Archives des mots-clés : research data

Journal data policies: Exploring how the understanding of editors and authors corresponds to the policies themselves

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Authors : Thu-Mai Christian, Amanda Gooch, Todd Vision, Elizabeth Hull

Despite the increase in the number of journals issuing data policies requiring authors to make data underlying reporting findings publicly available, authors do not always do so, and when they do, the data do not always meet standards of quality that allow others to verify or extend published results.

This phenomenon suggests the need to consider the effectiveness of journal data policies to present and articulate transparency requirements, and how well they facilitate (or hinder) authors’ ability to produce and provide access to data, code, and associated materials that meet quality standards for computational reproducibility.

This article describes the results of a research study that examined the ability of journal-based data policies to: 1) effectively communicate transparency requirements to authors, and 2) enable authors to successfully meet policy requirements.

To do this, we conducted a mixed-methods study that examined individual data policies alongside editors’ and authors’ interpretation of policy requirements to answer the following research questions.

Survey responses from authors and editors along with results from a content analysis of data policies found discrepancies among editors’ assertion of data policy requirements, authors’ understanding of policy requirements, and the requirements stated in the policy language as written.

We offer explanations for these discrepancies and offer recommendations for improving authors’ understanding of policies and increasing the likelihood of policy compliance.

URL : Journal data policies: Exploring how the understanding of editors and authors corresponds to the policies themselves

DOI : https://doi.org/10.1371/journal.pone.0230281

Digital Objects – FAIR Digital Objects: Which Services Are Required?

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Author : Ulrich Schwardmann

Some of the early Research Data Alliance working groups reused the notion of digital objects as digital entities described by metadata and referenced by a persistent identifier. In recent times the FAIR principles became a prominent role as framework for the sustainability of scientific data.

Both approaches had always machine actionability, the capability of computational systems to use services on data without human intervention, in their focus. The more technical approach of digital objects turned out to provide a complementary view on several aspects of the policy framework of FAIR from a technical perspective.

After a deeper analysis and integration of these concepts by a group of European data experts the discussion intensified on so called FAIR digital objects. But they need to be accompanied by services as building blocks for automated processes. We will describe the components of this framework and its potentials here, and also which services inside this framework are required.

URL : Digital Objects – FAIR Digital Objects: Which Services Are Required?

DOI : http://doi.org/10.5334/dsj-2020-015

Penser local. Développer une politique de données sur un campus SHS

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Auteur/Author : Joachim Schöpfel

Dans le cadre du Plan national pour la science ouverte, la structuration et le partage des données de recherche font désormais partie des priorités de la politique scientifique de la France.

Chaque établissement et chaque organisme scientifique doit se doter d’une politique de la science ouverte et mettre en place un ensemble de services et dispositifs pour la gestion des données de la recherche.

A partir d’enquêtes sur le terrain, l’article propose une feuille de route pour la mise en œuvre d’une telle politique sur un campus universitaire en sciences humaines et sociales.

Dix principes indiquent des pistes pour la gouvernance et le pilotage de cette politique, pour déterminer les priorités de développement et d’investissements, et pour faire le lien avec les infrastructures de recherche, dont notamment Huma-Num.

Il s’agit d’une démarche bottom-up, qui met l’accent sur les pratiques et besoins des chercheurs et qui place les chercheurs au cœur d’une politique institutionnelle dans le domaine des données de recherche.

URL : https://www.openscience.fr/Penser-local

The role of a data librarian in academic and research libraries

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Authors : Isaac K. Ohaji, Brenda Chawner, Pak Yoong

Introduction

This paper presents a data librarian role blueprint (the blueprint) in order to facilitate an understanding of the academic and research librarian’s role in research data management and e-research.

Method

The study employed a qualitative ase research approach to investigate the dimensions of the role of a data librarian in New Zealand research organizations, using semi-structured interviews as the main data collection instrument.

Analysis

A data analysis spiral was used to analyse the interview data, with the addition of a job analysis framework to organize the role performance components of a data librarian.

Results

The influencing factors, performance components and training needs for a data librarian role form the basis of the blueprint.

Conclusions

The findings which are reflected in the blueprint provide a conceptual understanding of the data librarian role which may be used to inform and enhance practice, or to develop relevant education and training programmes.

URL : http://informationr.net/ir/24-4/paper844.html

Research data sharing during the Zika virus public health emergency

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Authors : Vanessa de Arruda Jorge, Sarita Albagli

Introduction

In a public health emergency, sharing of research data is acknowledged as essential to manage treatment and control of the disease. The objective of this study was to examine how researchers reacted during the Zika virus emergency in Brazil.

Method

A literature review examined both unpublished reports and the published literature. Interviews were conducted with eleven researchers (from a sample of sixteen) in the Renezika network. Questions concerned sources of data used for research on the Zika virus, where this data was obtained, and what requirements by funding agencies influenced how data generated was shared – and how open the degree of sharing was.

Analysis

A content analysis matrix was developed based on the results of the interviews. The data were organised acording to categories, subcategories, records units and frequency of records units.

Results

Researchers stressed the importance of access to issue samples as well as pure research data. Collaboration – and publication – increased but also depended on trust in existing networks. Researchers were aware that many agencies and publishers required the deposit of research data in repositories – and several options existed for Zika research.

Conclusions

The findings show that research data were shared, but not necessarily as open data. Trust was necessary between researchers, and researchers in developing countries needed to be assured about their rights and ownership of data, and publications using that data.

URL : http://informationr.net/ir/25-1/paper846.html

Risk Assessment for Scientific Data

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Authors : Matthew S. Mayernik, Kelsey Breseman, Robert R. Downs, Ruth Duerr, Alexis Garretson, Chung-Yi (Sophie) Hou

Ongoing stewardship is required to keep data collections and archives in existence. Scientific data collections may face a range of risk factors that could hinder, constrain, or limit current or future data use.

Identifying such risk factors to data use is a key step in preventing or minimizing data loss. This paper presents an analysis of data risk factors that scientific data collections may face, and a data risk assessment matrix to support data risk assessments to help ameliorate those risks.

The goals of this work are to inform and enable effective data risk assessment by: a) individuals and organizations who manage data collections, and b) individuals and organizations who want to help to reduce the risks associated with data preservation and stewardship.

The data risk assessment framework presented in this paper provides a platform from which risk assessments can begin, and a reference point for discussions of data stewardship resource allocations and priorities.

URL : Risk Assessment for Scientific Data

DOI : http://doi.org/10.5334/dsj-2020-010

A Realistic Guide to Making Data Available Alongside Code to Improve Reproducibility

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Authors : Nicholas J Tierney, Karthik Ram

Data makes science possible. Sharing data improves visibility, and makes the research process transparent. This increases trust in the work, and allows for independent reproduction of results.

However, a large proportion of data from published research is often only available to the original authors. Despite the obvious benefits of sharing data, and scientists’ advocating for the importance of sharing data, most advice on sharing data discusses its broader benefits, rather than the practical considerations of sharing.

This paper provides practical, actionable advice on how to actually share data alongside research. The key message is sharing data falls on a continuum, and entering it should come with minimal barriers.

URL : https://arxiv.org/abs/2002.11626