Responsible data sharing in international health research: a systematic review of principles and norms

Authors : Shona Kalkman, Menno Mostert, Christoph Gerlinger, Johannes J. M. van Delden, Ghislaine J. M. W. van Thiel

Background

Large-scale linkage of international clinical datasets could lead to unique insights into disease aetiology and facilitate treatment evaluation and drug development.

Hereto, multi-stakeholder consortia are currently designing several disease-specific translational research platforms to enable international health data sharing.

Despite the recent adoption of the EU General Data Protection Regulation (GDPR), the procedures for how to govern responsible data sharing in such projects are not at all spelled out yet. In search of a first, basic outline of an ethical governance framework, we set out to explore relevant ethical principles and norms.

Methods

We performed a systematic review of literature and ethical guidelines for principles and norms pertaining to data sharing for international health research.

Results

We observed an abundance of principles and norms with considerable convergence at the aggregate level of four overarching themes: societal benefits and value; distribution of risks, benefits and burdens; respect for individuals and groups; and public trust and engagement.

However, at the level of principles and norms we identified substantial variation in the phrasing and level of detail, the number and content of norms considered necessary to protect a principle, and the contextual approaches in which principles and norms are used.

Conclusions

While providing some helpful leads for further work on a coherent governance framework for data sharing, the current collection of principles and norms prompts important questions about how to streamline terminology regarding de-identification and how to harmonise the identified principles and norms into a coherent governance framework that promotes data sharing while securing public trust.

URL : Responsible data sharing in international health research: a systematic review of principles and norms

DOI : https://doi.org/10.1186/s12910-019-0359-9

Assessing Data Management Support Needs of Bioengineering and Biomedical Research Faculty

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

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

The Time Efficiency Gain in Sharing and Reuse of Research Data

Author: Tessa E. Pronk

Among the frequently stated benefits of sharing research data are time efficiency or increased productivity. The assumption is that reuse or secondary use of research data saves researchers time in not having to produce data for a publication themselves.

This can make science more efficient and productive. However, if there is no reuse, time costs in making data available for reuse will have been made with no return on this investment.

In this paper a mathematical model is used to calculate the break-even point for time spent sharing in a scientific community, versus time gain by reuse. This is done for several scenarios; from simple to complex datasets to share and reuse, and at different sharing rates.

The results indicate that sharing research data can indeed cause an efficiency revenue for the scientific community. However, this is not a given in all modeled scenarios.

The scientific community with the lowest reuse needed to reach a break-even point is one that has few sharing researchers and low time investments for sharing and reuse.

This suggests it would be beneficial to have a critical selection of datasets that are worth the effort to prepare for reuse in other scientific studies. In addition, stimulating reuse of datasets in itself would be beneficial to increase efficiency in scientific communities.

URL : The Time Efficiency Gain in Sharing and Reuse of Research Data

DOI : http://doi.org/10.5334/dsj-2019-010

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

Authors : Kathleen Gregory, Paul Groth, Helena Cousijn, Andrea Scharnhorst, Sally Wyatt

A cross‐disciplinary examination of the user behaviors involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data in selected disciplines.

Two analytical frameworks, rooted in information retrieval and science and technology studies, are used to identify key similarities in practices as a first step toward developing a model describing data retrieval.

URL : Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

DOI : https://doi.org/10.1002/asi.24165

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

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

Methods to Evaluate Lifecycle Models for Research Data Management

Authors : Tobias Weber, Dieter Kranzlmüller

Lifecycle models for research data are often abstract and simple. This comes at the danger of oversimplifying the complex concepts of research data management.

The analysis of 90 different lifecycle models lead to two approaches to assess the quality of these models. While terminological issues make direct comparisons of models hard, an empirical evaluation seems possible.

URL : https://arxiv.org/abs/1901.11267