Archives des mots-clés : research data management

Doctoral Students’ Educational Needs in Research Data Management: Perceived Importance and Current Competencies

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Author : Jukka Rantasaari

Sound research data management (RDM) competencies are elementary tools used by researchers to ensure integrated, reliable, and re-usable data, and to produce high quality research results.

In this study, 35 doctoral students and faculty members were asked to self-rate or rate doctoral students’ current RDM competencies and rate the importance of these competencies.

Structured interviews were conducted, using close-ended and open-ended questions, covering research data lifecycle phases such as collection, storing, organization, documentation, processing, analysis, preservation, and data sharing.

The quantitative analysis of the respondents’ answers indicated a wide gap between doctoral students’ rated/self-rated current competencies and the rated importance of these competencies.

In conclusion, two major educational needs were identified in the qualitative analysis of the interviews: to improve and standardize data management planning, including awareness of the intellectual property and agreements issues affecting data processing and sharing; and to improve and standardize data documenting and describing, not only for the researcher themself but especially for data preservation, sharing, and re-using. Hence the study informs the development of RDM education for doctoral students.

URL : Doctoral Students’ Educational Needs in Research Data Management: Perceived Importance and Current Competencies

DOI : https://doi.org/10.2218/ijdc.v16i1.684

Open Data Policies among Library and Information Science Journals

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Author : Brian Jackson

Journal publishers play an important role in the open research data ecosystem. Through open data policies that include public data archiving mandates and data availability statements, journal publishers help promote transparency in research and wider access to a growing scholarly record.

The library and information science (LIS) discipline has a unique relationship with both open data initiatives and academic publishing and may be well-positioned to adopt rigorous open data policies.

This study examines the information provided on public-facing websites of LIS journals in order to describe the extent, and nature, of open data guidance provided to prospective authors.

Open access journals in the discipline have disproportionately adopted detailed, strict open data policies. Commercial publishers, which account for the largest share of publishing in the discipline, have largely adopted weaker policies. Rigorous policies, adopted by a minority of journals, describe the rationale, application, and expectations for open research data, while most journals that provide guidance on the matter use hesitant and vague language. Recommendations are provided for strengthening journal open data policies.

URL : Open Data Policies among Library and Information Science Journals

DOI : https://doi.org/10.3390/publications9020025

Data sharing practices and data availability upon request differ across scientific disciplines

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Authors : Leho tedersoo, Rainer Küngas, Ester Oras, Kajar Köster, Helen Eenmaa, Äli Leijen, Margus Pedaste, Marju Raju, Anastasiya Astapova, Heli Lukner, Karin Kogermann, Tuul Sepp

Data sharing is one of the cornerstones of modern science that enables large-scale analyses and reproducibility. We evaluated data availability in research articles across nine disciplines in Nature and Science magazines and recorded corresponding authors’ concerns, requests and reasons for declining data sharing.

Although data sharing has improved in the last decade and particularly in recent years, data availability and willingness to share data still differ greatly among disciplines. We observed that statements of data availability upon (reasonable) request are inefficient and should not be allowed by journals.

To improve data sharing at the time of manuscript acceptance, researchers should be better motivated to release their data with real benefits such as recognition, or bonus points in grant and job applications.

We recommend that data management costs should be covered by funding agencies; publicly available research data ought to be included in the evaluation of applications; and surveillance of data sharing should be enforced by both academic publishers and funders. These cross-discipline survey data are available from the plutoF repository.

URL : Data sharing practices and data availability upon request differ across scientific disciplines

DOI : https://doi.org/10.1038/s41597-021-00981-0

Data Management Plans in Horizon 2020: what beneficiaries think and what we can learn from their experience

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Author : Daniel Spichtinger

Background

Data Management Plans (DMPs) are at the heart of many research funder requirements for data management and open data, including the EU’s Framework Programme for Research and Innovation, Horizon 2020. This article provides a summary of the findings of the DMP Use Case study, conducted as part of OpenAIRE Advance.

Methods

As part of the study we created a vetted collection of over 800 Horizon 2020 DMPs. Primarily, however, we report the results of qualitative interviews and a quantitative survey on the experience of Horizon 2020 projects with DMPs.

Results & Conclusions

We find that a significant number of projects had to develop a DMP for the first time in the context of Horizon 2020, which points to the importance of funder requirements in spreading good data management practices. In total, 82% of survey respondents found DMPs useful or partially useful, beyond them being “just” an European Commission (EC) requirement.

DMPs are most prominently developed within a project’s Management Work Package. Templates were considered important, with 40% of respondents using the EC/European Research Council template. However, some argue for a more tailor-made approach.

The most frequent source for support with DMPs were other project partners, but many beneficiaries did not receive any support at all. A number of survey respondents and interviewees therefore ask for a dedicated contact point at the EC, which could take the form of an EC Data Management Helpdesk, akin to the IP helpdesk.

If DMPs are published, they are most often made available on the project website, which, however, is often taken offline after the project ends. There is therefore a need to further raise awareness on the importance of using repositories to ensure preservation and curation of DMPs.

The study identifies IP and licensing arrangements for DMPs as promising areas for further research.

URL : Data Management Plans in Horizon 2020: what beneficiaries think and what we can learn from their experience

DOI : https://doi.org/10.12688/openreseurope.13342.1

Digital Object Identifier (DOI) Under the Context of Research Data Librarianship

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Author : Jia Liu

A digital object identifier (DOI) is an increasingly prominent persistent identifier in finding and accessing scholarly information. This paper intends to present an overview of global development and approaches in the field of DOI and DOI services with a slight geographical focus on Germany.

At first, the initiation and components of the DOI system and the structure of a DOI name are explored. Next, the fundamental and specific characteristics of DOIs are described and DOIs for three (3) kinds of typical intellectual entities in the scholar communication are dealt with; then, a general DOI service pyramid is sketched with brief descriptions of functions of institutions at different levels.

After that, approaches of the research data librarianship community in the field of RDM, especially DOI services, are elaborated. As examples, the DOI services provided in German research libraries as well as best practices of DOI services in a German library are introduced; and finally, the current practices and some issues dealing with DOIs are summarized. It is foreseeable that DOI, which is crucial to FAIR research data, will gain extensive recognition in the scientific world.

URL : Digital Object Identifier (DOI) Under the Context of Research Data Librarianship

DOI : https://doi.org/10.7191/jeslib.2021.1180

Adaptable Methods for Training in Research Data Management

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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

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

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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