Archives des mots-clés : research data management

Are Research Datasets FAIR in the Long Run?

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Authors : Dennis Wehrle, Klaus Rechert

Currently, initiatives in Germany are developing infrastructure to accept and preserve dissertation data together with the dissertation texts (on state level – bwDATA Diss, on federal level – eDissPlus).

In contrast to specialized data repositories, these services will accept data from all kind of research disciplines. To ensure FAIR data principles (Wilkinson et al., 2016), preservation plans are required, because ensuring accessibility, interoperability and re-usability even for a minimum ten year data redemption period can become a major challenge.

Both for longevity and re-usability, file formats matter. In order to ensure access to data, the data’s encoding, i.e. their technical and structural representation in form of file formats, needs to be understood. Hence, due to a fast technical lifecycle, interoperability, re-use and in some cases even accessibility depends on the data’s format and our future ability to parse or render these.

This leads to several practical questions regarding quality assurance, potential access options and necessary future preservation steps. In this paper, we analyze datasets from public repositories and apply a file format based long-term preservation risk model to support workflows and services for non-domain specific data repositories.

URL : Are Research Datasets FAIR in the Long Run?

DOI : https://doi.org/10.2218/ijdc.v13i1.659

Remediation Data Management Plans : A Tool for Recovering Research Data from Messy, Messy Projects

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Author : Clara Llebot

Data Management Plans (DMPs) have been used in the last decade to encourage good data management practices among researchers. DMPs are widely used, preventive tools that encourage good data management practices. DMPs are traditionally used to manage data during the planning stage of the project, often required for grant proposals, and prior to data collection.

In this paper we will use a case study to argue that Data Management Plans can be useful in improving the management of the data of research projects that have moved beyond the planning stage of the research life cycle.

In particular, we focus on the case of active projects where data has already been collected and is still being analyzed.

We discuss the differences and commonalities in structure between preventive Data Management Plans and remedial Data Management Plans, and describe in detail the additional considerations that are needed when writing remedial Data Management Plans: the goals and audience of the document, the data inventory, and an implementation plan.

URL : Remediation Data Management Plans : A Tool for Recovering Research Data from Messy, Messy Projects

DOI : https://doi.org/10.2218/ijdc.v13i1.667

Ten principles for machine-actionable data management plans

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Authors : Tomasz Miksa, Stephanie Simms, Daniel Mietchen, Sarah Jones

Data management plans (DMPs) are documents accompanying research proposals and project outputs. DMPs are created as free-form text and describe the data and tools employed in scientific investigations. They are often seen as an administrative exercise and not as an integral part of research practice.

There is now widespread recognition that the DMP can have more thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, and others.

The research community is moving toward a shared goal of making DMPs machine-actionable to improve the experience for all involved by exchanging information across research tools and systems and embedding DMPs in existing workflows.

This will enable parts of the DMP to be automatically generated and shared, thus reducing administrative burdens and improving the quality of information within a DMP.

This paper presents 10 principles to put machine-actionable DMPs (maDMPs) into practice and realize their benefits. The principles contain specific actions that various stakeholders are already undertaking or should undertake in order to work together across research communities to achieve the larger aims of the principles themselves.

We describe existing initiatives to highlight how much progress has already been made toward achieving the goals of maDMPs as well as a call to action for those who wish to get involved.

URL : Ten principles for machine-actionable data management plans

DOI : https://doi.org/10.1371/journal.pcbi.1006750

Assessing Data Management Support Needs of Bioengineering and Biomedical Research Faculty

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Authors : Christie A. Wiley, Margaret H. Burnette

Objectives

This study explores data management knowledge, attitudes, and practices of bioengineering and biomedical researchers in the context of the National Institutes of Health-funded research projects. Specifically, this study seeks to answer the following questions:

  1. What is the nature of biomedical and bioengineering research on the Illinois campus and what kinds of data are being generated?
  2. To what degree are biomedical and bioengineering researchers aware of best practices for data management and what are the actual data management behaviors?
  3. What aspects of data management present the greatest challenges and frustrations?
  4. To what degree are biomedical and bioengineering researchers aware of data sharing opportunities and data repositories, and what are their attitudes towards data sharing?
  5. To what degree are researchers aware of campus services and support for data management planning, data sharing, and data deposit, and what is the level of interest in instruction in these areas?

Methods

Librarians on the University of Illinois at Urbana Champaign campus conducted semi-structured interviews with bioengineering and biomedical researchers to explore researchers’ knowledge of data management best practices, awareness of library campus services, data management behavior and challenges managing research data.

The topics covered during the interviews were current research projects, data types, format, description, campus repository usage, data-sharing, awareness of library campus services, data reuse, the anticipated impact of health on public and challenges (interview questions are provided in the Appendix).

Results

This study revealed the majority of researchers explore broad research topics, various file storage solutions, generate numerous amounts of data and adhere to differing discipline-specific practices. Researchers expressed both familiarity and unfamiliarity with DMP Tool.

Roughly half of the researchers interviewed reported having documented protocols for file names, file backup, and file storage. Findings also suggest that there is ambiguity about what it means to share research data and confusion about terminology such as “repository” and “data deposit”. Many researchers equate publication to data sharing.

Conclusions

The interviews reveal significant data literacy gaps that present opportunities for library instruction in the areas of file organization, project workflow and documentation, metadata standards, and data deposit options.

The interviews also provide invaluable insight into biomedical and bioengineering research in general and contribute to the authors’ understanding of the challenges facing the researchers we strive to support.

URL : Assessing Data Management Support Needs of Bioengineering and Biomedical Research Faculty

Alternative location  : https://escholarship.umassmed.edu/jeslib/vol8/iss1/1/

 

Establishing a Research Data Management Service on a Health Sciences Campus

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Authors : Kathryn Vela, Nancy Shin

Objective

Given the increasing need for research data management support and education, the Spokane Academic Library at Washington State University (WSU) sought to determine the data management practices, perceptions, and needs of researchers on the WSU Spokane health sciences campus.

Methods

A 23-question online survey was distributed to WSU researchers and research support staff through the campus listserv.

This online survey addressed data organization, documentation, storage & backup, security, preservation, and sharing, as well as challenges and desired support services.

Results

Survey results indicated that there was a clear need for more instruction with regard to data management planning, particularly as data management planning addresses the areas of metadata design, data sharing, data security, and data storage and backup.

Conclusions

This needs assessment will direct how RDM services are implemented on the WSU Spokane campus by the Spokane Academic Library (SAL). These services will influence both research data quality and integrity through improved data management practices.

URL : Establishing a Research Data Management Service on a Health Sciences Campus

DOI : https://doi.org/10.7191/jeslib.2019.1146

Les données de la recherche à l’Université Bordeaux Montaigne : Synthèse d’une enquête qualitative auprès des chercheurs

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Auteur/Author : Julie Duprat

Alors que ces dernières années l’importance de l’ouverture des publications écrites par les chercheurs des universités françaises a été largement abordée, les regards se tournent désormais sur une autre de leurs productions avec les données de la recherche.

Dans ce contexte, l’Université Bordeaux Montaigne, spécialisée en sciences humaines et sociales, souhaite mettre en place un service « données de la recherche » afin d’accompagner ses chercheurs dans la gestion et le partage de leurs données de recherche.

Au préalable du service à venir, une enquête a été menée entre septembre et décembre 2018 auprès des chercheurs de l’Université par la conservatrice-stagiaire Julie Duprat afin de faire remonter les besoins du terrain, dans une logique bottom up.

URL : https://hal.archives-ouvertes.fr/hal-02020141

A Principled Approach to Online Publication Listings and Scientific Resource Sharing

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Authors : Jacquelijn Ringersma, Karin Kastens, Ulla Tschida, Jos van Berkum

The Max Planck Institute (MPI) for Psycholinguistics has developed a service to manage and present the scholarly output of their researchers. The PubMan database manages publication metadata and full-texts of publications published by their scholars.

All relevant information regarding a researcher’s work is brought together in this database, including supplementary materials and links to the MPI database for primary research data.

The PubMan metadata is harvested into the MPI website CMS (Plone). The system developed for the creation of the publication lists, allows the researcher to create a selection of the harvested data in a variety of formats.

URL : https://journal.code4lib.org/articles/2520