Archives des mots-clés : research data

FAIREST: A Framework for Assessing Research Repositories

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Authors : Mathieu d’Aquin, Fabian Kirstein, Daniela Oliveira, Sonja Schimmler, Sebastian Urbanek

The open science movement has gained significant momentum within the last few years. This comes along with the need to store and share research artefacts, such as publications and research data. For this purpose, research repositories need to be established.

A variety of solutions exist for implementing such repositories, covering diverse features, ranging from custom depositing workflows to social media-like functions.

In this article, we introduce the FAIREST principles, a framework inspired by the well- known FAIR principles, but designed to provide a set of metrics for assessing and selecting solutions for creating digital repositories for research artefacts. The goal is to support decision makers in choosing such a solution when planning for a repository, especially at an institutional level.

The metrics included are therefore based on two pillars: (1) an analysis of established features and functionalities, drawn from existing dedicated, general purpose and commonly used solutions, and (2) a literature review on general requirements for digital repositories for research artefacts and related systems.

We further describe an assessment of 11 widespread solutions, with the goal to provide an overview of the current landscape of research data repository solutions, identifying gaps and research challenges to be addressed.

DOI : https://doi.org/10.1162/dint_a_00159

Reusable, FAIR Humanities Data : Creating Practical Guidance for Authors at Routledge Open Research

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Author : Rebecca Grant

While stakeholders including funding agencies and academic publishers implement more stringent data sharing policies, challenges remain for researchers in the humanities who are increasingly prompted to share their research data.

This paper outlines some key challenges of research data sharing in the humanities, and identifies existing work which has been undertaken to explore these challenges. It describes the current landscape regarding publishers’ research data sharing policies, and the impact which strong data policies can have, regardless of discipline.

Using Routledge Open Research as a case study, the development of a set of humanities-inclusive Open Data publisher data guidelines is then described. These include practical guidance in relation to data sharing for humanities authors, and a close alignment with the FAIR Data Principles.

URL : Reusable, FAIR Humanities Data : Creating Practical Guidance for Authors at Routledge Open Research

DOI : https://doi.org/10.2218/ijdc.v17i1.820

Increasing the Reuse of Data through FAIR-enabling the Certification of Trustworthy Digital Repositories

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Authors : Benjamin Jacob Mathers, Hervé L’Hours

The long-term preservation of digital objects, and the means by which they can be reused, are addressed by both the FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) and a number of standards bodies providing Trustworthy Digital Repository (TDR) certification, such as the CoreTrustSeal.

Though many of the requirements listed in the Core Trustworthy Data Repositories Requirements 2020–2022 Extended Guidance address the FAIR Data Principles indirectly, there is currently no formal ‘FAIR Certification’ offered by the CoreTrustSeal or other TDR standards bodies. To address this gap the FAIRsFAIR project developed a number of tools and resources that facilitate the assessment of FAIR-enabling practices at the repository level as well as the FAIRness of datasets within them.

These include the CoreTrustSeal+FAIRenabling Capability Maturity model (CTS+FAIR CapMat), a FAIR-Enabling Trustworthy Digital Repositories-Capability Maturity Self-Assessment template, and F-UJI ,  a web-based tool designed to assess the FAIRness of research data objects.

The success of such tools and resources ultimately depends upon community uptake. This requires a community-wide commitment to develop best practices to increase the reuse of data and to reach consensus on what these practices are.

One possible way of achieving community consensus would be through the creation of a network of FAIR-enabling TDRs, as proposed by FAIRsFAIR.

URL : Increasing the Reuse of Data through FAIR-enabling the Certification of Trustworthy Digital Repositories

DOI : https://doi.org/10.2218/ijdc.v17i1.852

Seeing oneself as a data reuser: How subjectification activates the drivers of data reuse in science

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Authors : Marcel LaFlamme, Marion Poetz, Daniel Spichtinger

Considerable resources are being invested in strategies to facilitate the sharing of data across domains, with the aim of addressing inefficiencies and biases in scientific research and unlocking potential for science-based innovation.

Still, we know too little about what determines whether scientific researchers actually make use of the unprecedented volume of data being shared. This study characterizes the factors influencing researcher data reuse in terms of their relationship to a specific research project, and introduces subjectification as the mechanism by which these influencing factors are activated.

Based on our analysis of semi-structured interviews with a purposive sample of 24 data reusers and intermediaries, we find that while both project-independent and project-dependent factors may have a direct effect on a single instance of data reuse, they have an indirect effect on recurring data reuse as mediated by subjectification.

We integrate our findings into a model of recurring data reuse behavior that presents subjectification as the mechanism by which influencing factors are activated in a propensity to engage in data reuse.

Our findings hold scientific implications for the theorization of researcher data reuse, as well as practical implications around the role of settings for subjectification in bringing about and sustaining changes in researcher behavior.

URL : Seeing oneself as a data reuser: How subjectification activates the drivers of data reuse in science

DOI : https://doi.org/10.1371/journal.pone.0272153

Multi-Stakeholder Research Data Management Training as a Tool to Improve the Quality, Integrity, Reliability and Reproducibility of Research

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Author : Jukka Rantasaari

To ensure the quality and integrity of data and the reliability of research, data must be well documented, organised, and described. This calls for research data management (RDM) education for researchers.

In light of 3 ECTS Basics of Research Data Management (BRDM) courses held between 2019 and 2021, we aim to find how a generic level multi-stakeholder training can improve STEM and HSS disciplines’ doctoral students’ and postdoc researchers’ competencies in RDM. The study uses quantitative, descriptive and inferential statistics to analyse respondents’ self-ratings of their competencies, and a qualitative grounded theory-inspired approach to code and analyse course participants’ feedback.

Results: On average, based on the post-course surveys, respondents’ (n = 123) competencies improved one point on a four-level scale, from “little competence” (2) to “somewhat competent” (3). Participants also reported that the training would change their current practices in planning research projects, data management and documentation, acknowledging legal and data privacy viewpoints, and data collecting and organising.

Participants indicated that it would be helpful to see legal and data privacy principles and regulations presented as concrete instructions, cases, and examples. The most requested continuing education topics were metadata and description, discipline specific cultures, and backup, version management, and storage.

Conclusions: Regarding to the widely used criteria for successful training containing 1) active participation during training; 2) demand for RDM training; 3) increased participants’ knowledge and understanding of RDM and confidence in enacting RDM practices; and 4) positive post-training feedback, BRDM meets the criteria.

This study shows that although reaching excellent competence in a RDM basics training is improbable, participants become aware of RDM and its contents and gain the elementary tools and basic skills to begin applying sound RDM practices in their research.

Furthermore, participants are introduced to the academic and research support professionals and vice versa: Stakeholders will get to know the challenges that young researchers and research students encounter when applying RDM. The study reveals valuable information on doctoral students’ and postdoc researchers’ competencies, the impact of education on competencies, and further learning needs in RDM.

URL : Multi-Stakeholder Research Data Management Training as a Tool to Improve the Quality, Integrity, Reliability and Reproducibility of Research

DOI : https://doi.org/10.53377/lq.11726

The financial maintenance of social science data archives: Four case studies of long-term infrastructure work

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Authors : Kristin R. Eschenfelder, Kalpana Shankar, Greg Downey

Contributing to the literature on knowledge infrastructure maintenance, this article describes a historical longitudinal analysis of revenue streams employed by four social science data organizations: the Roper Center for Public Opinion, the Inter-university Consortium for Political and Social Research (ICPSR), the UK Data Archive (UKDA), and the LIS Cross-National Data Center in Luxembourg (LIS).

Drawing on archival documentation and interviews, we describe founders’ assumptions about revenue, changes to revenue streams over the long term, practices for developing and maintaining revenue streams, the importance of financial support from host organizations, and how the context of each data organization shaped revenue possibilities.

We extend conversations about knowledge infrastructure revenue streams by showing the types of change that have occurred over time and how it occurs. We provide examples of the types of flexibility needed for data organizations to remain sustainable over 40–60 years of revenue changes.

We distinguish between Type A flexibilities, or development of new products and services, and Type B flexibilities, or continuous smaller adjustments to existing revenue streams. We argue that Type B flexibilities are as important as Type A, although they are easily overlooked. Our results are relevant to knowledge infrastructure managers and stakeholders facing similar revenue challenges.

URL : The financial maintenance of social science data archives: Four case studies of long-term infrastructure work

DOI : https://doi.org/10.1002/asi.24691

Practices Before Policy: Research Data Management Behaviours in Canada

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Authors : Melissa Cheung, Alexandra Cooper, Dylanne Dearborn, Elizabeth Hill, Erin Johnson, Marjorie Mitchell, Kristi Thompson

In anticipation of the then forthcoming Tri-Agency Research Data Management Policy, a consortium of professionals from Canadian university libraries surveyed researchers on their research data management (RDM) practices, attitudes, and interest in data management services.

Data collected from three surveys targeting researchers in science and engineering, humanities and social sciences, and health sciences and medicine were compiled to create a national dataset.

The present study is the first large-scale survey investigating researcher RDM practices in Canada, and one of the few recent multi-institutional and multidisciplinary surveys on this topic.

This article presents the results of the survey to assess researcher readiness to meet RDM policy requirements, namely the preparation of data management plans (DMPs) and data deposit in a digital repository.

The survey results also highlight common trends across the country while revealing differences in practices and attitudes between disciplines. Based on our survey results, most researchers would have to change their RDM behaviors to meet Tri-Agency RDM policy requirements.

The data we gathered provides insights that can help institutions prioritize service development and infrastructure that will meet researcher needs.

URL : Practices Before Policy: Research Data Management Behaviours in Canada

DOI : https://doi.org/10.21083/partnership.v17i1.6779