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Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients.
On March 19, 2014, the National Academies of Sciences, Engineering, and Medicine held a workshop on the topic of the sharing of data from environmental health research. Experts in the field of environmental health agree that there are benefits to sharing research data, but questions remain regarding how to effectively make these data available. The sharing of data derived from human subjects-making them both transparent and accessible to others-raises a host of ethical, scientific, and process questions that are not always present in other areas of science, such as physics, geology, or chemistry. The workshop participants explored key concerns, principles, and obstacles to the responsible sharing of data used in support of environmental health research and policy making while focusing on protecting the privacy of human subjects and addressing the concerns of the research community. Principles and Obstacles for Sharing Data from Environmental Health Research summarizes the presentations and discussions from the workshop.
In an increasingly interconnected world, perhaps it should come as no surprise that international collaboration in science and technology research is growing at a remarkable rate. As science and technology capabilities grow around the world, U.S.-based organizations are finding that international collaborations and partnerships provide unique opportunities to enhance research and training. International research agreements can serve many purposes, but data are always involved in these collaborations. The kinds of data in play within international research agreements varies widely and may range from financial and consumer data, to Earth and space data, to population behavior and health data, to specific project-generated dataâ€"this is just a narrow set of examples of research data but illustrates the breadth of possibilities. The uses of these data are various and require accounting for the effects of data access, use, and sharing on many different parties. Cultural, legal, policy, and technical concerns are also important determinants of what can be done in the realms of maintaining privacy, confidentiality, and security, and ethics is a lens through which the issues of data, data sharing, and research agreements can be viewed as well. A workshop held on March 14-16, 2018, in Washington, DC explored the changing opportunities and risks of data management and use across disciplinary domains. The third workshop in a series, participants gathered to examine advisory principles for consideration when developing international research agreements, in the pursuit of highlighting promising practices for sustaining and enabling international research collaborations at the highest ethical level possible. The intent of the workshop was to explore, through an ethical lens, the changing opportunities and risks associated with data management and use across disciplinary domainsâ€"all within the context of international research agreements. This publication summarizes the presentations and discussions from the workshop.
Genomic Data Sharing: Case Studies, Challenges, and Opportunities for Precision Medicine provides a comprehensive overview of current and emerging issues in genomic data sharing. In this book, international leaders in genomic data examine these issues in-depth, offering practical case studies that highlight key successes, challenges and opportunities. Sections discuss the eMERGE Network, Undiagnosed Disease Network, Vanderbilt Biobank, Marshfield Clinic Biobank, Minnesota Authorization, Rochester Epidemiology Project, NIH sponsored biobanks, GINA, and Global Alliance for Genomics and Health (GA4GH). In addition to these perspectives from the frontlines, the book also provides succinct overviews of ethical, legal, social and IT challenges.Clinician investigators, clinicians affiliated with academic medical centers, policymakers and regulators will also gain insights that will allow them to navigate the increasingly complex ethical, social and clinical landscape of genomic data sharing. - Covers both technical and ELSI (ethical, legal, and social implications) perspectives on genomic data sharing - Includes applied case studies of existing genomic data sharing consortia, including the eMERGE Network, Undiagnosed Disease Network, and the Global Alliance for Genomics and Health (GA4GH), among others - Features chapter contributions from international leaders in genomic data sharing
On November 18 and 19, 2019, the National Academies of Sciences, Engineering, and Medicine hosted a public workshop in Washington, DC, titled Sharing Clinical Trial Data: Challenges and a Way Forward. The workshop followed the release of the 2015 Institute of Medicine (IOM) consensus study report Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk, and was designed to examine the current state of clinical trial data sharing and reuse and to consider ways in which policy, technology, incentives, and governance could be leveraged to further encourage and enhance data sharing. This publication summarizes the presentations and discussions from the workshop.
For more than 20 years, supply chains have been optimized using electronic data interchange based on the EDIFACT standard. In 2010, the EU Commission launched a digital agenda to optimize public administration processes through uniform and cross-border regulations. Ongoing globalization and increased environmental awareness are creating increasing de-mands for the transport industry, such as shifting transport from road to rail. Several projects are focusing on the semantic standardization of data exchange based on web technologies, in order to optimize system interaction between all players of the transport supply chain. This study investigates, if using a semantic reference data model instead of a proprietary data model fosters the intensity of possible interactions of data exchange systems in the European goods transport industry. To be able to measure a change drivers, benefits, challenges, and success factors for collaborative data exchange are identified. The research in this study bases its solutions on the system interaction theory and performs a literature review as well as collects primary data. Six representatives of important players of the European goods transport industry are interviewed and surveyed.
With the development of big data, data sharing has become increasingly popular and important in optimizing resource allocation and improving information utilization. However, the expansion of data sharing means there is an urgent need to address the issue of the privacy protection – an area where the emerging blockchain technology offers considerable advantages. Although there are a large number of research papers on data sharing modeling and analysis of network security, there are few books dedicated to blockchain-based secure data sharing. Filing this gap in the literature, the book proposes a new data-sharing model based on the blockchain system, which is being increasingly used in medical and credit reporting contexts. It describes in detail various aspects of the model, including its role, transaction structure design, secure multi-party computing and homomorphic encryption services, and incentive mechanisms, and presents corresponding case studies. The book explains the security architecture model and the practice of building data sharing from the blockchain infrastructure, allowing readers to understand the importance of data sharing security based on the blockchain framework, as well as the threats to security and privacy. Further, by presenting specific data sharing case studies, it offers insights into solving data security sharing problems in more practical fields. The book is intended for readers with a basic understanding of the blockchain infrastructure, consensus mechanisms, smart contracts, secure multiparty computing, homomorphic encryption and image retrieval technologies.
Pharmaceutical companies, academic researchers, and government agencies such as the Food and Drug Administration and the National Institutes of Health all possess large quantities of clinical research data. If these data were shared more widely within and across sectors, the resulting research advances derived from data pooling and analysis could improve public health, enhance patient safety, and spur drug development. Data sharing can also increase public trust in clinical trials and conclusions derived from them by lending transparency to the clinical research process. Much of this information, however, is never shared. Retention of clinical research data by investigators and within organizations may represent lost opportunities in biomedical research. Despite the potential benefits that could be accrued from pooling and analysis of shared data, barriers to data sharing faced by researchers in industry include concerns about data mining, erroneous secondary analyses of data, and unwarranted litigation, as well as a desire to protect confidential commercial information. Academic partners face significant cultural barriers to sharing data and participating in longer term collaborative efforts that stem from a desire to protect intellectual autonomy and a career advancement system built on priority of publication and citation requirements. Some barriers, like the need to protect patient privacy, pre- sent challenges for both sectors. Looking ahead, there are also a number of technical challenges to be faced in analyzing potentially large and heterogeneous datasets. This public workshop focused on strategies to facilitate sharing of clinical research data in order to advance scientific knowledge and public health. While the workshop focused on sharing of data from preplanned interventional studies of human subjects, models and projects involving sharing of other clinical data types were considered to the extent that they provided lessons learned and best practices. The workshop objectives were to examine the benefits of sharing of clinical research data from all sectors and among these sectors, including, for example: benefits to the research and development enterprise and benefits to the analysis of safety and efficacy. Sharing Clinical Research Data: Workshop Summary identifies barriers and challenges to sharing clinical research data, explores strategies to address these barriers and challenges, including identifying priority actions and "low-hanging fruit" opportunities, and discusses strategies for using these potentially large datasets to facilitate scientific and public health advances.