Archives des mots-clés : research data

Sizing the Problem of Improving Discovery and Access to NIH-Funded Data: A Preliminary Study

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Objective

This study informs efforts to improve the discoverability of and access to biomedical datasets by providing a preliminary estimate of the number and type of datasets generated annually by research funded by the U.S. National Institutes of Health (NIH). It focuses on those datasets that are “invisible” or not deposited in a known repository.

Methods

We analyzed NIH-funded journal articles that were published in 2011, cited in PubMed and deposited in PubMed Central (PMC) to identify those that indicate data were submitted to a known repository. After excluding those articles, we analyzed a random sample of the remaining articles to estimate how many and what types of invisible datasets were used in each article.

Results

About 12% of the articles explicitly mention deposition of datasets in recognized repositories, leaving 88% that are invisible datasets. Among articles with invisible datasets, we found an average of 2.9 to 3.4 datasets, suggesting there were approximately 200,000 to 235,000 invisible datasets generated from NIH-funded research published in 2011. Approximately 87% of the invisible datasets consist of data newly collected for the research reported; 13% reflect reuse of existing data. More than 50% of the datasets were derived from live human or non-human animal subjects.

Conclusion

In addition to providing a rough estimate of the total number of datasets produced per year by NIH-funded researchers, this study identifies additional issues that must be addressed to improve the discoverability of and access to biomedical research data: the definition of a “dataset,” determination of which (if any) data are valuable for archiving and preservation, and better methods for estimating the number of datasets of interest. Lack of consensus amongst annotators about the number of datasets in a given article reinforces the need for a principled way of thinking about how to identify and characterize biomedical datasets.

URL : Sizing the Problem of Improving Discovery and Access to NIH-Funded Data: A Preliminary Study

DOI : 10.1371/journal.pone.0132735

Research Data Practices in Veterinary Medicine: A Case Study

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Objective
To determine trends in research data output, reuse, and sharing of the college of veterinary medicine faculty members at a large academic research institution.
Methods
This bibliographic study was conducted by examining original research articles for indication of the types of data produced, as well as evidence that the authors reused data or made provision for sharing their own data. Findings were recorded in the categories of research type, data type, data reuse, data sharing, author collaboration, and grants/funding and were analyzed to determine trends.
Results
A variety of different data types were encountered in this study, even within a single article, resulting primarily from clinical and laboratory animal studies. All of the articles resulted from author collaboration, both within the University of Illinois at Urbana – Champaign, as well as with researchers outside the institution. There was little indication that data was reused, except some instances where the authors acknowledged that data was obtained directly from a colleague. There was even less indication that the research data was shared, either as a supplementary file on the publisher’s website or by submission to a repository, except in the case of genetic data.
Conclusions
Veterinary researchers are prolific producers and users of a wide variety of data. Despite the large amount of collaborative research occurring in veterinary medicine, this study provided little evidence that veterinary researchers are reusing or sharing their data, except in an informal manner. Wider adoption of data management plans may serve to improve researchers’ data management practices.

Managing Research Data in Academic Institutions: Role of Libraries

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“One of the global emerging trends in academic libraries is to facilitate the management of research data for the benefit of researchers and institutions. The purpose of this paper is to explore the role of a library in offering such research data management services. The paper discusses the importance of research data, its preservation, organization, dissemination and critical role in the scholarly research life cycle. The authors attempt to provide a vivid description of Research Data Management (RDM) as a service and in the process review the existing literature on the topic in addition to the indicating the tools and technologies that could be adopted in successful RDM service implementation. The paper also is an attempt to share the experience of creating the Vikram Sarabhai Library’s research data repository that was developed by adopting the open source software – CKAN.”

URL : http://eprints.rclis.org/24911/

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