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Innovation from big science : enhancing big science impact agenda

Innovation from big science : enhancing big science impact agenda

Thus, different governance modes are required for “linear” and “non-linear” projects. The main point is that non-linear big-science projects often exhibit extraordinary complexity not only in terms of scientific missions and technological installations, but also in terms of the organisational and governance arrangements created to implement the projects. Because the projects are carried out in international networks consisting of research institutions and industrial suppliers that exhibit different emphasis over different stages of the project lifecycle, the resulting potential for innovation is similarly complex. During the early, mission-defining stages of a big-science project, fundamental and theoretical research dominate, often informed by empirical findings achieved in previous installations. This stage is important for subsequent innovation potential, because during this stage the project is sold to participating governments and its governance and funding structure is set up. Important decisions at this stage include, for example: (1) principles of procurement policies (including the design of the tendering process; possible countries-of-origin requirements; rules governing the choice between competing); (2) rules governing university collaborations; (3) emphasis laid on alternative missions (e.g., discovery orientation vs solution orientation; emphasis on service mission; etc); and (4) emphasis given to different impact delivery mechanisms (e.g., spin-outs and technology transfer activities; human resource development; support and services provided to universities and industry; and so on).
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Big science, learning and innovation: evidence from CERN procurement

Big science, learning and innovation: evidence from CERN procurement

This line of argument implies not only that the development and diffusion of innovations through PPI depend on user-producer interaction in the procurement process (Newcombe, 1999; Mowery and Rosenberg, 1979), but also that science organisations, such as CERN, can be seen as “lead-users” acting as learning environments for suppliers, who often strive to meet the stringent technological specifica- tions of the projects planned (Unnervik, 2009). Autio et al. (2004) argue that communication and inter- action in the dyad consisting of big science and industry mainly take the form of technological learning by the latter from the former.
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The Human Genome Project: big science transforms biology and medicine

The Human Genome Project: big science transforms biology and medicine

Fifth, the HGP, as conceived and implemented, was the first example of ‘big science’ in biology, and it clearly demonstrated both the power and the necessity of this approach for dealing with its integrated biological and technological aims. The HGP was characterized by a clear set of ambitious goals and plans for achieving them; a limited number of funded investigators typically organized around centers or consortia; a commitment to public data/resource release; and a need for significant funding to support project infrastructure and new tech- nology development. Big science and smaller-scope individual-investigator-oriented science are powerfully complementary, in that the former generates resources that are foundational for all researchers while the latter adds detailed experimental clarification of specific ques- tions, and analytical depth and detail to the data produced by big science. There are many levels of complexity in biology and medicine; big science projects are essential to tackle this complexity in a comprehensive and integrative manner [45].
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The ethos of Systems Biology as big science. Big Science - a characteristic feature of systems biology? How and why does it matter?

The ethos of Systems Biology as big science. Big Science - a characteristic feature of systems biology? How and why does it matter?

The ethos of Systems Biology as big science Big Science - a characteristic feature of systems?. biology.[r]

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Danish Big Science Network Civil Engineering and Building

Danish Big Science Network Civil Engineering and Building

www.amfitech.dk Balslev Consulting Engineers www.balslev.com BB Electronics A/S www.bbelectronics.dk COWI A/S www.cowi.com Danish Technological Institute www.dti.dk. Force Technology[r]

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Example Questions Big Science Competition

Example Questions Big Science Competition

However, the cells of DFTD tumours are slightly different from the non-cancerous cells of the infected Tasmanian devil itself. This suggests that[r]

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Big Data Science. Prof.dr.ir. Geert-Jan Houben. TU Delft Web Information Systems Delft Data Science KIVI chair Big Data Science

Big Data Science. Prof.dr.ir. Geert-Jan Houben. TU Delft Web Information Systems Delft Data Science KIVI chair Big Data Science

Scientific and Societal Challenges The quadruple helix: prosperous society & blooming economy & inventive academia & wise governance •   Enable data access & processing as a fundamental right in Europe •   Enable big science and engineering (2020: € 100 bn., 1 mil. jobs) •   “To out-compute is to out-compete”, but with energy footprint <5% •   Keep Internet-services affordable yet high quality in Europe

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Data Curation for Big Interdisciplinary Science: The Pulley Ridge Experience

Data Curation for Big Interdisciplinary Science: The Pulley Ridge Experience

Perhaps the most significant conclusion is that the lack of planning for post-project data curation in interdisciplinary big science is an opportunity for data curators to become data synthesizers, integrators of project results, and ultimately storytellers. Often research proposals that outline interdisciplinary approaches to wicked problems, little planning exists for the work necessary to integrate the final results from distinct disciplines, to use the integrated results to communicated findings either within the project or to decision makers and the general public, and ultimately to address the wicked problem as described in the original proposal. This lack of planning spans institutional to national levels, such as the U-LINK program described in the introduction and the Pulley Ridge Project described in this article, respectively. As a result, there are no resources set aside to perform this work, there is no overarching data management plan that allows for data interoperability within the project, and there are few incentives for disciplinary researchers to take on the task of integrating and communicating results to others outside of their discipline.
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ENGINEERING, SCIENCE, TECHNOLOGY, & BIG DATA FAIR SPRING 2015

ENGINEERING, SCIENCE, TECHNOLOGY, & BIG DATA FAIR SPRING 2015

www.sungard.com Accountancy, All Engineering, Business Administration, Business Minor, Computer Science, Finance, Information Science, Information Systems, Management, Marketing, M[r]

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Big Ideas. Working with. of Science Education. Edited by Wynne Harlen

Big Ideas. Working with. of Science Education. Edited by Wynne Harlen

In the case of big ideas 13 and 14, about the relationship between science and other STEM subjects and the applications of science, there are various ways in which these are included. In some cases it is through supplying cross-references, usually between the science and mathematics documents. However, these links tend to be regarded as optional when it comes to planning classroom programmes, which is often carried out by individual teachers or single subject groups, rather than in multi-disciplinary teams in which members bring their specialist expertise and together create coordinated learning experiences. Another approach is to embed reference to applications of science in the description of overall aims, as, for example, the discussion of moral and ethical questions arising from technological developments relating to DNA. A third, and possibly more effective way, is to make the links among subject domains an integral part of the curriculum framework. An example is the Framework for K-12 Science Education, where engineering and applications of science are identified as a disciplinary core idea in the same way as physical and life sciences. However, the extent to which these various attempts signal the growing importance of understanding links between science and other subject domains, particularly technology, engineering and mathematics, has yet to be seen.
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Principles and big ideas of science education Edited by Wynne Harlen

Principles and big ideas of science education Edited by Wynne Harlen

However, the reasons for wanting to define scientific big ideas provide strong arguments for including ideas about scientific activity. In a world increasingly dependent on the applications of science, young people may feel powerless without some understanding not just of how things can be explained but of how to evaluate the quality of the information on which explanations are based. In science this evaluation depends on the methods used in collecting, analysing and interpreting data. Questioning the basis of ideas enables all of us to reject claims that are based on false evidence and to recognise when evidence is being used selectively to support particular actions. This is a key part of using scientific knowledge to evaluate evidence in order to make decisions such as about the use of natural resources. These capabilities are frequently described as constituting ‘scientific literacy’. However, the compass of this phrase has been extended so far that its meaning has become uncertain, and for this reason we have not used it in this discussion.
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Thinking Big about Mathematics, Science, and Technology: Effective Teaching STEMS from big ideas

Thinking Big about Mathematics, Science, and Technology: Effective Teaching STEMS from big ideas

Current discussion amongst mathematics and science educators seeks to clarify the nature of STEM education. This paper considers the benefits of an integrated view of STEM. Recent mathematics curricula, such as the Common Core State Standards for Mathematics (USA) and the Australian Curriculum: Mathematics, present content in a traditional linear and compartmentalised manner, rather than accentuate the connections between the “big ideas” of mathematics. Both curricula pay lip service to the “big process ideas” (or proficiencies) that should be the vehicles for exposing links between and within the “big content ideas”. To some extent, the same criticism could be levelled at the Australian Curriculum: Science although it at least embeds key process ideas in one of three strands: Science Inquiry Skills. As well, both the Australian Curriculum: Science and the Australian Curriculum: Technologies acknowledge that understandings do not develop within the confines of a single year. It is suggested here that it may be beneficial to re-think the nature of key content and to organise it for teaching based on the “big ideas” of mathematics, science, and technology, emphasising the connections within and between them. This paper suggests that in attempting to deal with widely perceived “crowded curriculum”, teachers could consider the similarities between the big ideas of mathematics, science, and technology, and make the connections explicit for children.
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A Parallel Platform for Big Data Analytics : A Design Science Approach

A Parallel Platform for Big Data Analytics : A Design Science Approach

Methodologically, with this work we followed a pragmatic Design Science approach as it has been extensively covered in the academic IS literature (e.g. [30]- [32]). In particular, we adhered to the seven guidelines for Design Science studies as suggested by Hevner 19 . We provided the architecture and various technical elements of a parallel platform for truly Big Data analytics (Design as an Artifact). We underlined the problem relevance with the growing need and business potential of Big data analytics paired with needed technical solutions not yet being available in the market (Problem Relevance). With early real-life deployments of the proposed platform, we demonstrated the technical performance of the proposed platform in meeting client needs and outperforming traditional database solutions and pointed new business opportunities resulting from better analytic quality of big data sets and (Design Evaluation). Accordingly, with out study, we offer two contribution. First and foremost, we presented the design of a truly parallel, innovative platform for Big Data analytics. Secondly, we see outline business opportunities to be gained from generating state-of-the-art data quality from truly complex, close to real-time Big Data analytics (Research Contribution). We reached our contribution by harking back to insights from multiple scientific fields, such as data base technology, computer science, engineering, and web mining on the rather technical side and business model design on the management side (Research Rigor). Our design efforts have gone through numerous cycles and interactions in the labs and in the context of direct interactions with actual and potential users and clients (Design as a Search Process). Finally, we undertake ongoing efforts to communicate the proposed design and related, newly gained insights both in academic meetings and in presentations for practice (Communication of Research).
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Reproducible big data science: A case study in continuous FAIRness.

Reproducible big data science: A case study in continuous FAIRness.

We show here that these difficulties can be overcome via the use of relatively simple tools that either entirely automate or significantly streamline the many, often mundane, tasks that consume biomedical researcher time. These tools include Big Data Bags (BDBags) for data exchange and minimal identifiers (Minids) as persistent identifiers for intermediate data prod- ucts [ 7 ]; Globus cloud services for authentication and data transfer [ 8 , 9 ]; and the Galaxy- based Globus Genomics [ 10 ] and Docker containers [ 11 ] for reproducible cloud-based com- putations. Simple application programming interface (API)-level integration means that, for example, whenever a new BDBag is created to bundle outputs from a computation, a Minid can easily be created that can then be consumed by a subsequent computational step. We note that while the FAIR principles were originally stated with respect to published results, they should be applied to all aspects of the data lifecycle, including not only final results but also intermediate data and analysis code. To demonstrate what can be achieved in this space, we present here a case study of big data analysis, a transcription factor binding site (TFBS) analysis that creates an atlas of putative transcription factor binding sites from ENCODE DNase I hypersensitive sites sequencing (DNase-seq) data, across 27 tissue types. DNase-seq footprint- ing provides a means to predict genome-wide binding sites for hundreds of transcription fac- tors (TFs) simultaneously. This application involves the retrieval and analysis of multiple terabytes of publicly available DNase-seq data with an aggregated set of position weight matri- ces representing transcription factor binding sites; a range of open source analysis programs, Galaxy workflows, and customized R scripts; high-speed networks for data exchange; and tens of thousands of core-hours of computation on workstations and public clouds. We introduce the analysis method, review the tools used in its implementation, and present the implementa- tion itself, showing how the tools enable the principled capture of a complex computational workflow in a reusable form. In particular, we show how all resources used in this work, and the end-to-end process itself, are captured in reusable forms that are accessible via persistent identifiers. To evaluate the reproducibility and FAIRness of our methods we conducted a user study comprising 11 students and researchers. Each was asked to replicate the TFBS workflow on a subset of ENCODE data. All but one were able to replicate this analysis in full.
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Symposium on Big Data Science and Engineering: Research, Education, and Practice

Symposium on Big Data Science and Engineering: Research, Education, and Practice

Dr. Bina Ramamurthy, State University of New York at Buffalo, Buffalo, New York Dr. Bina Ramamurthy is a faculty at University at Buffalo, Computer Science and Engineering Department. She has been involved in the computer systems research, development and teaching for the past two decades. Her current research is in the area of distributed systems with emphasis on big-data, data-intensive computing and cloud infrastructures. She has been the principal investigator on National Science Foundation funded projects in the area of grid- computing (Project GridForce), embedded systems (Project Nexos) and currently in the area of data-intensive computing (Project TIDE) and in evolutionary biology on the cloud (Pop!World). She has given numerous invited presentations at prominent conferences in the areas of data- intensive computing and cloud computing. Dr. Ramamurthy is also an expert in curriculum development at all levels. She received the B.E. (Honors) in Electronics and Communication Madras University, India, and the Ph.D. in Computer Engineering from the University at Buffalo, NY.
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Program Proposal. Health Science Technology Associate in Applied Science. Big Sandy Community & Technical College

Program Proposal. Health Science Technology Associate in Applied Science. Big Sandy Community & Technical College

Library resources support course offerings under the Health Science Technology AAS degree. The library holds approximately 60 online databases that are purchased through a statewide consortium, the Kentucky Virtual Library (KYVL), system purchases or individual purchases. These electronic resources and the physical resources purchased by the library encompass books, electronic versions of print sources such as books and journal articles and audiovisuals. An example of a relevant database for this curriculum is Proquest Career and Technical Education: Science and Technology, available from the Kentucky Virtual Library. This is a source of vocational information focused on science and technology.
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Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm

Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm

whom we collect the data? What is needed are not more reports, more lists of publications, more software packages, and more data. Efforts like TCGA are reaching the “ bottleneck; ” it is hard to make significant breakthroughs in scientific challenges by focusing on big data. Since interdisciplinary research does not work well, how about post-interdisciplinary approaches such as transdisciplinary approaches [communication with a senior scientist]? Since many current methods and approaches are generic, how about looking into more granular layers and finely- detailed approaches?

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Newsletter 144 BIG Little Science Centre October 2009

Newsletter 144 BIG Little Science Centre October 2009

Born Gabrielle Emilie Le Tonnelier de Breteuil, a woman of the elite French aristocracy, she was raised in extreme luxury in a country chateau and a large house overlooking the Tuileries in Paris. Her father recognized her brilliance and had her tutored in several languages and trained as a musician. The combination of aristocratic roots and her family’s interest in science brought her into social contact with many of the leading scientific thinkers of her day, and she diligently sought them out. Among these were the great physicist and geographer Maupertuis and later, Voltaire. It is only probable that Maupertuis became her lover but it is known that he spent a great deal of time with her. She often attended his scientific gatherings at one of the early coffee houses of Paris where no women were served (except in their carriages, outside). When they refused to serve her, she dressed as a man but was made up as a woman and returned to the gathering. To avoid a scene, the café decided to accept her as a man and serve. It was the thin edge of the wedge, and eventually women were served in the coffee houses of Paris.
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High Performance Big Data Computing in the Digital Science Center

High Performance Big Data Computing in the Digital Science Center

– Separate bulk synchronous and data flow communication; – Task management as in Mesos, Yarn and Kubernetes – Dataflow graph execution models – Launching of the Harp-DAAL library – Strea[r]

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Critical Analysis On Data Science And Big Data Avenues

Critical Analysis On Data Science And Big Data Avenues

We are in an era of Data Science and Big Data. The paper describes the concept of Data Science and Big Data along with the concept of Artificial Intelligence, Machine Learning, Deep Learning, Supervised Learning, Unsupervised Learning and Reinforcement Learning with the help of cases and figures. The paper also focuses on Big Data processing problems and the 3Vs Concept (Volume, Velocity and variety) of Big Data. These technical challenges must needed efficient and fast processing for Big Data Analytic. The limitation include not just the obvious issues of scale, but also multidimensionality, lack of structuration, labialization, error-handling, provenance, and visualization, at all stages of the analysis pipeline from data acquisition to result interpretation. These technical limitations are common across a huge variety of presentation domains of data set, and therefore not cost-effective to address in the context of one domain alone.
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