380,717 research outputs found

    Sustainable Software Ecosystems: Software Engineers, Domain Scientists, and Engineers Collaborating for Science

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    The development of scientific software is often a partnership between domain scientists and scientific software engineers. It is especially important to embrace these collaborations when developing advanced scientific software, where sustainability, reproducibility, and extensibility are important. In the ideal case, as discussed in this manuscript, this brings together teams composed of the world's foremost scientific experts in a given field with seasoned software developers experienced in forming highly collaborative teams working on software to further scientific research.Comment: 4 pages, submission for WSSSPE

    Capacity building in Ocean Bathymetry: The Nippon Foundation GEBCO Training Programme at the University of New Hampshire

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    A successful Capacity Building project in hydrography is underway at the University of New Hampshire. Organised by the General Bathymetric Chart of the Oceans and sponsored by the Nippon Foundation, the programme trains hydrographers and other marine scientists in bathymetric mapping. Participants are formally prepared to produce bathymetric maps when they return to their home countries through a combination of graduate level courses and workshops, practical field training, participation in deep ocean research cruises, working visits to other laboratories and institutions, focused lectures from visiting experts, and the preparation of a bathymetry map of their area from public domain data. Intangible but necessary preparation includes the networking with professionals in bathymetry and related fields within Ocean Mapping, and the building of a cadre of graduates who will form the basis of international bathymetric mapping in the future

    Multi-user interface for co-located real-time work with digital mock-up: a way to foster collaboration?

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    Nowadays more and more industrial design activities adopt the strategy of Concurrent Engineering (CE), which changes the way to carry out all the activities along the product’s lifecycle from sequential to parallel. Various experts of different activities produce technical data using domain-specific software. To augment the interoperability among the technical data, a Digital Mock-Up (DMU), or a Building Information Model (BIM) in architectural engineering can be used. Through an appropriate Computer–Human Interface (CHI), each expert has his/her own point-of-view (POV) of a specific representation of DMU’s technical data according to an involved domain. When multiple experts work collaboratively in the same place and at the same time, the number of CHIs is also multiplied by the number of experts. Instead of multiple CHIs, therefore, a unique CHI should be developed to support the multiview and multi-interaction collaborative works. Our contributions in this paper are (a) a concept of a CHI system with multi-view and multi-interaction of DMU for multiple users in collaborative design; (b) a state of the art of multi-view and multi-interaction metaphors; (c) an experiment to evaluate a collaborative application using multi-view CHI. The experimental results indicate that, in multi-view CHI working condition, users are more efficient than in the other two working conditions (multiple CHIs and split view CHI). Moreover, in multi-view CHI working condition, the user, who is helping the other, takes less mutual awareness of where the other collaborator works than the other two working conditions.Bourse de thèse de CSC (China Scholarship Council

    Sticks, balls or a ribbon? Results of a formative user study with bioinformaticians

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    User interfaces in modern bioinformatics tools are designed for experts. They are too complicated for\ud novice users such as bench biologists. This report presents the full results of a formative user study as part of a\ud domain and requirements analysis to enhance user interfaces and collaborative environments for\ud multidisciplinary teamwork. Contextual field observations, questionnaires and interviews with bioinformatics\ud researchers of different levels of expertise and various backgrounds were performed in order to gain insight into\ud their needs and working practices. The analysed results are presented as a user profile description and user\ud requirements for designing user interfaces that support the collaboration of multidisciplinary research teams in\ud scientific collaborative environments. Although the number of participants limits the generalisability of the\ud findings, the combination of recurrent observations with other user analysis techniques in real-life settings\ud makes the contribution of this user study novel

    Knowledge Acquisition and Structuring by Multiple Experts in a Group Support Systems Environment

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    This study addresses the impact of Group Decision Support Systems (GDSS) on expert system development by multiple Domain Experts. Current approaches to building expert systems rely heavily on knowledge acquisition and prototyping by a Knowledge Engineer working directly with the Domain Expert. Although the complexity of knowledge domains and new organizational approaches demand the involvement of multiple experts, standard procedures limit the ability of the Knowledge Engineer to work with more than one expert at a time. Group Decision Support Systems offer a networked computerized environment for group work activities, in which multiple experts may express their ideas concurrently and anonymously through the electronic channel. GDSS systems have been widely used in other applications to support idea generation, conflict management, and the organizing, prioritizing, and synthesizing of ideas. The effects of many group process and technical factors on GDSS have been widely studied and documented. A review of the literature on expert systems, GDSS, and GDSS in relation to expert systems was conducted. Knowledge gained from this review was applied in the construction of an exploratory research model intended to provide the necessary breadth to identify factors worthy of future, more statistically-based, investigation. Domain Experts represented by college students were charged with developing and prioritizing ideas for creating a pre-prototypical expert system. The treatment group worked in a GDSS environment with a facilitator; a control group worked with a facilitator but without the assistance of GDSS. Each group then exchanged facilitators and technology to address another real-life problem. Additional groups worked with GDSS over time, addressing both problems. Data were gathered, analyzed and discussed relating to group efficiency factors, group process factors, attitudinal factors, and product quality factors. Independent Knowledge Engineers and Domain Experts evaluated the validity and verifiability of the group products. Analysis focused on the effect of GDSS in facilitating the acquisition and structuring of ideas for expert systems by multiple Domain Experts

    Automatic detection of accommodation steps as an indicator of knowledge maturing

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    Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed

    Unifying Multiple Knowledge Domains Using the ARTMAP Information Fusion System

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    Sensors working at different times, locations, and scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels that are reconciled by their implicit underlying relationships. Even when such relationships are unknown to the user, an ARTMAP information fusion system discovers a hierarchical knowledge structure for a labeled dataset. The present paper addresses the problem of integrating two or more independent knowledge hierarchies based on the same low-level classes. The new system fuses independent domains into a unified knowledge structure, discovering cross-domain rules in this process. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, ARTMAP information fusion system features distributed code representations that exploit the neural network’s capacity for one-to-many learning. The fusion system software and testbed datasets are available from http://cns.bu.edu/techlabNational Science Foundation (SBE-0354378); National Geospatial-Intelligence Agency (NMA 201-01-1-2016
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