Archives des mots-clés : research data management

An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive life-sciences data

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Authors : Romain David, Christian Ohmann, Jan‑Willem Boiten, Mónica Cano Abadía, Florence Bietrix, Steve Canham, Maria Luisa Chiusano, Walter Dastrù, Arnaud Laroquette, Dario Longo, Michaela Th. Mayrhofer, Maria Panagiotopoulou, Audrey S. Richard, Sergey Goryanin, Pablo Emilio Verde

For life science infrastructures, sensitive data generate an additional layer of complexity. Cross-domain categorisation and discovery of digital resources related to sensitive data presents major interoperability challenges. To support this FAIRification process, a toolbox demonstrator aiming at support for discovery of digital objects related to sensitive data (e.g., regulations, guidelines, best practice, tools) has been developed.

The toolbox is based upon a categorisation system developed and harmonised across a cluster of 6 life science research infrastructures. Three different versions were built, tested by subsequent pilot studies, finally leading to a system with 7 main categories (sensitive data type, resource type, research field, data type, stage in data sharing life cycle, geographical scope, specific topics).

109 resources attached with the tags in pilot study 3 were used as the initial content for the toolbox demonstrator, a software tool allowing searching of digital objects linked to sensitive data with filtering based upon the categorisation system.

Important next steps are a broad evaluation of the usability and user-friendliness of the toolbox, extension to more resources, broader adoption by different life-science communities, and a long-term vision for maintenance and sustainability.

URL : An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive life-sciences data

DOI : https://doi.org/10.1038/s41598-022-25278-z

Putting FAIR principles in the context of research information: FAIRness for CRIS and CRIS for FAIRness

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Authors : Otmane Azeroual, Joachim Schöpfel, Janne Pölönen, Anastasija Nikiforova

Digitization in the research domain refers to the increasing integration and analysis of research information in the process of research data management. However, it is not clear whether it is used and, more importantly, whether the data are of sufficient quality, and value and knowledge could be extracted from them.

FAIR principles (Findability, Accessibility, Interoperability, Reusability) represent a promising asset to achieve this. Since their publication, they have rapidly proliferated and have become part of (inter-)national research funding programs.

A special feature of the FAIR principles is the emphasis on the legibility, readability, and understandability of data. At the same time, they pose a prerequisite for data for their reliability, trustworthiness, and quality. In this sense, the importance of applying FAIR principles to research information and respective systems such as Current Research Information Systems (CRIS), which is an underrepresented subject for research, is the subject of the paper.

Supporting the call for the need for a ”one-stop-shop and register-onceuse-many approach”, we argue that CRIS is a key component of the research infrastructure landscape, directly targeted and enabled by operational application and the promotion of FAIR principles.

We hypothesize that the improvement of FAIRness is a bidirectional process, where CRIS promotes FAIRness of data and infrastructures, and FAIR principles push further improvements to the underlying CRIS.

URL https://hal.archives-ouvertes.fr/hal-03836525

First Line Research Data Management for Life Sciences: a Case Study

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Authors : J. Paul van Schayck, Maarten Coonen

Modern life sciences studies depend on the collection, management and analysis of comprehensive datasets in what has become data-intensive research. Life science research is also characterised by having relatively small groups of researchers.

This combination of data-intensive research performed by a few people has led to an increasing bottleneck in research data management (RDM). Parallel to this, there has been an urgent call by initiatives like FAIR and Open Science to openly publish research data which has put additional pressure on improving the quality of RDM.

Here, we reflect on the lessons learnt by DataHub Maastricht, a RDM support group of the Maastricht University Medical Centre (MUMC+) in Maastricht, the Netherlands, in providing first-line RDM support for life sciences.

DataHub Maastricht operates with a small core team, and is complemented with disciplinary data stewards, many of whom have joint positions with DataHub and a research group. This organisational model helps creating shared knowledge between DataHub and the data stewards, including insights how to focus support on the most reusable datasets. This model has shown to be very beneficial given limited time and personnel.

We found that co-hosting tailored platforms for specific domains, reducing storage costs by implementing tiered storage and promoting cross-institutional collaboration through federated authentication were all effective features to stimulate researchers to initiate RDM.

Overall, utilising the expertise and communication channel of the embedded data stewards was also instrumental in our RDM success. Looking into the future, we foresee the need to further embed the role of data stewards into the lifeblood of the research organisation, along with policies on how to finance long-term storage of research data.

The latter, to remain feasible, needs to be combined with a further formalising of appraisal and reappraisal of archived research data.

URL : First Line Research Data Management for Life Sciences: a Case Study

DOI : https://doi.org/10.2218/ijdc.v16i1.761

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

A focus groups study on data sharing and research data management

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Authors : Devan Ray Donaldson, Joshua Wolfgang Koepke

Data sharing can accelerate scientific discovery while increasing return on investment beyond the researcher or group that produced them. Data repositories enable data sharing and preservation over the long term, but little is known about scientists’ perceptions of them and their perspectives on data management and sharing practices.

Using focus groups with scientists from five disciplines (atmospheric and earth science, computer science, chemistry, ecology, and neuroscience), we asked questions about data management to lead into a discussion of what features they think are necessary to include in data repository systems and services to help them implement the data sharing and preservation parts of their data management plans.

Participants identified metadata quality control and training as problem areas in data management. Additionally, participants discussed several desired repository features, including: metadata control, data traceability, security, stable infrastructure, and data use restrictions. We present their desired repository features as a rubric for the research community to encourage repository utilization. Future directions for research are discussed.

URL : A focus groups study on data sharing and research data management

DOI : https://doi.org/10.1038/s41597-022-01428-w

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

Open research data: A case study into institutional and infrastructural arrangements to stimulate open research data sharing and reuse

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Authors : Thijmen van Gend, Anneke Zuiderwijk

This study investigates which combination of institutional and infrastructural arrangements positively impact research data sharing and reuse in a specific case. We conducted a qualitative case study of the institutional and infrastructural arrangements implemented at Delft University of Technology in the Netherlands.

In the examined case, it was fundamental to change the mindset of researchers and to make them aware of the benefits of sharing data. Therefore, arrangements should be designed bottom-up and used as a “carrot” rather than as a “stick.” Moreover, support offered to researchers should cover at least legal, financial, administrative, and practical issues of research data management and should be informal in nature.

Previous research describes generic institutional and infrastructural instruments that can stimulate open research data sharing and reuse. This study is among the first to analyze what and how infrastructural and institutional arrangements work in a particular context. It provides the basis for other scholars to study such arrangements in different contexts.

Open data policymakers, universities, and open data infrastructure providers can use our findings to stimulate data sharing and reuse in practice, adapted to the contextual situation. Our study focused on a single case and a particular part of the university.

We recommend repeating this research in other contexts, that is, at other universities, faculties, and involving other research data infrastructure providers.

URL : Open research data: A case study into institutional and infrastructural arrangements to stimulate open research data sharing and reuse

DOI : https://doi.org/10.1177/09610006221101200