Posted Content•
What Drives Academic Data Sharing
01 Jan 2014-
TL;DR: In this article, the authors developed a conceptual framework that explains the process of data sharing from the primary researcher's point of view, which can be divided into six descriptive categories: data donor, research organization, research community, norms, data infrastructure, and data recipients.
Abstract: 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 a knowledge commons, but research policies that better incentivize data sharing are needed to improve the quality of research results and foster scientific progress.
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TL;DR: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community providing the theory and key practical aspects offlow cytometry enabling immunologists to avoid the common errors that often undermine immunological data.
Abstract: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.
698 citations
01 Jan 2013
TL;DR: Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: to reproduce or to verify research, to make results of publicly funded research available to the public, to enable others to ask new questions of extant data, and to advance the state of research and innovation.
Abstract: We must all accept that science is data and that data are science, and thus provide for, and justify the need for the support of, much-improved data curation. (Hanson, Sugden, & Alberts)
Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data sharing practices in other fields vary widely. Moreover, research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice. © 2012 Wiley Periodicals, Inc.
634 citations
01 Jan 2017
TL;DR: It is rare to find an immunological paper or read a conference abstract in which the authors did not use flow cytometry as the main tool to dissect the immune system and identify its fine and complex functions, and recent developments have created the sophisticated technology of mass cytometry.
423 citations
TL;DR: A framework for analyzing knowledge utilization in social developmental differences in the understanding of integral integrating the scholarship of practice into the nurse discovery theory practice and problems is proposed.
Abstract: Review of 'Understanding knowledge as a commons. From theory to practice.' edited by Charlotte Hess and Elinor Ostrom
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Journal Article•
17,222 citations
TL;DR: In this article, the authors examined the trustworthiness of content analysis in nursing science studies and found that content analysis is commonly used for analyzing qualitative data, however, few articles have examined the use of QCA in nursing studies.
Abstract: Qualitative content analysis is commonly used for analyzing qualitative data. However, few articles have examined the trustworthiness of its use in nursing science studies. The trustworthiness of q...
5,401 citations
Book•
12 Aug 2008
TL;DR: The SAGE Encyclopedia of Qualitative Research Methods (two volumes) as discussed by the authors provides state-of-the-art information and ready-to-use techniques, facts, and examples from the field of qualitative methods in a non-intimidating and accessible style.
Abstract: Qualitative methods are central to research conducted in Education, Nursing, Sociology, Anthropology, and other disciplines in the humanities, social sciences and health sciences. The range of methods available to researchers is very broad (e.g., interviews, focus groups, observation) and projects are informed by various approaches (e.g., phenomenology, grounded theory, discourse analysis). Students, scholars and professionals who are new to qualitative research typically need guidance in defining the boundaries of this type of research, such as selecting specific methods, knowing what types of data are appropriate for qualitative research, identifying theoretical frameworks for particular projects, etc. It is important that both novice and established scholars understand the language, culture, and paradigmatic approaches used in qualitative research, especially as interdisciplinary projects increasingly link researchers across varied fields of study. Researchers and practitioners at all levels and across disciplines need a ready-reference tool that defines and explains core concepts, describes the techniques involved in the implementation of qualitative methods, and presents an overview of this approach to research. The SAGE Encyclopedia of Qualitative Research Methods (two volumes) presents state-of-the-art information and ready-to-use techniques, facts, and examples from the field of qualitative methods in a non-intimidating and accessible style. The encyclopedia is specifically written to appeal to beginning, intermediate, and advanced students, practitioners, researchers, consultants, and consumers of information across the social sciences, humanities, and health sciences. While there are 'how-to' guides and reference texts on qualitative methods, none provide as comprehensive a resource in as focused and approachable a manner as the proposed encyclopedia. From A to Z, the entries cover every major facet of qualitative methods, from gaining access to research participants, to data coding and collection, to research ethics, to the detailed aspects of specific qualitative methods, and much more u all without overwhelming the informed reader.
3,327 citations