Archives des mots-clés : data sharing

Factors that influence data sharing through data sharing platforms: A qualitative study on the views and experiences of cohort holders and platform developers

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Authors : Thijs Devriendt, Pascal Borry, Mahsa Shabani

Background

Infrastructures are being developed to enhance and facilitate the sharing of cohort data internationally. However, empirical studies show that many barriers impede sharing data broadly.

Purpose

Therefore, our aim is to describe the barriers and concerns for the sharing of cohort data, and the implications for data sharing platforms.

Methods

Seventeen participants involved in developing data sharing platforms or tied to cohorts that are to be submitted to platforms were recruited for semi-structured interviews to share views and experiences regarding data sharing.

Results

Credit and recognition, the potential misuse of data, loss of control, lack of resources, socio-cultural factors and ethical and legal barriers are elements that influence decisions on data sharing. Core values underlying these reasons are equality, reciprocity, trust, transparency, gratification and beneficence.

Conclusions

Data generators might use data sharing platforms primarily for collaborative modes of working and network building. Data generators might be unwilling to contribute and share for non-collaborative work, or if no financial resources are provided for sharing data.

URL : Factors that influence data sharing through data sharing platforms: A qualitative study on the views and experiences of cohort holders and platform developers

DOI : https://doi.org/10.1371/journal.pone.0254202

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

Le partage des données vu par les chercheurs : une approche par la valeur

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Auteur/Author : Violaine Rebouillat

Le propos de cet article porte sur la compréhension des logiques qui interviennent dans la définition de la valeur des données de la recherche, celles-ci pouvant avoir une influence sur les critères déterminant leur motivation au partage.

L’approche méthodologique repose sur une enquête qualitative, menée dans le cadre d’une recherche doctorale, qui a déployé 57 entretiens semi-directifs. Alors que les travaux menés autour des données sont focalisés sur les freins et motivations du partage, l’originalité de cette recherche consiste à identifier les différents prismes par lesquels la question de la valeur des données impacte la motivation et la décision de leur partage.

L’analyse des résultats montre que, tous domaines confondus, la valeur des données reste encore cristallisée autour de la publication et de la reconnaissance symbolique du travail du chercheur.

Les résultats permettent de comprendre que la question du partage est confrontée à un impensé : celui du cadre actuel de l’évaluation de la recherche, qui met l’article scientifique au cœur de son dispositif.

Ce travail contribue donc à montrer que l’avenir du partage des données dépend des systèmes alternatifs futurs d’évaluation de la recherche, associés à la science ouverte.

URL : https://lesenjeux.univ-grenoble-alpes.fr/2021/varia/03-le-partage-des-donnees-vu-par-les-chercheurs-une-approche-par-la-valeur/

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

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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

A survey of researchers’ needs and priorities for data sharing

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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

Repository Approaches to Improving the Quality of Shared Data and Code

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Authors : Ana Trisovic, Katherine Mika, Ceilyn Boyd, Sebastian Feger, Mercè Crosas

Sharing data and code for reuse has become increasingly important in scientific work over the past decade. However, in practice, shared data and code may be unusable, or published results obtained from them may be irreproducible.

Data repository features and services contribute significantly to the quality, longevity, and reusability of datasets.

This paper presents a combination of original and secondary data analysis studies focusing on computational reproducibility, data curation, and gamified design elements that can be employed to indicate and improve the quality of shared data and code.

The findings of these studies are sorted into three approaches that can be valuable to data repositories, archives, and other research dissemination platforms.

URL : Repository Approaches to Improving the Quality of Shared Data and Code

DOI : https://doi.org/10.3390/data6020015

An overview of biomedical platforms for managing research data

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Authors : Vivek Navale, Denis von Kaeppler, Matthew McAuliffe

Biomedical platforms provide the hardware and software to securely ingest, process, validate, curate, store, and share data. Many large-scale biomedical platforms use secure cloud computing technology for analyzing, integrating, and storing phenotypic, clinical, and genomic data. Several web-based platforms are available for researchers to access services and tools for biomedical research.

The use of bio-containers can facilitate the integration of bioinformatics software with various data analysis pipelines. Adoption of Common Data Models, Common Data Elements, and Ontologies can increase the likelihood of data reuse. Managing biomedical Big Data will require the development of strategies that can efficiently leverage public cloud computing resources.

The use of the research community developed standards for data collection can foster the development of machine learning methods for data processing and analysis. Increasingly platforms will need to support the integration of data from multiple disease area research.

URL : An overview of biomedical platforms for managing research data

DOI : https://doi.org/10.1007/s42488-020-00040-0