Archives des mots-clés : data management plan

Research Data Management in the Humanities: Challenges and Opportunities in the Canadian Context

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Authors : Stefan Higgins, Lisa Goddard, Shahira Khair

In recent years, research funders across the world have implemented mandates for research data management (RDM) that introduce new obligations for researchers seeking funding. Although data work is not new in the humanities, digital research infrastructures, best practices, and the development of highly qualified personnel to support humanist researchers are all still nascent.

Responding to these changes, this article offers four contributions to how humanists can consider the role of “data” in their research and succeed in its management. First, we define RDM and data management plans (DMP) and raise some exigent questions regarding their development and maintenance.

Second, acknowledging the unsettled status of “data” in the humanities, we offer some conceptual explanations of what data are, and gesture to some ways in which humanists are already (and have always been) engaged in data work.

Third, we argue that data work requires conscious design—attention to how data are produced—and that thinking of data work as involving design (e.g., experimental and interpretive work) can help humanists engage more fruitfully in RDM.

Fourth, we argue that RDM (and data work, generally) is labour that requires compensation in the form of funding, support, and tools, as well as accreditation and recognition that incentivizes researchers to make RDM an integral part of their research.

Finally, we offer a set of concrete recommendations to support humanist RDM in the Canadian context.

URL : Research Data Management in the Humanities: Challenges and Opportunities in the Canadian Context

DOI : https://doi.org/10.16995/dscn.9956

Data Management Plans: Implications for Automated Analyses

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Authors : Ngoc-Minh Pham, Heather Moulaison-Sandy, Bradley Wade Bishop, Hannah Gunderman

Data management plans (DMPs) are an essential part of planning data-driven research projects and ensuring long-term access and use of research data and digital objects; however, as text-based documents, DMPs must be analyzed manually for conformance to funder requirements.

This study presents a comparison of DMPs evaluations for 21 funded projects using 1) an automated means of analysis to identify elements that align with best practices in support of open research initiatives and 2) a manually-applied scorecard measuring these same elements.

The automated analysis revealed that terms related to availability (90% of DMPs), metadata (86% of DMPs), and sharing (81% of DMPs) were reliably supplied. Manual analysis revealed 86% (n = 18) of funded DMPs were adequate, with strong discussions of data management personnel (average score: 2 out of 2), data sharing (average score 1.83 out of 2), and limitations to data sharing (average score: 1.65 out of 2).

This study reveals that the automated approach to DMP assessment yields less granular yet similar results to manual assessments of the DMPs that are more efficiently produced. Additional observations and recommendations are also presented to make data management planning exercises and automated analysis even more useful going forward.

URL : Data Management Plans: Implications for Automated Analyses

DOI : http://doi.org/10.5334/dsj-2023-002

Data Services Librarians’ Responsibilities and Perspectives on Research Data Management

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Authors : Bradley Wade Bishop, Ashley M. Orehek, Christopher Eaker, Plato L. Smith

This study of data services librarians is part of a series of studies examining the current roles and perspectives on Research Data Management (RDM) services in higher education. Reviewing current best practices provides insights into the role-based responsibilities for RDM services that data services librarians perform, as well as ways to improve and create new services to meet the needs of their respective university communities.

Objectives

The objectives of this article are to provide the context of research data services through a review of past studies, explain how they informed this qualitative study, and provide the methods and results of the current study.

This study provides an in-depth overview of the overall job responsibilities of data services librarians and as well as their perspectives on RDM through job analyses.

Methods

Job analysis interviews provide insight and context to the tasks employees do as described in their own words. Interviews with 10 data services librarians recruited from the top 10 public and top 10 private universities according to the 2020 Best National University Rankings in the US News and World Reports were asked 30 questions concerning their overall job tasks and perspectives on RDM.

Five public and five private data services librarians were interviewed. The interviews were recorded and transcribed. The transcriptions were analyzed in NVivo using a grounded theory application of open, axial, and selective coding to generate categories and broad themes based on the responses using synonymous meanings.

Results: The results presented here provide the typical job tasks of data services librarians that include locating secondary data, reviewing data management plans (DMPs), conducting outreach, collaborating, and offering RDM training. Fewer data services librarians assisted with data curation or manage an institutional repository.

Discussion

The results indicate that there may be different types of data services librarians depending on the mix of responsibilities. Academic librarianship will benefit from further delineation of job titles using tasks while planning, advertising, hiring, and evaluating workers in this emerging area. There remain many other explorations needed to understand the challenges and opportunities for data services librarians related to RDM.

Conclusions

This article concludes with a proposed matrix of job tasks that indicates different types of data services librarians to inform further study. Future job descriptions, training, and education will all benefit from differentiating between the many associated research data services roles and with increased focus on research data greater specializations will emerge.

URL : Data Services Librarians’ Responsibilities and Perspectives on Research Data Management

DOI : https://doi.org/10.7191/jeslib.2022.1226

Research data management and services: Resources for different data practitioners

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Author : Gilbert Mushi

The emergence of data-driven research and demands for the establishment of Research Data Management (RDM) has created interest in academic institutions and research organizations globally.

Some of the libraries especially in developed countries have started offering RDM services to their communities. Although lagging behind, some academic libraries in developing countries are at the stage of planning or implementing the service. However, the level of RDM awareness is very low among researchers, librarians and other data practitioners.

The objective of this paper is to present available open resources for different data practitioners particularly researchers and librarians.

It includes training resources for both researchers and librarians, Data Management Plan (DMP) tool for researchers; data repositories available for researchers to freely archive and share their research data to the local and international communities.

A case study with a survey was conducted at the University of Dodoma to identify relevant RDM services so that librarians could assist researchers to make their data accessible to the local and international community.

The study findings revealed a low level of RDM awareness among researchers and librarians. Over 50% of the respondent indicated their perceived knowledge as poor in the following RDM knowledge areas; DMP, data repository, long term digital preservation, funders RDM mandates, metadata standards describing data and general awareness of RDM.

Therefore, this paper presents available open resources for different data practitioners to improve RDM knowledge and boost the confidence of academic and research libraries in establishing the service.

URL : Research data management and services: Resources for different data practitioners

DOI : https://doi.org/10.29173/iq995

Application Profile for Machine-Actionable Data Management Plans

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Authors : Tomasz Miksa, Paul Walk, Peter Neish, Simon Oblasser, Hollydawn Murray, Tom Renner, Marie-Christine Jacquemot-Perbal, João Cardoso, Trond Kvamme, Maria Praetzellis, Marek Suchánek, Rob Hooft, Benjamin Faure, Hanne Moa, Adil Hasan, Sarah Jones

This paper presents the application profile for machine-actionable data management plans that allows information from traditional data management plans to be expressed in a machine-actionable way.

We describe the methodology and research conducted to define the application profile. We also discuss design decisions made during its development and present systems which have adopted it.

The application profile was developed in an open and consensus-driven manner within the DMP Common Standards Working Group of the Research Data Alliance and is its official recommendation.

URL : Application Profile for Machine-Actionable Data Management Plans

DOI : http://doi.org/10.5334/dsj-2021-032

Data Management Plans in Horizon 2020: what beneficiaries think and what we can learn from their experience

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Author : Daniel Spichtinger

Background

Data Management Plans (DMPs) are at the heart of many research funder requirements for data management and open data, including the EU’s Framework Programme for Research and Innovation, Horizon 2020. This article provides a summary of the findings of the DMP Use Case study, conducted as part of OpenAIRE Advance.

Methods

As part of the study we created a vetted collection of over 800 Horizon 2020 DMPs. Primarily, however, we report the results of qualitative interviews and a quantitative survey on the experience of Horizon 2020 projects with DMPs.

Results & Conclusions

We find that a significant number of projects had to develop a DMP for the first time in the context of Horizon 2020, which points to the importance of funder requirements in spreading good data management practices. In total, 82% of survey respondents found DMPs useful or partially useful, beyond them being “just” an European Commission (EC) requirement.

DMPs are most prominently developed within a project’s Management Work Package. Templates were considered important, with 40% of respondents using the EC/European Research Council template. However, some argue for a more tailor-made approach.

The most frequent source for support with DMPs were other project partners, but many beneficiaries did not receive any support at all. A number of survey respondents and interviewees therefore ask for a dedicated contact point at the EC, which could take the form of an EC Data Management Helpdesk, akin to the IP helpdesk.

If DMPs are published, they are most often made available on the project website, which, however, is often taken offline after the project ends. There is therefore a need to further raise awareness on the importance of using repositories to ensure preservation and curation of DMPs.

The study identifies IP and licensing arrangements for DMPs as promising areas for further research.

URL : Data Management Plans in Horizon 2020: what beneficiaries think and what we can learn from their experience

DOI : https://doi.org/10.12688/openreseurope.13342.1

Foundational Practices of Research Data Management

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Authors : Kristin A Briney, Heather Coates, Abigail Goben

The importance of research data has grown as researchers across disciplines seek to ensure reproducibility, facilitate data reuse, and acknowledge data as a valuable scholarly commodity.

Researchers are under increasing pressure to share their data for validation and reuse. Adopting good data management practices allows researchers to efficiently locate their data, understand it, and use it throughout all of the stages of a project and in the future. Additionally, good data management can streamline data analysis, visualization, and reporting, thus making publication less stressful and time-consuming.

By implementing foundational practices of data management, researchers set themselves up for success by formalizing processes and reducing common errors in data handling, which can free up more time for research. This paper provides an introduction to best practices for managing all types of data.

URL : Foundational Practices of Research Data Management

DOI : https://doi.org/10.3897/rio.6.e56508