Étiquette : research data

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Affiliation Information in DataCite Dataset Metadata: a Flemish Case Study

Author/Auteur : Niek Van Wettere

This article aims to evaluate how and to what extent metadata of datasets indexed in DataCite offer clear human- or machine-readable information that enables the research data to be linked to a particular research institution.

Two main pathways are explored. First, researchers can encode their affiliation information at the moment of data submission. This can be done by means of free-text metadata fields or via the inclusion of identifiers such as GRID/ROR and ORCID. Second, affiliation information can be traced indirectly through linking between a dataset and associated publications, given that the metadata of publications is often more explicit about affiliation information than the metadata of datasets.

Both pathways of affiliation information encoding are evaluated on the basis of metadata pertaining to datasets created at the five Flemish universities. It is shown that good practices such as encoding of affiliation information in a dedicated metadata field or inclusion of ORCID in the metadata are on the rise, but could be expanded further.

Finally, the establishment of links between datasets and related publications is often lacking in dataset metadata, although there are important differences between data repositories, as is also demonstrated in a more data-intensive follow-up analysis based on random samples of metadata records.

It is important that data repositories address this issue by providing a metadata field clearly dedicated to associated publications, prominently displayed on the landing page of the dataset.

URL : Affiliation Information in DataCite Dataset Metadata: a Flemish Case Study

DOI : http://doi.org/10.5334/dsj-2021-013

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Open Research Data and Open Peer Review: Perceptions of a Medical and Health Sciences Community in Greece

Authors : Eirini Delikoura, Dimitrios Kouis

Recently significant initiatives have been launched for the dissemination of Open Access as part of the Open Science movement. Nevertheless, two other major pillars of Open Science such as Open Research Data (ORD) and Open Peer Review (OPR) are still in an early stage of development among the communities of researchers and stakeholders.

The present study sought to unveil the perceptions of a medical and health sciences community about these issues. Through the investigation of researchers‘ attitudes, valuable conclusions can be drawn, especially in the field of medicine and health sciences, where an explosive growth of scientific publishing exists.

A quantitative survey was conducted based on a structured questionnaire, with 179 valid responses. The participants in the survey agreed with the Open Peer Review principles. However, they ignored basic terms like FAIR (Findable, Accessible, Interoperable, and Reusable) and appeared incentivized to permit the exploitation of their data.

Regarding Open Peer Review (OPR), participants expressed their agreement, implying their support for a trustworthy evaluation system.

Conclusively, researchers need to receive proper training for both Open Research Data principles and Open Peer Review processes which combined with a reformed evaluation system will enable them to take full advantage of the opportunities that arise from the new scholarly publishing and communication landscape.

URL : Open Research Data and Open Peer Review: Perceptions of a Medical and Health Sciences Community in Greece

DOI : https://doi.org/10.3390/publications9020014

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Adaptable Methods for Training in Research Data Management

Authors: Katarzyna Biernacka, Kerstin Helbig, Petra Buchholz

The management of research data has become an essential aspect of good scientific practice. Education in research data management is, however, scarce. The low number of trainers can be attributed on the one hand to a lack of educational paths. On the other hand, qualification opportunities for academics who have already completed their studies and are in employment are missing.

Within the research project FDMentor a Train-the-Trainer programme was therefore developed to teach potential multipliers of research data management, and at the same time impart basic didactic knowledge.

The resulting concept was created, in addition to freely re-usable materials, to support researchers and research support staff in passing on this knowledge. In addition, the generic development and free licensing of the concept enables transferability to other thematic contexts, such as Open Access or Open Science.

URL : Adaptable Methods for Training in Research Data Management

DOI : http://doi.org/10.5334/dsj-2021-014

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Versioning Data Is About More than Revisions: A Conceptual Framework and Proposed Principles

Authors : Jens Klump, Lesley Wyborn, Mingfang Wu, Julia Martin, Robert R. Downs, Ari Asmi

A dataset, small or big, is often changed to correct errors, apply new algorithms, or add new data (e.g., as part of a time series), etc.

In addition, datasets might be bundled into collections, distributed in different encodings or mirrored onto different platforms. All these differences between versions of datasets need to be understood by researchers who want to cite the exact version of the dataset that was used to underpin their research.

Failing to do so reduces the reproducibility of research results. Ambiguous identification of datasets also impacts researchers and data centres who are unable to gain recognition and credit for their contributions to the collection, creation, curation and publication of individual datasets.

Although the means to identify datasets using persistent identifiers have been in place for more than a decade, systematic data versioning practices are currently not available. In this work, we analysed 39 use cases and current practices of data versioning across 33 organisations.

We noticed that the term ‘version’ was used in a very general sense, extending beyond the more common understanding of ‘version’ to refer primarily to revisions and replacements. Using concepts developed in software versioning and the Functional Requirements for Bibliographic Records (FRBR) as a conceptual framework, we developed six foundational principles for versioning of datasets: Revision, Release, Granularity, Manifestation, Provenance and Citation.

These six principles provide a high-level framework for guiding the consistent practice of data versioning and can also serve as guidance for data centres or data providers when setting up their own data revision and version protocols and procedures.

URL : Versioning Data Is About More than Revisions: A Conceptual Framework and Proposed Principles

DOI : http://doi.org/10.5334/dsj-2021-012

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Openness in Big Data and Data Repositories. The Application of an Ethics Framework for Big Data in Healthand Research

Authors : Vicki Xafis, Markus K. Labude

There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support.

This is particularly so in the context of big data, especially where health data is involved. There are, however, also challenges relating to the collection, storage, and re-use of research data.

This paper gives a brief overview of the landscape of data sharing via data repositories and discusses some of the key ethical issues raised by the sharing of health-related research data, including expectations of privacy and confidentiality, the transparency of repository governance structures, access restrictions, as well as data ownership and the fair attribution of credit.

To consider these issues and the values that are pertinent, the paper applies the deliberative balancing approach articulated in the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of Openness in Big Data and Data Repositories.

Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.

URL : Openness in Big Data and Data Repositories. The Application of an Ethics Framework for Big Data in Healthand Research

DOI : https://doi.org/10.1007/s41649-019-00097-z

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Why Won’t They Just Adopt Good Research Data Management Practices? An Exploration of Research Teams and Librarians’ Role in Facilitating RDM Adoption

Authors: Clara Llebot, Hannah Gascho Rempe

Adoption of good research data management practices is increasingly important for research teams. Despite the work the research community has done to define best data management practices, these practices are still difficult to adopt for many research teams.

Universities all around the world have been offering Research Data Services to help their research groups, and libraries are usually an important part of these services. A better understanding of the pressures and factors that affect research teams may help librarians serve these groups more effectively.

The social interactions between the members of a research team are a key element that influences the likelihood of a research group successfully adopting best practices in data management.

In this article we adapt the Unified Theory of the Acceptance and Use of Technology (UTAUT) model (Venkatesh, Morris, Davis, & Davis, 2003) to explain the variables that can influence whether new and better, data management practices will be adopted by a research group.

We describe six moderating variables: size of the team, disciplinary culture, group culture and leadership, team heterogeneity, funder, and dataset decisions.

We also develop three research group personas as a way of navigating the UTAUT model, and as a tool Research Data Services practitioners can use to target interactions between librarians and research groups to make them more effective.

URL : Why Won’t They Just Adopt Good Research Data Management Practices? An Exploration of Research Teams and Librarians’ Role in Facilitating RDM Adoption

DOI : https://doi.org/10.7710/2162-3309.2321

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A survey of researchers’ needs and priorities for data sharing

Authors : Iain Hrynaszkiewicz, James Harney, Lauren Cadwallader

PLOS has long supported Open Science. One of the ways in which we do so is via our stringent data availability policy established in 2014. Despite this policy, and more data sharing policies being introduced by other organizations, best practices for data sharing are adopted by a minority of researchers in their publications. Problems with effective research data sharing persist and these problems have been quantified by previous research as a lack of time, resources, incentives, and/or skills to share data.

In this study we built on this research by investigating the importance of tasks associated with data sharing, and researchers’ satisfaction with their ability to complete these tasks. By investigating these factors we aimed to better understand opportunities for new or improved solutions for sharing data.

In May-June 2020 we surveyed researchers from Europe and North America to rate tasks associated with data sharing on (i) their importance and (ii) their satisfaction with their ability to complete them. We received 728 completed and 667 partial responses. We calculated mean importance and satisfaction scores to highlight potential opportunities for new solutions to and compare different cohorts.

Tasks relating to research impact, funder compliance, and credit had the highest importance scores. 52% of respondents reuse research data but the average satisfaction score for obtaining data for reuse was relatively low. Tasks associated with sharing data were rated somewhat important and respondents were reasonably well satisfied in their ability to accomplish them. Notably, this included tasks associated with best data sharing practice, such as use of data repositories. However, the most common method for sharing data was in fact via supplemental files with articles, which is not considered to be best practice.

We presume that researchers are unlikely to seek new solutions to a problem or task that they are satisfied in their ability to accomplish, even if many do not attempt this task. This implies there are few opportunities for new solutions or tools to meet these researcher needs. Publishers can likely meet these needs for data sharing by working to seamlessly integrate existing solutions that reduce the effort or behaviour change involved in some tasks, and focusing on advocacy and education around the benefits of sharing data.

There may however be opportunities – unmet researcher needs – in relation to better supporting data reuse, which could be met in part by strengthening data sharing policies of journals and publishers, and improving the discoverability of data associated with published articles.

DOI : https://doi.org/10.31219/osf.io/njr5u