Tuuli project: accelerating data management planning in Finnish research organisations

Many research funders have requirements for data sharing and data management plans (DMP). DMP tools are services built to help researchers to create data management plans fitting their needs and based on funder and/or organisation guidelines. Project Tuuli (2015–2017) has provided DMPTuuli, a data management planning tool for Finnish researchers and research organisations offering DMP templates and guidance. In this paper we describe how project has helped both Finnish researchers and research organisations adopt research data management best practices. As a result of the project we have also created a national Tuuli network. With growing competence and collaboration of the network, the project has reached most of its goals. The project has also actively promoted DMP support and training in Finnish research organisations.

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Automating Research Data Management Using Machine-Actionable Data Management Plans

ACM Transactions on Management Information Systems ◽

10.1145/3490396 ◽

2022 ◽

Vol 13 (2) ◽

pp. 1-22

Author(s):

Tomasz Miksa ◽

Simon Oblasser ◽

Andreas Rauber

Keyword(s):

Data Management ◽

Research Data ◽

Use Cases ◽

Full Potential ◽

Institutional Research ◽

Reference Architecture ◽

Management Planning ◽

Research Data Management ◽

Management Plans ◽

New Generation

Many research funders mandate researchers to create and maintain data management plans (DMPs) for research projects that describe how research data is managed to ensure its reusability. A DMP, being a static textual document, is difficult to act upon and can quickly become obsolete and impractical to maintain. A new generation of machine-actionable DMPs (maDMPs) was therefore proposed by the Research Data Alliance to enable automated integration of information and updates. maDMPs open up a variety of use cases enabling interoperability of research systems and automation of data management tasks. In this article, we describe a system for machine-actionable data management planning in an institutional context. We identify common use cases within research that can be automated to benefit from machine-actionability of DMPs. We propose a reference architecture of an maDMP support system that can be embedded into an institutional research data management infrastructure. The system semi-automates creation and maintenance of DMPs, and thus eases the burden for the stakeholders responsible for various DMP elements. We evaluate the proposed system in a case study conducted at the largest technical university in Austria and quantify to what extent the DMP templates provided by the European Commission and a national funding body can be pre-filled. The proof-of-concept implementation shows that maDMP workflows can be semi-automated, thus workload on involved parties can be reduced and quality of information increased. The results are especially relevant to decision makers and infrastructure operators who want to design information systems in a systematic way that can utilize the full potential of maDMPs.

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

BACA JURNAL DOKUMENTASI DAN INFORMASI ◽

10.14203/j.baca.v41i2.707 ◽

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.

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Exploring research data management planning challenges in practice

it - Information Technology ◽

10.1515/itit-2019-0029 ◽

2020 ◽

Vol 62 (1) ◽

pp. 29-37

Author(s):

Armel Lefebvre ◽

Baharak Bakhtiari ◽

Marco Spruit

Keyword(s):

Data Management ◽

Scientific Information ◽

Public Funding ◽

Research Data ◽

High Quality ◽

Management Planning ◽

Research Data Management ◽

Barriers To Access ◽

Funding Agencies ◽

Management Plans

AbstractResearch data management planning (RDMP) is the process through which researchers first get acquainted with research data management (RDM) matters. In recent years, public funding agencies have implemented governmental policies for removing barriers to access to scientific information. Researchers applying for funding at public funding agencies need to define a strategy for guaranteeing that the acquired funds also yield high-quality and reusable research data. To achieve that, funding bodies ask researchers to elaborate on data management needs in documents called data management plans (DMP). In this study, we explore several organizational and technological challenges occurring during the planning phase of research data management, more precisely during the grant submission process. By doing so, we deepen our understanding of a crucial process within research data management and broaden our understanding of the current stakeholders, practices, and challenges in RDMP.

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Surveying the landscape of CIHR-funded research data sharing practices: An analysis of the published literature

10.31219/osf.io/h7byr ◽

2021 ◽

Author(s):

Kevin B Read ◽

Heather Ganshorn ◽

Sarah Rutley ◽

David R. Scott

Keyword(s):

Best Practices ◽

Data Sharing ◽

Descriptive Analysis ◽

Research Data ◽

Data Discovery ◽

Pubmed Central ◽

Funded Research ◽

Shared Data ◽

Research Data Management ◽

Is Value

Background:As Canada increases requirements for research data management (RDM) and sharing, there is value in identifying how research data are shared, and what has been done to make them findable and reusable. This study aims to understand Canada’s data sharing landscape by reviewing how Canadian Institutes of Health Research (CIHR) funded data are shared, and comparing researchers’ data sharing practices to RDM and sharing best practices. Methods:We performed a descriptive analysis of CIHR-funded publications from PubMed and PubMed Central that were published between 1946 and Dec 31, 2019 and that indicated the research data underlying the results of the publication were shared. Each publication was analyzed to identify how and where data were shared, who shared data, and what documentation was included to support data reuse.Results:Of 4,144 CIHR-funded publications, 45.2% (n=1,876) included accessible data, 21.9% (n=909) stated data were available by request, 7.3% (n=304) stated data sharing was not applicable/possible, and we found no evidence of data sharing in 37.6% (n=1,558) of publications. Frequent data sharing methods included via a repository (n=1,549, 37.3%), within supplementary files (n=1,048, 25.2%), and by request (n=919, 22.1%). 13.1% (n=554) of publications included documentation that would facilitate data reuse.Interpretation:Our findings reveal that CIHR-funded publications largely lack the metadata, access instructions, and documentation to facilitate data discovery and reuse. Without measures to address these concerns, and enhanced support for researchers seeking to implement RDM and sharing best practices, most CIHR-funded research data will remain hidden, inaccessible, and unusable.

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Research data management and services: Resources for novice data librarians

College & Research Libraries News ◽

10.5860/crln.78.5.274 ◽

2017 ◽

Vol 78 (5) ◽

pp. 274 ◽

Cited By ~ 3

Author(s):

Sarah Barbrow ◽

Denise Brush ◽

Julie Goldman

Keyword(s):

Life Cycle ◽

Data Management ◽

Academic Libraries ◽

Research Data ◽

Research Data Management ◽

Academic Fields ◽

Data Life Cycle ◽

Management Plans ◽

Share Data

Research in many academic fields today generates large amounts of data. These data not only must be processed and analyzed by the researchers, but also managed throughout the data life cycle. Recently, some academic libraries have begun to offer research data management (RDM) services to their communities. Often, this service starts with helping faculty write data management plans, now required by many federal granting agencies. Libraries with more developed services may work with researchers as they decide how to archive and share data once the grant work is complete.

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Challenges and Practices of Research Data Management in Selected Iraq Universities

DESIDOC Journal of Library & Information Technology ◽

10.14429/djlit.39.06.14443 ◽

2019 ◽

Vol 39 (06) ◽

pp. 308-314

Author(s):

Mahdi Salah Mohammed ◽

Rafea Ibrahim

Keyword(s):

Data Management ◽

Research Data ◽

Faculty Members ◽

Data Repositories ◽

Postgraduate Students ◽

Research Data Management ◽

Research Findings ◽

Management Policies ◽

Research Organisations

Research emphasises the fundamental role of research data management (RDM) in enhancing academic and scientific research. This paper intended to examine RDM in Iraqi Universities, identify the current challenges of RDM and propose influential RDM practices. Data collection employed a self-administered questionnaires distributed to 155 postgraduate students and 20 faculty members from five universities in Iraq. Research findings revealed that there is a lack of proper RDM. Postgraduate students and researchers were managing their own research data. Main challenges of maintaining a good RDM involve lack of guidelines on effective RDM practices, insufficient of adequate human resources, technological obsolescence, insecure and inefficient infrastructure, lack of financial resources, absence of research data management policies and lack of support by institutional authorities and researchers negatively influenced on research data management. Postgraduate students and researchers recommend building research data repositories and collaboration with other universities and research organisations.

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Research Data Management - Current status and future challenges for German non-university research institutions

Research Ideas and Outcomes ◽

10.3897/rio.6.e55141 ◽

2020 ◽

Vol 6 ◽

Author(s):

Mareike Petersen ◽

Bianca Pramann ◽

Ralf Toepfer ◽

Janna Neumann ◽

Harry Enke ◽

...

Keyword(s):

Data Management ◽

Data Sharing ◽

Working Group ◽

Research Data ◽

University Research ◽

Current Status ◽

Research Data Management ◽

World Cafe ◽

Future Challenges ◽

Scientific Fields

This report describes the results of a workshop on research data management (RDM) that took place in June 2019. More than 50 experts from 46 different non-university institutes covering all Leibniz Sections participated. The aim of the workshop was the intra- and transdisciplinary exchange among RDM experts of different institutions and sections within the Leibniz Association on current questions and challenges but also on experiences and activities with respect to RDM. The event was structured in inspiring talks, a World Café to discuss ideas and solutions related to RDM and an exchange of experts following their affiliation to the different Leibniz sections. The workshop revealed that most institutions, independent of scientific fields, face similar overarching problems with respect to RDM, e.g. missing incentives and no awareness of the benefits that would arise from a proper RDM and data sharing. The event also endorsed that the Research Data Working Group of the Leibniz Association (AK Forschungsdaten) is a place for the exchange of all topics around RDM and enables discussions on how to refine RDM at all institutions and in all scientific fields.

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

IASSIST Quarterly ◽

10.29173/iq12 ◽

2017 ◽

Vol 41 (1-4) ◽

pp. 12

Author(s):

Bhojaraju Gunjal ◽

Panorea Gaitanou

Keyword(s):

Literature Review ◽

Best Practices ◽

Data Management ◽

Skill Development ◽

Research Process ◽

Research Data ◽

Research Data Management ◽

Research Activities ◽

Educational Institute ◽

Higher Educational

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.

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

Data and Information Management ◽

10.2478/dim-2021-0002 ◽

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.

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A collective challenge: Open Science from the perspective of Science Europe

Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare ◽

10.31263/voebm.v72i2.2831 ◽

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.

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