Archives des mots-clés : research data

Research data sharing: Developing a stakeholder-driven model for journal policies

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“Conclusions of research articles depend on bodies of data that cannot be included in articles themselves. To share this data is important for reasons of both transparency and reuse. Science, Technology, and Medicine journals have a role in facilitating sharing, but by what mechanism is not yet clear. The Journal Research Data (JoRD) Project was a JISC (Joint Information Systems Committee)-funded feasibility study on the potential for a central service on journal research data policies. The objectives of the study included identifying the current state of journal data sharing policies and investigating stakeholders’ views and practices. The project confirmed that a large percentage of journals have no data sharing policy and that there are inconsistencies between those that are traceable. This state leaves authors unsure of whether they should share article related data and where and how to deposit those data. In the absence of a consolidated infrastructure to share data easily, a model journal data sharing policy was developed by comparing quantitative information from analyzing existing journal data policies with qualitative data collected from stakeholders. This article summarizes and outlines the process by which the model was developed and presents the model journal data sharing policy.”

URL : http://eprints.nottingham.ac.uk/3185/

Research Data Explored II: the Anatomy and Reception of figshare

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This is the second paper in a series of bibliometric studies of research data. In this paper, we present an analysis of figshare, one of the largest multidisciplinary repositories for research materials to date.

We analysed the structure of items archived in figshare, their usage, and their reception in two altmetrics sources (PlumX and ImpactStory). We found that figshare acts as a platform for newly published research materials, and as an archive for PLOS.

Depending on the function, we found different bibliometric characteristics. Items archived from PLOS tend to be coming from the natural sciences and are often unviewed and non-downloaded. Self-archived items, however, come from a variety of disciplines and exhibit some patterns of higher usage.

In the altmetrics analysis, we found that Twitter was the social media service where research data gained most attention; generally, research data published in 2014 were most popular across social media services.

PlumX detects considerably more items in social media and also finds higher altmetric scores than ImpactStory.

URL : http://arxiv.org/abs/1503.01298

A systematic review of barriers to data sharing in public health

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“Background : In the current information age, the use of data has become essential for decision making in public health at the local, national, and global level. Despite a global commitment to the use and sharing of public health data, this can be challenging in reality. No systematic framework or global operational guidelines have been created for data sharing in public health. Barriers at different levels have limited data sharing but have only been anecdotally discussed or in the context of specific case studies. Incomplete systematic evidence on the scope and variety of these barriers has limited opportunities to maximize the value and use of public health data for science and policy.

Methods : We conducted a systematic literature review of potential barriers to public health data sharing. Documents that described barriers to sharing of routinely collected public health data were eligible for inclusion and reviewed independently by a team of experts. We grouped identified barriers in a taxonomy for a focused international dialogue on solutions.

Results : Twenty potential barriers were identified and classified in six categories: technical, motivational, economic, political, legal and ethical. The first three categories are deeply rooted in well-known challenges of health information systems for which structural solutions have yet to be found; the last three have solutions that lie in an international dialogue aimed at generating consensus on policies and instruments for data sharing.

Conclusions : The simultaneous effect of multiple interacting barriers ranging from technical to intangible issues has greatly complicated advances in public health data sharing. A systematic framework of barriers to data sharing in public health will be essential to accelerate the use of valuable information for the global good.”

URL : A systematic review of barriers to data sharing in public health

Alternative URL : http://www.biomedcentral.com/1471-2458/14/1144

A Reputation Economy: Results from an Empirical Survey on Academic Data Sharing

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“Academic data sharing is a way for researchers to collaborate and thereby meet the needs of an increasingly complex research landscape. It enables researchers to verify results and to pursuit new research questions with “old” data. It is therefore not surprising that data sharing is advocated by funding agencies, journals, and researchers alike. We surveyed 2661 individual academic researchers across all disciplines on their dealings with data, their publication practices, and motives for sharing or withholding research data. The results for 1564 valid responses show that researchers across disciplines recognise the benefit of secondary research data for their own work and for scientific progress as a whole-still they only practice it in moderation. An explanation for this evidence could be an academic system that is not driven by monetary incentives, nor the desire for scientific progress, but by individual reputation-expressed in (high ranked journal) publications. We label this system a Reputation Economy. This special economy explains our findings that show that researchers have a nuanced idea how to provide adequate formal recognition for making data available to others-namely data citations. We conclude that data sharing will only be widely adopted among research professionals if sharing pays in form of reputation. Thus, policy measures that intend to foster research collaboration need to understand academia as a reputation economy. Successful measures must value intermediate products, such as research data, more highly than it is the case now.”

URL : http://arxiv.org/abs/1503.00481

What Drives Academic Data Sharing?

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“Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher’s point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress.”

URL : What Drives Academic Data Sharing?

DOI :10.1371/journal.pone.0118053

Issues in Open Research Data

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“In 2010 the Panton Principles for Open Data in Science were published. These principles were founded upon the idea that ‘Science is based on building on, reusing and openly criticising the published body of scientific knowledge’ (http://pantonprinciples.org) and they provide a succinct list of the fundamentals to observe when making your data open. Intended for a broad audience of academics, publishers and librarians, Issues in Research Data explores the implications of the Panton Principles through a number of perspectives on open research data in the sciences and beyond.

The book features chapters by open data experts in a range of academic disciplines, covering practical information on licensing, ethics, and advice for data curators, alongside more theoretical issues surrounding the adoption of open data. As the book is open access, each chapter can stand alone from the main volume so that communities can host, distribute, build upon and remix the content that is relevant to them.”

URL : https://microblogging.infodocs.eu/wp-content/uploads/2015/01/moore2014.pdf

DOI: http://dx.doi.org/10.5334/ban

Incentives and motivations for sharing research data: researcher’s perspectives

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“This study, commissioned by Knowledge Exchange, has gathered evidence, examples and opinions on current and future incentives for research data sharing from the researcher’s point of view, in order to provide recommendations for policy and practice development on how best to incentivise data access and reuse. Whilst most researchers appreciate the benefits of sharing research data, on an individual basis they may be reluctant to share their own data. This study is based on qualitative interviews with 22 selected researchers of five research teams that have established data sharing cultures, in the partner countries of Knowledge Exchange: Denmark, Finland, Germany, the Netherlands and the United Kingdom. The five case studies span various academic disciplines: arts and humanities, social sciences, biomedicine, chemistry and biology.”

URL : Incentives and motivations for sharing research data: researcher’s perspectives

Alternative URL : http://knowledge-exchange.info/Default.aspx?ID=733