Archives des mots-clés : research data management

Establishing, Developing, and Sustaining a Community of Data Champions

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Authors : James L. Savage, Lauren Cadwallader

Supporting good practice in Research Data Management (RDM) is challenging for higher education institutions, in part because of the diversity of research practices and data types across disciplines.

While centralised research data support units now exist in many universities, these typically possess neither the discipline-specific expertise nor the resources to offer appropriate targeted training and support within every academic unit.

One solution to this problem is to identify suitable individuals with discipline-specific expertise that are already embedded within each unit, and empower these individuals to advocate for good RDM and to deliver support locally.

This article focuses on an ongoing example of this approach: the Data Champion Programme at the University of Cambridge, UK.

We describe how the Data Champion programme was established; the programme’s reach, impact, strengths and weaknesses after two years of operation; and our anticipated challenges and planned strategies for maintaining the programme over the medium- and long-term.

URL : Establishing, Developing, and Sustaining a Community of Data Champions

DOI : http://doi.org/10.5334/dsj-2019-023

Data Sharing Practices among Researchers at South African Universities

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Authors : Siviwe Bangani, Mathew Moyo

Research data management practices have gained momentum the world over. This is due to increased demands by governments and other funding agencies to have research data archived and shared as widely as possible.

This paper sought to establish the data sharing practices of researchers in South Africa. The study further sought to establish the level of collaboration among researchers in sharing research data at the university level.

The outcomes of the survey will help the researchers to develop appropriate data literacy awareness programmes meant to stimulate growth in data sharing practices for the benefit of research, not only in South Africa, but the world at large.

A survey research method was used to gather data from willing public universities in South Africa. A similar study was conducted in other countries such as the United Kingdom, France and Turkey but the Researchers believe that circumstances in the developed world may differ with the South African research environment, hence the current study.

The major finding of this study was that most researchers preferred to use data produced by others but less keen on sharing their own data.

This study is the first of its kind in South Africa which investigates data sharing practices of researchers from multi-disciplinary fields at the university level and will contribute immensely to the growing body of literature in the area of research data management.

URL : Data Sharing Practices among Researchers at South African Universities

DOI : http://doi.org/10.5334/dsj-2019-028

A model for initiating research data management services at academic libraries

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Authors : Kevin B. Read, Jessica Koos, Rebekah S. Miller, Cathryn F. Miller, Gesina A. Phillips, Laurel Scheinfeld, Alisa Surkis

Background

Librarians developed a pilot program to provide training, resources, strategies, and support for medical libraries seeking to establish research data management (RDM) services. Participants were required to complete eight educational modules to provide the necessary background in RDM.

Each participating institution was then required to use two of the following three elements: (1) a template and strategies for data interviews, (2) a teaching tool kit to teach an introductory RDM class, or (3) strategies for hosting a data class series.

Case Presentation

Six libraries participated in the pilot, with between two and eight librarians participating from each institution. Librarians from each institution completed the online training modules.

Each institution conducted between six and fifteen data interviews, which helped build connections with researchers, and taught between one and five introductory RDM classes.

All classes received very positive evaluations from attendees. Two libraries conducted a data series, with one bringing in instructors from outside the library.

Conclusion

The pilot program proved successful in helping participating librarians learn about and engage with their research communities, jump-start their teaching of RDM, and develop institutional partnerships around RDM services.

The practical, hands-on approach of this pilot proved to be successful in helping libraries with different environments establish RDM services.

The success of this pilot provides a proven path forward for libraries that are developing data services at their own institutions.

URL : A model for initiating research data management services at academic libraries

Alternative location : http://jmla.pitt.edu/ojs/jmla/article/view/545

Developing a research data policy framework for all journals and publishers

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Authors : Iain Hrynaszkiewicz​, Natasha Simons​, Azhar Hussain​,​ Simon Goudie

More journals and publishers – and funding agencies and institutions – are introducing research data policies. But as the prevalence of policies increases, there is potential to confuse researchers and support staff with numerous or conflicting policy requirements.

We define and describe 14 features of journal research data policies and arrange these into a set of six standard policy types or tiers, which can be adopted by journals and publishers to promote data sharing in a way that encourages good practice and is appropriate for their audience’s perceived needs.

Policy features include coverage of topics such as data citation, data repositories, data availability statements, data standards and formats, and peer review of research data.

These policy features and types have been created by reviewing the policies of multiple scholarly publishers, which collectively publish more than 10,000 journals, and through discussions and consensus building with multiple stakeholders in research data policy via the Data Policy Standardisation and Implementation Interest Group of the Research Data Alliance.

Implementation guidelines for the standard research data policies for journals and publishers are also provided, along with template policy texts which can be implemented by journals in their Information for Authors and publishing workflows.

We conclude with a call for collaboration across the scholarly publishing and wider research community to drive further implementation and adoption of consistent research data policies.

URL : Developing a research data policy framework for all journals and publishers

Alternative location : https://figshare.com/articles/Developing_a_research_data_policy_framework_for_all_journals_and_publishers/8223365/1

A Generic Research Data Infrastructure for Long Tail Research Data Management

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Authors : Atif Latif, Fidan Limani, Klaus Tochtermann

The advent of data intensive science has fueled the generation of digital scientific data. Undoubtedly, digital research data plays a pivotal role in transparency and re-producibility of scientific results as well as in steering the innovation in a research process.

However, the main challenges for science policy and infrastructure projects are to develop practices and solutions for research data management which in compliance with good scientific standards make the research data discoverable, citeble and accessible for society potential reuse.

GeRDI – the Generic Research Data (RD) Infrastructure – is such a research data management initiative which targets long tail content that stems from research communities belonging to different domain and research practices.

It provides a generic and open software which connects research data infrastructures of communities to enable the investigation of multidisciplinary research questions.

URL : A Generic Research Data Infrastructure for Long Tail Research Data Management

DOI : http://doi.org/10.5334/dsj-2019-017

Are Research Datasets FAIR in the Long Run?

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Authors : Dennis Wehrle, Klaus Rechert

Currently, initiatives in Germany are developing infrastructure to accept and preserve dissertation data together with the dissertation texts (on state level – bwDATA Diss, on federal level – eDissPlus).

In contrast to specialized data repositories, these services will accept data from all kind of research disciplines. To ensure FAIR data principles (Wilkinson et al., 2016), preservation plans are required, because ensuring accessibility, interoperability and re-usability even for a minimum ten year data redemption period can become a major challenge.

Both for longevity and re-usability, file formats matter. In order to ensure access to data, the data’s encoding, i.e. their technical and structural representation in form of file formats, needs to be understood. Hence, due to a fast technical lifecycle, interoperability, re-use and in some cases even accessibility depends on the data’s format and our future ability to parse or render these.

This leads to several practical questions regarding quality assurance, potential access options and necessary future preservation steps. In this paper, we analyze datasets from public repositories and apply a file format based long-term preservation risk model to support workflows and services for non-domain specific data repositories.

URL : Are Research Datasets FAIR in the Long Run?

DOI : https://doi.org/10.2218/ijdc.v13i1.659

Remediation Data Management Plans : A Tool for Recovering Research Data from Messy, Messy Projects

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Author : Clara Llebot

Data Management Plans (DMPs) have been used in the last decade to encourage good data management practices among researchers. DMPs are widely used, preventive tools that encourage good data management practices. DMPs are traditionally used to manage data during the planning stage of the project, often required for grant proposals, and prior to data collection.

In this paper we will use a case study to argue that Data Management Plans can be useful in improving the management of the data of research projects that have moved beyond the planning stage of the research life cycle.

In particular, we focus on the case of active projects where data has already been collected and is still being analyzed.

We discuss the differences and commonalities in structure between preventive Data Management Plans and remedial Data Management Plans, and describe in detail the additional considerations that are needed when writing remedial Data Management Plans: the goals and audience of the document, the data inventory, and an implementation plan.

URL : Remediation Data Management Plans : A Tool for Recovering Research Data from Messy, Messy Projects

DOI : https://doi.org/10.2218/ijdc.v13i1.667