Archives des mots-clés : research data management

To protect and to serve: developing a road map for research data management services

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Authors: Andrea Chiarelli, Neil Beagrie, Lotte Boon, Ruth Mallalieu, Rob Johnson, Amy Warner May, Rowan Wilso

Research Data Management (RDM) has become a major issue for universities over the last decade. This case study outlines the review of RDM services carried out at the University of Oxford in partnership with external consultants between November 2019 and November 2020.

It aims to describe and discuss the processes in undertaking a university-wide review of services supporting RDM and developing a future road map for them, with a strong emphasis on the design processes, methodological approaches and infographics used. The future road map developed is a live document, which the consulting team handed over to the University at the end of the consultation process.

It provides a suggested RDM action plan for the University that will continue to evolve and be iterated in the light of additional internal costings, available resources and reprioritization in the budget cycle for each academic year.

It is hoped that the contents of this case study will be useful to other research-intensive universities with an interest in developing and planning RDM services to support their researchers.

URL : To protect and to serve: developing a road map for research data management services

DOI : http://doi.org/10.1629/uksg.566

Research data management systems and the organization of universities and research institutes: A systematic literature review

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Author : Eva Katharina Donner

New technological developments, the availability of big data, and the creation of research platforms open a variety of opportunities to generate, store, and analyze research data. To ensure the sustainable handling of research data, the European Commission as well as scientific commissions have recently highlighted the importance of implementing a research data management system (RDMS) in higher education institutes (HEI) which combines technical as well as organizational solutions.

A deep understanding of the requirements of research data management (RDM), as well as an overview of the different stakeholders, is a key prerequisite for the implementation of an RDMS.

Based on a scientific literature review, the aim of this study is to answer the following research questions: “What organizational factors need to be considered when implementing an RDMS? How do these organizational factors interact with each other and how do they constrain or facilitate the implementation of an RDMS?”

The structure of the analysis is built on the four components of Leavitt’s classical model of organizational change: task, structure, technology, and people. The findings reveal that the implementation of RDMS is strongly impacted by the organizational structure, infrastructure, labor culture as well as strategic considerations.

Overall, this literature review summarizes different approaches for the implementation of an RDMS. It also identifies areas for future research.

URL : Research data management systems and the organization of universities and research institutes: A systematic literature review

DOI : https://doi.org/10.1177%2F09610006211070282

Do I Have To Be An “Other” To Be Myself? Exploring Gender Diversity In Taxonomy, Data Collection, And Through The Research Data Lifecycle

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Authors : Ari Gofman, Sam A. Leif, Hannah Gunderman, Nina Exner

Objective

Existing studies estimate that between 0.3% and 2% of adults in the U.S. (between 900,000 and 2.6 million in 2020) identify as a nonbinary gender or otherwise gender nonconforming. In response to the RDAP 2021 theme of radical change, this article examines the need to change how datasets represent nonbinary persons and how research involving gender data should approach the curation of this data at each stage of the research lifecycle.

Methods

In this article, we examine some of the known challenges of gender inclusion in datasets and summarize some solutions underway. Using a critical lens, we examine the difference between current practice and inclusive practice in gender representation, describing inclusive practices at each stage of the research lifecycle from writing a data management plan to sharing data.

Results

Data structures that limit gender to “male” and “female” or ontological structures that use mapping to collapse gender demographics to binary values exclude nonbinary and gender diverse populations. Some data collection instruments attempt inclusivity by adding the gender category of “other,” but using the “other” gender category labels nonbinary persons as intrinsically alien.

Inclusive change must go farther, to move from alienation to inclusive categories. We describe several techniques for inclusively representing gender in data, from the data management planning stage, to collecting data, cleaning data, and sharing data.

To facilitate better sharing of gender data, repositories must also allow mapping that includes nonbinary genders explicitly and allow for ontological mapping for long-term representation of diverse gender identities.

Conclusions

A good practice during research design is to consider two levels of critique in the data collection plan. First, consider the research question at hand and remove unnecessary gendering from the data.

Secondly, if the research question needs gender, make sure to include nonbinary genders explicitly. Allies must take on this problem without leaving it to those who are most affected by it. Further, more voices calling for inclusionary practices surrounding data rises to a crescendo that cannot be ignored.

URL: Do I Have To Be An “Other” To Be Myself? Exploring Gender Diversity In Taxonomy, Data Collection, And Through The Research Data Lifecycle

DOI : https://doi.org/10.7191/jeslib.2021.1219

La gestion des données de la recherche agronomique : de la science ouverte à l’histoire des sciences

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Auteur/Author : Guillaume Tuloup

L’Institut national de recherche pour l’agriculture, l’alimentation et l’environnement (INRAE) est engagé depuis plusieurs années dans le développement de la science ouverte.

Se positionnant aujourd’hui en fer de lance des mouvements d’ouverture, il œuvre à la rationalisation de la production, de la préservation et de la diffusion des données de la recherche afin de faire des sciences agronomiques des sciences impliquées, dans la société.

En effet, confrontée aux problématiques environnementales, sanitaires et alimentaires, la recherche agronomique est engagée dans une restructuration de la production des savoirs sur la nature et le vivant, à l’appui du numérique qui se fait environnement totalisant.

L’Institut étant particulièrement sensibilisé aux enjeux mémoriels et historiques, l’histoire des sciences peut restituer les conditions historiques de cette restructuration, en mobilisant elle-même les ressorts du numérique et de la science ouverte.

URL : La gestion des données de la recherche agronomique : de la science ouverte à l’histoire des sciences

Original location : https://www.enssib.fr/bibliotheque-numerique/notices/70173-la-gestion-des-donnees-de-la-recherche-agronomique-de-la-science-ouverte-a-l-histoire-des-sciences

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

FAIR Forever? Accountabilities and Responsibilities in the Preservation of Research Data

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Author : Amy Currie, William Kilbride

Digital preservation is a fast-moving and growing community of practice of ubiquitous relevance, but in which capability is unevenly distributed. Within the open science and research data communities, digital preservation has a close alignment to the FAIR principles and is delivered through a complex specialist infrastructure comprising technology, staff and policy.

However, capacity erodes quickly, establishing a need for ongoing examination and review to ensure that skills, technology, and policy remain fit for changing purpose. To address this challenge, the Digital Preservation Coalition (DPC) conducted the FAIR Forever study, commissioned by the European Open Science Cloud (EOSC) Sustainability Working Group and funded by the EOSC Secretariat Project in 2020, to assess the current strengths, weaknesses, opportunities and threats to the preservation of research data across EOSC, and the feasibility of establishing shared approaches, workflows and services that would benefit EOSC stakeholders.

This paper draws from the FAIR Forever study to document and explore its key findings on the identified strengths, weaknesses, opportunities, and threats to the preservation of FAIR data in EOSC, and to the preservation of research data more broadly.

It begins with background of the study and an overview of the methodology employed, which involved a desk-based assessment of the emerging EOSC vision, interviews with representatives of EOSC stakeholders, and focus groups with digital preservation specialists and data managers in research organizations.

It summarizes key findings on the need for clarity on digital preservation in the EOSC vision and for elucidation of roles, responsibilities, and accountabilities to mitigate risks of data loss, reputation, and sustainability. It then outlines the recommendations provided in the final report presented to the EOSC Sustainability Working Group.

To better ensure that research data can be FAIRer for longer, the recommendations of the study are presented with discussion on how they can be extended and applied to various research data stakeholders in and outside of EOSC, and suggest ways to bring together research data curation, management, and preservation communities to better ensure FAIRness now and in the long term.

URL : FAIR Forever? Accountabilities and Responsibilities in the Preservation of Research Data

DOI : https://doi.org/10.2218/ijdc.v16i1.768

Do I-PASS for FAIR? Measuring the FAIR-ness of Research Organizations

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Authors : Jacquelijn Ringersma, Margriet Miedema

Given the increased use of the FAIR acronym as adjective for other contexts than data or data sets, the Dutch National Coordination Point for Research Data Management initiated a Task Group to work out the concept of a FAIR research organization.

The results of this Task Groups are a definition of a FAIR enabling organization and a method to measure the FAIR-ness of a research organization (The Do-I-PASS for FAIR method). The method can also aid in developing FAIR-enabling Road Maps for individual research institutions and at a national level.

This practice paper describes the development of the method and provides a couple of use cases for the application of the method in daily research data management practices in research organizations.

URL : Do I-PASS for FAIR? Measuring the FAIR-ness of Research Organizations

DOI : http://doi.org/10.5334/dsj-2021-030