216 research outputs found

    Cyberinfrastructure and the future of the World Research University

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    http://deepblue.lib.umich.edu/bitstream/2027.42/88581/1/2004_Draft_CLEAR_prospectus.pd

    A semantic service-oriented architecture for distributed model management systems

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    Decision models are organizational resources that need to be managed to facilitate sharing and reuse. In today\u27s networked economy, the ubiquity of the Internet and distributed computing environments further amplifies the need and the potential for distributed model management system (DMMS) that manages decision models throughout the modeling lifecycle and throughout the extended enterprise

    Operationalizing Personalized Medicine: Data Translation Practices in Bioinformatics Laboratories

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    This paper presents findings from an ethnographic study of two genomics and bioinformatics labs. The focus of this research is on the day-to-day practices of using multiple technologies to integrate data across different platforms. We argue that sociotechnical challenges (including technical, contextual, and political challenges) emerge when data integration practices are carried out, due to the embedded nature of the important, yet unrecorded and implicit historical information that each dataset carries. We observed that sociotechnical sensemaking was common place in lab work, and was the only method for working out the complexity of the challenges which arose during data integration activities. We suggest that due attention be given to this matter, as challenges related to assessing data are likely to arise once more when such data travels back to the bedside, where it is poised to directly impact human health

    Open Pedagogy

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    Chapter in the book Scholarly Communication Librarianship and Open Knowledge Editors: Maria Bonn, Joshua Bolick, and William Cross Publisher: The Association of College Research Libraries Year: 202

    Web 2.0 Broker: A standards-based service for spatio-temporal search of crowd-sourced information

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    Recent trends in information technology show that citizens are increasingly willing to share information using tools provided by Web 2.0 and crowdsourcing platforms to describe events that may have social impact. This is fuelled by the proliferation of location-aware devices such as smartphones and tablets; users are able to share information in these crowdsourcing platforms directly from the field at real time, augmenting this information with its location. Afterwards, to retrieve this information, users must deal with the different search mechanisms provided by the each Web 2.0 services. This paper explores how to improve on the interoperability of Web 2.0 services by providing a single service as a unique entry to search over several Web 2.0 services in a single step. This paper demonstrates the usefulness of the Open Geospatial Consortium's OpenSearch Geospatial and Time specification as an interface for a service that searches and retrieves information available in crowdsourcing services. We present how this information is valuable in complementing other authoritative information by providing an alternative, contemporary source. We demonstrate the intrinsic interoperability of the system showing the integration of crowd-sourced data in different scenarios

    Challenging Digital Archaeology

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    A keynote presentation at the 2012 Computer Applications in Archaeology (CAA) conference in Southampton (UK) proposed the use of grand challenges as a vehicle for identifying and pursuing major advances in Digital Archaeology. At the same time, it was argued that this should be a collaborative venture. This was taken forward at a round table session at the 2014 CAA in Paris, and a number of papers in this volume were presented there. This paper introduces the concept of grand challenges for Digital Archaeology and seeks to define their key characteristics

    Review: Artificial Intelligence for Liquid-Vapor Phase-Change Heat Transfer

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    Artificial intelligence (AI) is shifting the paradigm of two-phase heat transfer research. Recent innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not been addressed in the past, for making their insights available to other domains, and for solving for physical quantities based on first principles for phase-change thermofluidic systems. This review outlines core ideas of current AI technologies connected to thermal energy science to illustrate how they can be used to push the limit of our knowledge boundaries about boiling and condensation phenomena. AI technologies for meta-analysis, data extraction, and data stream analysis are described with their potential challenges, opportunities, and alternative approaches. Finally, we offer outlooks and perspectives regarding physics-centered machine learning, sustainable cyberinfrastructures, and multidisciplinary efforts that will help foster the growing trend of AI for phase-change heat and mass transfer

    Understanding the Role of Social, Technology, and Physical Infrastructures in Smart Communities: The Case of Rural Areas in the US

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    Smartness is a concept that frames a great variety of initiatives, particularly in the urban context. Smart cities are expected to be more resilient, more sustainable, and have highly engaged citizens, among many other expected outcomes. Given the focus on urban settings, many examples of smartness take for granted that the physical and technological infrastructures exist and are available to the majority of residents. For instance, Internet access, a reliable transportation system, or electrical power are rarely questioned or considered as a problem to be solved before becoming smart. In addition, formal education and technical skills are also expected as part of the social infrastructure of a city. However, when smartness goes beyond the urban settings, the availability and combination of these different infrastructures also differ. Based on a study of a rural community in the US, this paper begins to fill a gap in what is known about smartness in rural communities by analyzing how the physical, technology and social infrastructures in rural areas are different from urban settings, but still generate unique opportunities for building smart communities. Our results indicate that the unique conditions of rural communities create atypical strengths for becoming smarter
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