Suchergebnisse

In this paper we investigate the effect of different legal requirements and attitudes towards data protection, intellectual property, and ethical review processes and procedures in Germany and the UK to illustrate the divergence in regulation. We especially focus on formal and informal ways of data sharing across disciplines and countries, highlighting paths to ethical and responsible data sharing. Data archives often provide discipline specialized research data management support services. An aspect of support is addressing legal data protection requirements, intellectual property standards, and ethical research practices. Researchers encounter laws concerning data protection and intellectual property throughout the data-lifecycle, but established research practices can clash with new legal frameworks (see debate over the proposed reform of European Union data protection rules). Likewise, changing public attitudes to research and notions of consent can affect conceptions of what constitutes ethical research. Archives can help researchers navigate an environment which simultaneously pushes data sharing, and consideration of the individual's right to privacy and protection. However, doing so often requires negotiating the gaps and contradictions of different national laws, research cultures, and funding environments.

Watch the VIDEO.The case for sharing research data has been strongly made in many parts of the world, but noticeably so in Europe. Open access to research data delivers more value for every funding Euro by enabling data reuse and reducing unnecessary duplication of research. Further, open data can help speed the pace of discovery and allows for reproducibility studies. The European Commission has set out a clear vision for open data in their Horizon Europe proposal. Yet in 2018 only about half of research data are shared, according to surveys of researchers, and a much smaller proportion are shared openly or in ways that maximise discoverability and reuse. Whilst policy implementation remains critical to the uptake of data sharing, this must be joined by greater support and education for researchers, and faster, easier routes to sharing data optimally. We also need to make it worth a researcher's time to share their data. Starting with the case for better data practice, this talk showcases the findings of one of the largest author surveys of its kind on current practices, attitudes and perceptions in data-sharing at the point of scholarly publication. The survey, carried out by Springer Nature in 2018, is based on over 7700 responses from academic researchers - at various levels of their career – in Europe, Asia, America, and Australasia. Responses are from across all subject areas. The resulting data provides a valuable insight into how, where, and why data is currently shared and what the main obstacles to sharing it are.The talk identifies the most "critical areas" – as borne out of the survey findings – that need to be tackled with top priority if we are to accelerate the speed and scope of data-sharing. In closing, we therefore ask – how can we better work together across research libraries, institutions, funders, governments, and publishers, to address and action these "critical areas"? Indeed, it is only by working together that we can unlock the huge potential of research data, namely to improve our knowledge, to address the grand societal challenges, and to help solve some of the most pressing problems in science today.

"Effective Research Data Management (RDM) is a key component of research integrity and reproducible research, and its importance is increasingly emphasised by funding bodies, governments, and research institutions around the world. However, many researchers are unfamiliar with RDM best practices, and research support staff are faced with the difficult task of delivering support to researchers across different disciplines and career stages. What strategies can institutions use to solve these problems? Engaging Researchers with Data Management is an invaluable collection of 24 case studies, drawn from institutions across the globe, that demonstrate clearly and practically how to engage the research community with RDM. These case studies together illustrate the variety of innovative strategies research institutions have developed to engage with their researchers about managing research data. Each study is presented concisely and clearly, highlighting the essential ingredients that led to its success and challenges encountered along the way. By interviewing key staff about their experiences and the organisational context, the authors of this book have created an essential resource for organisations looking to increase engagement with their research communities. This handbook is a collaboration by research institutions, for research institutions. It aims not only to inspire and engage, but also to help drive cultural change towards better data management. It has been written for anyone interested in RDM, or simply, good research practice.

"This concise book uses narrative fiction to address how researchers can conduct qualitative research using both online and first-hand data and digital and face-to-face methods. The book is structured around four phases of the research process - accessing management field research, writing the literature review, collecting and analysing data and enacting qualitative research and finally the creative process of writing qualitative research. Theory and practice are merged through a situation-based case study within each chapter, with the methods and tools employed in each context explored through narrative fiction. The protagonists of each case have specific questions, emotions and ambiguities that qualitative researchers need to face, offering a unique approach to the practice of qualitative research and how it is used in real-life situations. Founded on the idea of enacting and not just doing qualitative research, this book offers toolkits that the researcher can use to operationalize research from start to finish. It will be of interest to postgraduate students conducting research-based projects in Business and Management, PhD researchers and academics looking for a fresh approach"--

The dynamic network of 31 research data centres (RDCs) accredited by the German Data Forum (Rat für Sozial- und Wirtschaftsdaten, RatSWD) provides science and research with comprehensive access to quality-assured and sensitive data. The annual number of processing requests and publications indicate that the data and services provided by RDCs are in high demand. They also underscore the importance of the research data centres for the social, behavioural and economic sciences. As shown in two new publications by the German Data Forum, the research data infrastructure is highly effective and can serve as a best practice model on a national and international level. The research data centres (RDCs) accredited by the German Data Forum (RatSWD) provide researchers from the empirical social, behavioural and economic sciences with comprehensive access to sensitive microdata. In doing so, RDCs satisfy the requirements of science and research as well as the requirements of data protection legislation. Moreover, RDCs provide their data users with a wide range of services, including professional support. This infrastructure serves as a best practice model not only for other disciplines on a national level, but also on an international level. The first RatSWD Output Paper of the 6th appointment period "The German Data Forum (RatSWD) and Research Data Infrastructure: Status Quo and Quality Management" demonstrates the capabilities and the broad scope of the services and data provided by the RDCs - as well as their extensive utilisation. It also reports in detail on the bottom-up governance and quality management of the research data infrastructure. The publication illustrates, for example, how accreditation standards and the continuous monitoring of the RDCs by the Standing Committee of the Research Data Infrastructure (FDI Committee) of the RatSWD contribute to the quality assurance of the infrastructure. The brochure is supplemented by a factsheet that gives a condensed overview of the dynamic network of RDCs and the data they offer.

Publishing in top-ranking journals in the social sciences and international relations requires writing with clarity. Accurately described and transparent methods sections ensure high-quality academic writing. The methodology section of empirical papers should explain the exact steps taken by the authors when operationalizing concepts and testing hypotheses to facilitate replication. This also allows for monitoring quality, challenging findings, and promoting good scientific practices. The quality of methodology sections is the result of the interaction between academic cultures of data sharing, effective application of rules, and good-quality research data management (RDM). This article evaluates the impact of standards on replicability. We present an empirical analysis of a set of sixty-six articles published during the period 1984-2013 that use data from all waves of the European Values Survey. We find differences demonstrating the impact of good RDM and data policies on good scientific practice.

"This book addresses different aspects of data management and ethical issues in research such as data collection, data processing, data storage and retention among others to produce a very comprehensive and highly informative book on Ethical Data Management and Research Integrity"--

The quality of methodology sections is the result of interaction between academic cultures of data sharing, effective application of rules, academic excellence and good quality Research Data Management (RDM).This data set is based on the coding of 66 published empirical articles that used data from at least one wave of the European Values Survey (http://dx.doi.org/10.4232/1.11005) and was published at least in pre-print form between 1984 and 2013. It tests for an article describing the methodology of data collection.

Beyond the complexities faced by typical asset management or institutional repository systems, research data presents a number of complications, including complex hierarchies of related objects that must be modeled and displayed, a wider array of data formats that must be supported, and domain-specific metadata that is necessary to make data intelligible. Managing these complications often leads to software that is tailored to a particular domain, making it difficult to maintain or share. This presentation will provide an overview of how Fedora support supports research data management, including the roadmap for future development and integrations. Fedora is a flexible, extensible, open source repository platform for storing, managing, and preserving digital content, including research data. Fedora is used in a wide variety of institutions including libraries, museums, archives, and government organizations. Fedora supports research data management by providing key repository features such as support for millions of resources and files of any size, native linked data functionality, advanced data modeling, and preservation services. Fedora is also extremely well-suited to integrations with existing researcher workflows via a well-documented REpresentational State Transfer (REST) API and event-based messaging service.

International audience ; How can political roadmaps, action plans and principles on open science be translated into pragmatic and realistic research data policy on a French university campus? How can an open science ecosystem be implemented in the specific environment field of social sciences and humanities? After a couple of scientific projects on research data conducted since 2013 at the University of Lille, we carried out interviews with about 50 researchers, PhD students, data engineers, laboratory and project managers, with three objectives:1.To place the researchers at the heart of the implementation of the open science ecosystem on the campus, with their needs, priorities and doubts.2.To identify opportunities and locks for a data policy.3.To recommend ten actions to develop the data culture on the campus.Conducted as an audit on the human and social sciences campus of the University of Lille, our study has a pragmatic scope: to identify the essential elements for a coherent policy of the production, management and reuse of research data on a campus in the humanities and social sciences, and thus contribute to the appropriation of the concept of open science by the development of a "culture of the data". The national action plan states that there is still a lot of work to be done to make open science a part of scientific practice. To succeed, such an approach requires knowledge of the reality of the field; it needs the support of research communities, the coordination of all actors on the campus, and institutional and scientific steering. It will take time. But it is a necessary investment to maintain excellence in research. This paper makes ten proposals how to go there.

International audience ; How can political roadmaps, action plans and principles on open science be translated into pragmatic and realistic research data policy on a French university campus? How can an open science ecosystem be implemented in the specific environment field of social sciences and humanities? After a couple of scientific projects on research data conducted since 2013 at the University of Lille, we carried out interviews with about 50 researchers, PhD students, data engineers, laboratory and project managers, with three objectives:1.To place the researchers at the heart of the implementation of the open science ecosystem on the campus, with their needs, priorities and doubts.2.To identify opportunities and locks for a data policy.3.To recommend ten actions to develop the data culture on the campus.Conducted as an audit on the human and social sciences campus of the University of Lille, our study has a pragmatic scope: to identify the essential elements for a coherent policy of the production, management and reuse of research data on a campus in the humanities and social sciences, and thus contribute to the appropriation of the concept of open science by the development of a "culture of the data". The national action plan states that there is still a lot of work to be done to make open science a part of scientific practice. To succeed, such an approach requires knowledge of the reality of the field; it needs the support of research communities, the coordination of all actors on the campus, and institutional and scientific steering. It will take time. But it is a necessary investment to maintain excellence in research. This paper makes ten proposals how to go there.

The management, publication, and preservation of datasets have become issues of increasing importance for universities, research institutions, and government agencies. While the reasons and mandates for these activities, and the kinds of datasets collected, differ among these types of institutions, other aspects of data management throughout the research lifecycle concern all of them, including (but not limited to): the discoverability of their data; the choice of metadata standard(s) and the creation of metadata; providing visualization and interaction with data; selection and migration of data formats for long-term preservation; policy development; and storage requirements. Yet, these types of institutions tend to follow different paths in data management and curation, choose different infrastructures, metadata standards and platforms. Are these different approaches inevitably rooted in the differences between these types of organizations and their missions and culture? Or are there lessons they could learn from each other to improve their own practice? The purpose of this symposium is to explore that question. We will have presentations first, then form breakout groups along the lines of different aspects such as platform choice, policy developments, metadata creation. At the end, all will come back together to share the results of their discussion.

Edith Cowan University (ECU) is one of twelve Australian universities striving to create world-class research capacity and capability through investment in the Collaborative Research Network (CRN) project. The CRN project at ECU aims to accelerate the growth of research activity in four key areas: Health, Education, ICT, and Environment. These areas align to both the Australian Government's National Research Priorities and ECU's strategic research plan. Managing eResearch data is essential, especially within a CRN to ensure the maximum benefit is achieved from the investigations. Currently, there is no set model to follow. Normally an institution will establish its own policy and procedures which are applicable to their researchers and data. However, this causes difficulties for researchers sharing data across a collaborative network as policies and procedures may differ. The CRN project will create a significant amount of new data and it is imperative that this data is managed so that it is secure, accessible and usable. Consequently, research data management policies, procedures and technical solutions are being developed to support this process.