Archives des mots-clés : research data

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

Data Quality Assurance at Research Data Repositories

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Authors : Maxi Kindling, Dorothea Strecker

This paper presents findings from a survey on the status quo of data quality assurance practices at research data repositories. The personalised online survey was conducted among repositories indexed in re3data in 2021. It covered the scope of the repository, types of data quality assessment, quality criteria, responsibilities, details of the review process, and data quality information and yielded 332 complete responses.

The results demonstrate that most repositories perform data quality assurance measures, and overall, research data repositories significantly contribute to data quality. Quality assurance at research data repositories is multifaceted and nonlinear, and although there are some common patterns, individual approaches to ensuring data quality are diverse.

The survey showed that data quality assurance sets high expectations for repositories and requires a lot of resources. Several challenges were discovered: for example, the adequate recognition of the contribution of data reviewers and repositories, the path dependence of data review on review processes for text publications, and the lack of data quality information. The study could not confirm that the certification status of a repository is a clear indicator of whether a repository conducts in-depth quality assurance.

URL : Data Quality Assurance at Research Data Repositories

DOI : http://doi.org/10.5334/dsj-2022-018

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

Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences

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Authors : Barbara McGillivray, Paola Marongiu, Nilo Pedrazzini, Marton Ribary, Mandy Wigdorowitz, Eleonora Zordan

The humanities and social sciences (HSS) have recently witnessed an exponential growth in data-driven research. In response, attention has been afforded to datasets and accompanying data papers as outputs of the research and dissemination ecosystem.

In 2015, two data journals dedicated to HSS disciplines appeared in this landscape: Journal of Open Humanities Data (JOHD) and Research Data Journal for the Humanities and Social Sciences (RDJ).

In this paper, we analyse the state of the art in the landscape of data journals in HSS using JOHD and RDJ as exemplars by measuring performance and the deep impact of data-driven projects, including metrics (citation count; Altmetrics, views, downloads, tweets) of data papers in relation to associated research papers and the reuse of associated datasets.

Our findings indicate: that data papers are published following the deposit of datasets in a repository and usually following research articles; that data papers have a positive impact on both the metrics of research papers associated with them and on data reuse; and that Twitter hashtags targeted at specific research campaigns can lead to increases in data papers’ views and downloads.

HSS data papers improve the visibility of datasets they describe, support accompanying research articles, and add to transparency and the open research agenda.

URL : Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences

DOI : https://doi.org/10.3390/publications10040039

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