3,065 research outputs found

    Designing Data Spaces

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    This open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries. To this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more. Overall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty

    The Federal Big Data Research and Development Strategic Plan

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    This document was developed through the contributions of the NITRD Big Data SSG members and staff. A special thanks and appreciation to the core team of editors, writers, and reviewers: Lida Beninson (NSF), Quincy Brown (NSF), Elizabeth Burrows (NSF), Dana Hunter (NSF), Craig Jolley (USAID), Meredith Lee (DHS), Nishal Mohan (NSF), Chloe Poston (NSF), Renata Rawlings-Goss (NSF), Carly Robinson (DOE Science), Alejandro Suarez (NSF), Martin Wiener (NSF), and Fen Zhao (NSF). A national Big Data1 innovation ecosystem is essential to enabling knowledge discovery from and confident action informed by the vast resource of new and diverse datasets that are rapidly becoming available in nearly every aspect of life. Big Data has the potential to radically improve the lives of all Americans. It is now possible to combine disparate, dynamic, and distributed datasets and enable everything from predicting the future behavior of complex systems to precise medical treatments, smart energy usage, and focused educational curricula. Government agency research and public-private partnerships, together with the education and training of future data scientists, will enable applications that directly benefit society and the economy of the Nation. To derive the greatest benefits from the many, rich sources of Big Data, the Administration announced a “Big Data Research and Development Initiative” on March 29, 2012.2 Dr. John P. Holdren, Assistant to the President for Science and Technology and Director of the Office of Science and Technology Policy, stated that the initiative “promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security.” The Federal Big Data Research and Development Strategic Plan (Plan) builds upon the promise and excitement of the myriad applications enabled by Big Data with the objective of guiding Federal agencies as they develop and expand their individual mission-driven programs and investments related to Big Data. The Plan is based on inputs from a series of Federal agency and public activities, and a shared vision: We envision a Big Data innovation ecosystem in which the ability to analyze, extract information from, and make decisions and discoveries based upon large, diverse, and real-time datasets enables new capabilities for Federal agencies and the Nation at large; accelerates the process of scientific discovery and innovation; leads to new fields of research and new areas of inquiry that would otherwise be impossible; educates the next generation of 21st century scientists and engineers; and promotes new economic growth. The Plan is built around seven strategies that represent key areas of importance for Big Data research and development (R&D). Priorities listed within each strategy highlight the intended outcomes that can be addressed by the missions and research funding of NITRD agencies. These include advancing human understanding in all branches of science, medicine, and security; ensuring the Nation’s continued leadership in research and development; and enhancing the Nation’s ability to address pressing societal and environmental issues facing the Nation and the world through research and development

    The Federal Big Data Research and Development Strategic Plan

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    This document was developed through the contributions of the NITRD Big Data SSG members and staff. A special thanks and appreciation to the core team of editors, writers, and reviewers: Lida Beninson (NSF), Quincy Brown (NSF), Elizabeth Burrows (NSF), Dana Hunter (NSF), Craig Jolley (USAID), Meredith Lee (DHS), Nishal Mohan (NSF), Chloe Poston (NSF), Renata Rawlings-Goss (NSF), Carly Robinson (DOE Science), Alejandro Suarez (NSF), Martin Wiener (NSF), and Fen Zhao (NSF). A national Big Data1 innovation ecosystem is essential to enabling knowledge discovery from and confident action informed by the vast resource of new and diverse datasets that are rapidly becoming available in nearly every aspect of life. Big Data has the potential to radically improve the lives of all Americans. It is now possible to combine disparate, dynamic, and distributed datasets and enable everything from predicting the future behavior of complex systems to precise medical treatments, smart energy usage, and focused educational curricula. Government agency research and public-private partnerships, together with the education and training of future data scientists, will enable applications that directly benefit society and the economy of the Nation. To derive the greatest benefits from the many, rich sources of Big Data, the Administration announced a “Big Data Research and Development Initiative” on March 29, 2012.2 Dr. John P. Holdren, Assistant to the President for Science and Technology and Director of the Office of Science and Technology Policy, stated that the initiative “promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security.” The Federal Big Data Research and Development Strategic Plan (Plan) builds upon the promise and excitement of the myriad applications enabled by Big Data with the objective of guiding Federal agencies as they develop and expand their individual mission-driven programs and investments related to Big Data. The Plan is based on inputs from a series of Federal agency and public activities, and a shared vision: We envision a Big Data innovation ecosystem in which the ability to analyze, extract information from, and make decisions and discoveries based upon large, diverse, and real-time datasets enables new capabilities for Federal agencies and the Nation at large; accelerates the process of scientific discovery and innovation; leads to new fields of research and new areas of inquiry that would otherwise be impossible; educates the next generation of 21st century scientists and engineers; and promotes new economic growth. The Plan is built around seven strategies that represent key areas of importance for Big Data research and development (R&D). Priorities listed within each strategy highlight the intended outcomes that can be addressed by the missions and research funding of NITRD agencies. These include advancing human understanding in all branches of science, medicine, and security; ensuring the Nation’s continued leadership in research and development; and enhancing the Nation’s ability to address pressing societal and environmental issues facing the Nation and the world through research and development

    Promoting access to public research data for scientific, economic, and social development

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    It is now commonplace to say that information and communications technologies are rapidly transforming the world of research. We are only beginning to recognize, however, that management of the scientific enterprise must adapt if we, as a society, are to take full advantage of the knowledge and understanding generated by researchers. One of the most important areas of information and communication technology (ICT)-driven change is the emergence of escience, briefly described as universal desktop access, via the Internet, to distributed resources, global collaboration, and the intellectual, analytical, and investigative output of the world’s scientific community.The vision of e-science is being realised in relation to the outputs of science, particularly journal articles and other forms of scholarly publication. This realisation extends less to research data, the raw material at the heart of the scientific process and the object of significant annual public investments.Ensuring research data are easily accessible, so that they can be used as often and as widely as possible, is a matter of sound stewardship of public resources. Moreover, as research becomes increasingly global, there is a growing need to systematically address data access and sharing issues beyond national jurisdictions. The goals of this report and its recommendations are to ensure that both researchers and the public receive optimum returns on the public investments in research, and to build on the value chain of investments in research and research data. To some extent, research data are shared today, often quite extensively within established networks, using both the latest technology and innovative management techniques. The Follow Up Group drew on the experiences of several of these networks to examine the roles and responsibilities of governments as they relate to data produced from publicly funded research. The objective was to seek good practices that can be used by national governments, international bodies, and scientists in other areas of research. In doing so, the Group developed an analytical framework for determining where further improvements can be made in the national and international organization, management, and regulation of research data.The findings and recommendations presented here are based on the central principle that publicly funded research data should be openly available to the maximum extent possible. Availability should be subject only to national security restrictions; protection of confidentiality and privacy; intellectual property rights; and time-limited exclusive use by principal investigators. Publicly funded research data are a public good, produced in the public interest. As such they should remain in the public realm. This does not preclude the subsequent commercialization of research results in patents and copyrights, or of the data themselves in databases, but it does mean that a copy of the data must be maintained and made openly accessible. Implicitly or explicitly, this principle is recognized by many of the world’s leading scientific institutions, organizations, andagencies. Expanding the adoption of this principle to national and international stages will enable researchers, empower citizens and convey tremendous scientific, economic, and social benefits. Evidence from the case studies and from other investigation undertaken for this report suggest that successful research data access and sharing arrangements, or regimes, share a number of key attributes and operating principles. These bring effective organization and management to the distribution and exchange of data. The key attributes include: openness; transparency of access and active dissemination; the assignment and assumption of formal responsibilities; interoperability; quality control; operational efficiency and flexibility; respect for private intellectual property and other ethical and legal matters; accountability; and professionalism. Whether they are discipline-specific or issue oriented, national or international, the regimes that adhere to these operating principles reap the greatest returns from the use of research data. There are five broad groups of issues that stand out in any examination of research data access and sharing regimes. The Follow Up Group used these as an analytical framework for examining the case studies that informed this report, and in doing so, came to several broad conclusions: • Technological issues: Broad access to research data, and their optimum exploitation, requires appropriately designed technological infrastructure, broad international agreement on interoperability, and effective data quality controls; • Institutional and managerial issues: While the core open access principle applies to all science communities, the diversity of the scientific enterprise suggests that a variety of institutional models and tailored data management approaches are most effective in meeting the needs of researchers; • Financial and budgetary issues: Scientific data infrastructure requires continued, and dedicated, budgetary planning and appropriate financial support. The use of research data cannot be maximized if access, management, and preservation costs are an add-on or after-thought in research projects; • Legal and policy issues: National laws and international agreements directly affect data access and sharing practices, despite the fact that they are often adopted without due consideration of the impact on the sharing of publicly funded research data; • Cultural and behavioural issues: Appropriate reward structures are a necessary component for promoting data access and sharing practices. These apply to both those who produce and those who manage research data.The case studies and other research conducted for this report suggest that concrete, beneficial actions can be taken by the different actors involved in making possible access to, and sharing of, publicly funded research data. This includes the OECD as an international organization with credibility and stature in the science policy area. The Follow Up Group recommends that the OECD consider the following: • Put the issues of data access and sharing on the agenda of the next Ministerial meeting; • In conjunction with relevant member country research organizations, o Conduct or coordinate a study to survey national laws and policies that affect data access and sharing practices; o Conduct or coordinate a study to compile model licensing agreements and templates for access to and sharing of publicly funded data; • With the rapid advances in scientific communications made possible by recent developments in ICTs, there are many aspects of research data access and sharing that have not been addressed sufficiently by this report, would benefit from further study, and will need further clarification. Accordingly, further possible actions areas include: o Governments from OECD expand their policy frameworks of research data access and sharing to include data produced from a mixture of public and private funds; o OECD consider examinations of research data access and sharing to include issues of interacting with developing countries; and o OECD promote further research, including a comprehensive economic analysis of existing data access regimes, at both the national and research project or program levels.National governments have a crucial role to play in promoting and supporting data accessibility since they provide the necessary resources, establish overall polices for data management, regulate matters such as the protection of confidentiality and privacy, and determine restrictions based on national security. Most importantly, national governments are responsible for major research support and funding organizations, and it is here that many of the managerial aspects ofdata sharing need to be addressed. Drawing on good practices worldwide, the Follow Up Group suggests that national governments should consider the following: • Adopt and effectively implement the principle that data produced from publicly funded research should be openly vailable to the maximum extent possible; • Encourage their research funding agencies and major data producing departments to work together to find ways to enhance access to statistical data, such as census materials and surveys; • Adopt free access or marginal cost pricing policies for the dissemination of researchuseful data produced by government departments and agencies; • Analyze, assess, and monitor policies, programs, and management practices related to data access and sharing polices within their national research and research funding organizations. The widespread national, international and cross-disciplinary sharing of research data is no longer a technological impossibility. Technology itself, however, will not fulfill the promise of escience.Information and communication technologies provide the physical infrastructure. It is up to national governments, international agencies, research institutions, and scientists themselves to ensure that the institutional, financial and economic, legal, and cultural and behavioural aspects of data sharing are taken into account

    The IPTS Report No. 53, April 2001

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    Data ecosystems for the sustainability transformation : a study commissioned by Huawei Technologies Deutschland GmbH

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    In the coming years, we must set a course that will allow as to protect our climate, reduce resource consumption, and preserve biodiversity. A profound ecological system change is on the horizon in all central areas of action of the economy and society, or transformation arenas. Digitalisation is a prerequisite for the success in this change and will impact these arenas at multiple levels: Digital technologies and applications will make it possible to improve current procedures, processes, and structures (Improve) and help us take the first steps towards new business models and frameworks (Convert). Despite this, digitalisation itself must be effective enough to facilitate a complete ecological restructuring of our society and lives to achieve more far-reaching economic transformation and value creation (Transform). The ability to obtain, link, and use data is a basic prerequisite for tapping into the potential of digitisation for sustainability transformation. However, data is not a homogeneous raw material. Data only gains value when we know the context in which it was collected and when we can use it for a specific purpose. The discussion on what structures and prerequisites are necessary for the system-changing use of data has only just begun. This study was conducted to serve as a starting point for this discussion as it describes the opportunities and prerequisites for a data-based sustainability transformation. This study focuses on environmental data, data from plants, machines, infrastructure, and IoT products. Our task will be to increase the use this data for systemic solutions (system innovation) within transformation arenas where different stakeholders are working together to initiate infrastructure, value chain, and business model transformation
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