of the course and developments in the Research Data Management landscape have led to a revision of the positioning, the structure and the content of the course. This paper describes both the three main drivers for the revision, the changes themselves and the lessons that can be drawn from them, after three training rounds in 2014 in the revised format under the new programmatic title of Essentials 4 Data Support.

scholarly journals Data Intelligence Training for Library Staff

2013 ◽  
Vol 8 (1) ◽  
pp. 218-228 ◽  
Author(s):  
Madeleine De Smaele ◽  
Ellen Verbakel ◽  
Nicole Potters ◽  
Marina Noordegraaf
Keyword(s):  
Data Management ◽  
Design Process ◽  
Digital Preservation ◽  
Online Resources ◽  
Research Groups ◽  
Data Repositories ◽  
Course Objectives ◽  
Library Staff ◽  
Further Development ◽  
And Training

The Data Intelligence 4 Librarians course was developed by 3TU.Datacentrum at the end of 2011 to provide online resources and training for digital preservation practitioners, specifically for library staff. The course objectives are to transfer and exchange knowledge about data management, and to provide participants with the skills required to advise researchers or research groups on efficient and effective ways of adding value to their data. The paper describes the process of creating the course, the methodology and the results of the first pilot, which took place from February to June 2012. It also demonstrates the choices made during the design process and discusses the implications of the evaluation of the pilot course for further development: in particular, how the course might be expanded to more disciplines and other data repositories.

scholarly journals Embedding Research Data Management Support in the Scholarly Publishing Workflow

2018 ◽  
Author(s):  
Iain Hrynaszkiewicz ◽  
Rebecca Grant
Keyword(s):  
Data Management ◽  
Support Service ◽  
Research Data ◽  
Scholarly Publishing ◽  
Lessons Learned ◽  
Third Party ◽  
Management Support ◽  
Research Data Management ◽  
Data Support ◽  
Publishing Workflow

In 2016 the publisher Springer Nature introduced four standard research data policies for its journals, enabling more journals to adopt a data policy appropriate for their discipline and community. These standard policies have been adopted by more than 1,500 journals, and similar initiatives to standardise journal research data policies have since been introduced by other large publishers. To support researchers and editors Springer Nature launched a Research Data Helpdesk, which to October 2017 had received more than 300 enquiries. A large survey of researchers with more than 7000 respondents in 2017 revealed that many researchers need support with data management and curation tasks. In 2017 Springer Nature introduced a pilot service to provide additional support to researchers who wish to make their data available alongside their published articles. This Research Data Support service provides hands-on assistance to researchers in uploading their data to a repository, selecting an appropriate licence, enhancing metadata, and cross-referencing the data and its associated publication. The data curation standards were subject to blinded testing by professional editors, and curated datasets scored much higher for metadata quality and completeness on average. We describe the implementation of – and lessons learned from providing – a third party data deposition and curation service at a large scholarly publisher, which has been used by authors publishing in journals including Nature, and BMC Ecology. We conclude with current and future developments, which extend the Research Data Support service to any published researcher, and to research institutions and conferences, providing opportunities to embed research data management support earlier in the scholarly publishing workflow. This paper was presented at the PV 2018 Conference: Adding value and preserving data.

scholarly journals Research Data Management Initiatives at University of Edinburgh

2011 ◽  
Vol 6 (2) ◽  
pp. 232-244 ◽  
Author(s):  
Robin Rice ◽  
Jeff Haywood
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Academic Staff ◽  
Research Data ◽  
Lessons Learned ◽  
The Public ◽  
Research Data Management ◽  
Online Tools ◽  
University Of Edinburgh ◽  
Future Direction

During the last decade, national and international attention has been increasingly focused on issues of research data management and access to publicly funded research data. The pressure brought to bear on researchers to improve their data management and data sharing practice has come from research funders seeking to add value to expensive research and solve cross-disciplinary grand challenges; publishers seeking to be responsive to calls for transparency and reproducibility of the scientific record; and the public seeking to gain and re-use knowledge for their own purposes using new online tools. Meanwhile higher education institutions have been rather reluctant to assert their role in either incentivising or supporting their academic staff in meeting these more demanding requirements for research practice, partly due to lack of knowledge as to how to provide suitable assistance or facilities for data storage and curation/preservation. This paper discusses the activities and drivers behind one institution’s recent attempts to address this gap, with reflection on lessons learned and future direction.

scholarly journals Research Librarians’ Experiences of Research Data Management Activities at an Academic Library in a Developing Country

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Johnson Masinde ◽  
Jing Chen ◽  
Daniel Wambiri ◽  
Angela Mumo
Keyword(s):  
Data Management ◽  
Academic Library ◽  
Research Data ◽  
Data Curation ◽  
Collaborative Partnerships ◽  
University Libraries ◽  
Model Framework ◽  
Technological Infrastructure ◽  
Research Data Management ◽  
And Training

Abstract University libraries have archaeologically augmented scientific research by collecting, organizing, maintaining, and availing research materials for access. Researchers reckon that with the expertise acquired from conventional cataloging, classification, and indexing coupled with that attained in the development, along with the maintenance of institutional repositories, it is only rational that libraries take a dominant and central role in research data management and further their capacity as curators. Accordingly, University libraries are expected to assemble capabilities, to manage and provide research data for sharing and reusing efficiently. This study examined research librarians’ experiences of RDM activities at the UON Library to recommend measures to enhance managing, sharing and reusing research data. The study was informed by the DCC Curation lifecycle model and the Community Capability Model Framework (CCMF) that enabled the Investigator to purposively capture qualitative data from a sample of 5 research librarians at the UON Library. The data was analysed thematically to generate themes that enabled the Investigator to address the research problem. Though the UON Library had policies on research data, quality assurance and intellectual property, study findings evidenced no explicit policies to guide each stage of data curation and capabilities. There were also inadequacies in skills and training capability, technological infrastructure and collaborative partnerships. Overall, RDM faced challenges in all the examined capabilities. These challenges limited the managing, sharing, and reusing of research data. The study recommends developing an RDM unit within the UON Library to oversee the implementation of RDM activities by assembling all the needed capabilities (policy guidelines, skills and training, technological infrastructure and collaborative partnerships) to support data curation activities and enable efficient managing, sharing and reusing research data.

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 Towards a Unified University Infrastructure: The Data Management Roll-Out at the University of Oxford

2013 ◽  
Vol 8 (2) ◽  
pp. 235-246 ◽  
Author(s):  
James A. J. Wilson ◽  
Paul Jeffreys
Keyword(s):  
Data Management ◽  
Research Data ◽  
Management Policy ◽  
Research Information ◽  
Institutional Level ◽  
Research Data Management ◽  
University Of Oxford ◽  
The University ◽  
And Training ◽  
Roll Out

Since presenting a paper at the International Digital Curation Conference 2010 conference entitled ‘An Institutional Approach to Developing Research Data Management Infrastructure’, the University of Oxford has come a long way in developing research data management (RDM) policy, tools and training to address the various phases of the research data lifecycle. Work has now begun on integrating these various elements into a unified infrastructure for the whole university, under the aegis of the Data Management Roll-out at Oxford (Damaro) Project.This paper will explain the process and motivation behind the project, and describes our vision for the future. It will also introduce the new tools and processes created by the university to tie the individual RDM components together. Chief among these is the ‘DataFinder’ – a hierarchically-structured metadata cataloguing system which will enable researchers to search for and locate research datasets hosted in a variety of different datastores from institutional repositories, through Web 2 services, to filing cabinets standing in department offices. DataFinder will be able to pull and associate research metadata from research information databases and data management plans, and is intended to be CERIF compatible. DataFinder is being designed so that it can be deployed at different levels within different contexts, with higher-level instances harvesting information from lower-level instances enabling, for example, an academic department to deploy one instance of DataFinder, which can then be harvested by another at an institutional level, which can then in turn be harvested by another at a national level.The paper will also consider the requirements of embedding tools and training within an institution and address the difficulties of ensuring the sustainability of an RDM infrastructure at a time when funding for such endeavours is limited. Our research shows that researchers (and indeed departments) are at present not exposed to the true costs of their (often suboptimal) data management solutions, whereas when data management services are centrally provided the full costs are visible and off-putting. There is, therefore, the need to sell the benefits of centrally-provided infrastructure to researchers. Furthermore, there is a distinction between training and services that can be most effectively provided at the institutional level, and those which need to be provided at the divisional or departmental level in order to be relevant and applicable to researchers. This is being addressed in principle by Oxford’s research data management policy, and in practice by the planning and piloting aspects of the Damaro Project.

scholarly journals Gathering Evidence of Benefits: A Structured Approach from the Jisc Managing Research Data Programme

2013 ◽  
Vol 8 (2) ◽  
pp. 123-133
Author(s):  
Laura Molloy ◽  
Simon Hodson ◽  
Meik Poschen ◽  
Jonathan Tedds
Keyword(s):  
Higher Education ◽  
Data Management ◽  
Research Funding ◽  
Research Data ◽  
Lessons Learned ◽  
Education Sector ◽  
Research Data Management ◽  
The Uk ◽  
Uk Higher Education ◽  
Structured Approach

The work of the Jisc Managing Research Data programme is – along with the rest of the UK higher education sector – taking place in an environment of increasing pressure on research funding. In order to justify the investment made by Jisc in this activity – and to help make the case more widely for the value of investing time and money in research data management – individual projects and the programme as a whole must be able to clearly express the resultant benefits to the host institutions and to the broader sector. This paper describes a structured approach to the measurement and description of benefits provided by the work of these projects for the benefit of funders, institutions and researchers. We outline the context of the programme and its work; discuss the drivers and challenges of gathering evidence of benefits; specify benefits as distinct from aims and outputs; present emerging findings and the types of metrics and other evidence which projects have provided; explain the value of gathering evidence in a structured way to demonstrate benefits generated by work in this field; and share lessons learned from progress to date.

scholarly journals “You say potato, I say potato“ Mapping Digital Preservation and Research Data Management Concepts towards Collective Curation and Preservation Strategies

2020 ◽  
Vol 15 (1) ◽  
pp. 26
Author(s):  
Michelle Lindlar ◽  
Pia Rudnik ◽  
Sarah Jones ◽  
Laurence Horton
Keyword(s):  
Data Management ◽  
Digital Preservation ◽  
Holistic Approach ◽  
Research Data ◽  
Research Data Management ◽  
Management Concepts ◽  
Professional Field

This paper explores models, concepts and terminology used in the Research Data Management and Digital Preservation communities. In doing so we identify several overlaps and mutual concerns where the advancements of one professional field can apply to and assist another. By focusing on what unites rather than divides us, and by adopting a more holistic approach we advance towards collective curation and preservation strategies.  

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 Incorporating Software Curation into Research Data Management Services: Lessons Learned

2018 ◽  
Vol 13 (1) ◽  
pp. 235-247
Author(s):  
Fernando Rios
Keyword(s):  
Data Management ◽  
Research University ◽  
Service Level ◽  
Research Data ◽  
Lessons Learned ◽  
Related Services ◽  
Management Planning ◽  
Research Data Management ◽  
Management Context ◽  
Large Research

Many large research universities provide research data management (RDM) support services for researchers. These may include support for data management planning, best practices (e.g., organization, support, and storage), archiving, sharing, and publication. However, these data-focused services may under-emphasize the importance of the software that is created to analyse said data. This is problematic for several reasons. First, because software is an integral part of research across all disciplines, it undermines the ability of said research to be understood, verified, and reused by others (and perhaps even the researcher themselves). Second, it may result in less visibility and credit for those involved in creating the software. A third reason is related to stewardship: if there is no clear process for how, when, and where the software associated with research can be accessed and who will be responsible for maintaining such access, important details of the research may be lost over time. This article presents the process by which the RDM services unit of a large research university addressed the lack of emphasis on software and source code in their existing service offerings. The greatest challenges were related to the need to incorporate software into existing data-oriented service workflows while minimizing additional resources required, and the nascent state of software curation and archiving in a data management context. The problem was addressed from four directions: building an understanding of software curation and preservation from various viewpoints (e.g., video games, software engineering), building a conceptual model of software preservation to guide service decisions, implementing software-related services, and documenting and evaluating the work to build expertise and establish a standard service level.

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