Semantic Web technologies for the big data in life sciences.

Science.gov (United States)

Wu, Hongyan; Yamaguchi, Atsuko

2014-08-01

The life sciences field is entering an era of big data with the breakthroughs of science and technology. More and more big data-related projects and activities are being performed in the world. Life sciences data generated by new technologies are continuing to grow in not only size but also variety and complexity, with great speed. To ensure that big data has a major influence in the life sciences, comprehensive data analysis across multiple data sources and even across disciplines is indispensable. The increasing volume of data and the heterogeneous, complex varieties of data are two principal issues mainly discussed in life science informatics. The ever-evolving next-generation Web, characterized as the Semantic Web, is an extension of the current Web, aiming to provide information for not only humans but also computers to semantically process large-scale data. The paper presents a survey of big data in life sciences, big data related projects and Semantic Web technologies. The paper introduces the main Semantic Web technologies and their current situation, and provides a detailed analysis of how Semantic Web technologies address the heterogeneous variety of life sciences big data. The paper helps to understand the role of Semantic Web technologies in the big data era and how they provide a promising solution for the big data in life sciences.

Can companies benefit from Big Science? Science and Industry

CERN Document Server

Autio, Erkko; Bianchi-Streit, M

2003-01-01

Several studies have indicated that there are significant returns on financial investment via "Big Science" centres. Financial multipliers ranging from 2.7 (ESA) to 3.7 (CERN) have been found, meaning that each Euro invested in industry by Big Science generates a two- to fourfold return for the supplier. Moreover, laboratories such as CERN are proud of their record in technology transfer, where research developments lead to applications in other fields - for example, with particle accelerators and detectors. Less well documented, however, is the effect of the experience that technological firms gain through working in the arena of Big Science. Indeed, up to now there has been no explicit empirical study of such benefits. Our findings reveal a variety of outcomes, which include technological learning, the development of new products and markets, and impact on the firm's organization. The study also demonstrates the importance of technologically challenging projects for staff at CERN. Together, these findings i...

Big data e data science

OpenAIRE

Cavique, Luís

2014-01-01

Neste artigo foram apresentados os conceitos básicos de Big Data e a nova área a que deu origem, a Data Science. Em Data Science foi discutida e exemplificada a noção de redução da dimensionalidade dos dados.

Big science transformed science, politics and organization in Europe and the United States

CERN Document Server

Hallonsten, Olof

2016-01-01

This book analyses the emergence of a transformed Big Science in Europe and the United States, using both historical and sociological perspectives. It shows how technology-intensive natural sciences grew to a prominent position in Western societies during the post-World War II era, and how their development cohered with both technological and social developments. At the helm of post-war science are large-scale projects, primarily in physics, which receive substantial funds from the public purse. Big Science Transformed shows how these projects, popularly called 'Big Science', have become symbols of progress. It analyses changes to the political and sociological frameworks surrounding publicly-funding science, and their impact on a number of new accelerator and reactor-based facilities that have come to prominence in materials science and the life sciences. Interdisciplinary in scope, this book will be of great interest to historians, sociologists and philosophers of science.

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

Science.gov (United States)

Huang, Xiuzhen; Jennings, Steven F; Bruce, Barry; Buchan, Alison; Cai, Liming; Chen, Pengyin; Cramer, Carole L; Guan, Weihua; Hilgert, Uwe Kk; Jiang, Hongmei; Li, Zenglu; McClure, Gail; McMullen, Donald F; Nanduri, Bindu; Perkins, Andy; Rekepalli, Bhanu; Salem, Saeed; Specker, Jennifer; Walker, Karl; Wunsch, Donald; Xiong, Donghai; Zhang, Shuzhong; Zhang, Yu; Zhao, Zhongming; Moore, Jason H

2015-01-01

Whether your interests lie in scientific arenas, the corporate world, or in government, you have certainly heard the praises of big data: Big data will give you new insights, allow you to become more efficient, and/or will solve your problems. While big data has had some outstanding successes, many are now beginning to see that it is not the Silver Bullet that it has been touted to be. Here our main concern is the overall impact of big data; the current manifestation of big data is constructing a Maginot Line in science in the 21st century. Big data is not "lots of data" as a phenomena anymore; The big data paradigm is putting the spirit of the Maginot Line into lots of data. Big data overall is disconnecting researchers and science challenges. We propose No-Boundary Thinking (NBT), applying no-boundary thinking in problem defining to address science challenges.

From big data to deep insight in developmental science.

Science.gov (United States)

Gilmore, Rick O

2016-01-01

The use of the term 'big data' has grown substantially over the past several decades and is now widespread. In this review, I ask what makes data 'big' and what implications the size, density, or complexity of datasets have for the science of human development. A survey of existing datasets illustrates how existing large, complex, multilevel, and multimeasure data can reveal the complexities of developmental processes. At the same time, significant technical, policy, ethics, transparency, cultural, and conceptual issues associated with the use of big data must be addressed. Most big developmental science data are currently hard to find and cumbersome to access, the field lacks a culture of data sharing, and there is no consensus about who owns or should control research data. But, these barriers are dissolving. Developmental researchers are finding new ways to collect, manage, store, share, and enable others to reuse data. This promises a future in which big data can lead to deeper insights about some of the most profound questions in behavioral science. © 2016 The Authors. WIREs Cognitive Science published by Wiley Periodicals, Inc.

Big Data and Data Science in Critical Care.

Science.gov (United States)

Sanchez-Pinto, L Nelson; Luo, Yuan; Churpek, Matthew M

2018-05-09

The digitalization of the healthcare system has resulted in a deluge of clinical Big Data and has prompted the rapid growth of data science in medicine. Data science, which is the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. The availability of large amounts of data in the intensive care unit, the need for better evidence-based care, and the complexity of critical illness makes the use of data science techniques and data-driven research particularly appealing to intensivists. Despite the increasing number of studies and publications in the field, so far there have been few examples of data science projects that have resulted in successful implementations of data-driven systems in the intensive care unit. However, given the expected growth in the field, intensivists should be familiar with the opportunities and challenges of Big Data and data science. In this paper, we review the definitions, types of algorithms, applications, challenges, and future of Big Data and data science in critical care. Copyright © 2018. Published by Elsevier Inc.

Finding the big bang

CERN Document Server

Page, Lyman A; Partridge, R Bruce

2009-01-01

Cosmology, the study of the universe as a whole, has become a precise physical science, the foundation of which is our understanding of the cosmic microwave background radiation (CMBR) left from the big bang. The story of the discovery and exploration of the CMBR in the 1960s is recalled for the first time in this collection of 44 essays by eminent scientists who pioneered the work. Two introductory chapters put the essays in context, explaining the general ideas behind the expanding universe and fossil remnants from the early stages of the expanding universe. The last chapter describes how the confusion of ideas and measurements in the 1960s grew into the present tight network of tests that demonstrate the accuracy of the big bang theory. This book is valuable to anyone interested in how science is done, and what it has taught us about the large-scale nature of the physical universe.

Big data in forensic science and medicine.

Science.gov (United States)

Lefèvre, Thomas

2018-07-01

In less than a decade, big data in medicine has become quite a phenomenon and many biomedical disciplines got their own tribune on the topic. Perspectives and debates are flourishing while there is a lack for a consensual definition for big data. The 3Vs paradigm is frequently evoked to define the big data principles and stands for Volume, Variety and Velocity. Even according to this paradigm, genuine big data studies are still scarce in medicine and may not meet all expectations. On one hand, techniques usually presented as specific to the big data such as machine learning techniques are supposed to support the ambition of personalized, predictive and preventive medicines. These techniques are mostly far from been new and are more than 50 years old for the most ancient. On the other hand, several issues closely related to the properties of big data and inherited from other scientific fields such as artificial intelligence are often underestimated if not ignored. Besides, a few papers temper the almost unanimous big data enthusiasm and are worth attention since they delineate what is at stakes. In this context, forensic science is still awaiting for its position papers as well as for a comprehensive outline of what kind of contribution big data could bring to the field. The present situation calls for definitions and actions to rationally guide research and practice in big data. It is an opportunity for grounding a true interdisciplinary approach in forensic science and medicine that is mainly based on evidence. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Accelerating Science Impact through Big Data Workflow Management and Supercomputing

Directory of Open Access Journals (Sweden)

De K.

2016-01-01

Full Text Available The Large Hadron Collider (LHC, operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. ATLAS, one of the largest collaborations ever assembled in the the history of science, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. To manage the workflow for all data processing on hundreds of data centers the PanDA (Production and Distributed AnalysisWorkload Management System is used. An ambitious program to expand PanDA to all available computing resources, including opportunistic use of commercial and academic clouds and Leadership Computing Facilities (LCF, is realizing within BigPanDA and megaPanDA projects. These projects are now exploring how PanDA might be used for managing computing jobs that run on supercomputers including OLCF’s Titan and NRC-KI HPC2. The main idea is to reuse, as much as possible, existing components of the PanDA system that are already deployed on the LHC Grid for analysis of physics data. The next generation of PanDA will allow many data-intensive sciences employing a variety of computing platforms to benefit from ATLAS experience and proven tools in highly scalable processing.

Nursing Knowledge: Big Data Science-Implications for Nurse Leaders.

Science.gov (United States)

Westra, Bonnie L; Clancy, Thomas R; Sensmeier, Joyce; Warren, Judith J; Weaver, Charlotte; Delaney, Connie W

2015-01-01

The integration of Big Data from electronic health records and other information systems within and across health care enterprises provides an opportunity to develop actionable predictive models that can increase the confidence of nursing leaders' decisions to improve patient outcomes and safety and control costs. As health care shifts to the community, mobile health applications add to the Big Data available. There is an evolving national action plan that includes nursing data in Big Data science, spearheaded by the University of Minnesota School of Nursing. For the past 3 years, diverse stakeholders from practice, industry, education, research, and professional organizations have collaborated through the "Nursing Knowledge: Big Data Science" conferences to create and act on recommendations for inclusion of nursing data, integrated with patient-generated, interprofessional, and contextual data. It is critical for nursing leaders to understand the value of Big Data science and the ways to standardize data and workflow processes to take advantage of newer cutting edge analytics to support analytic methods to control costs and improve patient quality and safety.

Data Management and Preservation Planning for Big Science

Directory of Open Access Journals (Sweden)

Juan Bicarregui

2013-06-01

Full Text Available ‘Big Science’ - that is, science which involves large collaborations with dedicated facilities, and involving large data volumes and multinational investments – is often seen as different when it comes to data management and preservation planning. Big Science handles its data differently from other disciplines and has data management problems that are qualitatively different from other disciplines. In part, these differences arise from the quantities of data involved, but possibly more importantly from the cultural, organisational and technical distinctiveness of these academic cultures. Consequently, the data management systems are typically and rationally bespoke, but this means that the planning for data management and preservation (DMP must also be bespoke.These differences are such that ‘just read and implement the OAIS specification’ is reasonable Data Management and Preservation (DMP advice, but this bald prescription can and should be usefully supported by a methodological ‘toolkit’, including overviews, case-studies and costing models to provide guidance on developing best practice in DMP policy and infrastructure for these projects, as well as considering OAIS validation, audit and cost modelling.In this paper, we build on previous work with the LIGO collaboration to consider the role of DMP planning within these big science scenarios, and discuss how to apply current best practice. We discuss the result of the MaRDI-Gross project (Managing Research Data Infrastructures – Big Science, which has been developing a toolkit to provide guidelines on the application of best practice in DMP planning within big science projects. This is targeted primarily at projects’ engineering managers, but intending also to help funders collaborate on DMP plans which satisfy the requirements imposed on them.

Legitimizing ESS Big Science as a collaboration across boundaries

CERN Document Server

O'Dell, Tom

2013-01-01

Legitimizing ESS 'Big Science' is a broad epithet that can be associated with research projects as different as the Manhattan Project, the Hubble Telescope-construction, and the CERN-establishment in Geneva. While the science produced by these projects is vastly different, they have in common the fact that they all involve huge budgets, big facilities, complex instrumentation, years of planning, and large multidis...

Big data science: A literature review of nursing research exemplars.

Science.gov (United States)

Westra, Bonnie L; Sylvia, Martha; Weinfurter, Elizabeth F; Pruinelli, Lisiane; Park, Jung In; Dodd, Dianna; Keenan, Gail M; Senk, Patricia; Richesson, Rachel L; Baukner, Vicki; Cruz, Christopher; Gao, Grace; Whittenburg, Luann; Delaney, Connie W

Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge. The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals. A literature review of studies published between 2009 and 2015. There were 650 journal articles identified in 17 key nursing informatics, general biomedical informatics, and nursing research journals in the Web of Science database. After screening for inclusion and exclusion criteria, 17 studies published in 18 articles were identified as big data nursing research applied to practice. Nurses clearly are beginning to conduct big data research applied to practice. These studies represent multiple data sources and settings. Although numerous analytic methods were used, the fundamental issue remains to define the types of analyses consistent with big data analytic methods. There are needs to increase the visibility of big data and data science research conducted by nurse scientists, further examine the use of state of the science in data analytics, and continue to expand the availability and use of a variety of scientific, governmental, and industry data resources. A major implication of this literature review is whether nursing faculty and preparation of future scientists (PhD programs) are prepared for big data and data science. Copyright © 2016 Elsevier Inc. All rights reserved.

'Big data' in pharmaceutical science: challenges and opportunities.

Science.gov (United States)

Dossetter, Al G; Ecker, Gerhard; Laverty, Hugh; Overington, John

2014-05-01

Future Medicinal Chemistry invited a selection of experts to express their views on the current impact of big data in drug discovery and design, as well as speculate on future developments in the field. The topics discussed include the challenges of implementing big data technologies, maintaining the quality and privacy of data sets, and how the industry will need to adapt to welcome the big data era. Their enlightening responses provide a snapshot of the many and varied contributions being made by big data to the advancement of pharmaceutical science.

The faces of Big Science.

Science.gov (United States)

Schatz, Gottfried

2014-06-01

Fifty years ago, academic science was a calling with few regulations or financial rewards. Today, it is a huge enterprise confronted by a plethora of bureaucratic and political controls. This change was not triggered by specific events or decisions but reflects the explosive 'knee' in the exponential growth that science has sustained during the past three-and-a-half centuries. Coming to terms with the demands and benefits of 'Big Science' is a major challenge for today's scientific generation. Since its foundation 50 years ago, the European Molecular Biology Organization (EMBO) has been of invaluable help in meeting this challenge.

Earth science big data at users' fingertips: the EarthServer Science Gateway Mobile

Science.gov (United States)

Barbera, Roberto; Bruno, Riccardo; Calanducci, Antonio; Fargetta, Marco; Pappalardo, Marco; Rundo, Francesco

2014-05-01

The EarthServer project (www.earthserver.eu), funded by the European Commission under its Seventh Framework Program, aims at establishing open access and ad-hoc analytics on extreme-size Earth Science data, based on and extending leading-edge Array Database technology. The core idea is to use database query languages as client/server interface to achieve barrier-free "mix & match" access to multi-source, any-size, multi-dimensional space-time data -- in short: "Big Earth Data Analytics" - based on the open standards of the Open Geospatial Consortium Web Coverage Processing Service (OGC WCPS) and the W3C XQuery. EarthServer combines both, thereby achieving a tight data/metadata integration. Further, the rasdaman Array Database System (www.rasdaman.com) is extended with further space-time coverage data types. On server side, highly effective optimizations - such as parallel and distributed query processing - ensure scalability to Exabyte volumes. In this contribution we will report on the EarthServer Science Gateway Mobile, an app for both iOS and Android-based devices that allows users to seamlessly access some of the EarthServer applications using SAML-based federated authentication and fine-grained authorisation mechanisms.

The Concept Lens Diagram: A New Mechanism for Presenting Biochemistry Content in Terms of "Big Ideas"

Science.gov (United States)

Rowland, Susan L.; Smith, Christopher A.; Gillam, Elizabeth M. A.; Wright, Tony

2011-01-01

A strong, recent movement in tertiary education is the development of conceptual, or "big idea" teaching. The emphasis in course design is now on promoting key understandings, core competencies, and an understanding of connections between different fields. In biochemistry teaching, this radical shift from the content-based tradition is…

The Ethics of Big Data and Nursing Science.

Science.gov (United States)

Milton, Constance L

2017-10-01

Big data is a scientific, social, and technological trend referring to the process and size of datasets available for analysis. Ethical implications arise as healthcare disciplines, including nursing, struggle over questions of informed consent, privacy, ownership of data, and its possible use in epistemology. The author offers straight-thinking possibilities for the use of big data in nursing science.

5th Annual Pan-European Science and Big Physics Symposium on March 5th, 2012, Zurich, Switzerland

CERN Multimedia

Balle, Ch

2012-01-01

The 5th Annual Pan-European Science and Big Physics Symposium on March 5th is a technical workshop that covers topics in the areas of control, measurement and diagnostics for accelerators, cyclotrons, tokamaks and telescopes. The symposium brings together over 60 scientists and engineers from major research labs around the world such as CERN, PSI, INFN, NPL, ESRF and other research institutions. Attend this event to share ideas and results and to learn from the presentations of your peers from different labs and experiments worldwide.

Towards Geo-spatial Information Science in Big Data Era

Directory of Open Access Journals (Sweden)

LI Deren

2016-04-01

Full Text Available Since the 1990s, with the advent of worldwide information revolution and the development of internet, geospatial information science have also come of age, which pushed forward the building of digital Earth and cyber city. As we entered the 21st century, with the development and integration of global information technology and industrialization, internet of things and cloud computing came into being, human society enters into the big data era. This article covers the key features (ubiquitous, multi-dimension and dynamics, internet+networking, full automation and real-time, from sensing to recognition, crowdsourcing and VGI, and service-oriented of geospatial information science in the big data era and addresses the key technical issues (non-linear four dimensional Earth reference frame system, space based enhanced GNSS, space-air and land unified network communication techniques, on board processing techniques for multi-sources image data, smart interface service techniques for space-borne information, space based resource scheduling and network security, design and developing of a payloads based multi-functional satellite platform. That needs to be resolved to provide a new definition of geospatial information science in big data era. Based on the discussion in this paper, the author finally proposes a new definition of geospatial information science (geomatics, i.e. Geomatics is a multiple discipline science and technology which, using a systematic approach, integrates all the means for spatio-temporal data acquisition, information extraction, networked management, knowledge discovering, spatial sensing and recognition, as well as intelligent location based services of any physical objects and human activities around the earth and its environment. Starting from this new definition, geospatial information science will get much more chances and find much more tasks in big data era for generation of smart earth and smart city . Our profession

Big Data: New science, new challenges, new dialogical opportunities

OpenAIRE

Fuller, Michael

2015-01-01

The advent of extremely large datasets, known as “big data”, has been heralded as the instantiation of a new science, requiring a new kind of practitioner: the “data scientist”. This paper explores the concept of big data, drawing attention to a number of new issues – not least ethical concerns, and questions surrounding interpretation – which big data sets present. It is observed that the skills required for data scientists are in some respects closer to those traditionally associated with t...

Investigating Elementary Teachers' Thinking About and Learning to Notice Students' Science Ideas

Science.gov (United States)

Luna, Melissa Jo

Children naturally use observations and everyday thinking to construct explanations as to why phenomena happen in the world. Science instruction can benefit by starting with these ideas to help children build coherent scientific understandings of how the physical world works. To do so, science teaching must involve attending to students' ideas so that those ideas become the basis for learning. Yet while science education reform requires teachers to pay close attention to their students' ideas, we know little about what teachers think this means in practice. To examine this issue, my dissertation research is two-fold. First, I examine teacher thinking by investigating how teachers understand what it means to pay attention to students' science ideas. Specifically, using new digital technology, three participating teachers captured moments of student thinking in the midst of instruction. Analysis of these moments reveals that teachers capture many different kinds of moments containing students' ideas and think about students' science ideas in different ways at different times. In particular, these three teachers most often think about students' ideas as being (a) from authority, (b) from experience, and (c) under construction. Second, I examine teacher learning through the development of an innovative science teaching video club model. The model differs from previous research on video clubs in several key ways in an attempt to focus teachers on student thinking in a sustained way. I investigate the ways in which this model was effective for engaging teachers in noticing and making sense of their students' science ideas during one implementation. Results indicate that teachers talked about student thinking early, often, and in meaningful ways. Science education leaders have recognized the potential of science teaching video clubs as a form of professional development, and the model presented in this work promotes the conditions for successful teacher learning. This

The Big Bang or not?;The year in ideas

CERN Multimedia

2007-01-01

It has been nicknamed the "God particle", and it is the keystone of modern physics. Without it, science's best explanation for the nature of the universe would come crashing down. The Higgs boson, first postulated in the Sixties by Professor Peter Higgs of Edinburgh University, is certainly among the most elegant ideas in the history of physics, but it has one small problem. Nobody knows whether it actually exists.

Forget the hype or reality. Big data presents new opportunities in Earth Science.

Science.gov (United States)

Lee, T. J.

2015-12-01

Earth science is arguably one of the most mature science discipline which constantly acquires, curates, and utilizes a large volume of data with diverse variety. We deal with big data before there is big data. For example, while developing the EOS program in the 1980s, the EOS data and information system (EOSDIS) was developed to manage the vast amount of data acquired by the EOS fleet of satellites. EOSDIS continues to be a shining example of modern science data systems in the past two decades. With the explosion of internet, the usage of social media, and the provision of sensors everywhere, the big data era has bring new challenges. First, Goggle developed the search algorithm and a distributed data management system. The open source communities quickly followed up and developed Hadoop file system to facility the map reduce workloads. The internet continues to generate tens of petabytes of data every day. There is a significant shortage of algorithms and knowledgeable manpower to mine the data. In response, the federal government developed the big data programs that fund research and development projects and training programs to tackle these new challenges. Meanwhile, comparatively to the internet data explosion, Earth science big data problem has become quite small. Nevertheless, the big data era presents an opportunity for Earth science to evolve. We learned about the MapReduce algorithms, in memory data mining, machine learning, graph analysis, and semantic web technologies. How do we apply these new technologies to our discipline and bring the hype to Earth? In this talk, I will discuss how we might want to apply some of the big data technologies to our discipline and solve many of our challenging problems. More importantly, I will propose new Earth science data system architecture to enable new type of scientific inquires.

Earth Science Data Analysis in the Era of Big Data

Science.gov (United States)

Kuo, K.-S.; Clune, T. L.; Ramachandran, R.

2014-01-01

Anyone with even a cursory interest in information technology cannot help but recognize that "Big Data" is one of the most fashionable catchphrases of late. From accurate voice and facial recognition, language translation, and airfare prediction and comparison, to monitoring the real-time spread of flu, Big Data techniques have been applied to many seemingly intractable problems with spectacular successes. They appear to be a rewarding way to approach many currently unsolved problems. Few fields of research can claim a longer history with problems involving voluminous data than Earth science. The problems we are facing today with our Earth's future are more complex and carry potentially graver consequences than the examples given above. How has our climate changed? Beside natural variations, what is causing these changes? What are the processes involved and through what mechanisms are these connected? How will they impact life as we know it? In attempts to answer these questions, we have resorted to observations and numerical simulations with ever-finer resolutions, which continue to feed the "data deluge." Plausibly, many Earth scientists are wondering: How will Big Data technologies benefit Earth science research? As an example from the global water cycle, one subdomain among many in Earth science, how would these technologies accelerate the analysis of decades of global precipitation to ascertain the changes in its characteristics, to validate these changes in predictive climate models, and to infer the implications of these changes to ecosystems, economies, and public health? Earth science researchers need a viable way to harness the power of Big Data technologies to analyze large volumes and varieties of data with velocity and veracity. Beyond providing speedy data analysis capabilities, Big Data technologies can also play a crucial, albeit indirect, role in boosting scientific productivity by facilitating effective collaboration within an analysis environment

Professor Ta-You Wu and his ideas of science

International Nuclear Information System (INIS)

Ye Minghan; Dai Nianzu

2005-01-01

The career of Professor Ta-You Wu and his contributions in the latter half of his life in Taiwan, China are briefly reviewed. The authors focus our discussion on some of his ideas in pushing forward the advancement of science and technology in Taiwan and in promoting scientific standards. These ideas include: emphasis on the importance of basic science and qualified personnel as the foundation for the development of science and technology, and while advocating applied science he pointed out that 'future profits depend on high technology'. Thus, he contributed greatly to the rise of the economy in Taiwan. His view of the dispute between Bohr and Einstein is also briefly reviewed, as well as his principles of leadership as the president of Academia Sinica, Taiwan. (author)

Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives

Science.gov (United States)

Miron-Shatz, T.; Lau, A. Y. S.; Paton, C.

2014-01-01

Summary Objectives As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. Methods A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Results Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to “small data” would also be useful. PMID:25123717

Detection and Characterisation of Meteors as a Big Data Citizen Science project

Science.gov (United States)

Gritsevich, M.

2017-12-01

Out of a total around 50,000 meteorites currently known to science, the atmospheric passage was recorded instrumentally in only 30 cases with the potential to derive their atmospheric trajectories and pre-impact heliocentric orbits. Similarly, while the observations of meteors, add thousands of new entries per month to existing databases, it is extremely rare they lead to meteorite recovery. Meteor studies thus represent an excellent example of the Big Data citizen science project, where progress in the field largely depends on the prompt identification and characterisation of meteor events as well as on extensive and valuable contributions by amateur observers. Over the last couple of decades technological advancements in observational techniques have yielded drastic improvements in the quality, quantity and diversity of meteor data, while even more ambitious instruments are about to become operational. This empowers meteor science to boost its experimental and theoretical horizons and seek more advanced scientific goals. We review some of the developments that push meteor science into the Big Data era that requires more complex methodological approaches through interdisciplinary collaborations with other branches of physics and computer science. We argue that meteor science should become an integral part of large surveys in astronomy, aeronomy and space physics, and tackle the complexity of micro-physics of meteor plasma and its interaction with the atmosphere. The recent increased interest in meteor science triggered by the Chelyabinsk fireball helps in building the case for technologically and logistically more ambitious meteor projects. This requires developing new methodological approaches in meteor research, with Big Data science and close collaboration between citizen science, geoscience and astronomy as critical elements. We discuss possibilities for improvements and promote an opportunity for collaboration in meteor science within the currently

Green data science : using big data in an "environmentally friendly" manner

NARCIS (Netherlands)

Van Der Aalst, W.M.P.

2016-01-01

The widespread use of "Big Data" is heavily impacting organizations and individuals for which these data are collected. Sophisticated data science techniques aim to extract as much value from data as possible. Powerful mixtures of Big Data and analytics are rapidly changing the way we do business,

Ernst Mach and the Epistemological Ideas Specific for Finnish Science Education

Science.gov (United States)

Siemsen, Hayo

2011-03-01

Where does Finnish science education come from? Where will it go? The following outside view reflects on relations, which Finns consider "normal" (and thus unrecognizable in introspection) in science education. But what is "normal" in Finnish culture cannot be considered "normal" for science education in other cultures, for example in Germany. The following article will trace the central ideas, which had a larger influence in the development of this difference. The question is, if and why the Finnish uniqueness in the philosophy of science education is empirically important. This puts Finnish science education into the perspective of a more general epistemological debate around Ernst Mach's Erkenntnistheorie (a German term similar to the meaning of history and philosophy of science, though more general; literally translated "cognition/knowledge theory"). From this perspective, an outlook will be given on open questions within the epistemology of Finnish science education. Following such questions could lead to the adaptation of the "successful" ideas in Finnish science education (indicated by empirical studies, such as the OECD PISA study) as well as the further development of the central ideas of Finnish science education.

Big Data and Data Science: Opportunities and Challenges of iSchools

Directory of Open Access Journals (Sweden)

Il-Yeol Song

2017-08-01

Full Text Available Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools’ opportunities and suggestions in data science education. We argue that iSchools should empower their students with “information computing” disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application-based. These three foci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework (DSEF with building blocks that include the three pillars of data science (people, technology, and data, computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.

Nursing Needs Big Data and Big Data Needs Nursing.

Science.gov (United States)

Brennan, Patricia Flatley; Bakken, Suzanne

2015-09-01

Contemporary big data initiatives in health care will benefit from greater integration with nursing science and nursing practice; in turn, nursing science and nursing practice has much to gain from the data science initiatives. Big data arises secondary to scholarly inquiry (e.g., -omics) and everyday observations like cardiac flow sensors or Twitter feeds. Data science methods that are emerging ensure that these data be leveraged to improve patient care. Big data encompasses data that exceed human comprehension, that exist at a volume unmanageable by standard computer systems, that arrive at a velocity not under the control of the investigator and possess a level of imprecision not found in traditional inquiry. Data science methods are emerging to manage and gain insights from big data. The primary methods included investigation of emerging federal big data initiatives, and exploration of exemplars from nursing informatics research to benchmark where nursing is already poised to participate in the big data revolution. We provide observations and reflections on experiences in the emerging big data initiatives. Existing approaches to large data set analysis provide a necessary but not sufficient foundation for nursing to participate in the big data revolution. Nursing's Social Policy Statement guides a principled, ethical perspective on big data and data science. There are implications for basic and advanced practice clinical nurses in practice, for the nurse scientist who collaborates with data scientists, and for the nurse data scientist. Big data and data science has the potential to provide greater richness in understanding patient phenomena and in tailoring interventional strategies that are personalized to the patient. © 2015 Sigma Theta Tau International.

French environmental labs may get 'big science' funds

CERN Multimedia

2000-01-01

France is considering expanding its network of enviromental laboratories to study the long term impacts of environmental change. It has been suggested that this could be funded using the 'big science' budget usually used for facilities such as particle accelerators (2 para).

A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science.

Science.gov (United States)

Faghmous, James H; Kumar, Vipin

2014-09-01

Global climate change and its impact on human life has become one of our era's greatest challenges. Despite the urgency, data science has had little impact on furthering our understanding of our planet in spite of the abundance of climate data. This is a stark contrast from other fields such as advertising or electronic commerce where big data has been a great success story. This discrepancy stems from the complex nature of climate data as well as the scientific questions climate science brings forth. This article introduces a data science audience to the challenges and opportunities to mine large climate datasets, with an emphasis on the nuanced difference between mining climate data and traditional big data approaches. We focus on data, methods, and application challenges that must be addressed in order for big data to fulfill their promise with regard to climate science applications. More importantly, we highlight research showing that solely relying on traditional big data techniques results in dubious findings, and we instead propose a theory-guided data science paradigm that uses scientific theory to constrain both the big data techniques as well as the results-interpretation process to extract accurate insight from large climate data .

Perspectives on Policy and the Value of Nursing Science in a Big Data Era.

Science.gov (United States)

Gephart, Sheila M; Davis, Mary; Shea, Kimberly

2018-01-01

As data volume explodes, nurse scientists grapple with ways to adapt to the big data movement without jeopardizing its epistemic values and theoretical focus that celebrate while acknowledging the authority and unity of its body of knowledge. In this article, the authors describe big data and emphasize ways that nursing science brings value to its study. Collective nursing voices that call for more nursing engagement in the big data era are answered with ways to adapt and integrate theoretical and domain expertise from nursing into data science.

Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics.

Science.gov (United States)

Popescu, George V; Noutsos, Christos; Popescu, Sorina C

2016-01-01

In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.

Big data in medical science--a biostatistical view.

Science.gov (United States)

Binder, Harald; Blettner, Maria

2015-02-27

Inexpensive techniques for measurement and data storage now enable medical researchers to acquire far more data than can conveniently be analyzed by traditional methods. The expression "big data" refers to quantities on the order of magnitude of a terabyte (1012 bytes); special techniques must be used to evaluate such huge quantities of data in a scientifically meaningful way. Whether data sets of this size are useful and important is an open question that currently confronts medical science. In this article, we give illustrative examples of the use of analytical techniques for big data and discuss them in the light of a selective literature review. We point out some critical aspects that should be considered to avoid errors when large amounts of data are analyzed. Machine learning techniques enable the recognition of potentially relevant patterns. When such techniques are used, certain additional steps should be taken that are unnecessary in more traditional analyses; for example, patient characteristics should be differentially weighted. If this is not done as a preliminary step before similarity detection, which is a component of many data analysis operations, characteristics such as age or sex will be weighted no higher than any one out of 10 000 gene expression values. Experience from the analysis of conventional observational data sets can be called upon to draw conclusions about potential causal effects from big data sets. Big data techniques can be used, for example, to evaluate observational data derived from the routine care of entire populations, with clustering methods used to analyze therapeutically relevant patient subgroups. Such analyses can provide complementary information to clinical trials of the classic type. As big data analyses become more popular, various statistical techniques for causality analysis in observational data are becoming more widely available. This is likely to be of benefit to medical science, but specific adaptations will

The Human Genome Project: big science transforms biology and medicine

OpenAIRE

Hood, Leroy; Rowen, Lee

2013-01-01

The Human Genome Project has transformed biology through its integrated big science approach to deciphering a reference human genome sequence along with the complete sequences of key model organisms. The project exemplifies the power, necessity and success of large, integrated, cross-disciplinary efforts - so-called ‘big science’ - directed towards complex major objectives. In this article, we discuss the ways in which this ambitious endeavor led to the development of novel technologies and a...

Big Surveys, Big Data Centres

Science.gov (United States)

Schade, D.

2016-06-01

Well-designed astronomical surveys are powerful and have consistently been keystones of scientific progress. The Byurakan Surveys using a Schmidt telescope with an objective prism produced a list of about 3000 UV-excess Markarian galaxies but these objects have stimulated an enormous amount of further study and appear in over 16,000 publications. The CFHT Legacy Surveys used a wide-field imager to cover thousands of square degrees and those surveys are mentioned in over 1100 publications since 2002. Both ground and space-based astronomy have been increasing their investments in survey work. Survey instrumentation strives toward fair samples and large sky coverage and therefore strives to produce massive datasets. Thus we are faced with the "big data" problem in astronomy. Survey datasets require specialized approaches to data management. Big data places additional challenging requirements for data management. If the term "big data" is defined as data collections that are too large to move then there are profound implications for the infrastructure that supports big data science. The current model of data centres is obsolete. In the era of big data the central problem is how to create architectures that effectively manage the relationship between data collections, networks, processing capabilities, and software, given the science requirements of the projects that need to be executed. A stand alone data silo cannot support big data science. I'll describe the current efforts of the Canadian community to deal with this situation and our successes and failures. I'll talk about how we are planning in the next decade to try to create a workable and adaptable solution to support big data science.

The big questions in science the quest to solve the great unknowns

CERN Document Server

Birch, Hayley; Stuart, Colin

2016-01-01

What are the great scientific questions of our modern age and why don't we know the answers? The Big Questions in Science takes on the most fascinating and pressing mysteries we have yet to crack and explains how tantalizingly close science is to solving them (or how frustratingly out of reach they remain). Some, such as "Can we live forever? and "What makes us human? " are eternal questions; others, such as "How do we solve the population problem? " and "How do we get more energy from the sun? " are essential to our future survival. Written by experienced science writers, adept at translating the complicated concepts of "hard science" into an engaging and insightful discussion for the general reader, The Big Questions in Science grapples with 20 hot topics across the disciplines of biology, chemistry, physics, astronomy and computer science to ignite the inquistitive scientist in all of us.

Big Science, co-publication and collaboration: getting to the core

Energy Technology Data Exchange (ETDEWEB)

Kahn, M.

2016-07-01

International collaboration in science has risen considerably in the last two decades (UNESCO, 2010). In the same period Big Science collaborations have proliferated in physics, astronomy, astrophysics, and medicine. Publications that use Big Science data draw on the expertise of those who design and build the equipment and software, as well as the scientific community. Over time a set of ‘rules of use’ has emerged that protects their intellectual property but that may have the unintended consequence of enhancing co-publication counts. This in turn distorts the use of co-publication data as a proxy for collaboration. The distorting effects are illustrated by means of a case study of the BRICS countries that recently issued a declaration on scientific and technological cooperation with specific fields allocated to each country. It is found that with a single exception the dominant research areas of collaboration are different to individual country specializations. The disjuncture between such ‘collaboration’ and the intent of the declaration raises questions of import to science policy, for the BRICS in particular and the measurement of scientific collaboration more generally. (Author)

Final Scientific/Technical Report to the U.S. Department of Energy on NOVA's Einstein's Big Idea (Project title: E-mc2, A Two-Hour Television Program on NOVA)

International Nuclear Information System (INIS)

Susanne Simpson

2007-01-01

A woman in the early 1700s who became one of Europe's leading interpreters of mathematics and a poor bookbinder who became one of the giants of nineteenth-century science are just two of the pioneers whose stories NOVA explored in Einstein's Big Idea. This two-hour documentary premiered on PBS in October 2005 and is based on the best-selling book by David Bodanis, E=mc2: A Biography of the World's Most Famous Equation. The film and book chronicle the scientific challenges and discoveries leading up to Einstein's startling conclusion that mass and energy are one, related by the formula E = mc2

From darwin to the census of marine life: marine biology as big science.

Science.gov (United States)

Vermeulen, Niki

2013-01-01

With the development of the Human Genome Project, a heated debate emerged on biology becoming 'big science'. However, biology already has a long tradition of collaboration, as natural historians were part of the first collective scientific efforts: exploring the variety of life on earth. Such mappings of life still continue today, and if field biology is gradually becoming an important subject of studies into big science, research into life in the world's oceans is not taken into account yet. This paper therefore explores marine biology as big science, presenting the historical development of marine research towards the international 'Census of Marine Life' (CoML) making an inventory of life in the world's oceans. Discussing various aspects of collaboration--including size, internationalisation, research practice, technological developments, application, and public communication--I will ask if CoML still resembles traditional collaborations to collect life. While showing both continuity and change, I will argue that marine biology is a form of natural history: a specific way of working together in biology that has transformed substantially in interaction with recent developments in the life sciences and society. As a result, the paper does not only give an overview of transformations towards large scale research in marine biology, but also shines a new light on big biology, suggesting new ways to deepen the understanding of collaboration in the life sciences by distinguishing between different 'collective ways of knowing'.

From darwin to the census of marine life: marine biology as big science.

Directory of Open Access Journals (Sweden)

Niki Vermeulen

Full Text Available With the development of the Human Genome Project, a heated debate emerged on biology becoming 'big science'. However, biology already has a long tradition of collaboration, as natural historians were part of the first collective scientific efforts: exploring the variety of life on earth. Such mappings of life still continue today, and if field biology is gradually becoming an important subject of studies into big science, research into life in the world's oceans is not taken into account yet. This paper therefore explores marine biology as big science, presenting the historical development of marine research towards the international 'Census of Marine Life' (CoML making an inventory of life in the world's oceans. Discussing various aspects of collaboration--including size, internationalisation, research practice, technological developments, application, and public communication--I will ask if CoML still resembles traditional collaborations to collect life. While showing both continuity and change, I will argue that marine biology is a form of natural history: a specific way of working together in biology that has transformed substantially in interaction with recent developments in the life sciences and society. As a result, the paper does not only give an overview of transformations towards large scale research in marine biology, but also shines a new light on big biology, suggesting new ways to deepen the understanding of collaboration in the life sciences by distinguishing between different 'collective ways of knowing'.

Big Data: Philosophy, Emergence, Crowdledge, and Science Education

Science.gov (United States)

dos Santos, Renato P.

2015-01-01

Big Data already passed out of hype, is now a field that deserves serious academic investigation, and natural scientists should also become familiar with Analytics. On the other hand, there is little empirical evidence that any science taught in school is helping people to lead happier, more prosperous, or more politically well-informed lives. In…

Harnessing the Power of Big Data to Improve Graduate Medical Education: Big Idea or Bust?

Science.gov (United States)

Arora, Vineet M

2018-06-01

With the advent of electronic medical records (EMRs) fueling the rise of big data, the use of predictive analytics, machine learning, and artificial intelligence are touted as transformational tools to improve clinical care. While major investments are being made in using big data to transform health care delivery, little effort has been directed toward exploiting big data to improve graduate medical education (GME). Because our current system relies on faculty observations of competence, it is not unreasonable to ask whether big data in the form of clinical EMRs and other novel data sources can answer questions of importance in GME such as when is a resident ready for independent practice.The timing is ripe for such a transformation. A recent National Academy of Medicine report called for reforms to how GME is delivered and financed. While many agree on the need to ensure that GME meets our nation's health needs, there is little consensus on how to measure the performance of GME in meeting this goal. During a recent workshop at the National Academy of Medicine on GME outcomes and metrics in October 2017, a key theme emerged: Big data holds great promise to inform GME performance at individual, institutional, and national levels. In this Invited Commentary, several examples are presented, such as using big data to inform clinical experience and provide clinically meaningful data to trainees, and using novel data sources, including ambient data, to better measure the quality of GME training.

The role of administrative data in the big data revolution in social science research.

Science.gov (United States)

Connelly, Roxanne; Playford, Christopher J; Gayle, Vernon; Dibben, Chris

2016-09-01

The term big data is currently a buzzword in social science, however its precise meaning is ambiguous. In this paper we focus on administrative data which is a distinctive form of big data. Exciting new opportunities for social science research will be afforded by new administrative data resources, but these are currently under appreciated by the research community. The central aim of this paper is to discuss the challenges associated with administrative data. We emphasise that it is critical for researchers to carefully consider how administrative data has been produced. We conclude that administrative datasets have the potential to contribute to the development of high-quality and impactful social science research, and should not be overlooked in the emerging field of big data. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Uncovering student ideas in physical science

CERN Document Server

Keeley, Page

2014-01-01

If you and your students can't get enough of a good thing, Volume 2 of Uncovering Student Ideas in Physical Science is just what you need. The book offers 39 new formative assessment probes, this time with a focus on electric charge, electric current, and magnets and electromagnetism. It can help you do everything from demystify electromagnetic fields to explain the real reason balloons stick to the wall after you rub them on your hair.

Big Science and Long-tail Science

CERN Document Server

2008-01-01

Jim Downing and I were privileged to be the guests of Salavtore Mele at CERN yesterday and to see the Atlas detector of the Large Hadron Collider . This is a wow experience - although I knew it was big, I hadnt realised how big.

Assessing Conceptual Understanding via Literacy-Infused, Inquiry-Based Science among Middle School English Learners and Economically-Challenged Students

Directory of Open Access Journals (Sweden)

Rafael Lara-Alecio

2018-02-01

Full Text Available The overarching purpose of our study was to compare performances of treatment and control condition students who completed a literacy-infused, inquiry-based science intervention through sixth grade as measured by a big idea assessment tool which we refer to as the Big Ideas in Science Assessment (BISA. First, we determine the concurrent validity of the BISA; second, we investigate the differences in the post-test of the BISA between treatment and control English Learners (ELs, controlling for their performance in the pre-test; third, we analyze the differences in the post-test of the BISA between treatment and control non-ELs, controlling for their performance in the pre-test; and fourth, we examine the relationship between students’ English language proficiency as measured by standardized assessment, and their performance in the BISA among ELs and non-ELs, respectively. Our findings indicate: (a literacy-infused science lessons with big ideas, implemented through the tested intervention, improved students’ language acquisition and science concept understanding for ELs and economically challenged students (ECs; (b there was a positive relationship between language and content for both ELs and non-ELs, with a similar magnitude, suggesting that students with a higher level of English proficiency score higher in science assessment; and (c the lesson plans prepared were successful for promoting a literacy-infused science curriculum via a 5E Model (Engage, Explore, Explain, Elaborate, and Evaluate that includes three to five of the Es used daily. A pedagogical approach for a literacy-infused science model with big ideas is proposed.

Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections

NARCIS (Netherlands)

C-L. Chang (Chia-Lin); M.J. McAleer (Michael); W.-K. Wong (Wing-Keung)

2018-01-01

textabstractThis paper provides a review of some connecting literature in Decision Sciences, Economics, Finance, Business, Computing, and Big Data. We then discuss some research that is related to the six cognate disciplines. Academics could develop theoretical models and subsequent

A Cross-cultural Exploration of Children's Everyday Ideas: Implications for science teaching and learning

Science.gov (United States)

Wee, Bryan

2012-03-01

Children's everyday ideas form critical foundations for science learning yet little research has been conducted to understand and legitimize these ideas, particularly from an international perspective. This paper explores children's everyday ideas about the environment across the US, Singapore and China to understand what they reveal about children's relationship to the environment and discuss its implications for science teaching and learning. A social constructivist lens guides research, and a visual methodology is used to frame children's realities. Participants' ages range from elementary to middle school, and a total of 210 children comprized mainly of Asians and Asian Americans were sampled from urban settings. Drawings are used to elicit children's everyday ideas and analyzed inductively using open coding and categorizing of data. Several categories support existing literature about how children view the environment; however, novel categories such as affect also emerged and lend new insight into the role that language, socio-cultural norms and perhaps ethnicity play in shaping children's everyday ideas. The findings imply the need for (a) a change in the role of science teachers from knowledge providers to social developers, (b) a science curriculum that is specific to learners' experiences in different socio-cultural settings, and (c) a shift away from inter-country comparisons using international science test scores.

Futuristic stories older than might appear: origin of ideas of science fiction screenplays

Directory of Open Access Journals (Sweden)

Carlos Alberto Machado

2013-12-01

Full Text Available The paper discusses the origin of the ideas of most movie scripts modern science fiction, and literaty concepts such as soft and hard, also present in the film. Pointed out the origin of these scripts mostly in the 1950s, 1960s and 1970s, they considered fertile periods in foreign science fiction literature. Also discusses about the casual predictions of the authors of this genre that end up bringing their ideas to contemporary unreasonably, but exciting, leading the media to call them visionary means. Some authors like Carrière, Xavier, Bez, Koff and Comparato assist in corroborating these ideas. Thus, the reader is led to reflect on the historical origin of these ideas.

Teaching students ideas-about-science: Five dimensions of effective practice

Science.gov (United States)

Bartholomew, Hannah; Osborne, Jonathan; Ratcliffe, Mary

2004-09-01

In this paper, we report work undertaken with a group of 11 UK teachers over a period of a year to teach aspects of the nature of science, its process, and its practices. The teachers, who taught science in a mix of elementary, junior high, and high schools, were asked to teach a set of ideas-about-scienc for which consensual support had been established using a Delphi study in the first phase of the project. Data were collected through field notes, videos of the teachers' lessons, teachers' reflective diaries, and instruments that measured their understanding of the nature of science and their views on the role and value of discussion in the classroom. In this paper, drawing on a sample of the data we explore the factors that afforded or inhibited the teachers' pedagogic performance in this domain. Using these data, we argue that there are five critical dimensions that distinguish and determine a teacher's ability to teach effectively about science. Whilst these dimensions are neither mutually independent nor equally important, they serve as a valuable analytical tool for evaluating and explaining the success, or otherwise, that individual teachers of science have when confronted with teaching aspects about science. In addition, we argue that they are an important means of identifying salient aspects of pedagogy for initial and in-service training of science teachers for curricula that incorporate elements of ideas-about-science

Principales parámetros para el estudio de la colaboración científica en Big Science

Directory of Open Access Journals (Sweden)

Ortoll, Eva

2014-12-01

Full Text Available In several scientific disciplines research has shifted from experiments of a reduced scale to large and complex collaborations. Many recent scientific achievements like the human genome sequencing or the discovery of the Higgs boson have taken place within the “big science” paradigm. The study of scientific collaboration needs to take into account all the diverse factors that have an influence on it. In the case of big science experiments, some of those aspects are particularly important: number of institutions involved, cultural differences, diversity of spaces and infrastructures or the conceptualization of research problems. By considering these specific factors we present a set of parameters for the analysis of scientific collaboration in big science projects. The utility of these parameters is illustrated through a comparative study of two large big science projects: the ATLAS experiment and the Human Genome Project.En varias áreas de la ciencia se ha pasado de trabajar en experimentos reducidos a participar en grandes y complejas colaboraciones. Muchos de los grandes avances científicos recientes como la secuenciación del genoma humano o el descubrimiento del bosón de Higgs se enmarcan en el paradigma denominado big science. El estudio de la colaboración científica debe tener en cuenta los factores de todo tipo que influyen en dicha colaboración. Los experimentos de big science inciden especialmente en algunos de estos aspectos: volumen de instituciones implicadas, diferencias culturales, diversidad de espacios e infraestructuras o la propia conceptualización del problema de investigación. Atendiendo a estas particularidades, en este trabajo presentamos un conjunto de parámetros para el análisis de la colaboración científica en proyectos big science. Ilustramos la utilidad de esos parámetros mediante un estudio comparativo de dos grandes proyectos de big science: el experimento ATLAS y el Proyecto Genoma Humano.

"Big Science" exhibition at Balexert

CERN Multimedia

2008-01-01

CERN is going out to meet those members of the general public who were unable to attend the recent Open Day. The Laboratory will be taking its "Big Science" exhibition from the Globe of Science and Innovation to the Balexert shopping centre from 19 to 31 May 2008. The exhibition, which shows the LHC and its experiments through the eyes of a photographer, features around thirty spectacular photographs measuring 4.5 metres high and 2.5 metres wide. Welcomed and guided around the exhibition by CERN volunteers, shoppers at Balexert will also have the opportunity to discover LHC components on display and watch films. "Fun with Physics" workshops will be held at certain times of the day. Main hall of the Balexert shopping centre, ground floor, from 9.00 a.m. to 7.00 p.m. Monday to Friday and from 10 a.m. to 6 p.m. on the two Saturdays. Call for volunteers All members of the CERN personnel are invited to enrol as volunteers to help welcom...

The Clubbers' Guide: Ideas for Science/STEM Clubs from... Books!

Science.gov (United States)

Howarth, Sue

2013-01-01

The internet is certainly a speedy way of finding plenty of information when searching for ideas for science or other STEM clubs. There are many helpful websites, such as that of the British Science Association, with their "free project resources" pages, which include "pick up and run" projects that can be linked to CREST…

Teaching computer science at school: some ideas

OpenAIRE

Bodei, Chiara; Grossi, Roberto; Lagan?, Maria Rita; Righi, Marco

2010-01-01

As a young discipline, Computer Science does not rely on longly tested didactic procedures. This allows the experimentation of innovative teaching methods at schools, especially in early childhood education. Our approach is based on the idea that abstracts notions should be gained as the final result of a learning path made of concrete and touchable steps. To illustrate our methodology, we present some of the teaching projects we proposed.

Big questions, big science: meeting the challenges of global ecology.

Science.gov (United States)

Schimel, David; Keller, Michael

2015-04-01

Ecologists are increasingly tackling questions that require significant infrastucture, large experiments, networks of observations, and complex data and computation. Key hypotheses in ecology increasingly require more investment, and larger data sets to be tested than can be collected by a single investigator's or s group of investigator's labs, sustained for longer than a typical grant. Large-scale projects are expensive, so their scientific return on the investment has to justify the opportunity cost-the science foregone because resources were expended on a large project rather than supporting a number of individual projects. In addition, their management must be accountable and efficient in the use of significant resources, requiring the use of formal systems engineering and project management to mitigate risk of failure. Mapping the scientific method into formal project management requires both scientists able to work in the context, and a project implementation team sensitive to the unique requirements of ecology. Sponsoring agencies, under pressure from external and internal forces, experience many pressures that push them towards counterproductive project management but a scientific community aware and experienced in large project science can mitigate these tendencies. For big ecology to result in great science, ecologists must become informed, aware and engaged in the advocacy and governance of large ecological projects.

Think big: learning contexts, algorithms and data science

Directory of Open Access Journals (Sweden)

Baldassarre Michele

2016-12-01

Full Text Available Due to the increasing growth in available data in recent years, all areas of research and the managements of institutions and organisations, specifically schools and universities, feel the need to give meaning to this availability of data. This article, after a brief reference to the definition of big data, intends to focus attention and reflection on their type to proceed to an extension of their characterisation. One of the hubs to make feasible the use of Big Data in operational contexts is to give a theoretical basis to which to refer. The Data, Information, Knowledge and Wisdom (DIKW model correlates these four aspects, concluding in Data Science, which in many ways could revolutionise the established pattern of scientific investigation. The Learning Analytics applications on online learning platforms can be tools for evaluating the quality of teaching. And that is where some problems arise. It becomes necessary to handle with care the available data. Finally, a criterion for deciding whether it makes sense to think of an analysis based on Big Data can be to think about the interpretability and relevance in relation to both institutional and personal processes.

Big Egos in Big Science

DEFF Research Database (Denmark)

Andersen, Kristina Vaarst; Jeppesen, Jacob

In this paper we investigate the micro-mechanisms governing structural evolution and performance of scientific collaboration. Scientific discovery tends not to be lead by so called lone ?stars?, or big egos, but instead by collaboration among groups of researchers, from a multitude of institutions...

Big Data and Big Science

OpenAIRE

Di Meglio, Alberto

2014-01-01

Brief introduction to the challenges of big data in scientific research based on the work done by the HEP community at CERN and how the CERN openlab promotes collaboration among research institutes and industrial IT companies. Presented at the FutureGov 2014 conference in Singapore.

The Human Genome Project: big science transforms biology and medicine.

Science.gov (United States)

Hood, Leroy; Rowen, Lee

2013-01-01

The Human Genome Project has transformed biology through its integrated big science approach to deciphering a reference human genome sequence along with the complete sequences of key model organisms. The project exemplifies the power, necessity and success of large, integrated, cross-disciplinary efforts - so-called 'big science' - directed towards complex major objectives. In this article, we discuss the ways in which this ambitious endeavor led to the development of novel technologies and analytical tools, and how it brought the expertise of engineers, computer scientists and mathematicians together with biologists. It established an open approach to data sharing and open-source software, thereby making the data resulting from the project accessible to all. The genome sequences of microbes, plants and animals have revolutionized many fields of science, including microbiology, virology, infectious disease and plant biology. Moreover, deeper knowledge of human sequence variation has begun to alter the practice of medicine. The Human Genome Project has inspired subsequent large-scale data acquisition initiatives such as the International HapMap Project, 1000 Genomes, and The Cancer Genome Atlas, as well as the recently announced Human Brain Project and the emerging Human Proteome Project.

Opening the Big Black Box: European study reveals visitors' impressions of science laboratories

CERN Multimedia

2004-01-01

"On 29 - 30 March the findings of 'Inside the Big Black Box'- a Europe-wide science and society project - will be revealed during a two-day seminar hosted by CERN*. The principle aim of Inside the Big Black Box (IN3B) is to determine whether a working scientific laboratory can capture the curiosity of the general public through visits" (1 page)

Education in the Field Influences Children's Ideas and Interest toward Science

Science.gov (United States)

Zoldosova, Kristina; Prokop, Pavol

2006-10-01

This paper explores the idea of informal science education in scientific field laboratory (The Science Field Centre). The experimental group of pupils ( N = 153) was experienced with approximately 5-day lasting field trips and experiments in the Field Centre in Slovakia. After finishing the course, two different research methods were used to discover their interest and ideas toward science. Pupils from the experimental group showed significant differences from those that did not experience education in the Field Centre (control group, N = 365). In comparison to the control group, pupils of the experimental group highly preferred book titles that were related to their program in the Field Centre. There were differences between the drawings of ideal school environment from both pupils groups. In the drawings of the experimental group, we found significantly more items connected with the educational environment of the Field Centre (e.g. laboratory equipment, live animals). We suppose field science education would be one of the most effective ways to increase interest of pupils to study science and to invaluable intrinsic motivation at the expense extrinsic motivation.

Undergraduates' Perceived Gains and Ideas about Teaching and Learning Science from Participating in Science Education Outreach Programs

Science.gov (United States)

Carpenter, Stacey L.

2015-01-01

This study examined what undergraduate students gain and the ideas about science teaching and learning they develop from participating in K-12 science education outreach programs. Eleven undergraduates from seven outreach programs were interviewed individually about their experiences with outreach and what they learned about science teaching and…

MiTEP's Collaborative Field Course Design Process Based on Earth Science Literacy Principles

Science.gov (United States)

Engelmann, C. A.; Rose, W. I.; Huntoon, J. E.; Klawiter, M. F.; Hungwe, K.

2010-12-01

Michigan Technological University has developed a collaborative process for designing summer field courses for teachers as part of their National Science Foundation funded Math Science Partnership program, called the Michigan Teacher Excellence Program (MiTEP). This design process was implemented and then piloted during two two-week courses: Earth Science Institute I (ESI I) and Earth Science Institute II (ESI II). Participants consisted of a small group of Michigan urban science teachers who are members of the MiTEP program. The Earth Science Literacy Principles (ESLP) served as the framework for course design in conjunction with input from participating MiTEP teachers as well as research done on common teacher and student misconceptions in Earth Science. Research on the Earth Science misconception component, aligned to the ESLP, is more fully addressed in GSA Abstracts with Programs Vol. 42, No. 5. “Recognizing Earth Science Misconceptions and Reconstructing Knowledge through Conceptual-Change-Teaching”. The ESLP were released to the public in January 2009 by the Earth Science Literacy Organizing Committee and can be found at http://www.earthscienceliteracy.org/index.html. Each day of the first nine days of both Institutes was focused on one of the nine ESLP Big Ideas; the tenth day emphasized integration of concepts across all of the ESLP Big Ideas. Throughout each day, Michigan Tech graduate student facilitators and professors from Michigan Tech and Grand Valley State University consistantly focused teaching and learning on the day's Big Idea. Many Earth Science experts from Michigan Tech and Grand Valley State University joined the MiTEP teachers in the field or on campus, giving presentations on the latest research in their area that was related to that Big Idea. Field sites were chosen for their unique geological features as well as for the “sense of place” each site provided. Preliminary research findings indicate that this collaborative design

Drawing, Visualisation and Young Children's Exploration of "Big Ideas"

Science.gov (United States)

Brooks, Margaret

2009-01-01

It is in the visualisation of ideas, and the expression or representation of our ideas, that we can bring something more clearly into consciousness. A drawing might be seen as an externalisation of a concept or idea. Drawing has the potential to play a mediating role in the visualisation of ideas and concepts in relation to young children…

Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections

NARCIS (Netherlands)

C-L. Chang (Chia-Lin); M.J. McAleer (Michael); W.-K. Wong (Wing-Keung)

2018-01-01

textabstractThe paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses some research that is related to the seven disciplines. Academics could develop theoretical models and subsequent

Children Explain the Rainbow: Using Young Children's Ideas to Guide Science Curricula

Science.gov (United States)

Siry, Christina; Kremer, Isabelle

2011-01-01

This study examines young children's ideas about natural science phenomena and explores possibilities in starting investigations in kindergarten from their ideas. Given the possibilities inherent in how young children make sense of their experiences, we believe it is critical to take children's perspectives into consideration when designing any…

"small problems, Big Trouble": An Art and Science Collaborative Exhibition Reflecting Seemingly small problems Leading to Big Threats

Science.gov (United States)

Waller, J. L.; Brey, J. A.

2014-12-01

"small problems, Big Trouble" (spBT) is an exhibition of artist Judith Waller's paintings accompanied by text panels written by Earth scientist Dr. James A. Brey and several science researchers and educators. The text panels' message is as much the focus of the show as the art--true interdisciplinarity! Waller and Brey's history of art and earth science collaborations include the successful exhibition "Layers: Places in Peril". New in spBT is extended collaboration with other scientists in order to create awareness of geoscience and other subjects (i.e. soil, parasites, dust, pollutants, invasive species, carbon, ground water contaminants, solar wind) small in scale which pose significant threats. The paintings are the size of a mirror, a symbol suggesting the problems depicted are those we increasingly need to face, noting our collective reflections of shared current and future reality. Naturalistic rendering and abstract form in the art helps reach a broad audience including those familiar with art and those familiar with science. The goal is that gallery visitors gain greater appreciation and understanding of both—and of the sober content of the show as a whole. "small problems, Big Trouble" premiers in Wisconsin April, 2015. As in previous collaborations, Waller and Brey actively utilize art and science (specifically geoscience) as an educational vehicle for active student learning. Planned are interdisciplinary university and area high school activities linked through spBT. The exhibition in a public gallery offers a means to enhance community awareness of and action on scientific issues through art's power to engage people on an emotional level. This AGU presentation includes a description of past Waller and Brey activities: incorporating art and earth science in lab and studio classrooms, producing gallery and museum exhibitions and delivering workshops and other presentations. They also describe how walking the paths of several past earth science

Growing a Primary Science Specialism: Assembling People, Places, Materials and Ideas

Science.gov (United States)

Lynch, Julianne; Frankel, Nadine; McCarthy, Kerry; Sharp, Lindy

2015-01-01

This paper derives from the authors' experiences of the development of a successful science specialism implemented in a large primary school in regional Victoria, Australia, since 2012. We discuss how diverse resources--people, spaces, equipment, materials and ideas--were brought together to support a science specialism that focuses on positioning…

Summary big data

CERN Document Server

2014-01-01

This work offers a summary of Cukier the book: "Big Data: A Revolution That Will Transform How we Live, Work, and Think" by Viktor Mayer-Schonberg and Kenneth. Summary of the ideas in Viktor Mayer-Schonberg's and Kenneth Cukier's book: " Big Data " explains that big data is where we use huge quantities of data to make better predictions based on the fact we identify patters in the data rather than trying to understand the underlying causes in more detail. This summary highlights that big data will be a source of new economic value and innovation in the future. Moreover, it shows that it will

Commercialisation of science in Australia

International Nuclear Information System (INIS)

Mitchell, G.

2003-01-01

Major changes are occurring across the science and technology (S and T) landscape in this country. Messages from Federal and State Governments in recent times could not have been clearer - in return for 'taxpayer $ into ideas' (in other words, funding for front end basic research) there is an expectation that 'ideas will be translated into $' (in other words, commercialisation will be pursued aggressively). As we in Australian S and T are constantly reminded, with part justification only, Australian researchers (especially in the life sciences) are good at generating a wealth of ideas but not much wealth from ideas. It is claimed that despite scientific excellence, many in the sector are risk averse, immobile, prone to academic snobbery, better employees than employers, not entrepreneurial etc, etc. Regardless of the veracity of any of this, the 1990s has seen a change with many more scientists interested in pursuing the progression of ideas to research to invention to intellectual property to competitive advantage to commercialisation to wealth, jobs and social development to profits and tax dollars to increased support for innovation, R and D, basic research etc. In regard to biomedical research, it has been said that '... medical biotechnology was the first business with enough glamour to persuade eminent scientists that the entrepreneurial spirit and academic respectability are not mutually exclusive. Maybe it's OK to be a science-literate businessman and to make money from science. Successful biotech companies emerge when good science meets excellent management and that combination, in an enabling environment, attracts informed investors and partners. Biotech companies may focus on a single product, a portfolio, or a technology platform and the majority are destined not to become, and have no intention of becoming, an integrated biopharmaceutical or agrochemical company. Their capacity to raise funds is influenced by 'signals' that the technology, the people

Mapping the heavens the radical scientific ideas that reveal the cosmos

CERN Document Server

Natarajan, Priyamvada

2016-01-01

This book provides a tour of the greatest hits of cosmological discoveries the ideas that reshaped our universe over the past century. The cosmos, once understood as a stagnant place, filled with the ordinary, is now a universe that is expanding at an accelerating pace, propelled by dark energy and structured by dark matter. Priyamvada Natarajan, our guide to these ideas, is someone at the forefront of the research an astrophysicist who literally creates maps of invisible matter in the universe. She not only explains for a wide audience the science behind these essential ideas but also provides an understanding of how radical scientific theories gain acceptance. The formation and growth of black holes, dark matter halos, the accelerating expansion of the universe, the echo of the big bang, the discovery of exoplanets, and the possibility of other universes these are some of the puzzling cosmological topics of the early twenty-first century. Natarajan discusses why the acceptance of new ideas about the univer...

Educators on the Edge: Big Ideas for Change and Innovation. Australian College of Educators (ACE) National Conference Proceedings (Brisbane, Australia, September 24-25, 2015)

Science.gov (United States)

Finger, Glenn, Ed.; Ghirelli, Paola S., Ed.

2015-01-01

The 2015 Australian College of Educators (ACE) National Conference theme is "Educators on the Edge: Big Ideas for Change and Innovation." ACE presented an opportunity for all education professionals to gather, discuss, and share cutting-edge, creative and innovative practices, nationally and globally at the conference held on September…

The Natural Science Underlying Big History

Directory of Open Access Journals (Sweden)

Eric J. Chaisson

2014-01-01

Full Text Available Nature’s many varied complex systems—including galaxies, stars, planets, life, and society—are islands of order within the increasingly disordered Universe. All organized systems are subject to physical, biological, or cultural evolution, which together comprise the grander interdisciplinary subject of cosmic evolution. A wealth of observational data supports the hypothesis that increasingly complex systems evolve unceasingly, uncaringly, and unpredictably from big bang to humankind. These are global history greatly extended, big history with a scientific basis, and natural history broadly portrayed across ∼14 billion years of time. Human beings and our cultural inventions are not special, unique, or apart from Nature; rather, we are an integral part of a universal evolutionary process connecting all such complex systems throughout space and time. Such evolution writ large has significant potential to unify the natural sciences into a holistic understanding of who we are and whence we came. No new science (beyond frontier, nonequilibrium thermodynamics is needed to describe cosmic evolution’s major milestones at a deep and empirical level. Quantitative models and experimental tests imply that a remarkable simplicity underlies the emergence and growth of complexity for a wide spectrum of known and diverse systems. Energy is a principal facilitator of the rising complexity of ordered systems within the expanding Universe; energy flows are as central to life and society as they are to stars and galaxies. In particular, energy rate density—contrasting with information content or entropy production—is an objective metric suitable to gauge relative degrees of complexity among a hierarchy of widely assorted systems observed throughout the material Universe. Operationally, those systems capable of utilizing optimum amounts of energy tend to survive, and those that cannot are nonrandomly eliminated.

From Big Data to Big Business

DEFF Research Database (Denmark)

Lund Pedersen, Carsten

2017-01-01

Idea in Brief: Problem: There is an enormous profit potential for manufacturing firms in big data, but one of the key barriers to obtaining data-driven growth is the lack of knowledge about which capabilities are needed to extract value and profit from data. Solution: We (BDBB research group at C...

Complementary social science? Quali-quantitative experiments in a Big Data world

Directory of Open Access Journals (Sweden)

Anders Blok

2014-08-01

Full Text Available The rise of Big Data in the social realm poses significant questions at the intersection of science, technology, and society, including in terms of how new large-scale social databases are currently changing the methods, epistemologies, and politics of social science. In this commentary, we address such epochal (“large-scale” questions by way of a (situated experiment: at the Danish Technical University in Copenhagen, an interdisciplinary group of computer scientists, physicists, economists, sociologists, and anthropologists (including the authors is setting up a large-scale data infrastructure, meant to continually record the digital traces of social relations among an entire freshman class of students ( N  > 1000. At the same time, fieldwork is carried out on friendship (and other relations amongst the same group of students. On this basis, the question we pose is the following: what kind of knowledge is obtained on this social micro-cosmos via the Big (computational, quantitative and Small (embodied, qualitative Data, respectively? How do the two relate? Invoking Bohr’s principle of complementarity as analogy, we hypothesize that social relations, as objects of knowledge, depend crucially on the type of measurement device deployed. At the same time, however, we also expect new interferences and polyphonies to arise at the intersection of Big and Small Data, provided that these are, so to speak, mixed with care. These questions, we stress, are important not only for the future of social science methods but also for the type of societal (self-knowledge that may be expected from new large-scale social databases.

Big Data and Clinicians: A Review on the State of the Science

Science.gov (United States)

Wang, Weiqi

2014-01-01

Background In the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality. Objective The objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm. Methods We conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest from the past 10 years. Results This paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type of data. Conclusions Big data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to collect and store data. PMID:25600256

Small Core, Big Network: A Comprehensive Approach to GIS Teaching Practice Based on Digital Three-Dimensional Campus Reconstruction

Science.gov (United States)

Cheng, Liang; Zhang, Wen; Wang, Jiechen; Li, Manchun; Zhong, Lishan

2014-01-01

Geographic information science (GIS) features a wide range of disciplines and has broad applicability. Challenges associated with rapidly developing GIS technology and the currently limited teaching and practice materials hinder universities from cultivating highly skilled GIS graduates. Based on the idea of "small core, big network," a…

How Big is Earth?

Science.gov (United States)

Thurber, Bonnie B.

2015-08-01

How Big is Earth celebrates the Year of Light. Using only the sunlight striking the Earth and a wooden dowel, students meet each other and then measure the circumference of the earth. Eratosthenes did it over 2,000 years ago. In Cosmos, Carl Sagan shared the process by which Eratosthenes measured the angle of the shadow cast at local noon when sunlight strikes a stick positioned perpendicular to the ground. By comparing his measurement to another made a distance away, Eratosthenes was able to calculate the circumference of the earth. How Big is Earth provides an online learning environment where students do science the same way Eratosthenes did. A notable project in which this was done was The Eratosthenes Project, conducted in 2005 as part of the World Year of Physics; in fact, we will be drawing on the teacher's guide developed by that project.How Big Is Earth? expands on the Eratosthenes project by providing an online learning environment provided by the iCollaboratory, www.icollaboratory.org, where teachers and students from Sweden, China, Nepal, Russia, Morocco, and the United States collaborate, share data, and reflect on their learning of science and astronomy. They are sharing their information and discussing their ideas/brainstorming the solutions in a discussion forum. There is an ongoing database of student measurements and another database to collect data on both teacher and student learning from surveys, discussions, and self-reflection done online.We will share our research about the kinds of learning that takes place only in global collaborations.The entrance address for the iCollaboratory is http://www.icollaboratory.org.

Lecture 10: The European Bioinformatics Institute - "Big data" for biomedical sciences

CERN Multimedia

CERN. Geneva; Dana, Jose

2013-01-01

Part 1: Big data for biomedical sciences (Tom Hancocks) Ten years ago witnessed the completion of the first international 'Big Biology' project that sequenced the human genome. In the years since biological sciences, have seen a vast growth in data. In the coming years advances will come from integration of experimental approaches and the translation into applied technologies is the hospital, clinic and even at home. This talk will examine the development of infrastructure, physical and virtual, that will allow millions of life scientists across Europe better access to biological data Tom studied Human Genetics at the University of Leeds and McMaster University, before completing an MSc in Analytical Genomics at the University of Birmingham. He has worked for the UK National Health Service in diagnostic genetics and in training healthcare scientists and clinicians in bioinformatics. Tom joined the EBI in 2012 and is responsible for the scientific development and delivery of training for the BioMedBridges pr...

Characterizing the changes in teaching practice during first semester implementation of an argument-based inquiry approach in a middle school science classroom

Science.gov (United States)

Pinney, Brian Robert John

The purpose of this study was to characterize ways in which teaching practice in classroom undergoing first semester implementation of an argument-based inquiry approach changes in whole-class discussion. Being that argument is explicitly called for in the Next Generation Science Standards and is currently a rare practice in teaching, many teachers will have to transform their teaching practice for inclusion of this feature. Most studies on Argument-Based Inquiry (ABI) agree that development of argument does not come easily and is only acquired through practice. Few studies have examined the ways in which teaching practice changes in relation to the big idea or disciplinary core idea (NGSS), the development of dialogue, and/or the development of argument during first semester implementation of an argument-based inquiry approach. To explore these areas, this study posed three primary research questions: (1) How does a teacher in his first semester of Science Writing Heuristic professional development make use of the "big idea"?, (1a) Is the indicated big idea consistent with NGSS core concepts?, (2) How did the dialogue in whole-class discussion change during the first semester of argument-based inquiry professional development?, (3) How did the argument in whole-class discussion change during the first semester of argument-based inquiry professional development? This semester-long study that took place in a middle school in a rural Midwestern city was grounded in interactive constructivism, and utilized a qualitative design to identify the ways in which the teacher utilized big ideas and how dialogue and argumentative dialogue developed over time. The purposefully selected teacher in this study provided a unique situation where he was in his first semester of professional development using the Science Writing Heuristic Approach to argument-based inquiry with 19 students who had two prior years' experience in ABI. Multiple sources of data were collected, including

Final Scientific/Technical Report to the U.S. Department of Energy on NOVA's Einstein's Big Idea (Project title: E-mc², A Two-Hour Television Program on NOVA)

Energy Technology Data Exchange (ETDEWEB)

Simpson, Susanne

2007-05-07

A woman in the early 1700s who became one of Europe’s leading interpreters of mathematics and a poor bookbinder who became one of the giants of nineteenth-century science are just two of the pioneers whose stories NOVA explored in Einstein’s Big Idea. This two-hour documentary premiered on PBS in October 2005 and is based on the best-selling book by David Bodanis, E=mc2: A Biography of the World’s Most Famous Equation. The film and book chronicle the scientific challenges and discoveries leading up to Einstein’s startling conclusion that mass and energy are one, related by the formula E = mc².

The idea of human prehistory: the natural sciences, the human sciences, and the problem of human origins in Victorian Britain.

Science.gov (United States)

Goodrum, Matthew R

2012-01-01

The idea of human prehistory was a provocative and profoundly influential new notion that took shape gradually during the nineteenth century. While archaeology played an important role in providing the evidence for this idea many other sciences such as geology, paleontology, ethnology, and physical anthropology all made critical contributions to discussions about human prehistory. Many works have explored the history of prehistoric archaeology but this paper examines the conceptual content of the idea of "human prehistory" as it developed in the British scientific community. Both the natural and the human sciences contributed to what was in fact a complex collection of individual elements that together constituted the prevailing idea of human prehistory, although there were other competing conceptions of human prehistory endorsed by various scientists and critics of the new view of early human history.

NASA IDEAS to Improve Instruction in Astronomy and Space Science

Science.gov (United States)

Malphrus, B.; Kidwell, K.

1999-12-01

The IDEAS to Improve Instructional Competencies in Astronomy and Space Science project is intended to develop and/or enhance teacher competencies in astronomy and space sciences of teacher participants (Grades 5-12) in Kentucky. The project is being implemented through a two-week summer workshop, a series of five follow-up meetings, and an academic year research project. The resources of Kentucky's only Radio Astronomy Observatory- the Morehead Radio Telescope (MRT), Goldstone Apple Valley Radio Telescope (GAVRT) (via remote observing using the Internet), and the Kentucky Department of Education regional service centers are combined to provide a unique educational experience. The project is designed to improve science teacher's instructional methodologies by providing pedagogical assistance, content training, involving the teachers and their students in research in radio astronomy, providing access to the facilities of the Morehead Astrophysical Observatory, and by working closely with a NASA-JOVE research astronomer. Participating teachers will ultimately produce curriculum units and research projects, the results of which will be published on the WWW. A major goal of this project is to share with teachers and ultimately students the excitement and importance of scientific research. The project represents a partnership of five agencies, each matching the commitment both financially and/or personnel. This project is funded by the NASA IDEAS initiative administered by the Space Telescope Science Institute and the National Air and Space Administration (NASA).

Big Data and Intellectual Property Rights in the Health and Life Sciences

DEFF Research Database (Denmark)

Minssen, Timo

The vast prospects of Big Data and the shift to more “personalized”, “open” and “transparent” innovation models highlight the importance of an effective governance, regulation and stimulation of high-quality data-uses in the health and life sciences. Intellectual Property Rights (IPRs) and related...... rights come into play when research is translated into safe and efficient “real world” uses. While the need of recalibrating IPRs to fully support Big Data advances is being intensely debated among multiple stakeholders, there seems to be much confusion about the availability of IPRs and their legal...... effects. In this very brief presentation I intend to provide a very brief overview on the most relevant IPRs for data-based life science research. Realizing that the choice of how to address, use and interact with IPRs differs among various areas of applications, I also intend to sketch out and discuss...

Learning of Core Disciplinary Ideas: Efficacy Comparison of Two Contrasting Modes of Science Instruction

Science.gov (United States)

Schuster, David; Cobern, William W.; Adams, Betty A. J.; Undreiu, Adriana; Pleasants, Brandy

2018-01-01

Science curricula and teaching methods vary greatly, depending in part on which facets of science are emphasized, e.g., core disciplinary ideas or science practices and process skills, and perspectives differ considerably on desirable pedagogies. Given the multi-faceted nature of science and the variety of teaching methods found in practice, it is…

Legal dimensions of Big Data in the Health and Life Sciences

DEFF Research Database (Denmark)

Minssen, Timo

2016-01-01

Please find below my welcome speech at last-weeks mini-symposium on “Legal dimensions of Big Data in the Health and Life Sciences – From Intellectual Property Rights and Global Pandemics to Privacy and Ethics at the University of Copenhagen (UCPH). The event was organized by our Global Genes –Local...

Big-Data-Driven Stem Cell Science and Tissue Engineering: Vision and Unique Opportunities.

Science.gov (United States)

Del Sol, Antonio; Thiesen, Hans J; Imitola, Jaime; Carazo Salas, Rafael E

2017-02-02

Achieving the promises of stem cell science to generate precise disease models and designer cell samples for personalized therapeutics will require harnessing pheno-genotypic cell-level data quantitatively and predictively in the lab and clinic. Those requirements could be met by developing a Big-Data-driven stem cell science strategy and community. Copyright © 2017 Elsevier Inc. All rights reserved.

Big data, big knowledge: big data for personalized healthcare.

Science.gov (United States)

Viceconti, Marco; Hunter, Peter; Hose, Rod

2015-07-01

The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the "physiological envelope" during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority.

An overview of big data and data science education at South African universities

Directory of Open Access Journals (Sweden)

Eduan Kotzé

2016-02-01

Full Text Available Man and machine are generating data electronically at an astronomical speed and in such a way that society is experiencing cognitive challenges to analyse this data meaningfully. Big data firms, such as Google and Facebook, identified this problem several years ago and are continuously developing new technologies or improving existing technologies in order to facilitate the cognitive analysis process of these large data sets. The purpose of this article is to contribute to our theoretical understanding of the role that big data might play in creating new training opportunities for South African universities. The article investigates emerging literature on the characteristics and main components of big data, together with the Hadoop application stack as an example of big data technology. Due to the rapid development of big data technology, a paradigm shift of human resources is required to analyse these data sets; therefore, this study examines the state of big data teaching at South African universities. This article also provides an overview of possible big data sources for South African universities, as well as relevant big data skills that data scientists need. The study also investigates existing academic programs in South Africa, where the focus is on teaching advanced database systems. The study found that big data and data science topics are introduced to students on a postgraduate level, but that the scope is very limited. This article contributes by proposing important theoretical topics that could be introduced as part of the existing academic programs. More research is required, however, to expand these programs in order to meet the growing demand for data scientists with big data skills.

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

Science.gov (United States)

Zhang, Xinzhi; Pérez-Stable, Eliseo J.; Bourne, Philip E.; Peprah, Emmanuel; Duru, O. Kenrik; Breen, Nancy; Berrigan, David; Wood, Fred; Jackson, James S.; Wong, David W.S.; Denny, Joshua

2017-01-01

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them. PMID:28439179

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century.

Science.gov (United States)

Zhang, Xinzhi; Pérez-Stable, Eliseo J; Bourne, Philip E; Peprah, Emmanuel; Duru, O Kenrik; Breen, Nancy; Berrigan, David; Wood, Fred; Jackson, James S; Wong, David W S; Denny, Joshua

2017-01-01

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.

Big Data: An Opportunity for Collaboration with Computer Scientists on Data-Driven Science

Science.gov (United States)

Baru, C.

2014-12-01

Big data technologies are evolving rapidly, driven by the need to manage ever increasing amounts of historical data; process relentless streams of human and machine-generated data; and integrate data of heterogeneous structure from extremely heterogeneous sources of information. Big data is inherently an application-driven problem. Developing the right technologies requires an understanding of the applications domain. Though, an intriguing aspect of this phenomenon is that the availability of the data itself enables new applications not previously conceived of! In this talk, we will discuss how the big data phenomenon creates an imperative for collaboration among domain scientists (in this case, geoscientists) and computer scientists. Domain scientists provide the application requirements as well as insights about the data involved, while computer scientists help assess whether problems can be solved with currently available technologies or require adaptaion of existing technologies and/or development of new technologies. The synergy can create vibrant collaborations potentially leading to new science insights as well as development of new data technologies and systems. The area of interface between geosciences and computer science, also referred to as geoinformatics is, we believe, a fertile area for interdisciplinary research.

Research Data Alliance: Understanding Big Data Analytics Applications in Earth Science

Science.gov (United States)

Riedel, Morris; Ramachandran, Rahul; Baumann, Peter

2014-01-01

The Research Data Alliance (RDA) enables data to be shared across barriers through focused working groups and interest groups, formed of experts from around the world - from academia, industry and government. Its Big Data Analytics (BDA) interest groups seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. BDA seeks to analyze different scientific domain applications (e.g. earth science use cases) and their potential use of various big data analytics techniques. These techniques reach from hardware deployment models up to various different algorithms (e.g. machine learning algorithms such as support vector machines for classification). A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. This contribution will outline initial parts of such a classification and recommendations in the specific context of the field of Earth Sciences. Given lessons learned and experiences are based on a survey of use cases and also providing insights in a few use cases in detail.

The distinction between key ideas in teaching school physics and key ideas in the discipline of physics

Science.gov (United States)

Deng, Zongyi

2001-05-01

The distinction between key ideas in teaching a high school science and key ideas in the corresponding discipline of science has been largely ignored in scholarly discourse about what science teachers should teach and about what they should know. This article clarifies this distinction through exploring how and why key ideas in teaching high school physics differ from key ideas in the discipline of physics. Its theoretical underpinnings include Dewey's (1902/1990) distinction between the psychological and the logical and Harré's (1986) epistemology of science. It analyzes how and why the key ideas in teaching color, the speed of light, and light interference at the high school level differ from the key ideas at the disciplinary level. The thesis is that key ideas in teaching high school physics can differ from key ideas in the discipline in some significant ways, and that the differences manifest Dewey's distinction. As a result, the article challenges the assumption of equating key ideas in teaching a high school science with key ideas in the corresponding discipline of science, and the assumption that having a college degree in science is sufficient to teach high school science. Furthermore, the article expands the concept of pedagogical content knowledge by arguing that key ideas in teaching high school physics constitute an essential component.

Winch, Wittgenstein and the Idea of a Critical Social Science

DEFF Research Database (Denmark)

Hermansen, Jens Christian

such phenomena. In the light of new uses ofWittgenstein within social theory and recent philosophical research on Wittgenstein (that challenge the orthodoxWinchian reception of Wittgenstein), the paper discusses the prospects of a critical social science after Wittgenstein.......In "The Idea of a Social Science" and in the article "Understanding a Primitive Society" Peter Winch develops what he believes to be the implications ofWittgenstein's late philosophy for the social sciences. Inspired byWittgenstein,Winch argues for a linguistic turn. Winch's basic ontological claim...... is that social life is conceptually organised: it is organised by the ways in which language is used by members of social life. This claim has methodological implications: the social sciences are, according to Winch, conceptual studies, that is, they are studies of the concepts possessed by members of social...

Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology.

Science.gov (United States)

Salazar, Brittany M; Balczewski, Emily A; Ung, Choong Yong; Zhu, Shizhen

2016-12-27

Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring "big data" applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which "big data" and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.

NASA's Big Data Task Force

Science.gov (United States)

Holmes, C. P.; Kinter, J. L.; Beebe, R. F.; Feigelson, E.; Hurlburt, N. E.; Mentzel, C.; Smith, G.; Tino, C.; Walker, R. J.

2017-12-01

Two years ago NASA established the Ad Hoc Big Data Task Force (BDTF - https://science.nasa.gov/science-committee/subcommittees/big-data-task-force), an advisory working group with the NASA Advisory Council system. The scope of the Task Force included all NASA Big Data programs, projects, missions, and activities. The Task Force focused on such topics as exploring the existing and planned evolution of NASA's science data cyber-infrastructure that supports broad access to data repositories for NASA Science Mission Directorate missions; best practices within NASA, other Federal agencies, private industry and research institutions; and Federal initiatives related to big data and data access. The BDTF has completed its two-year term and produced several recommendations plus four white papers for NASA's Science Mission Directorate. This presentation will discuss the activities and results of the TF including summaries of key points from its focused study topics. The paper serves as an introduction to the papers following in this ESSI session.

Big data uncertainties.

Science.gov (United States)

Maugis, Pierre-André G

2018-07-01

Big data-the idea that an always-larger volume of information is being constantly recorded-suggests that new problems can now be subjected to scientific scrutiny. However, can classical statistical methods be used directly on big data? We analyze the problem by looking at two known pitfalls of big datasets. First, that they are biased, in the sense that they do not offer a complete view of the populations under consideration. Second, that they present a weak but pervasive level of dependence between all their components. In both cases we observe that the uncertainty of the conclusion obtained by statistical methods is increased when used on big data, either because of a systematic error (bias), or because of a larger degree of randomness (increased variance). We argue that the key challenge raised by big data is not only how to use big data to tackle new problems, but to develop tools and methods able to rigorously articulate the new risks therein. Copyright © 2016. Published by Elsevier Ltd.

Idea-based, transformative experiences in science: What are they and how do you foster them?

Science.gov (United States)

Pugh, Kevin James

Many have argued that science education should enrich students' lives, but, surprisingly, this issue has not been systematically addressed. Much of the work in science education has focused on the issue of how enriched experience leads to the development of conceptual understanding, but relatively little work has focused on the issue of how conceptual understanding leads to the development of enriched experience. This dissertation is comprised of two articles, which address the latter issue. The first article, entitled "Applying Pragmatism and Deweyan Aesthetics to Science Education: A Look at How Concepts Can Enrich Everyday Experience," develops the construct of an idea-based, transformative experience (a particular type of enriched experience) and an understanding of the role that concepts play in such experience, by synthesizing Dewey's writings on experience, aesthetics, and education. Such experience is centrally defined by an expansion of perception, meaning, and value which results from active use of a concept. Three illustrative examples of idea-based, transformative experiences are provided. Implications include a focus on idea-based, transformative experience as the goal of science education. A discussion of how this goal compares, contrasts, and relates to the standard goals of conceptual understanding/change and the development of thinking/participatory skills is provided. The second article, entitled, "Teaching for Idea-based, Transformative Experiences in Science," is a report of a study which examines the effectiveness of two related teaching elements (the artistic crafting of content and the modeling and scaffolding of perception, meaning, and value) at fostering idea-based, transformative experiences. The elements were used in teaching a unit on adaptation and evolution in a high school zoology class and student outcomes were compared with those of students in a roughly equivalent class where case-based methods were used. Results indicate that a

"Big Science: the LHC in Pictures" in the Globe

CERN Multimedia

2008-01-01

An exhibition of spectacular photographs of the LHC and its experiments is about to open in the Globe. The LHC and its four experiments are not only huge in size but also uniquely beautiful, as the exhibition "Big Science: the LHC in Pictures" in the Globe of Science and Innovation will show. The exhibition features around thirty spectacular photographs measuring 4.5 metres high and 2.5 metres wide. These giant pictures reflecting the immense scale of the LHC and the mysteries of the Universe it is designed to uncover fill the Globe with shape and colour. The exhibition, which will open on 4 March, is divided into six different themes: CERN, the LHC and the four experiments ATLAS, LHCb, CMS and ALICE. Facts about all these subjects will be available at information points and in an explanatory booklet accompanying the exhibition (which visitors will be able to buy if they wish to take it home with them). Globe of Science and Innovatio...

Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives. Contribution of the IMIA Social Media Working Group.

Science.gov (United States)

Hansen, M M; Miron-Shatz, T; Lau, A Y S; Paton, C

2014-08-15

As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts: The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to "small data" would also be useful.

Quarks, leptons and the big bang

CERN Document Server

Allday, Jonathan

2016-01-01

Quarks, Leptons and The Big Bang, Third Edition, is a clear, readable and self-contained introduction to particle physics and related areas of cosmology. It bridges the gap between non-technical popular accounts and textbooks for advanced students. The book concentrates on presenting the subject from the modern perspective of quarks, leptons and the forces between them. This book will be of interest to students, teachers and general science readers interested in fundamental ideas of modern physics. This edition brings the book completely up to date by including advances in particle physics and cosmology, such as the discovery of the Higgs boson, the LIGO gravitational wave discovery and the WMAP and PLANCK results.

Young Idea People Mix with Old Idea People to Make the World Better

Science.gov (United States)

Hall, M.

2017-12-01

Groups of young idea people come to eat, drink, and talk about new ideas that old idea people are working on to change the world for the better. The ideas may fix our body and mind, make our lives easier or harder, and more. The young idea people lead, learn, listen and act, so they can become old idea people. The young idea people scare the old idea people because their ideas are different. And, sometimes, the young idea people have new ideas that the old idea people have not thought about. When this happens it makes the old idea people happy and better at their work. The old idea people get to go places and share their ideas around the world. They make good money and have fun lives. They write about their work and can be well known, or not. The young idea people learn from the old idea people how they can be like them. Together the young and old idea people build things and talk about crazy ideas that may come to be. Sometimes the old idea people talk too much and don't listen. They use big words that can be hard to understand. But, the young idea people help them learn to use known words so everyone learns. We know the young idea people learn and grow from this act and they grow happier about their life. We also know that the old idea people get happy that the young idea people are so bright.

Science teacher’s idea about environmental concepts in science learning as the first step of science teacher training

Science.gov (United States)

Tapilouw, M. C.; Firman, H.; Redjeki, S.; Chandra, D. T.

2018-05-01

To refresh natural environmental concepts in science, science teacher have to attend a teacher training. In teacher training, all participant can have a good sharing and discussion with other science teacher. This study is the first step of science teacher training program held by education foundation in Bandung and attended by 20 science teacher from 18 Junior High School. The major aim of this study is gathering science teacher’s idea of environmental concepts. The core of questions used in this study are basic competencies linked with environmental concepts, environmental concepts that difficult to explain, the action to overcome difficulties and references in teaching environmental concepts. There are four major findings in this study. First finding, most environmental concepts are taught in 7th grade. Second finding, most difficult environmental concepts are found in 7th grade. Third finding, there are five actions to overcome difficulties. Fourth finding, science teacher use at least four references in mastering environmental concepts. After all, teacher training can be a solution to reduce difficulties in teaching environmental concepts.

Numbers and other math ideas come alive

CERN Document Server

Pappas, Theoni

2012-01-01

Most people don't think about numbers, or take them for granted. For the average person numbers are looked upon as cold, clinical, inanimate objects. Math ideas are viewed as something to get a job done or a problem solved. Get ready for a big surprise with Numbers and Other Math Ideas Come Alive. Pappas explores mathematical ideas by looking behind the scenes of what numbers, points, lines, and other concepts are saying and thinking. In each story, properties and characteristics of math ideas are entertainingly uncovered and explained through the dialogues and actions of its math

Data Science as an Innovation Challenge: From Big Data to Value Proposition

Directory of Open Access Journals (Sweden)

Victoria Kayser

2018-03-01

Full Text Available Analyzing “big data” holds huge potential for generating business value. The ongoing advancement of tools and technology over recent years has created a new ecosystem full of opportunities for data-driven innovation. However, as the amount of available data rises to new heights, so too does complexity. Organizations are challenged to create the right contexts, by shaping interfaces and processes, and by asking the right questions to guide the data analysis. Lifting the innovation potential requires teaming and focus to efficiently assign available resources to the most promising initiatives. With reference to the innovation process, this article will concentrate on establishing a process for analytics projects from first ideas to realization (in most cases: a running application. The question we tackle is: what can the practical discourse on big data and analytics learn from innovation management? The insights presented in this article are built on our practical experiences in working with various clients. We will classify analytics projects as well as discuss common innovation barriers along this process.

Advanced statistical methods in data science

CERN Document Server

Chen, Jiahua; Lu, Xuewen; Yi, Grace; Yu, Hao

2016-01-01

This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a fu...

Data science and big data analytics discovering, analyzing, visualizing and presenting data

CERN Document Server

2014-01-01

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science teamDeploy a structured lifecycle approach to data analytics problemsApply appropriate analytic techniques and

Big Data, Big Problems: A Healthcare Perspective.

Science.gov (United States)

Househ, Mowafa S; Aldosari, Bakheet; Alanazi, Abdullah; Kushniruk, Andre W; Borycki, Elizabeth M

2017-01-01

Much has been written on the benefits of big data for healthcare such as improving patient outcomes, public health surveillance, and healthcare policy decisions. Over the past five years, Big Data, and the data sciences field in general, has been hyped as the "Holy Grail" for the healthcare industry promising a more efficient healthcare system with the promise of improved healthcare outcomes. However, more recently, healthcare researchers are exposing the potential and harmful effects Big Data can have on patient care associating it with increased medical costs, patient mortality, and misguided decision making by clinicians and healthcare policy makers. In this paper, we review the current Big Data trends with a specific focus on the inadvertent negative impacts that Big Data could have on healthcare, in general, and specifically, as it relates to patient and clinical care. Our study results show that although Big Data is built up to be as a the "Holy Grail" for healthcare, small data techniques using traditional statistical methods are, in many cases, more accurate and can lead to more improved healthcare outcomes than Big Data methods. In sum, Big Data for healthcare may cause more problems for the healthcare industry than solutions, and in short, when it comes to the use of data in healthcare, "size isn't everything."

Proportional Reasoning Ability and Concepts of Scale: Surface Area to Volume Relationships in Science

Science.gov (United States)

Taylor, Amy; Jones, Gail

2009-01-01

The "National Science Education Standards" emphasise teaching unifying concepts and processes such as basic functions of living organisms, the living environment, and scale. Scale influences science processes and phenomena across the domains. One of the big ideas of scale is that of surface area to volume. This study explored whether or not there…

Ontologies, methodologies, and new uses of Big Data in the social and cultural sciences

Directory of Open Access Journals (Sweden)

Robin Wagner-Pacifici

2015-12-01

Full Text Available In our Introduction to the Conceiving the Social with Big Data Special Issue of Big Data & Society , we survey the 18 contributions from scholars in the humanities and social sciences, and highlight several questions and themes that emerge within and across them. These emergent issues reflect the challenges, problems, and promises of working with Big Data to access and assess the social. They include puzzles about the locus and nature of human life, the nature of interpretation, the categorical constructions of individual entities and agents, the nature and relevance of contexts and temporalities, and the determinations of causality. As such, the Introduction reflects on the contributions along a series of binaries that capture the dualities and dynamisms of these themes: Life/Data; Mind/Machine; and Induction/Deduction.

Big data, computational science, economics, finance, marketing, management, and psychology: connections

OpenAIRE

Chang, Chia-Lin; McAleer, Michael; Wong, Wing-Keung

2018-01-01

textabstractThe paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses some research that is related to the seven disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models, as well as conduct simulation to examine whether the estimators in their theories on estimation and hypothesis testin...

Research funding. Big names or big ideas: do peer-review panels select the best science proposals?

Science.gov (United States)

Li, Danielle; Agha, Leila

2015-04-24

This paper examines the success of peer-review panels in predicting the future quality of proposed research. We construct new data to track publication, citation, and patenting outcomes associated with more than 130,000 research project (R01) grants funded by the U.S. National Institutes of Health from 1980 to 2008. We find that better peer-review scores are consistently associated with better research outcomes and that this relationship persists even when we include detailed controls for an investigator's publication history, grant history, institutional affiliations, career stage, and degree types. A one-standard deviation worse peer-review score among awarded grants is associated with 15% fewer citations, 7% fewer publications, 19% fewer high-impact publications, and 14% fewer follow-on patents. Copyright © 2015, American Association for the Advancement of Science.

Applying science and mathematics to big data for smarter buildings.

Science.gov (United States)

Lee, Young M; An, Lianjun; Liu, Fei; Horesh, Raya; Chae, Young Tae; Zhang, Rui

2013-08-01

Many buildings are now collecting a large amount of data on operations, energy consumption, and activities through systems such as a building management system (BMS), sensors, and meters (e.g., submeters and smart meters). However, the majority of data are not utilized and are thrown away. Science and mathematics can play an important role in utilizing these big data and accurately assessing how energy is consumed in buildings and what can be done to save energy, make buildings energy efficient, and reduce greenhouse gas (GHG) emissions. This paper discusses an analytical tool that has been developed to assist building owners, facility managers, operators, and tenants of buildings in assessing, benchmarking, diagnosing, tracking, forecasting, and simulating energy consumption in building portfolios. © 2013 New York Academy of Sciences.

Economics and econophysics in the era of Big Data

Science.gov (United States)

Cheong, Siew Ann

2016-12-01

There is an undeniable disconnect between theory-heavy economics and the real world, and some cross polination of ideas with econophysics, which is more balanced between data and models, might help economics along the way to become a truly scientific enterprise. With the coming of the era of Big Data, this transformation of economics into a data-driven science is becoming more urgent. In this article, I use the story of Kepler's discovery of his three laws of planetary motion to enlarge the framework of the scientific approach, from one that focuses on experimental sciences, to one that accommodates observational sciences, and further to one that embraces data mining and machine learning. I distinguish between the ontological values of Kepler's Laws vis-a-vis Newton's Laws, and argue that the latter is more fundamental because it is able to explain the former. I then argue that the fundamental laws of economics lie not in mathematical equations, but in models of adaptive economic agents. With this shift in mind set, it becomes possible to think about how interactions between agents can lead to the emergence of multiple stable states and critical transitions, and complex adaptive policies and regulations that might actually work in the real world. Finally, I discuss how Big Data, exploratory agent-based modeling, and predictive agent-based modeling can come together in a unified framework to make economics a true science.

Investigating Elementary Teachers' Thinking about and Learning to Notice Students' Science Ideas

Science.gov (United States)

Luna, Melissa Jo

2013-01-01

Children naturally use observations and everyday thinking to construct explanations as to why phenomena happen in the world. Science instruction can benefit by starting with these ideas to help children build coherent scientific understandings of how the physical world works. To do so, science teaching must involve attending to students'…

The Big Bang Singularity

Science.gov (United States)

Ling, Eric

The big bang theory is a model of the universe which makes the striking prediction that the universe began a finite amount of time in the past at the so called "Big Bang singularity." We explore the physical and mathematical justification of this surprising result. After laying down the framework of the universe as a spacetime manifold, we combine physical observations with global symmetrical assumptions to deduce the FRW cosmological models which predict a big bang singularity. Next we prove a couple theorems due to Stephen Hawking which show that the big bang singularity exists even if one removes the global symmetrical assumptions. Lastly, we investigate the conditions one needs to impose on a spacetime if one wishes to avoid a singularity. The ideas and concepts used here to study spacetimes are similar to those used to study Riemannian manifolds, therefore we compare and contrast the two geometries throughout.

Enabling a new Paradigm to Address Big Data and Open Science Challenges

Science.gov (United States)

Ramamurthy, Mohan; Fisher, Ward

2017-04-01

Data are not only the lifeblood of the geosciences but they have become the currency of the modern world in science and society. Rapid advances in computing, communi¬cations, and observational technologies — along with concomitant advances in high-resolution modeling, ensemble and coupled-systems predictions of the Earth system — are revolutionizing nearly every aspect of our field. Modern data volumes from high-resolution ensemble prediction/projection/simulation systems and next-generation remote-sensing systems like hyper-spectral satellite sensors and phased-array radars are staggering. For example, CMIP efforts alone will generate many petabytes of climate projection data for use in assessments of climate change. And NOAA's National Climatic Data Center projects that it will archive over 350 petabytes by 2030. For researchers and educators, this deluge and the increasing complexity of data brings challenges along with the opportunities for discovery and scientific breakthroughs. The potential for big data to transform the geosciences is enormous, but realizing the next frontier depends on effectively managing, analyzing, and exploiting these heterogeneous data sources, extracting knowledge and useful information from heterogeneous data sources in ways that were previously impossible, to enable discoveries and gain new insights. At the same time, there is a growing focus on the area of "Reproducibility or Replicability in Science" that has implications for Open Science. The advent of cloud computing has opened new avenues for not only addressing both big data and Open Science challenges to accelerate scientific discoveries. However, to successfully leverage the enormous potential of cloud technologies, it will require the data providers and the scientific communities to develop new paradigms to enable next-generation workflows and transform the conduct of science. Making data readily available is a necessary but not a sufficient condition. Data providers

Enacting the Common Script: Management Ideas at Finnish Universities of Applied Sciences

Science.gov (United States)

Vuori, Johanna

2015-01-01

This article discusses the work of mid-level management at Finnish universities of applied sciences. Based on in-depth interviews with 15 line managers, this study investigates how the standardized management ideas of rational management and employee empowerment are used in the leadership of lecturers at these institutions. The findings indicate…

Big Data Science Education: A Case Study of a Project-Focused Introductory Course

Science.gov (United States)

Saltz, Jeffrey; Heckman, Robert

2015-01-01

This paper reports on a case study of a project-focused introduction to big data science course. The pedagogy of the course leveraged boundary theory, where students were positioned to be at the boundary between a client's desire to understand their data and the academic class. The results of the case study demonstrate that using live clients…

Thinking about information work of nuclear science and technology in the age of big data: speaking of the information analysis and research

International Nuclear Information System (INIS)

Chen Tieyong

2014-01-01

Human society is entering a 'PB' (1024TB) the new era as the unit of structured and unstructured data, In the network era, with the development of mobile communications, electronic commerce, the emergence and development of social network. Now, a large-scale production, sharing and application data era is opening. How to explore the value of data, to conquer big data, to get useful information, is an important task of our science and technology information workers. This paper tries to analyze the development of the nuclear science and technology information work from big data obtain, analysis, application. Our analysis and research work for information will be increasingly based on all data and analysis, Instead of random sampling. The data 'sound' is possible. A lot of results of information analysis and research can be expressed quantitatively. We should attach great importance to data collection, careful analysis of the big data. We involves the professional division of labor, but also to cooperation In nuclear science and technology information analysis and research process. In addition, we should strengthen the nuclear science and technology information resource construction, improve Information supply; strengthen the analysis and research of nuclear science and technology information, improve the information service; strengthen information management of nuclear science and technology, pay attention to the security problems and intellectual property rights in information sharing; strengthen personnel training, continuously improve the nuclear science and technology information work efficiency and performance. In the age of big data, our nuclear science and technology information workers shall be based on the information analysis and study as the core, one hand grasping information collection, another hand grasping information service, forge ahead and innovation, continuous improvement working ability of nuclear science and technology information, improve the

Toward a Big Data Science: A challenge of "Science Cloud"

Science.gov (United States)

Murata, Ken T.; Watanabe, Hidenobu

2013-04-01

During these 50 years, along with appearance and development of high-performance computers (and super-computers), numerical simulation is considered to be a third methodology for science, following theoretical (first) and experimental and/or observational (second) approaches. The variety of data yielded by the second approaches has been getting more and more. It is due to the progress of technologies of experiments and observations. The amount of the data generated by the third methodologies has been getting larger and larger. It is because of tremendous development and programming techniques of super computers. Most of the data files created by both experiments/observations and numerical simulations are saved in digital formats and analyzed on computers. The researchers (domain experts) are interested in not only how to make experiments and/or observations or perform numerical simulations, but what information (new findings) to extract from the data. However, data does not usually tell anything about the science; sciences are implicitly hidden in the data. Researchers have to extract information to find new sciences from the data files. This is a basic concept of data intensive (data oriented) science for Big Data. As the scales of experiments and/or observations and numerical simulations get larger, new techniques and facilities are required to extract information from a large amount of data files. The technique is called as informatics as a fourth methodology for new sciences. Any methodologies must work on their facilities: for example, space environment are observed via spacecraft and numerical simulations are performed on super-computers, respectively in space science. The facility of the informatics, which deals with large-scale data, is a computational cloud system for science. This paper is to propose a cloud system for informatics, which has been developed at NICT (National Institute of Information and Communications Technology), Japan. The NICT science

Toward a manifesto for the 'public understanding of big data'.

Science.gov (United States)

Michael, Mike; Lupton, Deborah

2016-01-01

In this article, we sketch a 'manifesto' for the 'public understanding of big data'. On the one hand, this entails such public understanding of science and public engagement with science and technology-tinged questions as follows: How, when and where are people exposed to, or do they engage with, big data? Who are regarded as big data's trustworthy sources, or credible commentators and critics? What are the mechanisms by which big data systems are opened to public scrutiny? On the other hand, big data generate many challenges for public understanding of science and public engagement with science and technology: How do we address publics that are simultaneously the informant, the informed and the information of big data? What counts as understanding of, or engagement with, big data, when big data themselves are multiplying, fluid and recursive? As part of our manifesto, we propose a range of empirical, conceptual and methodological exhortations. We also provide Appendix 1 that outlines three novel methods for addressing some of the issues raised in the article. © The Author(s) 2015.

Big Data, Biostatistics and Complexity Reduction

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2018-01-01

Roč. 14, č. 2 (2018), s. 24-32 ISSN 1801-5603 R&D Projects: GA MZd(CZ) NV15-29835A Institutional support: RVO:67985807 Keywords : Biostatistics * Big data * Multivariate statistics * Dimensionality * Variable selection Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) https://www.ejbi.org/scholarly-articles/big-data-biostatistics-and-complexity-reduction.pdf

Quantum nature of the big bang.

Science.gov (United States)

Ashtekar, Abhay; Pawlowski, Tomasz; Singh, Parampreet

2006-04-14

Some long-standing issues concerning the quantum nature of the big bang are resolved in the context of homogeneous isotropic models with a scalar field. Specifically, the known results on the resolution of the big-bang singularity in loop quantum cosmology are significantly extended as follows: (i) the scalar field is shown to serve as an internal clock, thereby providing a detailed realization of the "emergent time" idea; (ii) the physical Hilbert space, Dirac observables, and semiclassical states are constructed rigorously; (iii) the Hamiltonian constraint is solved numerically to show that the big bang is replaced by a big bounce. Thanks to the nonperturbative, background independent methods, unlike in other approaches the quantum evolution is deterministic across the deep Planck regime.

Journalism and science: how to erode the idea of knowledge.

Science.gov (United States)

Meyer, Gitte

2006-01-01

This paper discusses aspects of the relationship between the scientific community and the public at large. Inspired by the European public debate on genetically modified crops and food, ethical challenges to the scientific community are highlighted. This is done by a discussion of changes that are likely to occur to journalistic attitudes--mirroring changing attitudes in the wider society--towards science and scientific researchers. Two journalistic conventions--those of science transmission and of investigative journalism--are presented and discussed in relation to the present drive towards commercialization within the world of science: how are journalists from these different schools of thought likely to respond to the trend of commercialization? Likely journalistic reactions could, while maintaining the authority of the scientific method, be expected to undermine public trust in scientists. In the long term, this may lead to an erosion of the idea of knowledge as something that cannot simply be reduced to the outcome of negotiation between stakeholders. It is argued that science is likely to be depicted as a fallen angel. This may be countered, it is posited, by science turning human, by recognizing its membership of society, and by recognizing that such membership entails more than just commercial relations. To rethink its relationship with the public at large--and, in particular, to rethink the ideal of disinterested science--is an ethical challenge facing the scientific community.

A Brief Review on Leading Big Data Models

Directory of Open Access Journals (Sweden)

Sugam Sharma

2014-11-01

Full Text Available Today, science is passing through an era of transformation, where the inundation of data, dubbed data deluge is influencing the decision making process. The science is driven by the data and is being termed as data science. In this internet age, the volume of the data has grown up to petabytes, and this large, complex, structured or unstructured, and heterogeneous data in the form of “Big Data” has gained significant attention. The rapid pace of data growth through various disparate sources, especially social media such as Facebook, has seriously challenged the data analytic capabilities of traditional relational databases. The velocity of the expansion of the amount of data gives rise to a complete paradigm shift in how new age data is processed. Confidence in the data engineering of the existing data processing systems is gradually fading whereas the capabilities of the new techniques for capturing, storing, visualizing, and analyzing data are evolving. In this review paper, we discuss some of the modern Big Data models that are leading contributors in the NoSQL era and claim to address Big Data challenges in reliable and efficient ways. Also, we take the potential of Big Data into consideration and try to reshape the original operationaloriented definition of “Big Science” (Furner, 2003 into a new data-driven definition and rephrase it as “The science that deals with Big Data is Big Science.”

Nursing Management Minimum Data Set: Cost-Effective Tool To Demonstrate the Value of Nurse Staffing in the Big Data Science Era.

Science.gov (United States)

Pruinelli, Lisiane; Delaney, Connie W; Garciannie, Amy; Caspers, Barbara; Westra, Bonnie L

2016-01-01

There is a growing body of evidence of the relationship of nurse staffing to patient, nurse, and financial outcomes. With the advent of big data science and developing big data analytics in nursing, data science with the reuse of big data is emerging as a timely and cost-effective approach to demonstrate nursing value. The Nursing Management Minimum Date Set (NMMDS) provides standard administrative data elements, definitions, and codes to measure the context where care is delivered and, consequently, the value of nursing. The integration of the NMMDS elements in the current health system provides evidence for nursing leaders to measure and manage decisions, leading to better patient, staffing, and financial outcomes. It also enables the reuse of data for clinical scholarship and research.

A Big Video Manifesto

DEFF Research Database (Denmark)

Mcilvenny, Paul Bruce; Davidsen, Jacob

2017-01-01

and beautiful visualisations. However, we also need to ask what the tools of big data can do both for the Humanities and for more interpretative approaches and methods. Thus, we prefer to explore how the power of computation, new sensor technologies and massive storage can also help with video-based qualitative......For the last few years, we have witnessed a hype about the potential results and insights that quantitative big data can bring to the social sciences. The wonder of big data has moved into education, traffic planning, and disease control with a promise of making things better with big numbers...

Conversaciones con Ramón: big questions for the millennium

Directory of Open Access Journals (Sweden)

Frank B. Golley

2001-12-01

Full Text Available Ramon Margalef and I have enjoyed many years of conversation about ecology and my remarks will celebrate our relationship. My life has gradually shifted from the hustle of the market place and consumption of money and goods to a academic career devoted to students and ideas. Students ask how they can avoid becoming trivial, how they can contribute to solving the dreadful problems that face human society? Students want to know from me, their senior, what are the big questions of ecological science. That is the motivation for me to think about the challenging questions ecologists are asking today, that may change the subject tomorrow.

Who Owns Educational Theory? Big Data, Algorithms and the Expert Power of Education Data Science

Science.gov (United States)

Williamson, Ben

2017-01-01

"Education data science" is an emerging methodological field which possesses the algorithm-driven technologies required to generate insights and knowledge from educational big data. This article consists of an analysis of the Lytics Lab, Stanford University's laboratory for research and development in learning analytics, and the Center…

Representation and Analysis of Chemistry Core Ideas in Science Education Standards between China and the United States

Science.gov (United States)

Wan, Yanlan; Bi, Hualin

2016-01-01

Chemistry core ideas play an important role in students' chemistry learning. On the basis of the representations of chemistry core ideas about "substances" and "processes" in the Chinese Chemistry Curriculum Standards (CCCS) and the U.S. Next Generation Science Standards (NGSS), we conduct a critical comparison of chemistry…

Diagramming Scientific Papers - A New Idea for Understanding/Teaching/Sharing Science

Science.gov (United States)

Saltus, R. W.; Fedi, M.

2014-12-01

How do we best communicate scientific results? As the number of scientists and scientific papers steadily increases, one of the greatest challenges is effective and efficient sharing of science. The official repository of scientific knowledge is the peer-reviewed journal archive. However, this primary knowledge can be difficult to access and understand by anyone but a relevant specialist. We propose some new ideas for diagramming the content and significance of scientific papers using a simple and intuitive graphical approach. We propose a visual mapping that highlights four fundamental aspects of most scientific papers: Data, Methods/Models, Results/Ideas, and Implications/Importance. Each of these aspects is illustrated within boxed fields which contain one or more labeled elements positioned to reflect novelty (aka originality) and impact relative to the vertical and horizontal axes. The relative position of the boxed fields themselves indicates the relative significance of data, methods, ideas, or implications to the paper. Optional lines between boxed elements indicate the flow and dependence of data/methods/ideas within the paper. As with any graphical depiction, you need to see it to best appreciate it -- this written abstract is only meant as an introduction to the idea.We anticipate that diagramming may prove useful in both communication of scientific ideas among scientists as well as in education and outreach. For example, professors could assign diagramming of papers as a way to help students organize their thoughts about the structure and impact of scientific articles. Students could compare and defend their diagrams as a way to facilitate discussion/debate. Authors could diagram their own work as a way to efficiently summarize the importance and significance of their work. We also imagine that (in the future) automatic diagramming might be used to help summarize or facilitate the discovery of archived work.

Stuck for words: multimodal representations of children’s ideas in science

OpenAIRE

Callinan, Carol

2016-01-01

Research which has aimed to understand how children come to acquire ideas about different science concepts has had a long history [1, 2, 3]. However, these studies have explored conceptual knowledge largely through verbal reports. Whilst these approaches have been successful in revealing what children know the bias towards language and linguistic capabilities at the expense of other forms of communication may prevent a comprehensive understanding of knowledge growth particularly if children a...

In response to an open invitation for comments on AAAS project 2061's Benchmark books on science. Part 1: documentation of serious errors in cell biology.

Science.gov (United States)

Ling, Gilbert

2006-01-01

Project 2061 was founded by the American Association for the Advancement of Science (AAAS) to improve secondary school science education. An in-depth study of ten 9 to 12th grade biology textbooks led to the verdict that none conveyed "Big Ideas" that would give coherence and meaning to the profusion of lavishly illustrated isolated details. However, neither the Project report itself nor the Benchmark books put out earlier by the Project carries what deserves the designation of "Big Ideas." Worse, in the two earliest-published Benchmark books, the basic unit of all life forms--the living cell--is described as a soup enclosed by a cell membrane, that determines what can enter or leave the cell. This is astonishing since extensive experimental evidence has unequivocally disproved this idea 60 years ago. A "new" version of the membrane theory brought in to replace the discredited (sieve) version is the pump model--currently taught as established truth in all high-school and college biology textbooks--was also unequivocally disproved 40 years ago. This comment is written partly in response to Bechmark's gracious open invitation for ideas to improve the books and through them, to improve US secondary school science education.

Data science and big data an environment of computational intelligence

CERN Document Server

Chen, Shyi-Ming

2017-01-01

This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business. Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today’s knowledge-driven economy. Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and...

Before big science the pursuit of modern chemistry and physics, 1800-1940

CERN Document Server

Nye, Mary Jo

1999-01-01

Today's vast multinational scientific monoliths bear little resemblance to the modest laboratories of the early nineteenth century. Yet early in the nineteenth century--when heat and electricity were still counted among the elements--changes were already under way that would revolutionize chemistry and physics into the "big science" of the late twentieth century, expanding tiny, makeshift laboratories into bustling research institutes and replacing the scientific amateurs and generalist savants of the early Victorian era with the professional specialists of contemporary physical science. Mary Jo Nye traces the social and intellectual history of the physical sciences from the early 1800s to the beginning of the Second World War, examining the sweeping transformation of scientific institutions and professions during the period and the groundbreaking experiments that fueled that change, from the earliest investigations of molecular chemistry and field dynamics to the revolutionary breakthroughs of quantum mecha...

The idea of Santa Claus in terms of Cognitive Sciences. Cultural persistence and interference with the Christian Religion

Directory of Open Access Journals (Sweden)

PhD. Paul SCARLAT

2017-01-01

Full Text Available The idea of Santa Claus is a universal one, which has been carried on for generations despite many obstacles. Although related to fantasy and imagination, he belongs to all cultures and for children he maintains a real presence. Cognitive Science examines the idea of this mysterious individual and brings clarification to his existence in society. Because this “superhero” plays a part in society, he needs a mental structure that can be imagined, a particular and specific cognitive structure. The study identifies the cognitive mechanisms by which the idea of Santa Claus is generated. The history of Santa has interfered with religion since ancient times. He is sometimes confused with religious figures. Cognitive Sciences as applied to religion seem to confirm the universality of religious beliefs and a certain similarity between the idea of Santa Claus and that of holy persons, such as St. Nicholas. However, there are opinions within this field of research that differentiate between the two areas: fantastic and religious.

Big data need big theory too.

Science.gov (United States)

Coveney, Peter V; Dougherty, Edward R; Highfield, Roger R

2016-11-13

The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their 'depth' and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote 'blind' big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'. © 2015 The Authors.

"A Ton of Faith in Science!" Nature and Role of Assumptions in, and Ideas about, Science and Epistemology Generated upon Watching a Sci-Fi Film

Science.gov (United States)

Myers, John Y.; Abd-El-Khalick, Fouad

2016-01-01

This study (i) explicates the sorts of ideas about science and the nature of knowing that were generated among participant graduate students who viewed the sci-fi film, "Contact," and (ii) examines the interactions between these ideas and ontic stances with which participants approached viewing the film. Eleven doctoral students of…

Modifying ``Six Ideas that Shaped Physics'' for a Life-Science major audience at Hope College

Science.gov (United States)

Mader, Catherine

2005-04-01

The ``Six Ideas That Shaped Physics'' textbook has been adapted and used for use in the algebra-based introductory physics course for non-physics science majors at Hope College. The results of the first use will be presented. Comparison of FCI for pre and post test scores will be compared with results from 8 years of results from both the algebra-based course and the calculus-based course (when we first adopted ``Six Ideas that Shaped Physcs" for the Calculus-based course). In addition, comparison on quantitative tests and homework problems with prior student groups will also be made. Because a large fraction of the audience in the algebra-based course is life-science majors, a goal of this project is to make the material relevant for these students. Supplemental materials that emphasize the connection between the life sciences and the fundamental physics concepts are being be developed to accompany the new textbook. Samples of these materials and how they were used (and received) during class testing will be presented.

Panel session: Part 1, In flux -- Science Policy and the social structure of Big Laboratories, 1964--1979

Energy Technology Data Exchange (ETDEWEB)

Westfall, C. [Michigan State Univ., East Lansing, MI (United States)]|[CEBAF, Newport News, VA (United States)]|[Fermilab History Collaboration, Batavia, IL (United States)

1993-09-01

This report discusses the in flux of science policy and the social structure of big laboratories during the period of 1964 to 1979 and some sociological consequences of high energy physicists` development of the standard model during the same period.

The transnational circulation of scientific ideas: importing behavioralism in European political science (1950-1970).

Science.gov (United States)

Boncourt, Thibaud

2015-01-01

This article aims to deepen our understanding of the transatlantic circulation of scientific ideas during the Cold War by looking at the importation of behavioralism in European political science. It analyses the social, institutional, and intellectual dynamics that led to the creation, in 1970, of a transnational organization that aimed to promote behavioralism in Europe: the European Consortium for Political Research (ECPR). Using qualitative material drawn from archives and interviews, the study shows that the creation of the ECPR was the joint product of academic, scientific, and political rivalries. It argues that the founding of the organization served a purpose for several agents (chiefly, academic entrepreneurs and philanthropic foundations) who pursued different strategies in different social fields in the context of the Cold War. More broadly, it suggests that the postwar development of the social sciences and the circulation of scientific ideas are best accounted for by mapping sociological interactions between scientific fields and neighboring social spheres. © 2015 Wiley Periodicals, Inc.

Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections

Directory of Open Access Journals (Sweden)

Chia-Lin Chang

2018-03-01

Full Text Available The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses research issues that are related to the various disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models, as well as conduct simulation to examine whether the estimators in their theories on estimation and hypothesis testing have good size and high power. Thereafter, academics and practitioners could apply theory to analyse some interesting issues in the seven disciplines and cognate areas.

The Big Challenge in Big Earth Science Data: Maturing to Transdisciplinary Data Platforms that are Relevant to Government, Research and Industry

Science.gov (United States)

Wyborn, Lesley; Evans, Ben

2016-04-01

Collecting data for the Earth Sciences has a particularly long history going back centuries. Initially scientific data came only from simple human observations recorded by pen on paper. Scientific instruments soon supplemented data capture, and as these instruments became more capable (e.g, automation, more information captured, generation of digitally-born outputs), Earth Scientists entered the 'Big Data' era where progressively data became too big to store and process locally in the old style vaults. To date, most funding initiatives for collection and storage of large volume data sets in the Earth Sciences have been specialised within a single discipline (e.g., climate, geophysics, and Earth Observation) or specific to an individual institution. To undertake interdisciplinary research, it is hard for users to integrate data from these individual repositories mainly due to limitations on physical access to/movement of the data, and/or data being organised without enough information to make sense of it without discipline specialised knowledge. Smaller repositories have also gradually been seen as inefficient in terms of the cost to manage and access (including scarce skills) and effective implementation of new technology and techniques. Within the last decade, the trend is towards fewer and larger data repositories that increasingly are collocated with HPC/cloud resources. There has also been a growing recognition that digital data can be a valuable resource that can be reused and repurposed - publicly funded data from either the academic of government sector is seen as a shared resource, and that efficiencies can be gained by co-location. These new, highly capable, 'transdisciplinary' data repositories are emerging as a fundamental 'infrastructure' both for research and other innovation. The sharing of academic and government data resources on the same infrastructures is enabling new research programmes that will enable integration beyond the traditional physical

In science communication, why does the idea of the public deficit always return? Exploring key influences.

Science.gov (United States)

Suldovsky, Brianne

2016-05-01

Despite mounting criticism, the deficit model remains an integral part of science communication research and practice. In this article, I advance three key factors that contribute to the idea of the public deficit in science communication, including the purpose of science communication, how communication processes and outcomes are conceptualized, and how science and scientific knowledge are defined. Affording science absolute epistemic privilege, I argue, is the most compelling factor contributing to the continued use of the deficit model. In addition, I contend that the deficit model plays a necessary, though not sufficient, role in science communication research and practice. Areas for future research are discussed. © The Author(s) 2016.

Can this kind of idea be a patent?

Energy Technology Data Exchange (ETDEWEB)

Yu, Jae Bok

2004-08-15

This book mentions patent such as application, what is patents, patent law, procedure and patent attorney, which includes business idea is patent. Is internet domain name or name-brand? We can make a big money with others idea, the difference among patent, trademark, utility model rights and registration of design, priority system, new weapon in digital period, patent is a knife and a shield, the cost from application to registration, what is hunting of patent information, writing document for patent, patent examination and patent lawyer.

Can this kind of idea be a patent?

International Nuclear Information System (INIS)

Yu, Jae Bok

2004-08-01

This book mentions patent such as application, what is patents, patent law, procedure and patent attorney, which includes business idea is patent. Is internet domain name or name-brand? We can make a big money with others idea, the difference among patent, trademark, utility model rights and registration of design, priority system, new weapon in digital period, patent is a knife and a shield, the cost from application to registration, what is hunting of patent information, writing document for patent, patent examination and patent lawyer.

Big Data and Intellectual Property Rights in the Health and Life Sciences

DEFF Research Database (Denmark)

Minssen, Timo; Pierce, Justin

2018-01-01

, especially in the life science sectors where competitive innovation and research and development (R&D) resources are persistent considerations. For private actors, the like of pharmaceutical companies, health care providers, laboratories and insurance companies, it is becoming common practice to accumulate R......Undeniably “Big Data” plays a crucial role in the ongoing evolution of health care and life science sector innovations. In recent years U.S. and European authorities have developed public platforms and infrastructures providing access to vast stores of health-care knowledge, including data from......&D data making it searchable through medical databases. This trend is advanced and supported by recent initiatives and legislation that are increasing the transparency of various forms of data, such as clinical trials data. As a result, researchers, companies, patients and health care providers gain...

Big data analytics a management perspective

CERN Document Server

Corea, Francesco

2016-01-01

This book is about innovation, big data, and data science seen from a business perspective. Big data is a buzzword nowadays, and there is a growing necessity within practitioners to understand better the phenomenon, starting from a clear stated definition. This book aims to be a starting reading for executives who want (and need) to keep the pace with the technological breakthrough introduced by new analytical techniques and piles of data. Common myths about big data will be explained, and a series of different strategic approaches will be provided. By browsing the book, it will be possible to learn how to implement a big data strategy and how to use a maturity framework to monitor the progress of the data science team, as well as how to move forward from one stage to the next. Crucial challenges related to big data will be discussed, where some of them are more general - such as ethics, privacy, and ownership – while others concern more specific business situations (e.g., initial public offering, growth st...

Big Math for Little Kids

Science.gov (United States)

Greenes, Carole; Ginsburg, Herbert P.; Balfanz, Robert

2004-01-01

"Big Math for Little Kids," a comprehensive program for 4- and 5-year-olds, develops and expands on the mathematics that children know and are capable of doing. The program uses activities and stories to develop ideas about number, shape, pattern, logical reasoning, measurement, operations on numbers, and space. The activities introduce the…

Computational Literacy and "The Big Picture" Concerning Computers in Mathematics Education

Science.gov (United States)

diSessa, Andrea A.

2018-01-01

This article develops some ideas concerning the "big picture" of how using computers might fundamentally change learning, with an emphasis on mathematics (and, more generally, STEM education). I develop the big-picture model of "computation as a new literacy" in some detail and with concrete examples of sixth grade students…

Communicating the Nature of Science through "The Big Bang Theory": Evidence from a Focus Group Study

Science.gov (United States)

Li, Rashel; Orthia, Lindy A.

2016-01-01

In this paper, we discuss a little-studied means of communicating about or teaching the nature of science (NOS)--through fiction television. We report some results of focus group research which suggest that the American sitcom "The Big Bang Theory" (2007-present), whose main characters are mostly working scientists, has influenced…

Idea Puzzle

OpenAIRE

Parente, C.; Ferro, L.

2016-01-01

WOS:000387124100017 (Nº de Acesso Web of Science) The Idea Puzzle is a software application created in 2007. It is a support tool to assist PhD students and researchers in the process of designing research projects through a focus on three central dimensions of research that are collectively represented by a triangle. Each side of the Idea Puzzle triangle corresponds to one of the three dimensions that every empirical research project should ideally include: ontology (data), epistemology (...

Slaves to Big Data. Or Are We?

Directory of Open Access Journals (Sweden)

Mireille Hildebrandt

2013-10-01

Full Text Available

In this contribution, the notion of Big Data is discussed in relation to the monetisation of personal data. The claim of some proponents, as well as adversaries, that Big Data implies that ‘n = all’, meaning that we no longer need to rely on samples because we have all the data, is scrutinised and found to be both overly optimistic and unnecessarily pessimistic. A set of epistemological and ethical issues is presented, focusing on the implications of Big Data for our perception, cognition, fairness, privacy and due process. The article then looks into the idea of user-centric personal data management to investigate to what extent it provides solutions for some of the problems triggered by the Big Data conundrum. Special attention is paid to the core principle of data protection legislation, namely purpose binding. Finally, this contribution seeks to inquire into the influence of Big Data politics on self, mind and society, and asks how we can prevent ourselves from becoming slaves to Big Data.

Innovations and Enhancements for a Consortium of Big-10 University Research and Training Reactors. Final Report

International Nuclear Information System (INIS)

Brenizer, Jack

2011-01-01

The Consortium of Big-10 University Research and Training Reactors was by design a strategic partnership of seven leading institutions. We received the support of both our industry and DOE laboratory partners. Investments in reactor, laboratory and program infrastructure, allowed us to lead the national effort to expand and improve the education of engineers in nuclear science and engineering, to provide outreach and education to pre-college educators and students and to become a key resource of ideas and trained personnel for our U.S. industrial and DOE laboratory collaborators.

South Africa’s BIG debate in comparative perspective

OpenAIRE

Marysse, Stefaan; Verschueren, Joris

2007-01-01

The idea of a Basic Income Grant (BIG) has for long been an appealing alternative to the means-tested social security nets associated with the welfare state as we know it. Proponents of BIG highlight as comparative advantages its unconditionality, its inclusiveness and its administrative simplicity. Moreover, as capital-intensive investment and demographic evolutions engender a decline in activity rates, social security nets that rely on labour as both a source of financing and a condition fo...

Addressing big data issues in Scientific Data Infrastructure

NARCIS (Netherlands)

Demchenko, Y.; Membrey, P.; Grosso, P.; de Laat, C.; Smari, W.W.; Fox, G.C.

2013-01-01

Big Data are becoming a new technology focus both in science and in industry. This paper discusses the challenges that are imposed by Big Data on the modern and future Scientific Data Infrastructure (SDI). The paper discusses a nature and definition of Big Data that include such features as Volume,

Victoria Stodden: Scholarly Communication in the Era of Big Data and Big Computation

OpenAIRE

Stodden, Victoria

2015-01-01

Victoria Stodden gave the keynote address for Open Access Week 2015. "Scholarly communication in the era of big data and big computation" was sponsored by the University Libraries, Computational Modeling and Data Analytics, the Department of Computer Science, the Department of Statistics, the Laboratory for Interdisciplinary Statistical Analysis (LISA), and the Virginia Bioinformatics Institute. Victoria Stodden is an associate professor in the Graduate School of Library and Information Scien...

IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research.

Science.gov (United States)

Chen, Ying; Elenee Argentinis, J D; Weber, Griff

2016-04-01

Life sciences researchers are under pressure to innovate faster than ever. Big data offer the promise of unlocking novel insights and accelerating breakthroughs. Ironically, although more data are available than ever, only a fraction is being integrated, understood, and analyzed. The challenge lies in harnessing volumes of data, integrating the data from hundreds of sources, and understanding their various formats. New technologies such as cognitive computing offer promise for addressing this challenge because cognitive solutions are specifically designed to integrate and analyze big datasets. Cognitive solutions can understand different types of data such as lab values in a structured database or the text of a scientific publication. Cognitive solutions are trained to understand technical, industry-specific content and use advanced reasoning, predictive modeling, and machine learning techniques to advance research faster. Watson, a cognitive computing technology, has been configured to support life sciences research. This version of Watson includes medical literature, patents, genomics, and chemical and pharmacological data that researchers would typically use in their work. Watson has also been developed with specific comprehension of scientific terminology so it can make novel connections in millions of pages of text. Watson has been applied to a few pilot studies in the areas of drug target identification and drug repurposing. The pilot results suggest that Watson can accelerate identification of novel drug candidates and novel drug targets by harnessing the potential of big data. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Machine learning for Big Data analytics in plants.

Science.gov (United States)

Ma, Chuang; Zhang, Hao Helen; Wang, Xiangfeng

2014-12-01

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Competence-Based Science Learning Framework Illustrated through the Study of Natural Hazards and Disaster Risk Reduction

Science.gov (United States)

Oyao, Sheila G.; Holbrook, Jack; Rannikmäe, Miia; Pagunsan, Marmon M.

2015-01-01

This article proposes a competence-based learning framework for science teaching, applied to the study of "big ideas", in this case to the study of natural hazards and disaster risk reduction (NH&DRR). The framework focuses on new visions of competence, placing emphasis on nurturing connectedness and behavioral actions toward…

"Air Toxics under the Big Sky": Examining the Effectiveness of Authentic Scientific Research on High School Students' Science Skills and Interest

Science.gov (United States)

Ward, Tony J.; Delaloye, Naomi; Adams, Earle Raymond; Ware, Desirae; Vanek, Diana; Knuth, Randy; Hester, Carolyn Laurie; Marra, Nancy Noel; Holian, Andrij

2016-01-01

"Air Toxics Under the Big Sky" is an environmental science outreach/education program that incorporates the Next Generation Science Standards (NGSS) 8 Practices with the goal of promoting knowledge and understanding of authentic scientific research in high school classrooms through air quality research. This research explored: (1)…

Big Data as Information Barrier

Directory of Open Access Journals (Sweden)

Victor Ya. Tsvetkov

2014-07-01

Full Text Available The article covers analysis of ‘Big Data’ which has been discussed over last 10 years. The reasons and factors for the issue are revealed. It has proved that the factors creating ‘Big Data’ issue has existed for quite a long time, and from time to time, would cause the informational barriers. Such barriers were successfully overcome through the science and technologies. The conducted analysis refers the “Big Data” issue to a form of informative barrier. This issue may be solved correctly and encourages development of scientific and calculating methods.

Principales parámetros para el estudio de la colaboración científica en Big Science

OpenAIRE

Ortoll, Eva; Canals, Agustí; Garcia, Montserrat; Cobarsí, Josep

2014-01-01

In several scientific disciplines research has shifted from experiments of a reduced scale to large and complex collaborations. Many recent scientific achievements like the human genome sequencing or the discovery of the Higgs boson have taken place within the “big science” paradigm. The study of scientific collaboration needs to take into account all the diverse factors that have an influence on it. In the case of big science experiments, some of those aspects are particularly important: num...

Preservice Elementary Teachers' Ideas About Scientific Practices

Science.gov (United States)

Ricketts, Amy

2014-10-01

With the goal of producing scientifically literate citizens who are able to make informed decisions and reason critically when science intersects with their everyday lives, the National Research Council (NRC) has produced two recent documents that call for a new approach to K-12 science education that is based on scientific practices, crosscutting concepts, and disciplinary core ideas. These documents will potentially influence future state standards and K-12 curricula. Teachers will need support in order to teach science using a practices based approach, particularly if they do not have strong science backgrounds, which is often the case with elementary teachers. This study investigates one cohort (n = 19) of preservice elementary teachers' ideas about scientific practices, as developed in a one-semester elementary science teaching methods course. The course focused on eight particular scientific practices, as defined by the National Research Council's A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas (2012). Participants' written reflections, lesson plans and annotated teaching videos were analyzed in fine detail to better understand their ideas about what it means to engage in each of the practices. The findings suggest that preservice elementary teachers hold promising ideas about scientific practices (such as an emphasis on argumentation and communication between scientists, critical thinking, and answering and asking questions as the goal of science) as well as problematic ideas (including confusion over the purpose of modeling and the process of analysis, and conflating argumentation and explanation building). These results highlight the strengths and limitations of using the Framework (NRC 2012) as an instructional text and the difficulties of differentiating between preservice teachers' content knowledge about doing the practices and their pedagogical knowledge about teaching the practices.

How to Use TCM Informatics to Study Traditional Chinese Medicine in Big Data Age.

Science.gov (United States)

Shi, Cheng; Gong, Qing-Yue; Zhou, Jinhai

2017-01-01

This paper introduces the characteristics and complexity of traditional Chinese medicine (TCM) data, considers that modern big data processing technology has brought new opportunities for the research of TCM, and gives some ideas and methods to apply big data technology in TCM.

The BigBOSS Experiment

Energy Technology Data Exchange (ETDEWEB)

Schelgel, D.; Abdalla, F.; Abraham, T.; Ahn, C.; Allende Prieto, C.; Annis, J.; Aubourg, E.; Azzaro, M.; Bailey, S.; Baltay, C.; Baugh, C.; /APC, Paris /Brookhaven /IRFU, Saclay /Marseille, CPPM /Marseille, CPT /Durham U. / /IEU, Seoul /Fermilab /IAA, Granada /IAC, La Laguna

2011-01-01

BigBOSS will obtain observational constraints that will bear on three of the four 'science frontier' questions identified by the Astro2010 Cosmology and Fundamental Phyics Panel of the Decadal Survey: Why is the universe accelerating; what is dark matter and what are the properties of neutrinos? Indeed, the BigBOSS project was recommended for substantial immediate R and D support the PASAG report. The second highest ground-based priority from the Astro2010 Decadal Survey was the creation of a funding line within the NSF to support a 'Mid-Scale Innovations' program, and it used BigBOSS as a 'compelling' example for support. This choice was the result of the Decadal Survey's Program Priorization panels reviewing 29 mid-scale projects and recommending BigBOSS 'very highly'.

Data Science and its Relationship to Big Data and Data-Driven Decision Making.

Science.gov (United States)

Provost, Foster; Fawcett, Tom

2013-03-01

Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot-even "sexy"-career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.

Modeling of the Nuclear Power Plant Life cycle for 'Big Data' Management System: A Systems Engineering Viewpoint

International Nuclear Information System (INIS)

Ha, Bui Hoang; Khanh, Tran Quang Diep; Shakirah, Wan; Kahar, Wan Abdul; Jung, Jae Cheon

2012-01-01

Together with the significant development of Internet and Web technologies, the rapid evolution of 'Big Data' idea has been observed since it is first introduced in 1941 as an 'information explosion'(OED). Using the '3Vs' model, as proposed by Gartner, 'Big Data' can be defined as 'high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.' Big Data technologies and tools have been developed to address the way large quantities of data are stored, accessed and presented for manipulation or analysis. The idea also focuses on how the users can easily access and extract the 'useful and right' data, information, or even knowledge from the 'Big Data'.

Reference Data Layers for Earth and Environmental Science: History, Frameworks, Science Needs, Approaches, and New Technologies

Science.gov (United States)

Lenhardt, W. C.

2015-12-01

Global Mapping Project, Web-enabled Landsat Data (WELD), International Satellite Land Surface Climatology Project (ISLSCP), hydrology, solid earth dynamics, sedimentary geology, climate modeling, integrated assessments and so on all have needs for or have worked to develop consistently integrated data layers for Earth and environmental science. This paper will present an overview of an abstract notion of data layers of this types, what we are referring to as reference data layers for Earth and environmental science, highlight some historical examples, and delve into new approaches. The concept of reference data layers in this context combines data availability, cyberinfrastructure and data science, as well as domain science drivers. We argue that current advances in cyberinfrastructure such as iPython notebooks and integrated science processing environments such as iPlant's Discovery Environment coupled with vast arrays of new data sources warrant another look at the how to create, maintain, and provide reference data layers. The goal is to provide a context for understanding science needs for reference data layers to conduct their research. In addition, to the topics described above this presentation will also outline some of the challenges to and present some ideas for new approaches to addressing these needs. Promoting the idea of reference data layers is relevant to a number of existing related activities such as EarthCube, RDA, ESIP, the nascent NSF Regional Big Data Innovation Hubs and others.

Integration of Culturally Relevant Pedagogy Into the Science Learning Progression Framework

Science.gov (United States)

Bernardo, Cyntra

This study integrated elements of culturally relevant pedagogy into a science learning progression framework, with the goal of enhancing teachers' cultural knowledge and thereby creating better teaching practices in an urban public high school science classroom. The study was conducted using teachers, an administrator, a science coach, and students involved in science courses in public high school. Through a qualitative intrinsic case study, data were collected and analyzed using traditional methods. Data from primary participants (educators) were analyzed through identification of big ideas, open coding, and themes. Through this process, patterns and emergent ideas were reported. Outcomes of this study demonstrated that educators lack knowledge about research-based academic frameworks and multicultural education strategies, but benefit through institutionally-based professional development. Students from diverse cultures responded positively to culturally-based instruction. Their progress was further manifested in better communication and discourse with their teacher and peers, and increased academic outcomes. This study has postulated and provided an exemplar for science teachers to expand and improve multicultural knowledge, ultimately transferring these skills to their pedagogical practice.

Examining the Big-Fish-Little-Pond Effect on Students' Self-Concept of Learning Science in Taiwan Based on the TIMSS Databases

Science.gov (United States)

Liou, Pey-Yan

2014-01-01

The purpose of this study is to examine the relationship between student self-concept and achievement in science in Taiwan based on the big-fish-little-pond effect (BFLPE) model using the Trends in International Mathematics and Science Study (TIMSS) 2003 and 2007 databases. Hierarchical linear modeling was used to examine the effects of the…

Big data is not a monolith

CERN Document Server

Ekbia, Hamid R; Mattioli, Michael

2016-01-01

Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies. The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control throu...

In science communication, why does the idea of a public deficit always return?

Science.gov (United States)

Meyer, Gitte

2016-05-01

For centuries, science communication has been widely perceived, irrespective of context, as a didactic enterprise. That understanding does not accommodate a political category of science communication, featuring citizens on an equal footing - some of them scientists - who share responsibility for public affairs and represent different points of view and ways of reasoning. That may harm, at the same time and for the same reasons, democratic knowledge societies as political entities and science as a body of knowledge and rational methodology. Scientists are discursively excluded from the public. The public is perceived in terms of knowledge deficiency. The latter perception has survived decades of critique, accompanied by attempts, along an everyman-as-scientist logic, to include all citizens in the scientific endeavour. But why should all be scientists? With respect to practical-political issues - as distinct from technical-scientific ones - the acknowledgement of the citizenship of scientists seems more relevant. Only, this would challenge the widespread understanding of science as an all-purpose problem solver and the consequent ideas of politics. © The Author(s) 2016.

Big Data Challenges in Climate Science: Improving the Next-Generation Cyberinfrastructure

Science.gov (United States)

Schnase, John L.; Lee, Tsengdar J.; Mattmann, Chris A.; Lynnes, Christopher S.; Cinquini, Luca; Ramirez, Paul M.; Hart, Andre F.; Williams, Dean N.; Waliser, Duane; Rinsland, Pamela;

Big physics quartet win government backing

Science.gov (United States)

Banks, Michael

2014-09-01

Four major physics-based projects are among 10 to have been selected by Japan’s Ministry of Education, Culture, Sports, Science and Technology for funding in the coming decade as part of its “roadmap” of big-science projects.

Artificial intelligence and big data management: the dynamic duo for moving forward data centric sciences

OpenAIRE

Vargas Solar, Genoveva

2017-01-01

After vivid discussions led by the emergence of the buzzword “Big Data”, it seems that industry and academia have reached an objective understanding about data properties (volume, velocity, variety, veracity and value), the resources and “know how” it requires, and the opportunities it opens. Indeed, new applications promising fundamental changes in society, industry and science, include face recognition, machine translation, digital assistants, self-driving cars, ad-serving, chat-bots, perso...

Science and Theatre Education: A Cross-Disciplinary Approach of Scientific Ideas Addressed to Student Teachers of Early Childhood Education

Science.gov (United States)

Tselfes, Vasilis; Paroussi, Antigoni

2009-01-01

There is, in Greece, an ongoing attempt to breach the boundaries established between the different teaching-learning subjects of compulsory education. In this context, we are interested in exploring to what degree the teaching and learning of ideas from the sciences' "internal life" (Hacking, in: Pickering (ed) "Science as practice…

Big Data in Health: a Literature Review from the Year 2005.

Science.gov (United States)

de la Torre Díez, Isabel; Cosgaya, Héctor Merino; Garcia-Zapirain, Begoña; López-Coronado, Miguel

2016-09-01

The information stored in healthcare systems has increased over the last ten years, leading it to be considered Big Data. There is a wealth of health information ready to be analysed. However, the sheer volume raises a challenge for traditional methods. The aim of this article is to conduct a cutting-edge study on Big Data in healthcare from 2005 to the present. This literature review will help researchers to know how Big Data has developed in the health industry and open up new avenues for research. Information searches have been made on various scientific databases such as Pubmed, Science Direct, Scopus and Web of Science for Big Data in healthcare. The search criteria were "Big Data" and "health" with a date range from 2005 to the present. A total of 9724 articles were found on the databases. 9515 articles were discarded as duplicates or for not having a title of interest to the study. 209 articles were read, with the resulting decision that 46 were useful for this study. 52.6 % of the articles used were found in Science Direct, 23.7 % in Pubmed, 22.1 % through Scopus and the remaining 2.6 % through the Web of Science. Big Data has undergone extremely high growth since 2011 and its use is becoming compulsory in developed nations and in an increasing number of developing nations. Big Data is a step forward and a cost reducer for public and private healthcare.

A Big Bang Lab

Science.gov (United States)

Scheider, Walter

2005-01-01

The February 2005 issue of The Science Teacher (TST) reminded everyone that by learning how scientists study stars, students gain an understanding of how science measures things that can not be set up in lab, either because they are too big, too far away, or happened in a very distant past. The authors of "How Far are the Stars?" show how the…

Beyond Big Science

CERN Multimedia

Boyle, Alan

2007-01-01

"Billion-dollar science projects end up being about much more than the science, whether we're talking about particle physics, or fusion research, or the international space station, or missions to the moon and beyond, or the next-generation radio telescope." (3 pages)

A big bang in a little room the quest to create new universes

CERN Document Server

Merali, Zeeya

2017-01-01

What if you could become God, with the ability to build a whole new universe? As startling as it sounds, modern physics suggests that within the next two decades, scientists may be able to perform this seemingly divine feat-to concoct an entirely new baby universe, complete with its own physical laws, star systems, galaxies, and even intelligent life. A Big Bang in a Little Room takes the reader on a journey through the history of cosmology and unravels-particle by particle, theory by theory, and experiment by experiment-the ideas behind this provocative claim made by some of the most respected physicists alive today. Beyond simply explaining the science, A Big Bang in a Little Room also tells the story of the people who have been laboring for more than thirty years to make this seemingly impossible dream a reality. What has driven them to continue on what would seem, at first glance, to be a quixotic quest? This mind-boggling book reveals that we can nurse other worlds in the tiny confines of a lab, raising...

Speaking sociologically with big data: symphonic social science and the future for big data research

OpenAIRE

Halford, Susan; Savage, Mike

2017-01-01

Recent years have seen persistent tension between proponents of big data analytics, using new forms of digital data to make computational and statistical claims about ‘the social’, and many sociologists sceptical about the value of big data, its associated methods and claims to knowledge. We seek to move beyond this, taking inspiration from a mode of argumentation pursued by Putnam (2000), Wilkinson and Pickett (2009) and Piketty (2014) that we label ‘symphonic social science’. This bears bot...

Ludwik Fleck on proto-ideas in medicine

DEFF Research Database (Denmark)

Brorson, S

2000-01-01

and his nominalist view on medical taxonomy. Finally, I discuss four philosophical problems implied by Fleck's concept of proto-ideas: (a) the problem of combining two conflicting perspectives on the history of science (b) the problem of accounting for the notion of 'continuity' within a nonrealist theory......'Proto-idea' was a central concept in the thinking of the Polish microbiologist and philosopher of science Ludwik Fleck (1896-1961). Based on studies of the origin of the modern concept of syphilis, Fleck claimed that many established scientific facts are best understood as interpretations...... of prescientific, somewhat hazy 'proto-ideas' in the framework of a certain 'thought-style'. As an example, Fleck saw the modern knowledge of infection as an interpretation of the ancient proto-idea of diseases as caused by minute 'animalcules'. However, the epistemological aspects of the concept of proto-ideas...

Opportunities and challenges of big data for the social sciences: The case of genomic data.

Science.gov (United States)

Liu, Hexuan; Guo, Guang

2016-09-01

In this paper, we draw attention to one unique and valuable source of big data, genomic data, by demonstrating the opportunities they provide to social scientists. We discuss different types of large-scale genomic data and recent advances in statistical methods and computational infrastructure used to address challenges in managing and analyzing such data. We highlight how these data and methods can be used to benefit social science research. Copyright © 2016 Elsevier Inc. All rights reserved.

Big Data Science Cafés: High School Students Experiencing Real Research with Scientists

Science.gov (United States)

Walker, C. E.; Pompea, S. M.

2017-12-01

The Education and Public Outreach group at the National Optical Astronomy Observatory has designed an outside-of-school education program to excite the interest of talented youth in future projects like the Large Synoptic Survey Telescope (LSST) and the NOAO (archival) Data Lab - their data approaches and key science projects. Originally funded by the LSST Corporation, the program cultivates talented youth to enter STEM disciplines and serves as a model to disseminate to the 40+ institutions involved in LSST. One Saturday a month during the academic year, high school students have the opportunity to interact with expert astronomers who work with large astronomical data sets in their scientific work. Students learn about killer asteroids, the birth and death of stars, colliding galaxies, the structure of the universe, gravitational waves, dark energy, dark matter, and more. The format for the Saturday science cafés has been a short presentation, discussion (plus food), computer lab activity and more discussion. They last about 2.5 hours and have been planned by a group of interested local high school students, an undergraduate student coordinator, the presenting astronomers, the program director and an evaluator. High school youth leaders help ensure an enjoyable and successful program for fellow students. They help their fellow students with the activities and help evaluate how well the science café went. Their remarks shape the next science café and improve the program. The experience offers youth leaders ownership of the program, opportunities to take on responsibilities and learn leadership and communication skills, as well as foster their continued interests in STEM. The prototype Big Data Science Academy was implemented successfully in the Spring 2017 and engaged almost 40 teens from greater Tucson in the fundamentals of astronomy concepts and research. As with any first implementation there were bumps. However, staff, scientists, and student leaders all

Data Prospecting Framework - a new approach to explore "big data" in Earth Science

Science.gov (United States)

Ramachandran, R.; Rushing, J.; Lin, A.; Kuo, K.

2012-12-01

Due to advances in sensors, computation and storage, cost and effort required to produce large datasets have been significantly reduced. As a result, we are seeing a proliferation of large-scale data sets being assembled in almost every science field, especially in geosciences. Opportunities to exploit the "big data" are enormous as new hypotheses can be generated by combining and analyzing large amounts of data. However, such a data-driven approach to science discovery assumes that scientists can find and isolate relevant subsets from vast amounts of available data. Current Earth Science data systems only provide data discovery through simple metadata and keyword-based searches and are not designed to support data exploration capabilities based on the actual content. Consequently, scientists often find themselves downloading large volumes of data, struggling with large amounts of storage and learning new analysis technologies that will help them separate the wheat from the chaff. New mechanisms of data exploration are needed to help scientists discover the relevant subsets We present data prospecting, a new content-based data analysis paradigm to support data-intensive science. Data prospecting allows the researchers to explore big data in determining and isolating data subsets for further analysis. This is akin to geo-prospecting in which mineral sites of interest are determined over the landscape through screening methods. The resulting "data prospects" only provide an interaction with and feel for the data through first-look analytics; the researchers would still have to download the relevant datasets and analyze them deeply using their favorite analytical tools to determine if the datasets will yield new hypotheses. Data prospecting combines two traditional categories of data analysis, data exploration and data mining within the discovery step. Data exploration utilizes manual/interactive methods for data analysis such as standard statistical analysis and

Big inquiry

Energy Technology Data Exchange (ETDEWEB)

Wynne, B [Lancaster Univ. (UK)

1979-06-28

The recently published report entitled 'The Big Public Inquiry' from the Council for Science and Society and the Outer Circle Policy Unit is considered, with especial reference to any future enquiry which may take place into the first commercial fast breeder reactor. Proposals embodied in the report include stronger rights for objectors and an attempt is made to tackle the problem that participation in a public inquiry is far too late to be objective. It is felt by the author that the CSS/OCPU report is a constructive contribution to the debate about big technology inquiries but that it fails to understand the deeper currents in the economic and political structure of technology which so influence the consequences of whatever formal procedures are evolved.

Taking a 'Big Data' approach to data quality in a citizen science project.

Science.gov (United States)

Kelling, Steve; Fink, Daniel; La Sorte, Frank A; Johnston, Alison; Bruns, Nicholas E; Hochachka, Wesley M

2015-11-01

Data from well-designed experiments provide the strongest evidence of causation in biodiversity studies. However, for many species the collection of these data is not scalable to the spatial and temporal extents required to understand patterns at the population level. Only data collected from citizen science projects can gather sufficient quantities of data, but data collected from volunteers are inherently noisy and heterogeneous. Here we describe a 'Big Data' approach to improve the data quality in eBird, a global citizen science project that gathers bird observations. First, eBird's data submission design ensures that all data meet high standards of completeness and accuracy. Second, we take a 'sensor calibration' approach to measure individual variation in eBird participant's ability to detect and identify birds. Third, we use species distribution models to fill in data gaps. Finally, we provide examples of novel analyses exploring population-level patterns in bird distributions.

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science through Cloud-Enabled Climate Analytics-as-a-Service

Science.gov (United States)

Schnase, J. L.; Duffy, D.; Tamkin, G. S.; Nadeau, D.; Thompson, J. H.; Grieg, C. M.; McInerney, M.; Webster, W. P.

2013-12-01

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS built on this principle. MERRA/AS enables MapReduce analytics over NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRA/AS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRA/AS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Science.gov (United States)

Schnase, John L.; Duffy, Daniel Quinn; Tamkin, Glenn S.; Nadeau, Denis; Thompson, John H.; Grieg, Christina M.; McInerney, Mark A.; Webster, William P.

2014-01-01

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to

3rd International Symposium on Big Data and Cloud Computing Challenges

CERN Document Server

Neelanarayanan, V

2016-01-01

This proceedings volume contains selected papers that were presented in the 3rd International Symposium on Big data and Cloud Computing Challenges, 2016 held at VIT University, India on March 10 and 11. New research issues, challenges and opportunities shaping the future agenda in the field of Big Data and Cloud Computing are identified and presented throughout the book, which is intended for researchers, scholars, students, software developers and practitioners working at the forefront in their field. This book acts as a platform for exchanging ideas, setting questions for discussion, and sharing the experience in Big Data and Cloud Computing domain.

Footprint of Sandia's August 15 2016 Informal Idea Exploration Session on "Towards an Engineering and Applied Science of Research".

Energy Technology Data Exchange (ETDEWEB)

Tsao, Jeffrey Y. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Fleming Lindsley, Elizabeth S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Heffelfinger, Grant S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Narayanamurti, Venkatesh [Harvard Univ., Cambridge, MA (United States); Schneider, Rick [glo USA, Sunnyvale, CA (United States); Starkweather, Lynne M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ting, Christina [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Yajima, Rieko [Stanford Univ., CA (United States); Bauer, Travis L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Coltrin, Michael E. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Guy, Donald W. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jones, Wendell [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mareda, John F. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Nenoff, Tina M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Turnley, Jessica Glicken [Galisteo Consulting Group, Albuquerque, NM (United States)

2017-02-01

On August 15, 2016, Sandia hosted a visit by Professor Venkatesh Narayanamurti. Prof Narayanamurti (Benjamin Peirce Research Professor of Technology and Public Policy at Harvard, Board Member of the Belfer Center for Science and International Affairs, former Dean of the School of Engineering and Applied Science at Harvard, former Dean of Engineering at UC Santa Barbara, and former Vice President of Division 1000 at Sandia). During the visit, a small, informal, all-day idea exploration session on "Towards an Engineering and Applied Science of Research" was conducted. This document is a brief synopsis or "footprint" of the presentations and discussions at this Idea Exploration Session. The intent of this document is to stimulate further discussion about pathways Sandia can take to improve its Research practices.

Big Data and reality

Directory of Open Access Journals (Sweden)

Ryan Shaw

2015-11-01

Full Text Available DNA sequencers, Twitter, MRIs, Facebook, particle accelerators, Google Books, radio telescopes, Tumblr: what do these things have in common? According to the evangelists of “data science,” all of these are instruments for observing reality at unprecedentedly large scales and fine granularities. This perspective ignores the social reality of these very different technological systems, ignoring how they are made, how they work, and what they mean in favor of an exclusive focus on what they generate: Big Data. But no data, big or small, can be interpreted without an understanding of the process that generated them. Statistical data science is applicable to systems that have been designed as scientific instruments, but is likely to lead to confusion when applied to systems that have not. In those cases, a historical inquiry is preferable.

The SIKS/BiGGrid Big Data Tutorial

NARCIS (Netherlands)

Hiemstra, Djoerd; Lammerts, Evert; de Vries, A.P.

2011-01-01

The School for Information and Knowledge Systems SIKS and the Dutch e-science grid BiG Grid organized a new two-day tutorial on Big Data at the University of Twente on 30 November and 1 December 2011, just preceding the Dutch-Belgian Database Day. The tutorial is on top of some exciting new

Discourse, Power, and Knowledge in the Management of "Big Science": The Production of Consensus in a Nuclear Fusion Research Laboratory.

Science.gov (United States)

Kinsella, William J.

1999-01-01

Extends a Foucauldian view of power/knowledge to the archetypical knowledge-intensive organization, the scientific research laboratory. Describes the discursive production of power/knowledge at the "big science" laboratory conducting nuclear fusion research and illuminates a critical incident in which the fusion research…

The Role of Science Education in the Nuclear Age

DEFF Research Database (Denmark)

Christensen, Ivan Lind

2016-01-01

The ramifications of the atomic bombings of Hiroshima and Nagasaki in 1945 and the Atom for Peace resolution adopted by the UN in 1954 has been the object of study for some time now, especially with regard to international relations, national security policies and popular culture. Far less...... attention has been paid to the impact of the subsequent UNESCO Atoms for Peace initiatives within science education. This article traces the international ideas about the role of education in the atomic age, as they were formulated by central agents within UNESCO’s Natural Science Department, Section...... of Science Teaching, Social Science Department and the Department of Education. Moving from the rhetoric of international ‘Big Politics’ to the local level of primary schools, the article explores how the Atom for Peace initiative was related to the general science teaching discourse and the already ongoing...

Big Data, data integrity, and the fracturing of the control zone

Directory of Open Access Journals (Sweden)

Carl Lagoze

2014-11-01

Full Text Available Despite all the attention to Big Data and the claims that it represents a “paradigm shift” in science, we lack understanding about what are the qualities of Big Data that may contribute to this revolutionary impact. In this paper, we look beyond the quantitative aspects of Big Data (i.e. lots of data and examine it from a sociotechnical perspective. We argue that a key factor that distinguishes “Big Data” from “lots of data” lies in changes to the traditional, well-established “control zones” that facilitated clear provenance of scientific data, thereby ensuring data integrity and providing the foundation for credible science. The breakdown of these control zones is a consequence of the manner in which our network technology and culture enable and encourage open, anonymous sharing of information, participation regardless of expertise, and collaboration across geographic, disciplinary, and institutional barriers. We are left with the conundrum—how to reap the benefits of Big Data while re-creating a trust fabric and an accountable chain of responsibility that make credible science possible.

Big data has big potential for applications to climate change adaptation

NARCIS (Netherlands)

Ford, James D.; Tilleard, Simon E.; Berrang-Ford, Lea; Araos, Malcolm; Biesbroek, Robbert; Lesnikowski, Alexandra C.; MacDonald, Graham K.; Hsu, Angel; Chen, Chen; Bizikova, Livia

2016-01-01

The capacity to collect and analyze massive amounts
of data is transforming research in the natural and social
sciences (1). And yet, the climate change adaptation
community has largely overlooked these developments.
Here, we examine how “big data” can inform adaptation
research

Big Biomedical data as the key resource for discovery science

Energy Technology Data Exchange (ETDEWEB)

Toga, Arthur W.; Foster, Ian; Kesselman, Carl; Madduri, Ravi; Chard, Kyle; Deutsch, Eric W.; Price, Nathan D.; Glusman, Gustavo; Heavner, Benjamin D.; Dinov, Ivo D.; Ames, Joseph; Van Horn, John; Kramer, Roger; Hood, Leroy

2015-07-21

Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an “-ome to home” approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center’s computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson’s and Alzheimer’s.

Big data in biomedicine.

Science.gov (United States)

Costa, Fabricio F

2014-04-01

The increasing availability and growth rate of biomedical information, also known as 'big data', provides an opportunity for future personalized medicine programs that will significantly improve patient care. Recent advances in information technology (IT) applied to biomedicine are changing the landscape of privacy and personal information, with patients getting more control of their health information. Conceivably, big data analytics is already impacting health decisions and patient care; however, specific challenges need to be addressed to integrate current discoveries into medical practice. In this article, I will discuss the major breakthroughs achieved in combining omics and clinical health data in terms of their application to personalized medicine. I will also review the challenges associated with using big data in biomedicine and translational science. Copyright © 2013 Elsevier Ltd. All rights reserved.

Big Java late objects

CERN Document Server

Horstmann, Cay S

2012-01-01

Big Java: Late Objects is a comprehensive introduction to Java and computer programming, which focuses on the principles of programming, software engineering, and effective learning. It is designed for a two-semester first course in programming for computer science students.

A Multidisciplinary Perspective of Big Data in Management Research

OpenAIRE

Sheng, Jie; Amankwah-Amoah, J.; Wang, X.

2017-01-01

In recent years, big data has emerged as one of the prominent buzzwords in business and management. In spite of the mounting body of research on big data across the social science disciplines, scholars have offered little synthesis on the current state of knowledge. To take stock of academic research that contributes to the big data revolution, this paper tracks scholarly work's perspectives on big data in the management domain over the past decade. We identify key themes emerging in manageme...

Big Data Analyses in Health and Opportunities for Research in Radiology.

Science.gov (United States)

Aphinyanaphongs, Yindalon

2017-02-01

This article reviews examples of big data analyses in health care with a focus on radiology. We review the defining characteristics of big data, the use of natural language processing, traditional and novel data sources, and large clinical data repositories available for research. This article aims to invoke novel research ideas through a combination of examples of analyses and domain knowledge. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

The BIG Data Center: from deposition to integration to translation.

Science.gov (United States)

2017-01-04

Biological data are generated at unprecedentedly exponential rates, posing considerable challenges in big data deposition, integration and translation. The BIG Data Center, established at Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, provides a suite of database resources, including (i) Genome Sequence Archive, a data repository specialized for archiving raw sequence reads, (ii) Gene Expression Nebulas, a data portal of gene expression profiles based entirely on RNA-Seq data, (iii) Genome Variation Map, a comprehensive collection of genome variations for featured species, (iv) Genome Warehouse, a centralized resource housing genome-scale data with particular focus on economically important animals and plants, (v) Methylation Bank, an integrated database of whole-genome single-base resolution methylomes and (vi) Science Wikis, a central access point for biological wikis developed for community annotations. The BIG Data Center is dedicated to constructing and maintaining biological databases through big data integration and value-added curation, conducting basic research to translate big data into big knowledge and providing freely open access to a variety of data resources in support of worldwide research activities in both academia and industry. All of these resources are publicly available and can be found at http://bigd.big.ac.cn. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Managing globally distributed expertise with new competence management solutions: a big-science collaboration as a pilot case.

OpenAIRE

Ferguson, J; Koivula, T; Livan, M; Nordberg, M; Salmia, T; Vuola, O

2003-01-01

In today's global organisations and networks, a critical factor for effective innovation and project execution is appropriate competence and skills management. The challenges include selection of strategic competences, competence development, and leveraging the competences and skills to drive innovation and collaboration for shared goals. This paper presents a new industrial web-enabled competence management and networking solution and its implementation and piloting in a complex big-science ...

The role of big laboratories

CERN Document Server

Heuer, Rolf-Dieter

2013-01-01

This paper presents the role of big laboratories in their function as research infrastructures. Starting from the general definition and features of big laboratories, the paper goes on to present the key ingredients and issues, based on scientific excellence, for the successful realization of large-scale science projects at such facilities. The paper concludes by taking the example of scientific research in the field of particle physics and describing the structures and methods required to be implemented for the way forward.

The role of big laboratories

International Nuclear Information System (INIS)

Heuer, R-D

2013-01-01

This paper presents the role of big laboratories in their function as research infrastructures. Starting from the general definition and features of big laboratories, the paper goes on to present the key ingredients and issues, based on scientific excellence, for the successful realization of large-scale science projects at such facilities. The paper concludes by taking the example of scientific research in the field of particle physics and describing the structures and methods required to be implemented for the way forward. (paper)

What Role for Law, Human Rights, and Bioethics in an Age of Big Data, Consortia Science, and Consortia Ethics? The Importance of Trustworthiness.

Science.gov (United States)

Dove, Edward S; Özdemir, Vural

2015-09-01

The global bioeconomy is generating new paradigm-shifting practices of knowledge co-production, such as collective innovation; large-scale, data-driven global consortia science (Big Science); and consortia ethics (Big Ethics). These bioeconomic and sociotechnical practices can be forces for progressive social change, but they can also raise predicaments at the interface of law, human rights, and bioethics. In this article, we examine one such double-edged practice: the growing, multivariate exploitation of Big Data in the health sector, particularly by the private sector. Commercial exploitation of health data for knowledge-based products is a key aspect of the bioeconomy and is also a topic of concern among publics around the world. It is exacerbated in the current age of globally interconnected consortia science and consortia ethics, which is characterized by accumulating epistemic proximity, diminished academic independence, "extreme centrism", and conflicted/competing interests among innovation actors. Extreme centrism is of particular importance as a new ideology emerging from consortia science and consortia ethics; this relates to invariably taking a middle-of-the-road populist stance, even in the event of human rights breaches, so as to sustain the populist support needed for consortia building and collective innovation. What role do law, human rights, and bioethics-separate and together-have to play in addressing these predicaments and opportunities in early 21st century science and society? One answer we propose is an intertwined ethico-legal normative construct, namely trustworthiness . By considering trustworthiness as a central pillar at the intersection of law, human rights, and bioethics, we enable others to trust us, which in turns allows different actors (both nonprofit and for-profit) to operate more justly in consortia science and ethics, as well as to access and responsibly use health data for public benefit.

What Role for Law, Human Rights, and Bioethics in an Age of Big Data, Consortia Science, and Consortia Ethics? The Importance of Trustworthiness

Science.gov (United States)

Dove, Edward S.; Özdemir, Vural

2015-01-01

The global bioeconomy is generating new paradigm-shifting practices of knowledge co-production, such as collective innovation; large-scale, data-driven global consortia science (Big Science); and consortia ethics (Big Ethics). These bioeconomic and sociotechnical practices can be forces for progressive social change, but they can also raise predicaments at the interface of law, human rights, and bioethics. In this article, we examine one such double-edged practice: the growing, multivariate exploitation of Big Data in the health sector, particularly by the private sector. Commercial exploitation of health data for knowledge-based products is a key aspect of the bioeconomy and is also a topic of concern among publics around the world. It is exacerbated in the current age of globally interconnected consortia science and consortia ethics, which is characterized by accumulating epistemic proximity, diminished academic independence, “extreme centrism”, and conflicted/competing interests among innovation actors. Extreme centrism is of particular importance as a new ideology emerging from consortia science and consortia ethics; this relates to invariably taking a middle-of-the-road populist stance, even in the event of human rights breaches, so as to sustain the populist support needed for consortia building and collective innovation. What role do law, human rights, and bioethics—separate and together—have to play in addressing these predicaments and opportunities in early 21st century science and society? One answer we propose is an intertwined ethico-legal normative construct, namely trustworthiness. By considering trustworthiness as a central pillar at the intersection of law, human rights, and bioethics, we enable others to trust us, which in turns allows different actors (both nonprofit and for-profit) to operate more justly in consortia science and ethics, as well as to access and responsibly use health data for public benefit. PMID:26345196

What Role for Law, Human Rights, and Bioethics in an Age of Big Data, Consortia Science, and Consortia Ethics? The Importance of Trustworthiness

Directory of Open Access Journals (Sweden)

Edward S. Dove

2015-08-01

Full Text Available The global bioeconomy is generating new paradigm-shifting practices of knowledge co-production, such as collective innovation; large-scale, data-driven global consortia science (Big Science; and consortia ethics (Big Ethics. These bioeconomic and sociotechnical practices can be forces for progressive social change, but they can also raise predicaments at the interface of law, human rights, and bioethics. In this article, we examine one such double-edged practice: the growing, multivariate exploitation of Big Data in the health sector, particularly by the private sector. Commercial exploitation of health data for knowledge-based products is a key aspect of the bioeconomy and is also a topic of concern among publics around the world. It is exacerbated in the current age of globally interconnected consortia science and consortia ethics, which is characterized by accumulating epistemic proximity, diminished academic independence, “extreme centrism”, and conflicted/competing interests among innovation actors. Extreme centrism is of particular importance as a new ideology emerging from consortia science and consortia ethics; this relates to invariably taking a middle-of-the-road populist stance, even in the event of human rights breaches, so as to sustain the populist support needed for consortia building and collective innovation. What role do law, human rights, and bioethics—separate and together—have to play in addressing these predicaments and opportunities in early 21st century science and society? One answer we propose is an intertwined ethico-legal normative construct, namely trustworthiness. By considering trustworthiness as a central pillar at the intersection of law, human rights, and bioethics, we enable others to trust us, which in turns allows different actors (both nonprofit and for-profit to operate more justly in consortia science and ethics, as well as to access and responsibly use health data for public benefit.

Big Sib Students' Perceptions of the Educational Environment at the School of Medical Sciences, Universiti Sains Malaysia, using Dundee Ready Educational Environment Measure (DREEM) Inventory.

Science.gov (United States)

Arzuman, Hafiza; Yusoff, Muhamad Saiful Bahri; Chit, Som Phong

2010-07-01

A cross-sectional descriptive study was conducted among Big Sib students to explore their perceptions of the educational environment at the School of Medical Sciences, Universiti Sains Malaysia (USM) and its weak areas using the Dundee Ready Educational Environment Measure (DREEM) inventory. The DREEM inventory is a validated global instrument for measuring educational environments in undergraduate medical and health professional education. The English version of the DREEM inventory was administered to all Year 2 Big Sib students (n = 67) at a regular Big Sib session. The purpose of the study as well as confidentiality and ethical issues were explained to the students before the questionnaire was administered. The response rate was 62.7% (42 out of 67 students). The overall DREEM score was 117.9/200 (SD 14.6). The DREEM indicated that the Big Sib students' perception of educational environment of the medical school was more positive than negative. Nevertheless, the study also revealed some problem areas within the educational environment. This pilot study revealed that Big Sib students perceived a positive learning environment at the School of Medical Sciences, USM. It also identified some low-scored areas that require further exploration to pinpoint the exact problems. The relatively small study population selected from a particular group of students was the major limitation of the study. This small sample size also means that the study findings cannot be generalised.

Processes meet big data : connecting data science with process science

NARCIS (Netherlands)

van der Aalst, W.; Damiani, E.

2015-01-01

As more and more companies are embracing Big data, it has become apparent that the ultimate challenge is to relate massive amounts of event data to processes that are highly dynamic. To unleash the value of event data, events need to be tightly connected to the control and management of operational

[Some reflections on evidenced-based medicine, precision medicine, and big data-based research].

Science.gov (United States)

Tang, J L; Li, L M

2018-01-10

Evidence-based medicine remains the best paradigm for medical practice. However, evidence alone is not decisions; decisions must also consider resources available and the values of people. Evidence shows that most of those treated with blood pressure-lowering, cholesterol-lowering, glucose-lowering and anti-cancer drugs do not benefit from preventing severe complications such as cardiovascular events and deaths. This implies that diagnosis and treatment in modern medicine in many circumstances is imprecise. It has become a dream to identify and treat only those few who can respond to the treatment. Precision medicine has thus come into being. Precision medicine is however not a new idea and cannot rely solely on gene sequencing as it was initially proposed. Neither is the large cohort and multi-factorial approach a new idea; in fact it has been used widely since 1950s. Since its very beginning, medicine has never stopped in searching for more precise diagnostic and therapeutic methods and already made achievements at various levels of our understanding and knowledge, such as vaccine, blood transfusion, imaging, and cataract surgery. Genetic biotechnology is not the only path to precision but merely a new method. Most genes are found only weakly associated with disease and are thus unlikely to lead to great improvement in diagnostic and therapeutic precision. The traditional multi-factorial approach by embracing big data and incorporating genetic factors is probably the most realistic way ahead for precision medicine. Big data boasts of possession of the total population and large sample size and claims correlation can displace causation. They are serious misleading concepts. Science has never had to observe the totality in order to draw a valid conclusion; a large sample size is required only when the anticipated effect is small and clinically less meaningful; emphasis on correlation over causation is equivalent to rejection of the scientific principles and methods

Disaggregating asthma: Big investigation versus big data.

Science.gov (United States)

Belgrave, Danielle; Henderson, John; Simpson, Angela; Buchan, Iain; Bishop, Christopher; Custovic, Adnan

2017-02-01

We are facing a major challenge in bridging the gap between identifying subtypes of asthma to understand causal mechanisms and translating this knowledge into personalized prevention and management strategies. In recent years, "big data" has been sold as a panacea for generating hypotheses and driving new frontiers of health care; the idea that the data must and will speak for themselves is fast becoming a new dogma. One of the dangers of ready accessibility of health care data and computational tools for data analysis is that the process of data mining can become uncoupled from the scientific process of clinical interpretation, understanding the provenance of the data, and external validation. Although advances in computational methods can be valuable for using unexpected structure in data to generate hypotheses, there remains a need for testing hypotheses and interpreting results with scientific rigor. We argue for combining data- and hypothesis-driven methods in a careful synergy, and the importance of carefully characterized birth and patient cohorts with genetic, phenotypic, biological, and molecular data in this process cannot be overemphasized. The main challenge on the road ahead is to harness bigger health care data in ways that produce meaningful clinical interpretation and to translate this into better diagnoses and properly personalized prevention and treatment plans. There is a pressing need for cross-disciplinary research with an integrative approach to data science, whereby basic scientists, clinicians, data analysts, and epidemiologists work together to understand the heterogeneity of asthma. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Visualizing big energy data

DEFF Research Database (Denmark)

Hyndman, Rob J.; Liu, Xueqin Amy; Pinson, Pierre

2018-01-01

Visualization is a crucial component of data analysis. It is always a good idea to plot the data before fitting models, making predictions, or drawing conclusions. As sensors of the electric grid are collecting large volumes of data from various sources, power industry professionals are facing th...... the challenge of visualizing such data in a timely fashion. In this article, we demonstrate several data-visualization solutions for big energy data through three case studies involving smart-meter data, phasor measurement unit (PMU) data, and probabilistic forecasts, respectively....

In science communication, why does the idea of a public deficit always return? The eternal recurrence of the public deficit.

Science.gov (United States)

Cortassa, Carina

2016-05-01

After several years of loud and clear rejection, the idea of a public cognitive deficit insistently reappears in the agenda of Science Communication and Public Understanding of Science studies. This essay addresses two different kinds of reason - practical and epistemic - converging at that point. In the first part, it will be argued that the hypothesis of the lack of knowledge among laypeople and its controversial relationships with their interests and attitudes towards science prevails because it is an intuitive and optimistic way to frame the gap between science and society and, therefore, to cope with its causes and consequences. In the second part, a deeper level of reasons will be examined, in order to show that the persistence of the idea has its roots in the objective epistemic asymmetry between scientists and the public, the scope of which is not always properly judged. To recognize this asymmetry as a previous condition for their interactions may help to surpass the byzantine debate: deficit yes or no and open up original questions for the field, summarized in the closing remarks. © The Author(s) 2016.

Research in an emerging 'big science' discipline. The case of neutron scattering in Spain

International Nuclear Information System (INIS)

Borja Gonzalez-Albo; Maria Bordons; Pedro Gorria

2010-01-01

Neutron scattering (NS) is a 'big science' discipline whose research spans over a wide spectrum of fields, from fundamental or basic science to technological applications. The objective of this paper is to track the evolution of Spanish research in NS from a bibliometric perspective and to place it in the international context. Scientific publications of Spanish authors included in the Web of Science (WoS 1970-2006) are analysed with respect to five relevant dimensions: volume of research output, impact, disciplinary diversity, structural field features and internationalisation. NS emerges as a highly internationalised fast-growing field whose research is firmly rooted in Physics, Chemistry and Engineering, but with applications in a wide range of fields. International collaboration links -present in around 70% of the documents- and national links have largely contributed to mould the existing structure of research in the area, which evolves around major neutron scattering facilities abroad. The construction of a new European neutron source (ESS) would contribute to the consolidation of the field within the EU, since it will strengthen research and improve current activity. (author)

A New Coherent Science Content Storyline Astronomy Course for Pre-Service Teachers at Penn State

Science.gov (United States)

Palma, Christopher; Plummer, Julia; Earth and Space Science Partnership

2016-01-01

The Earth and Space Science Partnership (ESSP) is a collaboration among Penn State scientists, science educators and seven school districts across Pennsylvania. One of the ESSP goals has been to provide pre-service teachers with new or improved science course offerings at Penn State in the Earth and Space Science domains. In particular, we aim to provide students with opportunities to learn astronomy content knowledge through teaching methods that engage them in investigations where they experience the practices used by astronomers. We have designed a new course that builds on our research into students' ideas about Solar System astronomy (Plummer et al. 2015) and the curriculum our team created for a professional development workshop for in-service teachers (Palma et al. 2013) with this same theme. The course was offered for the first time in the spring 2015 semester. We designed the course using a coherent science content storyline approach (see, e.g., Palma et al. 2014), which requires all of the student investigations to build towards a big idea in science; in this case, we chose the model for formation of our Solar System. The course led pre-service teachers through a series of investigations that model the type of instruction we hope they will adopt in their own classrooms. They were presented with a series of research questions that all tie in to the big idea of Solar System formation, and they were responsible for collecting and interpreting their own data to draw evidence-based conclusions about one aspect of this model. Students in the course were assessed on their astronomy content knowledge, but also on their ability to construct arguments using scientific reasoning to answer astronomy questions. In this poster, we will present descriptions of the investigations, the assessments used, and our preliminary results about how the course led this group of pre-service teachers to improved understanding of astronomy content and the practices astronomers use in

Focus: new perspectives on science and the Cold War. Introduction.

Science.gov (United States)

Heyck, Hunter; Kaiser, David

2010-06-01

Twenty years after the fall of the Berlin Wall, the Cold War looks ever more like a slice of history rather than a contemporary reality. During those same twenty years, scholarship on science, technology, and the state during the Cold War era has expanded dramatically. Building on major studies of physics in the American context--often couched in terms of "big science"--recent work has broached scientific efforts in other domains as well, scrutinizing Cold War scholarship in increasingly international and comparative frameworks. The essays in this Focus section take stock of current thinking about science and the Cold War, revisiting the question of how best to understand tangled (and sometimes surprising) relationships between government patronage and the world of ideas.

An atomic model of the Big Bang

Science.gov (United States)

Lasukov, V. V.

2013-03-01

An atomic model of the Big Bang has been developed on the basis of quantum geometrodynamics with a nonzero Hamiltonian and on the concept of gravitation developed by Logunov asymptotically combined with the Gliner's idea of a material interpretation of the cosmological constant. The Lemaître primordial atom in superpace-time, whose spatial coordinate is the so-called scaling factor of the Logunov metric of the effective Riemann space, acts as the Big Bang model. The primordial atom in superspace-time corresponds to spatialtime structures(spheres, lines, and surfaces of a level) of the Minkowski spacetime real within the Logunov gravitation theory, the foregoing structures being filled with a scalar field with a negative density of potential energy.

Spatial big data for disaster management

Science.gov (United States)

Shalini, R.; Jayapratha, K.; Ayeshabanu, S.; Chemmalar Selvi, G.

2017-11-01

Big data is an idea of informational collections that depicts huge measure of information and complex that conventional information preparing application program is lacking to manage them. Presently, big data is a widely known domain used in research, academic, and industries. It is utilized to store substantial measure of information in a solitary brought together one. Challenges integrate capture, allocation, analysis, information precise, visualization, distribution, interchange, delegation, inquiring, updating and information protection. In this digital world, to put away the information and recovering the data is enormous errand for the huge organizations and some time information ought to be misfortune due to circulated information putting away. For this issue the organization individuals are chosen to actualize the huge information to put away every one of the information identified with the organization they are put away in one enormous database that is known as large information. Remote sensor is a science getting data used to distinguish the items or break down the range from a separation. It is anything but difficult to discover the question effortlessly with the sensor. It makes geographic data from satellite and sensor information so in this paper dissect what are the structures are utilized for remote sensor in huge information and how the engineering is vary from each other and how they are identify with our investigations. This paper depicts how the calamity happens and figuring consequence of informational collection. And applied a seismic informational collection to compute the tremor calamity in view of classification and clustering strategy. The classical data mining algorithms for classification used are k-nearest, naive bayes and decision table and clustering used are hierarchical, make density based and simple k_means using XLMINER and WEKA tool. This paper also helps to predicts the spatial dataset by applying the XLMINER AND WEKA tool and

The Whole Shebang: How Science Produced the Big Bang Model.

Science.gov (United States)

Ferris, Timothy

2002-01-01

Offers an account of the accumulation of evidence that has led scientists to have confidence in the big bang theory of the creation of the universe. Discusses the early work of Ptolemy, Copernicus, Kepler, Galileo, and Newton, noting the rise of astrophysics, and highlighting the birth of the big bang model (the cosmic microwave background theory…

Exploiting big data for critical care research.

Science.gov (United States)

Docherty, Annemarie B; Lone, Nazir I

2015-10-01

Over recent years the digitalization, collection and storage of vast quantities of data, in combination with advances in data science, has opened up a new era of big data. In this review, we define big data, identify examples of critical care research using big data, discuss the limitations and ethical concerns of using these large datasets and finally consider scope for future research. Big data refers to datasets whose size, complexity and dynamic nature are beyond the scope of traditional data collection and analysis methods. The potential benefits to critical care are significant, with faster progress in improving health and better value for money. Although not replacing clinical trials, big data can improve their design and advance the field of precision medicine. However, there are limitations to analysing big data using observational methods. In addition, there are ethical concerns regarding maintaining confidentiality of patients who contribute to these datasets. Big data have the potential to improve medical care and reduce costs, both by individualizing medicine, and bringing together multiple sources of data about individual patients. As big data become increasingly mainstream, it will be important to maintain public confidence by safeguarding data security, governance and confidentiality.

Modeling of the Nuclear Power Plant Life cycle for 'Big Data' Management System: A Systems Engineering Viewpoint

Energy Technology Data Exchange (ETDEWEB)

Ha, Bui Hoang; Khanh, Tran Quang Diep; Shakirah, Wan; Kahar, Wan Abdul; Jung, Jae Cheon [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)

2012-10-15

Together with the significant development of Internet and Web technologies, the rapid evolution of 'Big Data' idea has been observed since it is first introduced in 1941 as an 'information explosion'(OED). Using the '3Vs' model, as proposed by Gartner, 'Big Data' can be defined as 'high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.' Big Data technologies and tools have been developed to address the way large quantities of data are stored, accessed and presented for manipulation or analysis. The idea also focuses on how the users can easily access and extract the 'useful and right' data, information, or even knowledge from the 'Big Data'.

Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats

Science.gov (United States)

Ghiringhelli, Luca M.; Carbogno, Christian; Levchenko, Sergey; Mohamed, Fawzi; Huhs, Georg; Lüders, Martin; Oliveira, Micael; Scheffler, Matthias

2017-11-01

With big-data driven materials research, the new paradigm of materials science, sharing and wide accessibility of data are becoming crucial aspects. Obviously, a prerequisite for data exchange and big-data analytics is standardization, which means using consistent and unique conventions for, e.g., units, zero base lines, and file formats. There are two main strategies to achieve this goal. One accepts the heterogeneous nature of the community, which comprises scientists from physics, chemistry, bio-physics, and materials science, by complying with the diverse ecosystem of computer codes and thus develops "converters" for the input and output files of all important codes. These converters then translate the data of each code into a standardized, code-independent format. The other strategy is to provide standardized open libraries that code developers can adopt for shaping their inputs, outputs, and restart files, directly into the same code-independent format. In this perspective paper, we present both strategies and argue that they can and should be regarded as complementary, if not even synergetic. The represented appropriate format and conventions were agreed upon by two teams, the Electronic Structure Library (ESL) of the European Center for Atomic and Molecular Computations (CECAM) and the NOvel MAterials Discovery (NOMAD) Laboratory, a European Centre of Excellence (CoE). A key element of this work is the definition of hierarchical metadata describing state-of-the-art electronic-structure calculations.

No ``explosion'' in Big Bang cosmology: teaching kids the truth of what cosmologists really know

Science.gov (United States)

Gangui, Alejandro

2011-06-01

Common wisdom says that cosmologists are smart: they have developed a theory that can explain the ``origin of the universe''. Every time an astro-related, heavily funded ``big-science'' project comes to the media, naturally the question arises: will science -through this or that experiment- explain the origin of the cosmos? Can this be done with the LHC, for example? Will this dream machine create other universes? Of course, the very words we employ in cosmology reinforce this misconception: so Big Bang must be associated with an ``explosion'', even if a ``peculiar'' one, as it took place nowhere (there was presumably no space before the beginning) and happened virtually in no time (supposedly, space-time was created on this peculiar -singular- event). Right, the issue sounds confusing. Let us imagine what kids may get out of all this. We have recently presented a series of brief astronomy and cosmology books aimed at helping both kids and their teachers in these and other arcane subjects, all introduced with carefully chosen words and images that young children can understand. In particular, Volume Four deals with the Big Bang and emphasizes the notion of ``evolution'' as opposed to the -wrong- notion of ``origin'' behind the scientific model. We then explain some of the pillars of Big Bang cosmology: the expansion of space that drags away distant galaxies, as seen in the redshift of their emitted light; the build-up of light elements in a cooling bath of radiation, as explained by primordial nucleosynthesis; and the existence and main features of the ubiquitous cosmic microwave background radiation, where theory and observations agree to a highly satisfactory degree. Of course, one cannot attempt to answer the ``origins'' question when it is well known that all theories so far break down close to this origin (if there was actually an origin). It is through observations, analyses, lively discussions and recognition of the basic limitations of current theories and

The big data-big model (BDBM) challenges in ecological research

Science.gov (United States)

Luo, Y.

2015-12-01

The field of ecology has become a big-data science in the past decades due to development of new sensors used in numerous studies in the ecological community. Many sensor networks have been established to collect data. For example, satellites, such as Terra and OCO-2 among others, have collected data relevant on global carbon cycle. Thousands of field manipulative experiments have been conducted to examine feedback of terrestrial carbon cycle to global changes. Networks of observations, such as FLUXNET, have measured land processes. In particular, the implementation of the National Ecological Observatory Network (NEON), which is designed to network different kinds of sensors at many locations over the nation, will generate large volumes of ecological data every day. The raw data from sensors from those networks offer an unprecedented opportunity for accelerating advances in our knowledge of ecological processes, educating teachers and students, supporting decision-making, testing ecological theory, and forecasting changes in ecosystem services. Currently, ecologists do not have the infrastructure in place to synthesize massive yet heterogeneous data into resources for decision support. It is urgent to develop an ecological forecasting system that can make the best use of multiple sources of data to assess long-term biosphere change and anticipate future states of ecosystem services at regional and continental scales. Forecasting relies on big models that describe major processes that underlie complex system dynamics. Ecological system models, despite great simplification of the real systems, are still complex in order to address real-world problems. For example, Community Land Model (CLM) incorporates thousands of processes related to energy balance, hydrology, and biogeochemistry. Integration of massive data from multiple big data sources with complex models has to tackle Big Data-Big Model (BDBM) challenges. Those challenges include interoperability of multiple

My Summer with Science Policy

Science.gov (United States)

Murray, Marissa

This past summer I interned at the American Institute of Physics and helped research and write articles for the FYI Science Policy Bulletin. FYI is an objective digest of science policy developments in Washington, D.C. that impact the greater physical sciences community. Over the course of the summer, I independently attended, analyzed, and reported on a variety of science, technology, and funding related events including congressional hearings, government agency advisory committee meetings, and scientific society events. I wrote and co-wrote three articles on basic energy research legislation, the National Institute of Standards and Technology improvement act, and the National Science Foundation's big ideas for future investment. I had the opportunity to examine some challenging questions such as what is the role of government in funding applied research? How should science priorities be set? What is the right balance of funding across different agencies and programs? I learned about how science policy is a two-way street: science is used to inform policy decisions and policy is made to fund and regulate the conduct of science. I will conclude with how my summer working with FYI showed me the importance of science advocacy, being informed, and voting. Society of Physics Students.

L'universo prima del Big Bang cosmologia e teoria delle stringhe

CERN Document Server

Gasperini, Maurizio

2002-01-01

Termini come "universo in espansione", "big bang", "singolarità iniziale" sono ormai entrati a far parte del linguaggio comune. L'idea che l'universo che oggi osserviamo abbia avuto origine da una grossa esplosione (big bang) è ormai ampiamente diffusa e accettata nella moderna cultura popolare, a tutti i libelli. Ma cosa c'era prima del big bang? E ha senso porsi questo interrogativo in un contesto scientifico? I recenti progressi della fisica teoria, e in particolare della cosiddetta teoria delle stringhe, suggeriscono una risposta a questa domanda, fornendo degli strumenti matematici capaci, in linea di principio, di ricostruire la storia dell'universo spingendosi anche oltre l'istante del big bang. Ne emerge un possibile scenario cosmologico nel quale l'universo, anzichè essere "appena nato" al momento del big bang, era piuttosto nel punto di mezzo della sua evoluzione, di durata probabilmente infinita. In questo libro si cerca di illustrare tale scenario usando un linguaggio non troppo tecnico, rivolt...

Big Data: You Are Adding to . . . and Using It

Science.gov (United States)

Makela, Carole J.

2016-01-01

"Big data" prompts a whole lexicon of terms--data flow; analytics; data mining; data science; smart you name it (cars, houses, cities, wearables, etc.); algorithms; learning analytics; predictive analytics; data aggregation; data dashboards; digital tracks; and big data brokers. New terms are being coined frequently. Are we paying…

Managing globally distributed expertise with new competence management solutions a big-science collaboration as a pilot case.

CERN Document Server

Ferguson, J; Livan, M; Nordberg, M; Salmia, T; Vuola, O

2003-01-01

In today's global organisations and networks, a critical factor for effective innovation and project execution is appropriate competence and skills management. The challenges include selection of strategic competences, competence development, and leveraging the competences and skills to drive innovation and collaboration for shared goals. This paper presents a new industrial web-enabled competence management and networking solution and its implementation and piloting in a complex big-science environment of globally distributed competences.

Database Resources of the BIG Data Center in 2018.

Science.gov (United States)

2018-01-04

The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides freely open access to a suite of database resources in support of worldwide research activities in both academia and industry. With the vast amounts of omics data generated at ever-greater scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big-data integration and value-added curation, including BioCode (a repository archiving bioinformatics tool codes), BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Gene Expression Nebulas (GEN, a database of gene expression profiles based on RNA-Seq data), Methylation Bank (MethBank, an integrated databank of DNA methylomes), and Science Wikis (a series of biological knowledge wikis for community annotations). In addition, three featured web services are provided, viz., BIG Search (search as a service; a scalable inter-domain text search engine), BIG SSO (single sign-on as a service; a user access control system to gain access to multiple independent systems with a single ID and password) and Gsub (submission as a service; a unified submission service for all relevant resources). All of these resources are publicly accessible through the home page of the BIG Data Center at http://bigd.big.ac.cn. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Big data and visual analytics in anaesthesia and health care.

Science.gov (United States)

Simpao, A F; Ahumada, L M; Rehman, M A

2015-09-01

Advances in computer technology, patient monitoring systems, and electronic health record systems have enabled rapid accumulation of patient data in electronic form (i.e. big data). Organizations such as the Anesthesia Quality Institute and Multicenter Perioperative Outcomes Group have spearheaded large-scale efforts to collect anaesthesia big data for outcomes research and quality improvement. Analytics--the systematic use of data combined with quantitative and qualitative analysis to make decisions--can be applied to big data for quality and performance improvements, such as predictive risk assessment, clinical decision support, and resource management. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces, and it can facilitate performance of cognitive activities involving big data. Ongoing integration of big data and analytics within anaesthesia and health care will increase demand for anaesthesia professionals who are well versed in both the medical and the information sciences. © The Author 2015. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The universe before the Big Bang cosmology and string theory

CERN Document Server

Gasperini, Maurizio

2008-01-01

Terms such as "expanding Universe", "big bang", and "initial singularity", are nowadays part of our common language. The idea that the Universe we observe today originated from an enormous explosion (big bang) is now well known and widely accepted, at all levels, in modern popular culture. But what happens to the Universe before the big bang? And would it make any sense at all to ask such a question? In fact, recent progress in theoretical physics, and in particular in String Theory, suggests answers to the above questions, providing us with mathematical tools able in principle to reconstruct the history of the Universe even for times before the big bang. In the emerging cosmological scenario the Universe, at the epoch of the big bang, instead of being a "new born baby" was actually a rather "aged" creature in the middle of its possibly infinitely enduring evolution. The aim of this book is to convey this picture in non-technical language accessibile also to non-specialists. The author, himself a leading cosm...

Earth Science Literacy: Building Community Consensus

Science.gov (United States)

Wysession, M.; Ladue, N.; Budd, D.; Campbell, K.; Conklin, M.; Lewis, G.; Raynolds, R.; Ridky, R.; Ross, R.; Taber, J.; Tewksbury, B.; Tuddenham, P.

2008-12-01

During 2008, the Earth Sciences Literacy Initiative (ESLI) constructed a framework of earth science "Big Ideas" and "Supporting Concepts". Following the examples of recent literacy efforts in the ocean, atmosphere and climate research communities, ESLI has distilled the fundamental understandings of the earth science community into a document that all members of the community will be able to refer to when working with educators, policy-makers, the press and members of the general public. This document is currently in draft form for review and will be published for public distribution in 2009. ESLI began with the construction of an organizing committee of a dozen people who represent a wide array of earth science backgrounds. This group then organized and ran two workshops in 2008: a 2-week online content workshop and a 3-day intensive writing workshop. For both workshops, participants were chosen so as to cover the full breadth of earth science related to the solid earth, surficial processes, and fresh-water hydrology. The asynchronous online workshop included 350 scientists and educators participating from around the world and was a powerful way to gather ideas and information while retaining a written record of all interactions. The writing workshop included 35 scientists, educators and agency representatives to codify the extensive input of the online workshop. Since September, 2008, drafts of the ESLI literacy framework have been circulated through many different channels to make sure that the document accurately reflects the current understandings of earth scientists and to ensure that it is widely accepted and adopted by the earth science communities.

The Choice Is Yours: The Role of Cognitive Processes for IT-Supported Idea Selection

DEFF Research Database (Denmark)

Seeber, Isabella; Weber, Barbara; Maier, Ronald

2018-01-01

of selection direction and selection type. A laboratory experiment using eye-tracking will investigate variations in selection type and selection direction. Moreover, the experiment will test the effects on the decision-making process and the number and quality of ideas in a filtered set. Findings will provide......The selection of good ideas out of hundreds or even thousands has proven to be the next big challenge for organizations that conduct open idea contests for innovation. Cognitive load and attention loss hinder crowds to effectively run their idea selection process. Facilitation techniques...... for the reduction and clarification of ideas could help with such problems, but have not yet been researched in crowd settings that are prevalent in idea contests. This research-in-progress paper aims to contribute to this research gap by investigating IT-supported selection techniques that differ in terms...

Big Data and Regional Science: Opportunities, Challenges, and Directions for Future Research

OpenAIRE

Schintler, Laurie A.; Fischer, Manfred M.

2018-01-01

Recent technological, social, and economic trends and transformations are contributing to the production of what is usually referred to as Big Data. Big Data, which is typically defined by four dimensions -- Volume, Velocity, Veracity, and Variety -- changes the methods and tactics for using, analyzing, and interpreting data, requiring new approaches for data provenance, data processing, data analysis and modeling, and knowledge representation. The use and analysis of Big Data involves severa...

History of Great Ideas: An Honors Seminar.

Science.gov (United States)

Terrill, Marty; And Others

The History of Great Ideas is an interdisciplinary seminar course for sophomore honor students at North Arkansas Community Technical College that teaches the intellectual history of western civilization. Each semester, students study 14 ideas from science, philosophy, history, religion, sociology, and economics to discover how philosophical…

The scientific production on data quality in big data: a study in the Web of Science database

Directory of Open Access Journals (Sweden)

Priscila Basto Fagundes

2017-11-01

Full Text Available More and more, the big data theme has attracted interest in researchers from different areas of knowledge, among them information scientists who need to understand their concepts and applications in order to contribute with new proposals for the management of the information generated from the data stored in these environments. The objective of this article is to present a survey of publications about data quality in big data in the Web of Science database until the year 2016. Will be presented the total number of publications indexed in the database, the number of publications per year, the location the origin of the research and a synthesis of the studies found. The survey in the database was conducted in July 2017 and resulted in a total of 23 publications. In order to make it possible to present a summary of the publications in this article, searches were made of the full texts of all the publications on the Internet and read the ones that were available. With this survey it was possible to conclude that the studies on data quality in big data had their publications starting in 2013, most of which present literature reviews and few effective proposals for the monitoring and management of data quality in environments with large volumes of data. Therefore, it is intended with this survey to contribute and foster new research on the context of data quality in big data environments.

Big data analytics turning big data into big money

CERN Document Server

Ohlhorst, Frank J

2012-01-01

Unique insights to implement big data analytics and reap big returns to your bottom line Focusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportuni

Analysis of Big Data technologies for use in agro-environmental science

NARCIS (Netherlands)

Lokers, Rob; Knapen, Rob; Janssen, Sander; Randen, van Yke; Jansen, Jacques

2016-01-01

Recent developments like the movements of open access and open data and the unprecedented growth of data, which has come forward as Big Data, have shifted focus to methods to effectively handle such data for use in agro-environmental research. Big Data technologies, together with the increased

Epidemiology in the Era of Big Data

Science.gov (United States)

Mooney, Stephen J; Westreich, Daniel J; El-Sayed, Abdulrahman M

2015-01-01

Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called ‘3 Vs’: variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that, while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field’s future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future. PMID:25756221

Modeling and Analysis in Marine Big Data: Advances and Challenges

Directory of Open Access Journals (Sweden)

Dongmei Huang

2015-01-01

Full Text Available It is aware that big data has gathered tremendous attentions from academic research institutes, governments, and enterprises in all aspects of information sciences. With the development of diversity of marine data acquisition techniques, marine data grow exponentially in last decade, which forms marine big data. As an innovation, marine big data is a double-edged sword. On the one hand, there are many potential and highly useful values hidden in the huge volume of marine data, which is widely used in marine-related fields, such as tsunami and red-tide warning, prevention, and forecasting, disaster inversion, and visualization modeling after disasters. There is no doubt that the future competitions in marine sciences and technologies will surely converge into the marine data explorations. On the other hand, marine big data also brings about many new challenges in data management, such as the difficulties in data capture, storage, analysis, and applications, as well as data quality control and data security. To highlight theoretical methodologies and practical applications of marine big data, this paper illustrates a broad view about marine big data and its management, makes a survey on key methods and models, introduces an engineering instance that demonstrates the management architecture, and discusses the existing challenges.

PANGAEA® - Data Publisher for Earth & Environmental Science - Research data enters scholarly communication and big data analysis

Science.gov (United States)

Diepenbroek, Michael; Schindler, Uwe; Riedel, Morris; Huber, Robert

2014-05-01

The ISCU World Data Center PANGAEA is an information system for acquisition, processing, long term storage, and publication of geo-referenced data related to earth science fields. Storing more than 350.000 data sets from all fields of geosciences it belongs to the largest archives for observational earth science data. Standard conform interfaces (ISO, OGC, W3C, OAI) enable access from a variety of data and information portals, among them the search engine of PANGAEA itself ((www.pangaea.de) and e.g. GBIF. All data sets in PANGAEA are citable, fully documented, and can be referenced via persistent identifiers (Digital Object Identifier - DOI) - a premise for data publication. Together with other ICSU World Data Centers (www.icsu-wds.org) and the Technical Information Library in Germany (TIB) PANGAEA had a share in the implementation of a DOI based registry for scientific data, which by now is supported by a worldwide consortium of libraries (www.datacite.org). A further milestone was building up strong co-operations with science publishers as Elsevier, Springer, Wiley, AGU, Nature and others. A common web service allows to reference supplementary data in PANGAEA directly from an articles abstract page (e.g. Science Direct). The next step with science publishers is to further integrate the editorial process for the publication of supplementary data with the publication procedures on the journal side. Data centric research efforts such as environmental modelling or big data analysing approaches represent new challenges for PANGAEA. Integrated data warehouse technologies are used for highly efficient retrievals and compilations of time slices or surface data matrixes on any measurement parameters out of the whole data continuum. Further, new and emerging big data approaches are currently investigated within PANGAEA to e.g. evaluate its usability for quality control or data clustering. PANGAEA is operated as a joint long term facility by MARUM at the University Bremen

Opening the Black Box: Understanding the Science Behind Big Data and Predictive Analytics.

Science.gov (United States)

Hofer, Ira S; Halperin, Eran; Cannesson, Maxime

2018-05-25

Big data, smart data, predictive analytics, and other similar terms are ubiquitous in the lay and scientific literature. However, despite the frequency of usage, these terms are often poorly understood, and evidence of their disruption to clinical care is hard to find. This article aims to address these issues by first defining and elucidating the term big data, exploring the ways in which modern medical data, both inside and outside the electronic medical record, meet the established definitions of big data. We then define the term smart data and discuss the transformations necessary to make big data into smart data. Finally, we examine the ways in which this transition from big to smart data will affect what we do in research, retrospective work, and ultimately patient care.

Big biomedical data as the key resource for discovery science.

Science.gov (United States)

Toga, Arthur W; Foster, Ian; Kesselman, Carl; Madduri, Ravi; Chard, Kyle; Deutsch, Eric W; Price, Nathan D; Glusman, Gustavo; Heavner, Benjamin D; Dinov, Ivo D; Ames, Joseph; Van Horn, John; Kramer, Roger; Hood, Leroy

2015-11-01

Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an "-ome to home" approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center's computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson's and Alzheimer's. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Lithium isotopic abundances in metal-poor stars: a problem for standard big bang nucleosynthesis?

International Nuclear Information System (INIS)

Nissen, P.E.; Asplund, M.; Lambert, D.L.; Primas, F.; Smith, V.V.

2005-01-01

Spectral obtained with VLT/UVES suggest the existence of the 6 Li isotope in several metal-poor stars at a level that challenges ideas about its synthesis. The 7 Li abundance is, on the other hand, a factor of three lower than predicted by standard Big Bang nucleosynthesis theory. Both problems may be explained if decaying suppersymmetric particles affect the synthesis of light elements in the Big Bang. (orig.)

Making big sense from big data in toxicology by read-across.

Science.gov (United States)

Hartung, Thomas

2016-01-01

Modern information technologies have made big data available in safety sciences, i.e., extremely large data sets that may be analyzed only computationally to reveal patterns, trends and associations. This happens by (1) compilation of large sets of existing data, e.g., as a result of the European REACH regulation, (2) the use of omics technologies and (3) systematic robotized testing in a high-throughput manner. All three approaches and some other high-content technologies leave us with big data--the challenge is now to make big sense of these data. Read-across, i.e., the local similarity-based intrapolation of properties, is gaining momentum with increasing data availability and consensus on how to process and report it. It is predominantly applied to in vivo test data as a gap-filling approach, but can similarly complement other incomplete datasets. Big data are first of all repositories for finding similar substances and ensure that the available data is fully exploited. High-content and high-throughput approaches similarly require focusing on clusters, in this case formed by underlying mechanisms such as pathways of toxicity. The closely connected properties, i.e., structural and biological similarity, create the confidence needed for predictions of toxic properties. Here, a new web-based tool under development called REACH-across, which aims to support and automate structure-based read-across, is presented among others.

Big Opportunities and Big Concerns of Big Data in Education

Science.gov (United States)

Wang, Yinying

2016-01-01

Against the backdrop of the ever-increasing influx of big data, this article examines the opportunities and concerns over big data in education. Specifically, this article first introduces big data, followed by delineating the potential opportunities of using big data in education in two areas: learning analytics and educational policy. Then, the…

A Proposed Concentration Curriculum Design for Big Data Analytics for Information Systems Students

Science.gov (United States)

Molluzzo, John C.; Lawler, James P.

2015-01-01

Big Data is becoming a critical component of the Information Systems curriculum. Educators are enhancing gradually the concentration curriculum for Big Data in schools of computer science and information systems. This paper proposes a creative curriculum design for Big Data Analytics for a program at a major metropolitan university. The design…

Big Data in Caenorhabditis elegans: quo vadis?

Science.gov (United States)

Hutter, Harald; Moerman, Donald

2015-11-05

A clear definition of what constitutes "Big Data" is difficult to identify, but we find it most useful to define Big Data as a data collection that is complete. By this criterion, researchers on Caenorhabditis elegans have a long history of collecting Big Data, since the organism was selected with the idea of obtaining a complete biological description and understanding of development. The complete wiring diagram of the nervous system, the complete cell lineage, and the complete genome sequence provide a framework to phrase and test hypotheses. Given this history, it might be surprising that the number of "complete" data sets for this organism is actually rather small--not because of lack of effort, but because most types of biological experiments are not currently amenable to complete large-scale data collection. Many are also not inherently limited, so that it becomes difficult to even define completeness. At present, we only have partial data on mutated genes and their phenotypes, gene expression, and protein-protein interaction--important data for many biological questions. Big Data can point toward unexpected correlations, and these unexpected correlations can lead to novel investigations; however, Big Data cannot establish causation. As a result, there is much excitement about Big Data, but there is also a discussion on just what Big Data contributes to solving a biological problem. Because of its relative simplicity, C. elegans is an ideal test bed to explore this issue and at the same time determine what is necessary to build a multicellular organism from a single cell. © 2015 Hutter and Moerman. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

SETI as a part of Big History

Science.gov (United States)

Maccone, Claudio

2014-08-01

Big History is an emerging academic discipline which examines history scientifically from the Big Bang to the present. It uses a multidisciplinary approach based on combining numerous disciplines from science and the humanities, and explores human existence in the context of this bigger picture. It is taught at some universities. In a series of recent papers ([11] through [15] and [17] through [18]) and in a book [16], we developed a new mathematical model embracing Darwinian Evolution (RNA to Humans, see, in particular, [17] and Human History (Aztecs to USA, see [16]) and then we extrapolated even that into the future up to ten million years (see 18), the minimum time requested for a civilization to expand to the whole Milky Way (Fermi paradox). In this paper, we further extend that model in the past so as to let it start at the Big Bang (13.8 billion years ago) thus merging Big History, Evolution on Earth and SETI (the modern Search for ExtraTerrestrial Intelligence) into a single body of knowledge of a statistical type. Our idea is that the Geometric Brownian Motion (GBM), so far used as the key stochastic process of financial mathematics (Black-Sholes models and related 1997 Nobel Prize in Economics!) may be successfully applied to the whole of Big History. In particular, in this paper we derive the Statistical Drake Equation (namely the statistical extension of the classical Drake Equation typical of SETI) can be regarded as the “frozen in time” part of GBM. This makes SETI a subset of our Big History Theory based on GBMs: just as the GBM is the “movie” unfolding in time, so the Statistical Drake Equation is its “still picture”, static in time, and the GBM is the time-extension of the Drake Equation. Darwinian Evolution on Earth may be easily described as an increasing GBM in the number of living species on Earth over the last 3.5 billion years. The first of them was RNA 3.5 billion years ago, and now 50 million living species or more exist, each

Core Ideas of Engineering and Technology

Science.gov (United States)

Sneider, Cary

2012-01-01

Last month, Rodger Bybee's article, "Scientific and Engineering Practices in K-12 Classrooms," provided an overview of Chapter 3 in "A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas" (NRC 2011). Chapter 3 describes the practices of science and engineering that students are expected to develop during 13 years…

Veracity in big data: How good is good enough.

Science.gov (United States)

Reimer, Andrew P; Madigan, Elizabeth A

2018-01-01

Veracity, one of the five V's used to describe big data, has received attention when it comes to using electronic medical record data for research purposes. In this perspective article, we discuss the idea of data veracity and associated concepts as it relates to the use of electronic medical record data and administrative data in research. We discuss the idea that electronic medical record data are "good enough" for clinical practice and, as such, are "good enough" for certain applications. We then propose three primary issues to attend to when establishing data veracity: data provenance, cross validation, and context.

Big Data and Intelligence: Applications, Human Capital, and Education

Directory of Open Access Journals (Sweden)

Michael Landon-Murray

2016-06-01

Full Text Available The potential for big data to contribute to the US intelligence mission goes beyond bulk collection, social media and counterterrorism. Applications will speak to a range of issues of major concern to intelligence agencies, from military operations to climate change to cyber security. There are challenges too: procurement lags, data stovepiping, separating signal from noise, sources and methods, a range of normative issues, and central to managing these challenges, human capital. These potential applications and challenges are discussed and a closer look at what data scientists do in the Intelligence Community (IC is offered. Effectively filling the ranks of the IC’s data science workforce will depend on the provision of well-trained data scientists from the higher education system. Program offerings at America’s top fifty universities will thus be surveyed (just a few years ago there were reportedly no degrees in data science. One Master’s program that has melded data science with intelligence is examined as well as a university big data research center focused on security and intelligence. This discussion goes a long way to clarify the prospective uses of data science in intelligence while probing perhaps the key challenge to optimal application of big data in the IC.

Cosmic Evolution: The History of an Idea

Science.gov (United States)

Dick, S. J.

2004-12-01

Cosmic evolution has become the conceptual framework within which modern astronomy is undertaken, and is the guiding principle of major NASA programs such as Origins and Astrobiology. While there are 19th- and early 20th century antecedents, as in the work of Robert Chambers, Herbert Spencer and Lawrence Henderson, it was only at mid-20th century that full-blown cosmic evolution began to be articulated and accepted as a research paradigm extending from the Big Bang to life, intelligence and the evolution of culture. Harlow Shapley was particularly important in spreading the idea to the public in the 1950s, and NASA embraced the idea in the 1970s as part of its SETI program and later its exobiology and astrobiology programs. Eric Chaisson, Carl Sagan and others were early proponents of cosmic evolution, and it continues to be elaborated in ever more subtle form as a research program and a philosophy. It has even been termed "Genesis for the 21st century." This paper documents the origin and development of the idea and offers a glimpse of where it could lead if cultural evolution is taken seriously, possibly leading to the concept of a postbiological universe.

Crowd-sourcing the smart city: Using big geosocial media metrics in urban governance

Directory of Open Access Journals (Sweden)

Matthew Zook

2017-05-01

Full Text Available Using Big Data to better understand urban questions is an exciting field with challenging methodological and theoretical problems. It is also, however, potentially troubling when Big Data (particularly derived from social media is applied uncritically to urban governance via the ideas and practices of “smart cities”. This essay reviews both the historical depth of central ideas within smart city governance —particular the idea that enough data/information/knowledge can solve society problems—but also the ways that the most recent version differs. Namely, that the motivations and ideological underpinning behind the goal of urban betterment is largely driven by technology advocates and neoliberalism rather than the strong social justice themes associated with earlier applications of data to cities. Geosocial media data and metrics derived from them can provide useful insight and policy direction. But one must be ever mindful that metrics don’t simply measure; in the process of deciding what is important and possible to measure, these data are simultaneously defining what cities are.

Examining the Big-Fish-Little-Pond Effect on Students' Self-Concept of Learning Science in Taiwan Based on the TIMSS Databases

Science.gov (United States)

Liou, Pey-Yan

2014-08-01

The purpose of this study is to examine the relationship between student self-concept and achievement in science in Taiwan based on the big-fish-little-pond effect (BFLPE) model using the Trends in International Mathematics and Science Study (TIMSS) 2003 and 2007 databases. Hierarchical linear modeling was used to examine the effects of the student-level and school-level science achievement on student self-concept of learning science. The results indicated that student science achievement was positively associated with individual self-concept of learning science in both TIMSS 2003 and 2007. On the contrary, while school-average science achievement was negatively related to student self-concept in TIMSS 2003, it had no statistically significant relationship with student self-concept in TIMSS 2007. The findings of this study shed light on possible explanations for the existence of BFLPE and also lead to an international discussion on the generalization of BFLPE.

LLNL's Big Science Capabilities Help Spur Over $796 Billion in U.S. Economic Activity Sequencing the Human Genome

Energy Technology Data Exchange (ETDEWEB)

Stewart, Jeffrey S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2015-07-28

LLNL’s successful history of taking on big science projects spans beyond national security and has helped create billions of dollars per year in new economic activity. One example is LLNL’s role in helping sequence the human genome. Over $796 billion in new economic activity in over half a dozen fields has been documented since LLNL successfully completed this Grand Challenge.

WBP: The wood Brazilian BIG-GT demonstration project

Energy Technology Data Exchange (ETDEWEB)

Carpentieri, E. [Companhia Hidro Eletrica do Sao Francisco, Recife (Brazil)

1993-12-31

Brazil is one of the leading countries in the use of renewable energy. Most of its electricity comes from hydro power, about 200,000 barrels a day of ethanol from sugar cane is used as fuel, around 38% of the pig iron, and 20% of the steel production, uses charcoal as a reducing medium. Located in the tropics, with the sun shining all year round, and with its vast territory, the Country may be regarded as having all the basic conditions to develop a modern Biomass for Electricity industry. The conjunction of those characteristics with, the necessity of developing new energy resources for electricity production in the Northeast of the Country, the results of the studies made by Princeton University, Shell and Chesf, the progress achieved by the BIG-GT (Biomass Integrated Gasification Gas Turbine) technology in Europe, and the organization of the Global Environment Facility (GEF), provided the unique opportunity for the implementation of a commercial demonstration in Brazil. This paper describes the idea, the scope, the technical challenges, and actual status of development of the WBP, a project which aims to demonstrate the commercial viability of the BIG-GT technology. It also highlights, the project management structure, the role of the GEF, World Bank and of the United Nations Development Program (UNDP), and the participation of the Brazilian Federal Government, through the Ministry of Science and Technology (MCT). Finally it describes the Participants (ELETROBRAS, CVRD, CIENTEC, SHELL, and CHESF), their role in the project, and how the group was formed and operates.

The Role of Social Responsibility in Big Business Practics

OpenAIRE

V A Gurinov

2010-01-01

The study of corporate social responsibility has become especially relevant in national science in the context of the development of big business able to assume significant social responsibilities. The article focuses on the issues of the nature and specificity of social responsibility of big business in Russia. The levels of social responsibility and the arrangements for social programmes implementation are also highlighted.

Classical propagation of strings across a big crunch/big bang singularity

International Nuclear Information System (INIS)

Niz, Gustavo; Turok, Neil

2007-01-01

One of the simplest time-dependent solutions of M theory consists of nine-dimensional Euclidean space times 1+1-dimensional compactified Milne space-time. With a further modding out by Z 2 , the space-time represents two orbifold planes which collide and re-emerge, a process proposed as an explanation of the hot big bang [J. Khoury, B. A. Ovrut, P. J. Steinhardt, and N. Turok, Phys. Rev. D 64, 123522 (2001).][P. J. Steinhardt and N. Turok, Science 296, 1436 (2002).][N. Turok, M. Perry, and P. J. Steinhardt, Phys. Rev. D 70, 106004 (2004).]. When the two planes are near, the light states of the theory consist of winding M2-branes, describing fundamental strings in a particular ten-dimensional background. They suffer no blue-shift as the M theory dimension collapses, and their equations of motion are regular across the transition from big crunch to big bang. In this paper, we study the classical evolution of fundamental strings across the singularity in some detail. We also develop a simple semiclassical approximation to the quantum evolution which allows one to compute the quantum production of excitations on the string and implement it in a simplified example

Middle School Teacher Misconceptions and Anxieties Concerning Space Science Disciplinary Core Ideas in NGSS

Science.gov (United States)

Larsen, Kristine

2017-01-01

The Disciplinary Core Ideas (DCI) of the Next Generation Science Standards (NGSS) are grouped into the broad disciplinary areas of Physical Sciences, Life Sciences, Earth and Space Sciences, and Engineering, Technology and Application of Science, and feature learning progressions based on endpoint targets for each grade band. Since the Middle School DCIs build on the expected learning achievements to be reached by the end of Fifth Grade, and High School DCI similarly build on the expected learning achievements expected for the end of Eighth Grade, the Middle School grade band is of particular importance as the bridge between the Elementary and High School curriculum. In states where there is not a special Middle School Certification many of these science classes are taught by teachers prepared to teach at the Elementary level (and who may have limited content background). As a result, some pre-service and in-service teachers have expressed reduced self-confidence in both their own science content knowledge and their ability to apply it in the NGSS-based classroom, while decades of research has demonstrated the pervasiveness of science misconceptions among teachers. Thus the adoption of NGSS has the potential to drive talented teachers out of the profession who feel that they are ill-prepared for this sweeping transition. The key is providing rigorous education in both content and pedagogy for pre-service teachers and quality targeted professional development for in-service teachers. This report focuses on the Middle School Space Sciences grade band DCIs and presents research on specific difficulties, misconceptions and uncertainties with the material demonstrated by pre-service education students over the past four years in a required university science content course, as well as two year-long granted workshop series for current Middle School teachers. This information is relevant to the development of both new content courses aligned with NGSS for pre

Don’t Trust the Big Man

Science.gov (United States)

2008-06-06

on the scholar questioned, these entities began as either natural alliances of people already united by colocation, culture , ethnicity, religion and...anger to provide a channel for the pent-up anger at a failing government. This is where the tribal culture becomes interesting. The Big Man can find...Zulu, Dinka, and Maasai; the Shona and Yoruba and Somali - militates against the very idea of borders drawn through tribal and nomadic areas by the

Addressing the Biggest (Baddest) and Best Ideas Ever: Through the Lens of Humility

Science.gov (United States)

Sowcik, Matthew J.; Andenoro, Anthony C.; Council, Austin

2017-01-01

Now and into the foreseeable future, both effective leadership and creativity are going to be important when addressing complex problems. The connection between effective leadership and creativity will be critical as leaders look to turn big ideas into innovative solutions. However, it seems that there is often a disconnect between the two…

Implications of Big Data for cell biology

OpenAIRE

Dolinski, Kara; Troyanskaya, Olga G.

2015-01-01

“Big Data” has surpassed “systems biology” and “omics” as the hottest buzzword in the biological sciences, but is there any substance behind the hype? Certainly, we have learned about various aspects of cell and molecular biology from the many individual high-throughput data sets that have been published in the past 15–20 years. These data, although useful as individual data sets, can provide much more knowledge when interrogated with Big Data approaches, such as applying integrative methods ...

Primary school mathematics teachers' ideas, beliefs, and practices ...

African Journals Online (AJOL)

kofi.mereku

African Journal of Educational Studies in Mathematics and Sciences Vol. 12, 2016. 45 ... The study explored Ghanaian primary school mathematics teachers' ideas, beliefs and ...... Journal of science and technology, 24(2), 106 -115. Palmer ...

Small Bodies, Big Concepts: Engaging Teachers and Their Students in Visual Analysis of Comets and Asteroids

Science.gov (United States)

Cobb, W. H.; Buxner, S.; Lebofsky, L. A.; Ristvey, J.; Weeks, S.; Zolensky, M.

2011-12-01

Small Bodies, Big Concepts is a multi-disciplinary, professional development project that engages 5th - 8th grade teachers in high end planetary science using a research-based pedagogical framework, Designing Effective Science Instruction (DESI). In addition to developing sound background knowledge with a focus on visual analysis, teachers' awareness of the process of learning new content is heightened, and they use that experience to deepen their science teaching practice. Culling from NASA E/PO educational materials, activities are sequenced to enhance conceptual understanding of big ideas in space science: what do we know, how do we know it, why do we care? Helping teachers develop a picture of the history and evolution of our understanding of the solar system, and honing in on the place of comets and asteroids in helping us answer old questions and discover new ones, teachers see the power and excitement underlying planetary science as human endeavor. Research indicates that science inquiry is powerful in the classroom and mission scientists are real-life models of science inquiry in action. Using guest scientist facilitators from the Planetary Science Institute, NASA Johnson Space Center, Lockheed Martin, and NASA E/PO professionals from McREL and NASA AESP, teachers practice framing scientific questions, using current visual data, and adapting NASA E/PO activities related to current exploration of asteroids and comets in our Solar System. Cross-curricular elements included examining research-based strategies for enhancing English language learners' ability to engage in higher order questions and a professional astronomy artist's insight into how visual analysis requires not just our eyes engaged, but our brains: comparing, synthesizing, questioning, evaluating, and wondering. This summer we pilot tested the SBBC curriculum with thirteen 5th- 10th grade teachers modeling a variety of instructional approaches over eight days. Each teacher developed lesson plans

From ecological records to big data: the invention of global biodiversity.

Science.gov (United States)

Devictor, Vincent; Bensaude-Vincent, Bernadette

2016-12-01

This paper is a critical assessment of the epistemological impact of the systematic quantification of nature with the accumulation of big datasets on the practice and orientation of ecological science. We examine the contents of big databases and argue that it is not just accumulated information; records are translated into digital data in a process that changes their meanings. In order to better understand what is at stake in the 'datafication' process, we explore the context for the emergence and quantification of biodiversity in the 1980s, along with the concept of the global environment. In tracing the origin and development of the global biodiversity information facility (GBIF) we describe big data biodiversity projects as a techno-political construction dedicated to monitoring a new object: the global diversity. We argue that, biodiversity big data became a powerful driver behind the invention of the concept of the global environment, and a way to embed ecological science in the political agenda.

Commentary: Epidemiology in the era of big data.

Science.gov (United States)

Mooney, Stephen J; Westreich, Daniel J; El-Sayed, Abdulrahman M

2015-05-01

Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called "three V's": variety, volume, and velocity. From this definition, we argue that Big Data has evolutionary and revolutionary implications for identifying and intervening on the determinants of population health. We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field's future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future.

The International Big History Association

Science.gov (United States)

Duffy, Michael; Duffy, D'Neil

2013-01-01

IBHA, the International Big History Association, was organized in 2010 and "promotes the unified, interdisciplinary study and teaching of history of the Cosmos, Earth, Life, and Humanity." This is the vision that Montessori embraced long before the discoveries of modern science fleshed out the story of the evolving universe. "Big…

Big data in psychology: Introduction to the special issue.

Science.gov (United States)

Harlow, Lisa L; Oswald, Frederick L

2016-12-01

The introduction to this special issue on psychological research involving big data summarizes the highlights of 10 articles that address a number of important and inspiring perspectives, issues, and applications. Four common themes that emerge in the articles with respect to psychological research conducted in the area of big data are mentioned, including: (a) The benefits of collaboration across disciplines, such as those in the social sciences, applied statistics, and computer science. Doing so assists in grounding big data research in sound theory and practice, as well as in affording effective data retrieval and analysis. (b) Availability of large data sets on Facebook, Twitter, and other social media sites that provide a psychological window into the attitudes and behaviors of a broad spectrum of the population. (c) Identifying, addressing, and being sensitive to ethical considerations when analyzing large data sets gained from public or private sources. (d) The unavoidable necessity of validating predictive models in big data by applying a model developed on 1 dataset to a separate set of data or hold-out sample. Translational abstracts that summarize the articles in very clear and understandable terms are included in Appendix A, and a glossary of terms relevant to big data research discussed in the articles is presented in Appendix B. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The Use of Online Citizen-Science Projects to Provide Experiential Learning Opportunities for Nonmajor Science Students

Directory of Open Access Journals (Sweden)

Donna M. Kridelbaugh

2015-11-01

Full Text Available Citizen science is becoming even more accessible to the general public through technological advances in the development of mobile applications, facilitating information dissemination and data collection. With the advent of “big data,” many citizen-science projects designed to help researchers sift through piles of research data now exist entirely online, either in the form of playing a game or via other digital avenues. Recent trends in citizen science have also focused on “crowdsourcing” solutions from the general public to help solve societal issues, often requiring nothing more than brainstorming and a computer to submit ideas. Online citizen science thus provides an excellent platform to expand the accessibility of experiential learning opportunities for a broad range of nonmajor science students at institutions with limited resources (e.g., community colleges. I created an activity for a general microbiology lecture to engage students in hands-on experiences via participation in online citizen-science projects. The objectives of the assignment were for students to: 1 understand that everyone can be a scientist; 2 learn to be creative and innovative in designing solutions to health and science challenges; and 3 further practice science communication skills with a written report. This activity is designed for introductory science courses with nonmajor science students who have limited opportunities to participate in undergraduate research experiences.

Small decisions with big impact on data analytics

OpenAIRE

Jana Diesner

2015-01-01

Big social data have enabled new opportunities for evaluating the applicability of social science theories that were formulated decades ago and were often based on small- to medium-sized samples. Big Data coupled with powerful computing has the potential to replace the statistical practice of sampling and estimating effects by measuring phenomena based on full populations. Preparing these data for analysis and conducting analytics involves a plethora of decisions, some of which are already em...

WBP/SIGAME the Brazilian BIG-GT demonstration project actual status and perspectives

International Nuclear Information System (INIS)

Carpentier, E.; Silva, A.

1998-01-01

Located in the tropics, with the sun shining all year round, and with its vast territory, Brazil may be regarded as having all the basic conditions to develop a modern Biomass for Electricity industry. Those characteristics together with: (a) the necessity of developing new energy resources for electricity production, in the northeast of the country; (b) the results of studies made by various entities, including CHESF; (c) the progress achieved by the BIG-GT technology; (d) the organisation of the Global Environment Facility (GEF); (e) and the support of the Brazilian government, through the Ministry of Science and Technology (MCT), provided the unique opportunity for the implementation of a commercial demonstration of that technology in Brazil. This paper describes the idea, scope, challenges, lessons, and actual status of development of the WBP/SIGAME project. It also highlights some institutional issues, budget figures, and energy prices. (author)

Big Bang, Blowup, and Modular Curves: Algebraic Geometry in Cosmology

Science.gov (United States)

Manin, Yuri I.; Marcolli, Matilde

2014-07-01

We introduce some algebraic geometric models in cosmology related to the ''boundaries'' of space-time: Big Bang, Mixmaster Universe, Penrose's crossovers between aeons. We suggest to model the kinematics of Big Bang using the algebraic geometric (or analytic) blow up of a point x. This creates a boundary which consists of the projective space of tangent directions to x and possibly of the light cone of x. We argue that time on the boundary undergoes the Wick rotation and becomes purely imaginary. The Mixmaster (Bianchi IX) model of the early history of the universe is neatly explained in this picture by postulating that the reverse Wick rotation follows a hyperbolic geodesic connecting imaginary time axis to the real one. Penrose's idea to see the Big Bang as a sign of crossover from ''the end of previous aeon'' of the expanding and cooling Universe to the ''beginning of the next aeon'' is interpreted as an identification of a natural boundary of Minkowski space at infinity with the Big Bang boundary.

Challenges in data science

DEFF Research Database (Denmark)

Carbone, Anna; Jensen, M.; Sato, Aki-Hiro

2016-01-01

of global properties from locally interacting data entities and clustering phenomena demand suitable approaches and methodologies recently developed in the foundational area of Data Science by taking a Complex Systems standpoint. Here, we deal with challenges that can be summarized by the question: "What...... can Complex Systems Science contribute to Big Data? ". Such question can be reversed and brought to a superior level of abstraction by asking "What Knowledge can be drawn from Big Data?" These aspects constitute the main motivation behind this article to introduce a volume containing a collection...... of papers presenting interdisciplinary advances in the Big Data area by methodologies and approaches typical of the Complex Systems Science, Nonlinear Systems Science and Statistical Physics. (C) 2016 Elsevier Ltd. All rights reserved....

The Role of Social Responsibility in Big Business Practics

Directory of Open Access Journals (Sweden)

V A Gurinov

2010-06-01

Full Text Available The study of corporate social responsibility has become especially relevant in national science in the context of the development of big business able to assume significant social responsibilities. The article focuses on the issues of the nature and specificity of social responsibility of big business in Russia. The levels of social responsibility and the arrangements for social programmes implementation are also highlighted.

Survey of Cyber Crime in Big Data

Science.gov (United States)

Rajeswari, C.; Soni, Krishna; Tandon, Rajat

2017-11-01

Big data is like performing computation operations and database operations for large amounts of data, automatically from the data possessor’s business. Since a critical strategic offer of big data access to information from numerous and various areas, security and protection will assume an imperative part in big data research and innovation. The limits of standard IT security practices are notable, with the goal that they can utilize programming sending to utilize programming designers to incorporate pernicious programming in a genuine and developing risk in applications and working frameworks, which are troublesome. The impact gets speedier than big data. In this way, one central issue is that security and protection innovation are sufficient to share controlled affirmation for countless direct get to. For powerful utilization of extensive information, it should be approved to get to the information of that space or whatever other area from a space. For a long time, dependable framework improvement has arranged a rich arrangement of demonstrated ideas of demonstrated security to bargain to a great extent with the decided adversaries, however this procedure has been to a great extent underestimated as “needless excess” and sellers In this discourse, essential talks will be examined for substantial information to exploit this develop security and protection innovation, while the rest of the exploration difficulties will be investigated.

The BigBoss Experiment

Energy Technology Data Exchange (ETDEWEB)

Schelgel, D.; Abdalla, F.; Abraham, T.; Ahn, C.; Allende Prieto, C.; Annis, J.; Aubourg, E.; Azzaro, M.; Bailey, S.; Baltay, C.; Baugh, C.; Bebek, C.; Becerril, S.; Blanton, M.; Bolton, A.; Bromley, B.; Cahn, R.; Carton, P.-H.; Cervanted-Cota, J.L.; Chu, Y.; Cortes, M.; /APC, Paris /Brookhaven /IRFU, Saclay /Marseille, CPPM /Marseille, CPT /Durham U. / /IEU, Seoul /Fermilab /IAA, Granada /IAC, La Laguna / /IAC, Mexico / / /Madrid, IFT /Marseille, Lab. Astrophys. / / /New York U. /Valencia U.

2012-06-07

BigBOSS is a Stage IV ground-based dark energy experiment to study baryon acoustic oscillations (BAO) and the growth of structure with a wide-area galaxy and quasar redshift survey over 14,000 square degrees. It has been conditionally accepted by NOAO in response to a call for major new instrumentation and a high-impact science program for the 4-m Mayall telescope at Kitt Peak. The BigBOSS instrument is a robotically-actuated, fiber-fed spectrograph capable of taking 5000 simultaneous spectra over a wavelength range from 340 nm to 1060 nm, with a resolution R = {lambda}/{Delta}{lambda} = 3000-4800. Using data from imaging surveys that are already underway, spectroscopic targets are selected that trace the underlying dark matter distribution. In particular, targets include luminous red galaxies (LRGs) up to z = 1.0, extending the BOSS LRG survey in both redshift and survey area. To probe the universe out to even higher redshift, BigBOSS will target bright [OII] emission line galaxies (ELGs) up to z = 1.7. In total, 20 million galaxy redshifts are obtained to measure the BAO feature, trace the matter power spectrum at smaller scales, and detect redshift space distortions. BigBOSS will provide additional constraints on early dark energy and on the curvature of the universe by measuring the Ly-alpha forest in the spectra of over 600,000 2.2 < z < 3.5 quasars. BigBOSS galaxy BAO measurements combined with an analysis of the broadband power, including the Ly-alpha forest in BigBOSS quasar spectra, achieves a FOM of 395 with Planck plus Stage III priors. This FOM is based on conservative assumptions for the analysis of broad band power (k{sub max} = 0.15), and could grow to over 600 if current work allows us to push the analysis to higher wave numbers (k{sub max} = 0.3). BigBOSS will also place constraints on theories of modified gravity and inflation, and will measure the sum of neutrino masses to 0.024 eV accuracy.

The Impact of Globalization on Employment Generation in India: The case of emerging 'Big Shopping Malls and Retailers'

OpenAIRE

Kaliappa Kalirajan; Kanhaiya Singh

2009-01-01

Globalization in this paper concerns diffusion of idea, and technique of doing business. Organised retailing and retailing through big shopping complexes and malls is an idea, which is drawn from within and across nations. Thus, this idea is necessarily global and expansion of this idea is an integral part of globalisation. Growth of organized retail sector in India is being seen by some as the next driver of the Indian economy after the information technology boom. Some have argued that the ...

Salmona´s idea of city report on an analysis of the human science postgraduate building

OpenAIRE

Ulloa, Miguel

2010-01-01

This article is a comparative study between the Park Towers and the building of Postgraduate Studies of Human Sciences Faculty, at the National University of Colombia designed by the architect Rogelio Salmona. Reviewing both constructions the interaction between building and city, and by the use of social aspects as ‘the encounter with the other’ and the politics, we hope to find that original idea, referred to the city, which sustains conceptually the work of this architect, which we believe...

Idea Generation in Highly Institutionalized Fields

DEFF Research Database (Denmark)

Agoguè, Marine; Boxenbaum, Eva

innovation. An important question facing innovation research is thus how actors can generate ideas that break with the field frame in highly institutionalized fields? To answer this question, we draw on insights into dual process modeling from cognitive sciences. Dual process modeling emphasizes...... the different nature of the conscious (deliberate) and subconscious (implicit) systems involved in ideation. We further elaborate on how these two systems relate to four streams of research that management scholars evoke to model microprocesses of generating new ideas, namely metaphors, conceptual blending......The early phase of innovation processes in highly institutionalized fields relies on the capabilities of actors to generate new ideas that break with the field frame. Informed by a dominant logic, a field frame shapes collective cognition and can thus prevent the generation of new ideas and block...

Improving Healthcare Using Big Data Analytics

Directory of Open Access Journals (Sweden)

Revanth Sonnati

2017-03-01

Full Text Available In daily terms we call the current era as Modern Era which can also be named as the era of Big Data in the field of Information Technology. Our daily lives in todays world are rapidly advancing never quenching ones thirst. The fields of science engineering and technology are producing data at an exponential rate leading to Exabytes of data every day. Big data helps us to explore and re-invent many areas not limited to education health and law. The primary purpose of this paper is to provide an in-depth analysis in the area of Healthcare using the big data and analytics. The main purpose is to emphasize on the usage of the big data which is being stored all the time helping to look back in the history but this is the time to emphasize on the analyzation to improve the medication and services. Although many big data implementations happen to be in-house development this proposed implementation aims to propose a broader extent using Hadoop which just happen to be the tip of the iceberg. The focus of this paper is not limited to the improvement and analysis of the data it also focusses on the strengths and drawbacks compared to the conventional techniques available.

Axiologies to develop new ideas on Intellectual Property

Directory of Open Access Journals (Sweden)

Fernando Martínez Cabezudo

2015-07-01

Full Text Available A philosophical approach to copyleft is a big deal. It brings together copyrights, TICs and, sometimes, the democratic model itself. Copyleft is the device that conducts all these social empowerments articulated on this new society of knowledge. However, it is needed to make a research on the origins of these new ideas and the implications that discourses and practices of the pioneering collectives have had from the beginning. We have pick out two of the main actors of the field of the production of Free Software: the Free Software Foundation and the Open Source Initiative. Through the analysis of the discourses we will elucidate if we can appreciate differences in significance of "free work" notion for both actors, the propositions that underlie its main ideas and the factual possibility of the copyleft as to its subversive perspective for the discourse of the traditional operators of cultural market.

Re-inventing NDE as science — How student ideas will help adapt NDE to the new ecosystem of science and technology

Science.gov (United States)

Meyendorf, Norbert

2018-04-01

Industry 4.0 stands for the fourth industrial revolution that is ongoing at present. Industry 4.0 is a terminology generally used in Europe to characterize the integration of production and communication technologies, the so called "smart factory". Lowering costs and efficient in-time production will be possible for low numbers of unique parts, for example by additive manufacturing (3D printing). A significant aspect is also quality and maintainability of these sometimes unique structures and components. NDE has to follow these trends, but introduce the capability of cyber systems into the inspection and maintenance processes. The author initiated in his NDE introductory class student projects where small groups of students had to identify everyday problems that can be solved by NDE techniques and suggest technical solutions based on today's technology. The results where exiting. After discussing the ecosystem and the present situation of NDE as a science, several of these ideas were presented. Let us listen to the ideas and needs of the young generation to re-invent NDE!

How Big Science Came to Long Island: the Birth of Brookhaven Lab (429th Brookhaven Lecture)

International Nuclear Information System (INIS)

Crease, Robert P.

2007-01-01

Robert P. Crease, historian for the U.S. Department of Energy's Brookhaven National Laboratory and Chair of the Philosophy Department at Stony Brook University, will give two talks on the Laboratory's history on October 31 and December 12. Crease's October 31 talk, titled 'How Big Science Came to Long Island: The Birth of Brookhaven Lab,' will cover the founding of the Laboratory soon after World War II as a peacetime facility to construct and maintain basic research facilities, such as nuclear reactors and particle accelerators, that were too large for single institutions to build and operate. He will discuss the key figures involved in starting the Laboratory, including Nobel laureates I.I. Rabi and Norman Ramsey, as well as Donald Dexter Van Slyke, one of the most renowned medical researchers in American history. Crease also will focus on the many problems that had to be overcome in creating the Laboratory and designing its first big machines, as well as the evolving relations of the Laboratory with the surrounding Long Island community and news media. Throughout his talk, Crease will tell fascinating stories about Brookhaven's scientists and their research.

Big Sites, Big Questions, Big Data, Big Problems: Scales of Investigation and Changing Perceptions of Archaeological Practice in the Southeastern United States

Directory of Open Access Journals (Sweden)

Cameron B Wesson

2014-08-01

Full Text Available Since at least the 1930s, archaeological investigations in the southeastern United States have placed a priority on expansive, near-complete, excavations of major sites throughout the region. Although there are considerable advantages to such large–scale excavations, projects conducted at this scale are also accompanied by a series of challenges regarding the comparability, integrity, and consistency of data recovery, analysis, and publication. We examine the history of large–scale excavations in the southeast in light of traditional views within the discipline that the region has contributed little to the ‘big questions’ of American archaeology. Recently published analyses of decades old data derived from Southeastern sites reveal both the positive and negative aspects of field research conducted at scales much larger than normally undertaken in archaeology. Furthermore, given the present trend toward the use of big data in the social sciences, we predict an increased use of large pre–existing datasets developed during the New Deal and other earlier periods of archaeological practice throughout the region.

Traffic measurement for big network data

CERN Document Server

Chen, Shigang; Xiao, Qingjun

2017-01-01

This book presents several compact and fast methods for online traffic measurement of big network data. It describes challenges of online traffic measurement, discusses the state of the field, and provides an overview of the potential solutions to major problems. The authors introduce the problem of per-flow size measurement for big network data and present a fast and scalable counter architecture, called Counter Tree, which leverages a two-dimensional counter sharing scheme to achieve far better memory efficiency and significantly extend estimation range. Unlike traditional approaches to cardinality estimation problems that allocate a separated data structure (called estimator) for each flow, this book takes a different design path by viewing all the flows together as a whole: each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. A framework of virtual estimators is designed to apply the idea of sharing to an array of cardinality estimation solutions, achi...

Science Big Bang comes to the Alps

CERN Multimedia

2008-01-01

The most extensive and expensive scientific instrument in history is due to start working this summer at Cern, the European particle physics laboratory near Geneva. Two beams of protons will accelerate in opposite directions around a 27km tunnel under the Alpine foothills until they are travelling almost at the speed of light - and then smash together, reproducing on a tiny scale the intense energy of the new-born universe after the inaugural Big Bang 15bn years ago.

Time, space, stars and man the story of the Big Bang

CERN Document Server

Woolfson, Michael M

2013-01-01

The three greatest scientific mysteries, which remain poorly understood, are the origin of the universe, the origin of life and the development of consciousness. This book describes the processes preceding the Big Bang, the creation of matter, the concentration of that matter into stars and planets, the development of simple life forms and the theory of evolution that has given higher life forms, including mankind. Readership: Members of the general public who have an interest in popular science. There are many popular and excellent science books that present various aspects of science. However, this book follows a narrow scientific pathway from the Big Bang to mankind, and depicts the causal relationship between each step and the next. The science covered will be enough to satisfy most readers. Many important areas of science are dealt with, and these include cosmology, particle physics, atomic physics, galaxy and star formation, planet formation and aspects of evolution. The necessary science is described i...

Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology

Directory of Open Access Journals (Sweden)

Brittany M. Salazar

2016-12-01

Full Text Available Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring “big data” applications in pediatric oncology. Computational strategies derived from big data science–network- and machine learning-based modeling and drug repositioning—hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which “big data” and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.

e-Science Paradigm for Astroparticle Physics at KISTI

Directory of Open Access Journals (Sweden)

Kihyeon Cho

2016-03-01

Full Text Available The Korea Institute of Science and Technology Information (KISTI has been studying the e-Science paradigm. With its successful application to particle physics, we consider the application of the paradigm to astroparticle physics. The Standard Model of particle physics is still not considered perfect even though the Higgs boson has recently been discovered. Astrophysical evidence shows that dark matter exists in the universe, hinting at new physics beyond the Standard Model. Therefore, there are efforts to search for dark matter candidates using direct detection, indirect detection, and collider detection. There are also efforts to build theoretical models for dark matter. Current astroparticle physics involves big investments in theories and computing along with experiments. The complexity of such an area of research is explained within the framework of the e-Science paradigm. The idea of the e-Science paradigm is to unify experiment, theory, and computing. The purpose is to study astroparticle physics anytime and anywhere. In this paper, an example of the application of the paradigm to astrophysics is presented.

Reflection on Quality Assurance System of Higher Vocational Education under Big Data Era

Directory of Open Access Journals (Sweden)

Jiang Xinlan

2015-01-01

Full Text Available Big data has the features like Volume, Variety, Value and Velocity. Here come the new opportunities and challenges for construction of Chinese quality assurance system of higher vocational education under big data era. There are problems in current quality assurance system of higher vocational education, such as imperfect main body, non-formation of internally and externally incorporated quality assurance system, non-scientific security standard and insufficiency in security investment. The construction of higher vocational education under big data era requires a change in the idea of quality assurance system construction to realize the multiple main bodies and multiple layers development trend for educational quality assurance system, and strengthen the construction of information platform for quality assurance system.

Communicating Science

Science.gov (United States)

Russell, Nicholas

2009-10-01

Introduction: what this book is about and why you might want to read it; Prologue: three orphans share a common paternity: professional science communication, popular journalism, and literary fiction are not as separate as they seem; Part I. Professional Science Communication: 1. Spreading the word: the endless struggle to publish professional science; 2. Walk like an Egyptian: the alien feeling of professional science writing; 3. The future's bright? Professional science communication in the age of the internet; 4. Counting the horse's teeth: professional standards in science's barter economy; 5. Separating the wheat from the chaff: peer review on trial; Part II. Science for the Public: What Science Do People Need and How Might They Get It?: 6. The Public Understanding of Science (PUS) movement and its problems; 7. Public engagement with science and technology (PEST): fine principle, difficult practice; 8. Citizen scientists? Democratic input into science policy; 9. Teaching and learning science in schools: implications for popular science communication; Part III. Popular Science Communication: The Press and Broadcasting: 10. What every scientist should know about mass media; 11. What every scientist should know about journalists; 12. The influence of new media; 13. How the media represents science; 14. How should science journalists behave?; Part IV. The Origins of Science in Cultural Context: Five Historic Dramas: 15. A terrible storm in Wittenberg: natural knowledge through sorcery and evil; 16. A terrible storm in the Mediterranean: controlling nature with white magic and religion; 17. Thieving magpies: the subtle art of false projecting; 18. Foolish virtuosi: natural philosophy emerges as a distinct discipline but many cannot take it seriously; 19. Is scientific knowledge 'true' or should it just be 'truthfully' deployed?; Part V. Science in Literature: 20. Science and the Gothic: the three big nineteenth-century monster stories; 21. Science fiction: serious

Science Fiction and the Big Questions

Science.gov (United States)

O'Keefe, M.

Advocates of space science promote investment in science education and the development of new technologies necessary for space travel. Success in these areas requires an increase of interest and support among the general public. What role can entertainment media play in inspiring the public ­ especially young people ­ to support the development of space science? Such inspiration is badly needed. Science education and funding in the United States are in a state of crisis. This bleak situation exists during a boom in the popularity of science-oriented television shows and science fiction movies. This paper draws on interviews with professionals in science, technology, engineering and mathematics (STEM) fields, as well as students interested in those fields. The interviewees were asked about their lifelong media-viewing habits. Analysis of these interviews, along with examples from popular culture, suggests that science fiction can be a valuable tool for space advocates. Specifically, the aspects of character, story, and special effects can provide viewers with inspiration and a sense of wonder regarding space science and the prospect of long-term human space exploration.

Is it possible to give scientific solutions to Grand Challenges? On the idea of grand challenges for life science research.

Science.gov (United States)

Efstathiou, Sophia

2016-04-01

This paper argues that challenges that are grand in scope such as "lifelong health and wellbeing", "climate action", or "food security" cannot be addressed through scientific research only. Indeed scientific research could inhibit addressing such challenges if scientific analysis constrains the multiple possible understandings of these challenges into already available scientific categories and concepts without translating between these and everyday concerns. This argument builds on work in philosophy of science and race to postulate a process through which non-scientific notions become part of science. My aim is to make this process available to scrutiny: what I call founding everyday ideas in science is both culturally and epistemologically conditioned. Founding transforms a common idea into one or more scientifically relevant ones, which can be articulated into descriptively thicker and evaluatively deflated terms and enable operationalisation and measurement. The risk of founding however is that it can invisibilise or exclude from realms of scientific scrutiny interpretations that are deemed irrelevant, uninteresting or nonsensical in the domain in question-but which may remain salient for addressing grand-in-scope challenges. The paper considers concepts of "wellbeing" in development economics versus in gerontology to illustrate this process. Copyright © 2015 Elsevier Ltd. All rights reserved.

Science: Big Bang comes to the Alps

CERN Multimedia

Cookson, Clive

2008-01-01

"The most extensive and expensive scientific instrument in history is due to start working this summer at CERN, the European particle physics laboratory near Geneva. Two beams of protons will accelerate in opposite directions around a 27 km tunnel under the alpine foothills until they are travelling almost at the speed of light - and then smash together, reproducing on a tiny scale the intense energy of the new-born universe after the inaugural Big Bang 15bn years ago. (1 page)

The Promise and Potential Perils of Big Data for Advancing Symptom Management Research in Populations at Risk for Health Disparities.

Science.gov (United States)

Bakken, Suzanne; Reame, Nancy

2016-01-01

Symptom management research is a core area of nursing science and one of the priorities for the National Institute of Nursing Research, which specifically focuses on understanding the biological and behavioral aspects of symptoms such as pain and fatigue, with the goal of developing new knowledge and new strategies for improving patient health and quality of life. The types and volume of data related to the symptom experience, symptom management strategies, and outcomes are increasingly accessible for research. Traditional data streams are now complemented by consumer-generated (i.e., quantified self) and "omic" data streams. Thus, the data available for symptom science can be considered big data. The purposes of this chapter are to (a) briefly summarize the current drivers for the use of big data in research; (b) describe the promise of big data and associated data science methods for advancing symptom management research; (c) explicate the potential perils of big data and data science from the perspective of the ethical principles of autonomy, beneficence, and justice; and (d) illustrate strategies for balancing the promise and the perils of big data through a case study of a community at high risk for health disparities. Big data and associated data science methods offer the promise of multidimensional data sources and new methods to address significant research gaps in symptom management. If nurse scientists wish to apply big data and data science methods to advance symptom management research and promote health equity, they must carefully consider both the promise and perils.

BIG: a large-scale data integration tool for renal physiology.

Science.gov (United States)

Zhao, Yue; Yang, Chin-Rang; Raghuram, Viswanathan; Parulekar, Jaya; Knepper, Mark A

2016-10-01

Due to recent advances in high-throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: "How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?" This is the type of problem that has motivated the "Big-Data" revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/.

Translating Big Data into Big Climate Ideas: Communicating Future Climate Scenarios to Increase Interdisciplinary Engagement.

Science.gov (United States)

Climate change has emerged as the significant environmental challenge of the 21st century. Therefore, understanding our changing world has forced researchers from many different fields of science to join together to tackle complicated research questions. The climate change resear...

Big data analysis new algorithms for a new society

CERN Document Server

Stefanowski, Jerzy

2016-01-01

This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued...

Big data, advanced analytics and the future of comparative effectiveness research.

Science.gov (United States)

Berger, Marc L; Doban, Vitalii

2014-03-01

The intense competition that accompanied the growth of internet-based companies ushered in the era of 'big data' characterized by major innovations in processing of very large amounts of data and the application of advanced analytics including data mining and machine learning. Healthcare is on the cusp of its own era of big data, catalyzed by the changing regulatory and competitive environments, fueled by growing adoption of electronic health records, as well as efforts to integrate medical claims, electronic health records and other novel data sources. Applying the lessons from big data pioneers will require healthcare and life science organizations to make investments in new hardware and software, as well as in individuals with different skills. For life science companies, this will impact the entire pharmaceutical value chain from early research to postcommercialization support. More generally, this will revolutionize comparative effectiveness research.

DEVELOPING THE TRANSDISCIPLINARY AGING RESEARCH AGENDA: NEW DEVELOPMENTS IN BIG DATA.

Science.gov (United States)

Callaghan, Christian William

2017-07-19

In light of dramatic advances in big data analytics and the application of these advances in certain scientific fields, new potentialities exist for breakthroughs in aging research. Translating these new potentialities to research outcomes for aging populations, however, remains a challenge, as underlying technologies which have enabled exponential increases in 'big data' have not yet enabled a commensurate era of 'big knowledge,' or similarly exponential increases in biomedical breakthroughs. Debates also reveal differences in the literature, with some arguing big data analytics heralds a new era associated with the 'end of theory' or which makes the scientific method obsolete, where correlation supercedes causation, whereby science can advance without theory and hypotheses testing. On the other hand, others argue theory cannot be subordinate to data, no matter how comprehensive data coverage can ultimately become. Given these two tensions, namely between exponential increases in data absent exponential increases in biomedical research outputs, and between the promise of comprehensive data coverage and data-driven inductive versus theory-driven deductive modes of enquiry, this paper seeks to provide a critical review of certain theory and literature that offers useful perspectives of certain developments in big data analytics and their theoretical implications for aging research. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Lowering the barriers for accessing distributed geospatial big data to advance spatial data science: the PolarHub solution

Science.gov (United States)

Li, W.

2017-12-01

Data is the crux of science. The widespread availability of big data today is of particular importance for fostering new forms of geospatial innovation. This paper reports a state-of-the-art solution that addresses a key cyberinfrastructure research problem—providing ready access to big, distributed geospatial data resources on the Web. We first formulate this data-access problem and introduce its indispensable elements, including identifying the cyber-location, space and time coverage, theme, and quality of the dataset. We then propose strategies to tackle each data-access issue and make the data more discoverable and usable for geospatial data users and decision makers. Among these strategies is large-scale web crawling as a key technique to support automatic collection of online geospatial data that are highly distributed, intrinsically heterogeneous, and known to be dynamic. To better understand the content and scientific meanings of the data, methods including space-time filtering, ontology-based thematic classification, and service quality evaluation are incorporated. To serve a broad scientific user community, these techniques are integrated into an operational data crawling system, PolarHub, which is also an important cyberinfrastructure building block to support effective data discovery. A series of experiments were conducted to demonstrate the outstanding performance of the PolarHub system. We expect this work to contribute significantly in building the theoretical and methodological foundation for data-driven geography and the emerging spatial data science.

Big Data, epistemology and causality: Knowledge in and knowledge out in EXPOsOMICS

Directory of Open Access Journals (Sweden)

Stefano Canali

2016-09-01

Full Text Available Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data.

[Application of big data analyses for musculoskeletal cell differentiation].

Science.gov (United States)

Imai, Yuuki

2016-04-01

Next generation sequencer has strongly progress big data analyses in life science. Among various kinds of sequencing data sets, epigenetic platform has just been important key to clarify the questions on broad and detail phenomenon in various forms of life. In this report, it is introduced that the research on identification of novel transcription factors in osteoclastogenesis using DNase-seq. Big data on musculoskeletal research will be organized by IFMRS and is getting more crucial.

The ethics of big data as a public good: which public? Whose good?

Science.gov (United States)

Taylor, Linnet

2016-12-28

International development and humanitarian organizations are increasingly calling for digital data to be treated as a public good because of its value in supplementing scarce national statistics and informing interventions, including in emergencies. In response to this claim, a 'responsible data' movement has evolved to discuss guidelines and frameworks that will establish ethical principles for data sharing. However, this movement is not gaining traction with those who hold the highest-value data, particularly mobile network operators who are proving reluctant to make data collected in low- and middle-income countries accessible through intermediaries. This paper evaluates how the argument for 'data as a public good' fits with the corporate reality of big data, exploring existing models for data sharing. I draw on the idea of corporate data as an ecosystem involving often conflicting rights, duties and claims, in comparison to the utilitarian claim that data's humanitarian value makes it imperative to share them. I assess the power dynamics implied by the idea of data as a public good, and how differing incentives lead actors to adopt particular ethical positions with regard to the use of data.This article is part of the themed issue 'The ethical impact of data science'. © 2016 The Author(s).

The Great Plains IDEA Gerontology Program: An Online, Interinstitutional Graduate Degree

Science.gov (United States)

Sanders, Gregory F.

2011-01-01

The Great-Plains IDEA Gerontology Program is a graduate program developed and implemented by the Great Plains Interactive Distance Education Alliance (Great Plains IDEA). The Great Plains IDEA (Alliance) originated as a consortium of Colleges of Human Sciences ranging across the central United States. This Alliance's accomplishments have included…

How Big Are "Martin's Big Words"? Thinking Big about the Future.

Science.gov (United States)

Gardner, Traci

"Martin's Big Words: The Life of Dr. Martin Luther King, Jr." tells of King's childhood determination to use "big words" through biographical information and quotations. In this lesson, students in grades 3 to 5 explore information on Dr. King to think about his "big" words, then they write about their own…

Cardiovascular proteomics in the era of big data: experimental and computational advances.

Science.gov (United States)

Lam, Maggie P Y; Lau, Edward; Ng, Dominic C M; Wang, Ding; Ping, Peipei

2016-01-01

Proteomics plays an increasingly important role in our quest to understand cardiovascular biology. Fueled by analytical and computational advances in the past decade, proteomics applications can now go beyond merely inventorying protein species, and address sophisticated questions on cardiac physiology. The advent of massive mass spectrometry datasets has in turn led to increasing intersection between proteomics and big data science. Here we review new frontiers in technological developments and their applications to cardiovascular medicine. The impact of big data science on cardiovascular proteomics investigations and translation to medicine is highlighted.

Research Ethics in Big Data.

Science.gov (United States)

Hammer, Marilyn J

2017-05-01

The ethical conduct of research includes, in part, patient agreement to participate in studies and the protection of health information. In the evolving world of data science and the accessibility of large quantities of web-based data created by millions of individuals, novel methodologic approaches to answering research questions are emerging. This article explores research ethics in the context of big data.

Adapting bioinformatics curricula for big data

Science.gov (United States)

Greene, Anna C.; Giffin, Kristine A.; Greene, Casey S.

2016-01-01

Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are changing in the era of big data. We identify key competencies that scientists dealing with big data are expected to possess across fields, and we use this information to propose courses to meet these growing needs. While bioinformatics programs have traditionally trained students in data-intensive science, we identify areas of particular biological, computational and statistical emphasis important for this era that can be incorporated into existing curricula. For each area, we propose a course structured around these topics, which can be adapted in whole or in parts into existing curricula. In summary, specific challenges associated with big data provide an important opportunity to update existing curricula, but we do not foresee a wholesale redesign of bioinformatics training programs. PMID:25829469

Georg Cantor i idea jedności nauki

Directory of Open Access Journals (Sweden)

Jerzy Dadaczyński

2009-06-01

Full Text Available G. Cantor presented - in an unpublished paper (1884 - a vision of the unity of science. He argued all sciences can be reduced directly to the set theory. A source of this idea was for Cantor the unity of mathematics (on the basis of set theory. Cantor represented thesis about the unity of science irrespective of the representatives of positivism (E. Mach.

[The Big Data Game : On the Ludic Constitution of the Collaborative Production of Knowledge in High-Energy Physics at CERN].

Science.gov (United States)

Dippel, Anne

2017-12-01

This article looks at how games and play contribute to the big data-driven production of knowledge in High-Energy Physics, with a particular focus on the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN), where the author has been conducting anthropological fieldwork since 2014. The ludic (playful) aspect of knowledge production is analyzed here in three different dimensions: the Symbolic, the Ontological, and the Epistemic. The first one points towards CERN as place where a cosmological game of probability is played with the help of Monte-Carlo simulations. The second one can be seen in the agonistic infrastructures of competing experimental collaborations. The third dimension unfolds in ludic platforms, such as online Challenges and citizen science games, which contribute to the development of machine learning algorithms, whose function is necessary in order to process the huge amount of data gathered from experimental events. Following Clifford Geertz, CERN itself is characterized as a site of deep play, a concept that contributes to understanding wider social and cultural orders through the analysis of ludic collective phenomena. The article also engages with Peter Galison's idea of the trading zone, proposing to comprehend it in the age of big data as a Playground. Thus the author hopes to contribute to a wider discussion in the historiographical and social study of science and technology, as well as in cultural anthropology, by recognizing the ludic in science as a central element of understanding collaborative knowledge production.

Accelerators: Sparking Innovation and Transdisciplinary Team Science in Disparities Research

Science.gov (United States)

Horowitz, Carol R.; Shameer, Khader; Gabrilove, Janice; Atreja, Ashish; Shepard, Peggy; Goytia, Crispin N.; Smith, Geoffrey W.; Dudley, Joel; Manning, Rachel; Bickell, Nina A.; Galvez, Maida P.

2017-01-01

Development and implementation of effective, sustainable, and scalable interventions that advance equity could be propelled by innovative and inclusive partnerships. Readied catalytic frameworks that foster communication, collaboration, a shared vision, and transformative translational research across scientific and non-scientific divides are needed to foster rapid generation of novel solutions to address and ultimately eliminate disparities. To achieve this, we transformed and expanded a community-academic board into a translational science board with members from public, academic and private sectors. Rooted in team science, diverse board experts formed topic-specific “accelerators”, tasked with collaborating to rapidly generate new ideas, questions, approaches, and projects comprising patients, advocates, clinicians, researchers, funders, public health and industry leaders. We began with four accelerators—digital health, big data, genomics and environmental health—and were rapidly able to respond to funding opportunities, transform new ideas into clinical and community programs, generate new, accessible, actionable data, and more efficiently and effectively conduct research. This innovative model has the power to maximize research quality and efficiency, improve patient care and engagement, optimize data democratization and dissemination among target populations, contribute to policy, and lead to systems changes needed to address the root causes of disparities. PMID:28241508

Delivering Science from Big Data

Science.gov (United States)

Quinn, Peter Joseph

2015-08-01

The SKA will be capable of producing a stream of science data products that are Exa-scale in terms of their storage and processing requirements. This Google-scale enterprise is attracting considerable international interest and excitement from within the industrial and academic communities. In this paper we examine the data flow, storage and processing requirements of a number of key SKA survey science projects to be executed on the baseline SKA1 configuration. Based on a set of conservative assumptions about trends for HPC and storage costs, and the data flow process within the SKA Observatory, it is apparent that survey projects of the scale proposed will potentially drive construction and operations costs beyond the current anticipated SKA1 budget. This implies a sharing of the resources and costs to deliver SKA science between the community and what is contained within the SKA Observatory. A similar situation was apparent to the designers of the LHC more than 10 years ago. We propose that it is time for the SKA project and broader community to consider the effort and process needed to design and implement a distributed science data system that leans on the lessons of other projects and looks to recent developments in Cloud technologies to ensure an affordable, effective and global achievement of science goals.

Combining Ideas in Crowdsourced Idea Generation

Directory of Open Access Journals (Sweden)

Wang Kai

2017-02-01

Full Text Available Collecting ideas through crowdsourcing has become a common practice for companies to benefit from external ideas and innovate. It is desirable that crowd members build on each other's ideas to achieve synergy. This study proposes and verifies a new method for idea combination which can result in combined ideas that are both novel and useful. The domain-specific knowledge of crowd members does not influence the effectiveness of such idea combination. The new method can be used for collecting highly creative ideas from the crowd. The implications for future research are discussed.

Implementing the “Big Data” Concept in Official Statistics

Directory of Open Access Journals (Sweden)

О. V.

2017-02-01

Full Text Available Big data is a huge resource that needs to be used at all levels of economic planning. The article is devoted to the study of the development of the concept of “Big Data” in the world and its impact on the transformation of statistical simulation of economic processes. Statistics at the current stage should take into account the complex system of international economic relations, which functions in the conditions of globalization and brings new forms of economic development in small open economies. Statistical science should take into account such phenomena as gig-economy, common economy, institutional factors, etc. The concept of “Big Data” and open data are analyzed, problems of implementation of “Big Data” in the official statistics are shown. The ways of implementation of “Big Data” in the official statistics of Ukraine through active use of technological opportunities of mobile operators, navigation systems, surveillance cameras, social networks, etc. are presented. The possibilities of using “Big Data” in different sectors of the economy, also on the level of companies are shown. The problems of storage of large volumes of data are highlighted. The study shows that “Big Data” is a huge resource that should be used across the Ukrainian economy.

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

OpenAIRE

Zhuge, Hai

2015-01-01

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing ...

US Cosmic Visions: New Ideas in Dark Matter 2017 : Community Report

Energy Technology Data Exchange (ETDEWEB)

Feng, J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Fox, P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Dawson, W. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Ammons, M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Axelrod, T. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Chapline, G. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Drlica-Wagner, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Golovich, N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Schneider, M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-05-08

This white paper summarizes the workshop “U.S. Cosmic Visions: New Ideas in Dark Matter” held at University of Maryland from March 23-25. The flagships of the US Dark Matter search program are the G2 experiments ADMX, LZ, and SuperCDMS, which will cover well-motivated axion and WIMP dark matter over a range of masses. The workshop assumes that a complete exploration of this parameter space remains the highest priority of the dark matter community, and focuses instead on the science case for additional new small-scale projects in dark matter science that complement the G2 program (and other ongoing projects worldwide). It therefore concentrates on exploring distinct, well-motivated parameter space that will not be covered by the existing program; on surveying ideas for such projects (i.e. projects costing ~$10M or less); and on placing these ideas in a global context. The workshop included over 100 presentations of new ideas, proposals and recent science and R&D results from the US and international scientific community.

Cartography in the Age of Spatio-temporal Big Data

Directory of Open Access Journals (Sweden)

WANG Jiayao

2017-10-01

Full Text Available Cartography is an ancient science with almost the same long history as the world's oldest culture.Since ancient times,the movement and change of anything and any phenomena,including human activities,have been carried out in a certain time and space.The development of science and technology and the progress of social civilization have made social management and governance more and more dependent on time and space.The information source,theme,content,carrier,form,production methods and application methods of map are different in different historical periods,so that its all-round value is different. With the arrival of the big data age,the scientific paradigm has now entered the era of "data-intensive" paradigm,so is the cartography,with obvious characteristics of big data science.All big data are caused by movement and change of all things and phenomena in the geographic world,so they have space and time characteristics and thus cannot be separated from the spatial reference and time reference.Therefore,big data is big spatio-temporal data essentially.Since the late 1950s and early 1960s,modern cartography,that is,the cartography in the information age,takes spatio-temporal data as the object,and focuses on the processing and expression of spatio-temporal data,but not in the face of the large scale multi-source heterogeneous and multi-dimensional dynamic data flow(or flow datafrom sky to the sea.The real-time dynamic nature,the theme pertinence,the content complexity,the carrier diversification,the expression form personalization,the production method modernization,the application ubiquity of the map,is incomparable in the past period,which leads to the great changes of the theory,technology and application system of cartography.And all these changes happen to occur in the 60 years since the late 1950s and early 1960s,so this article was written to commemorate the 60th anniversary of the "Acta Geodaetica et Cartographica Sinica".

Reviews Book: Extended Project Student Guide Book: My Inventions Book: ASE Guide to Research in Science Education Classroom Video: The Science of Starlight Software: SPARKvue Book: The Geek Manifesto Ebook: A Big Ball of Fire Apps

Science.gov (United States)

2014-05-01

WE RECOMMEND Level 3 Extended Project Student Guide A non-specialist, generally useful and nicely put together guide to project work ASE Guide to Research in Science Education Few words wasted in this handy introduction and reference The Science of Starlight Slow but steady DVD covers useful ground SPARKvue Impressive software now available as an app WORTH A LOOK My Inventions and Other Writings Science, engineering, autobiography, visions and psychic phenomena mixed in a strange but revealing concoction The Geek Manifesto: Why Science Matters More enthusiasm than science, but a good motivator and interesting A Big Ball of Fire: Your questions about the Sun answered Free iTunes download made by and for students goes down well APPS Collider visualises LHC experiments ... Science Museum app enhances school trips ... useful information for the Cambridge Science Festival

Complementary Social Science?

DEFF Research Database (Denmark)

Blok, Anders; Pedersen, Morten Axel

2014-01-01

of measurement device deployed. At the same time, however, we also expect new interferences and polyphonies to arise at the intersection of Big and Small Data, provided that these are, so to speak, mixed with care. These questions, we stress, are important not only for the future of social science methods......The rise of Big Data in the social realm poses significant questions at the intersection of science, technology, and society, including in terms of how new large-scale social databases are currently changing the methods, epistemologies, and politics of social science. In this commentary, we address...

Is big data risk assessment a novelty?

NARCIS (Netherlands)

Swuste, P.H.J.J.

2016-01-01

Objective: What metaphors, models and theories were developed in the safety science domain? And which research was based upon ‘big data’? Method: The study was confined to original articles and documents, written in English or Dutch from the period under consideration. Results and conclusions: From

The phytotronist and the phenotype: plant physiology, Big Science, and a Cold War biology of the whole plant.

Science.gov (United States)

Munns, David P D

2015-04-01

This paper describes how, from the early twentieth century, and especially in the early Cold War era, the plant physiologists considered their discipline ideally suited among all the plant sciences to study and explain biological functions and processes, and ranked their discipline among the dominant forms of the biological sciences. At their apex in the late-1960s, the plant physiologists laid claim to having discovered nothing less than the "basic laws of physiology." This paper unwraps that claim, showing that it emerged from the construction of monumental big science laboratories known as phytotrons that gave control over the growing environment. Control meant that plant physiologists claimed to be able to produce a standard phenotype valid for experimental biology. Invoking the standards of the physical sciences, the plant physiologists heralded basic biological science from the phytotronic produced phenotype. In the context of the Cold War era, the ability to pursue basic science represented the highest pinnacle of standing within the scientific community. More broadly, I suggest that by recovering the history of an underappreciated discipline, plant physiology, and by establishing the centrality of the story of the plant sciences in the history of biology can historians understand the massive changes wrought to biology by the conceptual emergence of the molecular understanding of life, the dominance of the discipline of molecular biology, and the rise of biotechnology in the 1980s. Copyright © 2015 Elsevier Ltd. All rights reserved.

Recht voor big data, big data voor recht

NARCIS (Netherlands)

Lafarre, Anne

Big data is een niet meer weg te denken fenomeen in onze maatschappij. Het is de hype cycle voorbij en de eerste implementaties van big data-technieken worden uitgevoerd. Maar wat is nu precies big data? Wat houden de vijf V's in die vaak genoemd worden in relatie tot big data? Ter inleiding van

Passport to the Big Bang moves across the road

CERN Document Server

Corinne Pralavorio

2015-01-01

The ATLAS platform of the Passport to the Big Bang circuit has been relocated in front of the CERN Reception.   The ATLAS platform of the Passport to the Big Bang, outside the CERN Reception building. The Passport to the Big Bang platform of the ATLAS Experiment has been moved in front of the CERN Reception to make it more visible and accessible. It had to be dismantled and moved from its previous location in the garden of the Globe of Science and Innovation due to the major refurbishment work in progress on the Globe, and is now fully operational in its new location on the other side of the road, in the Main Reception car-park. The Passport to the Big Bang circuit, inaugurated in 2013, comprises ten platforms installed in front of ten CERN sites and aims to help local residents and visitors to the region understand CERN's research. Dedicated Passport to the Big Bang flyers, containing all necessary information and riddles for you to solve, are available at the CERN Rec...

2. A Circle of ideas

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 2. Geometry A Circle of Ideas. Kapil H Paranjape. Series Article Volume 1 Issue 2 February 1996 pp 26-31. Fulltext. Click here to view fulltext PDF. Permanent link: https://www.ias.ac.in/article/fulltext/reso/001/02/0026-0031. Author Affiliations.

Towards cloud based big data analytics for smart future cities

OpenAIRE

Khan, Zaheer; Anjum, Ashiq; Soomro, Kamran; Tahir, Muhammad

2015-01-01

A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A proto...

Enhancing Teachers' Awareness About Relations Between Science and Religion. The Debate Between Steady State and Big Bang Theories

Science.gov (United States)

Bagdonas, Alexandre; Silva, Cibelle Celestino

2015-11-01

Educators advocate that science education can help the development of more responsible worldviews when students learn not only scientific concepts, but also about science, or "nature of science". Cosmology can help the formation of worldviews because this topic is embedded in socio-cultural and religious issues. Indeed, during the Cold War period, the cosmological controversy between Big Bang and Steady State theory was tied up with political and religious arguments. The present paper discusses a didactic sequence developed for and applied in a pre-service science teacher-training course on history of science. After studying the historical case, pre-service science teachers discussed how to deal with possible conflicts between scientific views and students' personal worldviews related to religion. The course focused on the study of primary and secondary sources about cosmology and religion written by cosmologists such as Georges Lemaître, Fred Hoyle and the Pope Pius XII. We used didactic strategies such as short seminars given by groups of pre-service teachers, videos, computer simulations, role-play, debates and preparation of written essays. Along the course, most pre-service teachers emphasized differences between science and religion and pointed out that they do not feel prepared to conduct classroom discussions about this topic. Discussing the relations between science and religion using the history of cosmology turned into an effective way to teach not only science concepts but also to stimulate reflections about nature of science. This topic may contribute to increasing students' critical stance on controversial issues, without the need to explicitly defend certain positions, or disapprove students' cultural traditions. Moreover, pre-service teachers practiced didactic strategies to deal with this kind of unusual content.

A Guided Inquiry on Hubble Plots and the Big Bang

Science.gov (United States)

Forringer, Ted

2014-01-01

In our science for non-science majors course "21st Century Physics," we investigate modern "Hubble plots" (plots of velocity versus distance for deep space objects) in order to discuss the Big Bang, dark matter, and dark energy. There are two potential challenges that our students face when encountering these topics for the…

Quantum nature of the big bang: An analytical and numerical investigation

International Nuclear Information System (INIS)

Ashtekar, Abhay; Pawlowski, Tomasz; Singh, Parampreet

2006-01-01

Analytical and numerical methods are developed to analyze the quantum nature of the big bang in the setting of loop quantum cosmology. They enable one to explore the effects of quantum geometry both on the gravitational and matter sectors and significantly extend the known results on the resolution of the big bang singularity. Specifically, the following results are established for the homogeneous isotropic model with a massless scalar field: (i) the scalar field is shown to serve as an internal clock, thereby providing a detailed realization of the 'emergent time' idea; (ii) the physical Hilbert space, Dirac observables, and semiclassical states are constructed rigorously; (iii) the Hamiltonian constraint is solved numerically to show that the big bang is replaced by a big bounce. Thanks to the nonperturbative, background independent methods, unlike in other approaches the quantum evolution is deterministic across the deep Planck regime. Our constructions also provide a conceptual framework and technical tools which can be used in more general models. In this sense, they provide foundations for analyzing physical issues associated with the Planck regime of loop quantum cosmology as a whole

A Big Data Task Force Review of Advances in Data Access and Discovery Within the Science Disciplines of the NASA Science Mission Directorate (SMD)

Science.gov (United States)

Walker, R. J.; Beebe, R. F.

2017-12-01

One of the basic problems the NASA Science Mission Directorate (SMD) faces when dealing with preservation of scientific data is the variety of the data. This stems from the fact that NASA's involvement in the sciences spans a broad range of disciplines across the Science Mission Directorate: Astrophysics, Earth Sciences, Heliophysics and Planetary Science. As the ability of some missions to produce large data volumes has accelerated, the range of problems associated with providing adequate access to the data has demanded diverse approaches for data access. Although mission types, complexity and duration vary across the disciplines, the data can be characterized by four characteristics: velocity, veracity, volume, and variety. The rate of arrival of the data (velocity) must be addressed at the individual mission level, validation and documentation of the data (veracity), data volume and the wide variety of data products present huge challenges as the science disciplines strive to provide transparent access to their available data. Astrophysics, supports an integrated system of data archives based on frequencies covered (UV, visible, IR, etc.) or subject areas (extrasolar planets, extra galactic, etc.) and is accessed through the Astrophysics Data Center (https://science.nasa.gov/astrophysics/astrophysics-data-centers/). Earth Science supports the Earth Observing System (https://earthdata.nasa.gov/) that manages the earth science satellite data. The discipline supports 12 Distributed Active Archive Centers. Heliophysics provides the Space Physics Data Facility (https://spdf.gsfc.nasa.gov/) that supports the heliophysics community and Solar Data Analysis Center (https://umbra.nascom.nasa.gov/index.html) that allows access to the solar data. The Planetary Data System (https://pds.nasa.gov) is the main archive for planetary science data. It consists of science discipline nodes (Atmospheres, Geosciences, Cartography and Imaging Sciences, Planetary Plasma Interactions

Challenges of Big Data in Educational Assessment

Science.gov (United States)

Gibson, David C.; Webb, Mary; Ifenthaler, Dirk

2015-01-01

This paper briefly discusses four measurement challenges of data science or "big data" in educational assessments that are enabled by technology: 1. Dealing with change over time via time-based data. 2. How a digital performance space's relationships interact with learner actions, communications and products. 3. How layers of…

Technical challenges for big data in biomedicine and health: data sources, infrastructure, and analytics.

Science.gov (United States)

Peek, N; Holmes, J H; Sun, J

2014-08-15

To review technical and methodological challenges for big data research in biomedicine and health. We discuss sources of big datasets, survey infrastructures for big data storage and big data processing, and describe the main challenges that arise when analyzing big data. The life and biomedical sciences are massively contributing to the big data revolution through secondary use of data that were collected during routine care and through new data sources such as social media. Efficient processing of big datasets is typically achieved by distributing computation over a cluster of computers. Data analysts should be aware of pitfalls related to big data such as bias in routine care data and the risk of false-positive findings in high-dimensional datasets. The major challenge for the near future is to transform analytical methods that are used in the biomedical and health domain, to fit the distributed storage and processing model that is required to handle big data, while ensuring confidentiality of the data being analyzed.

Agrupamentos epistemológicos de artigos publicados sobre big data analytics

OpenAIRE

FURLAN, Patricia Kuzmenko; LAURINDO, Fernando José Barbin

2017-01-01

Resumo A era do big data já é realidade para empresas e indivíduos, e a literatura acadêmica sobre o tema tem crescido rapidamente nos últimos anos. Neste artigo, pretendeu-se identificar quais são os principais nichos e vertentes de publicação sobre o big data analytics. A opção metodológica foi realizar pesquisa bibliométrica na base de dados ISI Web of Science, utilizando-se aquele termo para focar as práticas de gestão de big data. Foi possível identificar cinco grupos distintos dentre os...

Generating a hot big bang via a change in topology

International Nuclear Information System (INIS)

Kandvup, H.E.

1990-01-01

This paper uses ideas developed recently in semiclassical quantum gravity to argue that many qualitative features of the hot big bang generally assumed in cosmology may be explained by the hypothesis that, interpreted semiclassically, the universe tunnelled into being via a quantum fluctuation from a small (Planck-sized), topologically complex entity to a topologically trivial entity (like a Friedmann universe) that rapidly grew to a more macroscopic size

Generating a hot big bang via a change in topology

Energy Technology Data Exchange (ETDEWEB)

Kandvup, H.E. (Florida Univ., Gainesville, FL (USA). Space Astronomy Lab.); Masur, P.O. (Institute for Fundamental Theory, Univ. of Florida, Gainesville, FL (US))

1990-08-01

This paper uses ideas developed recently in semiclassical quantum gravity to argue that many qualitative features of the hot big bang generally assumed in cosmology may be explained by the hypothesis that, interpreted semiclassically, the universe tunnelled into being via a quantum fluctuation from a small (Planck-sized), topologically complex entity to a topologically trivial entity (like a Friedmann universe) that rapidly grew to a more macroscopic size.

Creation a Geo Big Data Outreach and Training Collaboratory for Wildfire Community

Science.gov (United States)

Altintas, I.; Sale, J.; Block, J.; Cowart, C.; Crawl, D.

2015-12-01

A major challenge for the geoscience community is the training and education of current and next generation big data geoscientists. In wildfire research, there are an increasing number of tools, middleware and techniques to use for data science related to wildfires. The necessary computing infrastructures are often within reach and most of the software tools for big data are freely available. But what has been lacking is a transparent platform and training program to produce data science experts who can use these integrated tools effectively. Scientists well versed to take advantage of big data technologies in geoscience applications is of critical importance to the future of research and knowledge advancement. To address this critical need, we are developing learning modules to teach process-based thinking to capture the value of end-to-end systems of reusable blocks of knowledge and integrate the tools and technologies used in big data analysis in an intuitive manner. WIFIRE is an end-to-end cyberinfrastructure for dynamic data-driven simulation, prediction and visualization of wildfire behavior.To this end, we are openly extending an environment we have built for "big data training" (biobigdata.ucsd.edu) to similar MOOC-based approaches to the wildfire community. We are building an environment that includes training modules for distributed platforms and systems, Big Data concepts, and scalable workflow tools, along with other basics of data science including data management, reproducibility and sharing of results. We also plan to provide teaching modules with analytical and dynamic data-driven wildfire behavior modeling case studies which address the needs not only of standards-based K-12 science education but also the needs of a well-educated and informed citizenry.Another part our outreach mission is to educate our community on all aspects of wildfire research. One of the most successful ways of accomplishing this is through high school and undergraduate

Addressing big data challenges for scientific data infrastructure

NARCIS (Netherlands)

Demchenko, Y.; Zhao, Z.; Grosso, P.; Wibisono, A.; de Laat, C.

2012-01-01

This paper discusses the challenges that are imposed by Big Data Science on the modern and future Scientific Data Infrastructure (SDI). The paper refers to different scientific communities to define requirements on data management, access control and security. The paper introduces the Scientific

Making a Big Bang on the small screen

Science.gov (United States)

Thomas, Nick

2010-01-01

While the quality of some TV sitcoms can leave viewers feeling cheated out of 30 minutes of their lives, audiences and critics are raving about the science-themed US comedy The Big Bang Theory. First shown on the CBS network in 2007, the series focuses on two brilliant postdoc physicists, Leonard and Sheldon, who are totally absorbed by science. Adhering to the stereotype, they also share a fanatical interest in science fiction, video-gaming and comic books, but unfortunately lack the social skills required to connect with their 20-something nonacademic contemporaries.

Small data in the era of big data

OpenAIRE

Kitchin, Rob; Lauriault, Tracey P.

2015-01-01

Academic knowledge building has progressed for the past few centuries using small data studies characterized by sampled data generated to answer specific questions. It is a strategy that has been remarkably successful, enabling the sciences, social sciences and humanities to advance in leaps and bounds. This approach is presently being challenged by the development of big data. Small data studies will however, we argue, continue to be popular and valuable in the fut...

Police Spatial Big Data Location Code and Its Application Prospect

Directory of Open Access Journals (Sweden)

HU Xiaoguang

2016-12-01

Full Text Available The rich decision-making basis are provided for police work by police spatial big data. But some challenges are also brought by it, such as:large data integration complex, multi scale information related difficulties, the location identification is not unique. Thus, how to make the data better service to the police work reform and development is a problem need to be study. In this paper, we propose location identification method to solve the existing problems. Based on subdivision grid, we design the location encoding method of police spatial big data, and choose domicile location identification as a case. Finally, the prospect of its application is presented. So, a new idea is proposed to solve the problem existing in the police spatial data organization and application.

Adapting bioinformatics curricula for big data.

Science.gov (United States)

Greene, Anna C; Giffin, Kristine A; Greene, Casey S; Moore, Jason H

2016-01-01

Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are changing in the era of big data. We identify key competencies that scientists dealing with big data are expected to possess across fields, and we use this information to propose courses to meet these growing needs. While bioinformatics programs have traditionally trained students in data-intensive science, we identify areas of particular biological, computational and statistical emphasis important for this era that can be incorporated into existing curricula. For each area, we propose a course structured around these topics, which can be adapted in whole or in parts into existing curricula. In summary, specific challenges associated with big data provide an important opportunity to update existing curricula, but we do not foresee a wholesale redesign of bioinformatics training programs. © The Author 2015. Published by Oxford University Press.

A Study of the Application of Big Data in a Rural Comprehensive Information Service

Directory of Open Access Journals (Sweden)

Leifeng Guo

2015-05-01

Full Text Available Big data has attracted extensive interest due to its potential tremendous social and scientific value. Researchers are also trying to extract potential value from agriculture big data. This paper presents a study of information services based on big data from the perspective of a rural comprehensive information service. First, we introduce the background of the rural comprehensive information service, and then we present in detail the National Rural Comprehensive Information Service Platform (NRCISP, which is supported by the national science and technology support program. Next, we discuss big data in the NRCISP according to data characteristics, data sources, and data processing. Finally, we discuss a service model and services based on big data in the NRCISP.

Ecological caring-Revisiting the original ideas of caring science.

Science.gov (United States)

Dahlberg, Helena; Ranheim, Albertine; Dahlberg, Karin

2016-01-01

The aim of this empirically grounded philosophical paper is to explore the notion of holistic care with the intention to expand it into a notion of ecological care and in such a way revisit the original ideas of caring science. The philosophical analysis, driven by lifeworld theory and especially Merleau-Ponty's philosophy, is firmly rooted in contemporary clinical care. We used interview data from patients in a study at an anthroposophic clinic in Sweden, which forms part of an ecological community with, for example, ecological agriculture. The empirical study is analysed according to reflective lifeworld research. Starting from the fact that illness can be defined as a loss of homelikeness in the body and in the familiar world, our findings illustrate how ecological care helps the patient to once again find one's place in a world that is characterized by interconnectedness. The task of ecological care is thus not only to see the patient within a world of relationships but to help the patient find his/her place again, to understand himself/herself and the world anew . Ecological care is not only about fighting an illness, but also recognizes a patient from inside a world that s/he is affected by and affects, that s/he is understood and understands from. Such care tries to restore this connection by making possible the rhythmical movement as well as the space in-between activity and rest, between being cared for and actively involving oneself in one's recovery and between closing oneself off from the world and once again going out into it.

Ecological caring—Revisiting the original ideas of caring science

Directory of Open Access Journals (Sweden)

Helena Dahlberg

2016-11-01

Full Text Available The aim of this empirically grounded philosophical paper is to explore the notion of holistic care with the intention to expand it into a notion of ecological care and in such a way revisit the original ideas of caring science. The philosophical analysis, driven by lifeworld theory and especially Merleau-Ponty's philosophy, is firmly rooted in contemporary clinical care. We used interview data from patients in a study at an anthroposophic clinic in Sweden, which forms part of an ecological community with, for example, ecological agriculture. The empirical study is analysed according to reflective lifeworld research. Starting from the fact that illness can be defined as a loss of homelikeness in the body and in the familiar world, our findings illustrate how ecological care helps the patient to once again find one's place in a world that is characterized by interconnectedness. The task of ecological care is thus not only to see the patient within a world of relationships but to help the patient find his/her place again, to understand himself/herself and the world anew. Ecological care is not only about fighting an illness, but also recognizes a patient from inside a world that s/he is affected by and affects, that s/he is understood and understands from. Such care tries to restore this connection by making possible the rhythmical movement as well as the space in-between activity and rest, between being cared for and actively involving oneself in one's recovery and between closing oneself off from the world and once again going out into it.

Ecological caring—Revisiting the original ideas of caring science

Science.gov (United States)

Dahlberg, Helena; Ranheim, Albertine; Dahlberg, Karin

2016-01-01

The aim of this empirically grounded philosophical paper is to explore the notion of holistic care with the intention to expand it into a notion of ecological care and in such a way revisit the original ideas of caring science. The philosophical analysis, driven by lifeworld theory and especially Merleau-Ponty's philosophy, is firmly rooted in contemporary clinical care. We used interview data from patients in a study at an anthroposophic clinic in Sweden, which forms part of an ecological community with, for example, ecological agriculture. The empirical study is analysed according to reflective lifeworld research. Starting from the fact that illness can be defined as a loss of homelikeness in the body and in the familiar world, our findings illustrate how ecological care helps the patient to once again find one's place in a world that is characterized by interconnectedness. The task of ecological care is thus not only to see the patient within a world of relationships but to help the patient find his/her place again, to understand himself/herself and the world anew. Ecological care is not only about fighting an illness, but also recognizes a patient from inside a world that s/he is affected by and affects, that s/he is understood and understands from. Such care tries to restore this connection by making possible the rhythmical movement as well as the space in-between activity and rest, between being cared for and actively involving oneself in one's recovery and between closing oneself off from the world and once again going out into it. PMID:27914196

Steering with big words: articulating ideographs in nanotechnology

NARCIS (Netherlands)

Bos, Colette; Walhout, Albert; Peine, Alex; van Lente, Harro

2014-01-01

Nowadays, science should address societal challenges, such as ‘sustainability’, or ‘responsible research and innovation’. This emerging form of steering toward broad and generic goals involves the use of ‘big words’: encompassing concepts that are uncontested themselves, but that allow for multiple

Unleashing the Potential of Big Data: A white paper based on the 2013 World Summit on Big Data and Organization Design

DEFF Research Database (Denmark)

HO, Diem; Snow, Charles; Obel, Børge

-in-hand with new data-protection legislation, the EC wants to formulate an overall cybersecurity strategy to ensure that individual and organizational data are properly used and protected. Alongside harmonized rules for how data is handled, the EC is pushing for standards to allow the interoperability...... and integration of data. Other government initiatives focus on technological development and infrastructure projects. This White Paper offers ideas and recommendations to further increase the value of Big Data initiatives while protecting against their risks. Governments, universities, and business all have...

Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data.

Science.gov (United States)

Dinov, Ivo D

2016-01-01

Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be 'team science'.

Analyzing Big Data in Psychology: A Split/Analyze/Meta-Analyze Approach

Directory of Open Access Journals (Sweden)

Mike W.-L. Cheung

2016-05-01

Full Text Available Big data is a field that has traditionally been dominated by disciplines such as computer science and business, where mainly data-driven analyses have been performed. Psychology, a discipline in which a strong emphasis is placed on behavioral theories and empirical research, has the potential to contribute greatly to the big data movement. However, one challenge to psychologists – and probably the most crucial one – is that most researchers may not have the necessary programming and computational skills to analyze big data. In this study we argue that psychologists can also conduct big data research and that, rather than trying to acquire new programming and computational skills, they should focus on their strengths, such as performing psychometric analyses and testing theories using multivariate analyses to explain phenomena. We propose a split/analyze/meta-analyze approach that allows psychologists to easily analyze big data. Two real datasets are used to demonstrate the proposed procedures in R. A new research agenda related to the analysis of big data in psychology is outlined at the end of the study.

Analyzing Big Data in Psychology: A Split/Analyze/Meta-Analyze Approach.

Science.gov (United States)

Cheung, Mike W-L; Jak, Suzanne

2016-01-01

Big data is a field that has traditionally been dominated by disciplines such as computer science and business, where mainly data-driven analyses have been performed. Psychology, a discipline in which a strong emphasis is placed on behavioral theories and empirical research, has the potential to contribute greatly to the big data movement. However, one challenge to psychologists-and probably the most crucial one-is that most researchers may not have the necessary programming and computational skills to analyze big data. In this study we argue that psychologists can also conduct big data research and that, rather than trying to acquire new programming and computational skills, they should focus on their strengths, such as performing psychometric analyses and testing theories using multivariate analyses to explain phenomena. We propose a split/analyze/meta-analyze approach that allows psychologists to easily analyze big data. Two real datasets are used to demonstrate the proposed procedures in R. A new research agenda related to the analysis of big data in psychology is outlined at the end of the study.

The Mathematics of Infinity A Guide to Great Ideas

CERN Document Server

Faticoni, Theodore G

2012-01-01

Praise for the First Edition ". . . an enchanting book for those people in computer science or mathematics who are fascinated by the concept of infinity."—Computing Reviews ". . . a very well written introduction to set theory . . . easy to read and well suited for self-study . . . highly recommended."—Choice The concept of infinity has fascinated and confused mankind for centuries with theories and ideas that cause even seasoned mathematicians to wonder. The Mathematics of Infinity: A Guide to Great Ideas, Second Edition uniquely explores how we can manipulate these ideas when

EKALAVYA MODEL OF HIGHER EDUCATION – AN INNOVATION OF IBM’S BIG DATA UNIVERSITY

OpenAIRE

Dr. P. S. Aithal; Shubhrajyotsna Aithal

2016-01-01

Big Data Science is a new multi-disciplinary subject in the society, comprising of business intelligence, data analytics, and the related fields have become increasingly important in both the academic and the business communities during the 21st century. Many organizations and business intelligence experts have foreseen the significant development in the big data field as next big wave in future research arena in many industry sectors and the society. To become an expert and skilled in this n...

How to Generate Economic and Sustainability Reports from Big Data? Qualifications of Process Industry

Directory of Open Access Journals (Sweden)

Esa Hämäläinen

2017-11-01

Full Text Available Big Data may introduce new opportunities, and for this reason it has become a mantra among most industries. This paper focuses on examining how to develop cost and sustainable reporting by utilizing Big Data that covers economic values, production volumes, and emission information. We assume strongly that this use supports cleaner production, while at the same time offers more information for revenue and profitability development. We argue that Big Data brings company-wide business benefits if data queries and interfaces are built to be interactive, intuitive, and user-friendly. The amount of information related to operations, costs, emissions, and the supply chain would increase enormously if Big Data was used in various manufacturing industries. It is essential to expose the relevant correlations between different attributes and data fields. Proper algorithm design and programming are key to making the most of Big Data. This paper introduces ideas on how to refine raw data into valuable information, which can serve many types of end users, decision makers, and even external auditors. Concrete examples are given through an industrial paper mill case, which covers environmental aspects, cost-efficiency management, and process design.

The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery.

Science.gov (United States)

Swan, Melanie

2013-06-01

A key contemporary trend emerging in big data science is the quantified self (QS)-individuals engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information as n=1 individuals or in groups. There are opportunities for big data scientists to develop new models to support QS data collection, integration, and analysis, and also to lead in defining open-access database resources and privacy standards for how personal data is used. Next-generation QS applications could include tools for rendering QS data meaningful in behavior change, establishing baselines and variability in objective metrics, applying new kinds of pattern recognition techniques, and aggregating multiple self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. The long-term vision of QS activity is that of a systemic monitoring approach where an individual's continuous personal information climate provides real-time performance optimization suggestions. There are some potential limitations related to QS activity-barriers to widespread adoption and a critique regarding scientific soundness-but these may be overcome. One interesting aspect of QS activity is that it is fundamentally a quantitative and qualitative phenomenon since it includes both the collection of objective metrics data and the subjective experience of the impact of these data. Some of this dynamic is being explored as the quantified self is becoming the qualified self in two new ways: by applying QS methods to the tracking of qualitative phenomena such as mood, and by understanding that QS data collection is just the first step in creating qualitative feedback loops for behavior change. In the long-term future, the quantified self may become additionally transformed into the extended exoself as data quantification and self-tracking enable the development of new sense capabilities that are not possible with ordinary senses. The

Students' Ideas on Cooperative Learning Method

Science.gov (United States)

Yoruk, Abdulkadir

2016-01-01

Aim of this study is to investigate students' ideas on cooperative learning method. For that purpose students who are studying at elementary science education program are distributed into two groups through an experimental design. Factors threaten the internal validity are either eliminated or reduced to minimum value. Data analysis is done…

BigOP: Generating Comprehensive Big Data Workloads as a Benchmarking Framework

OpenAIRE

Zhu, Yuqing; Zhan, Jianfeng; Weng, Chuliang; Nambiar, Raghunath; Zhang, Jinchao; Chen, Xingzhen; Wang, Lei

2014-01-01

Big Data is considered proprietary asset of companies, organizations, and even nations. Turning big data into real treasure requires the support of big data systems. A variety of commercial and open source products have been unleashed for big data storage and processing. While big data users are facing the choice of which system best suits their needs, big data system developers are facing the question of how to evaluate their systems with regard to general big data processing needs. System b...

THE SYSTEMIC APPROACH TO TEACHING AND LEARNING:

African Journals Online (AJOL)

Rita Wilkinson

CHEMISTRY AND THE BIG IDEAS OF SCIENCE EDUCATION. JD Bradley and ... Since then several papers have reported on research in the classroom focused on the ... Ideas. On the one hand is the almost irresistible tide of new information.

Big Data analytics in the Geo-Spatial Domain

NARCIS (Netherlands)

R.A. Goncalves (Romulo); M.G. Ivanova (Milena); M.L. Kersten (Martin); H. Scholten; S. Zlatanova; F. Alvanaki (Foteini); P. Nourian (Pirouz); E. Dias

2014-01-01

htmlabstractBig data collections in many scientific domains have inherently rich spatial and geo-spatial features. Spatial location is among the core aspects of data in Earth observation sciences, astronomy, and seismology to name a few. The goal of our project is to design an efficient data

Recreating big Ban to learn more about universe

CERN Multimedia

2005-01-01

A multi-nation effort at Gemeva-based CERN laboratory to recreate conditions existing just after the Big Ban could give vital clues to the creation of the universe and help overcome prejudices against this widely held scientific theory, an eminent science writer said in Kolkata on Tuesday

The Rise of Big Data in Oncology.

Science.gov (United States)

Fessele, Kristen L

2018-05-01

To describe big data and data science in the context of oncology nursing care. Peer-reviewed and lay publications. The rapid expansion of real-world evidence from sources such as the electronic health record, genomic sequencing, administrative claims and other data sources has outstripped the ability of clinicians and researchers to manually review and analyze it. To promote high-quality, high-value cancer care, big data platforms must be constructed from standardized data sources to support extraction of meaningful, comparable insights. Nurses must advocate for the use of standardized vocabularies and common data elements that represent terms and concepts that are meaningful to patient care. Copyright © 2018 Elsevier Inc. All rights reserved.

The (Big Data-security assemblage: Knowledge and critique

Directory of Open Access Journals (Sweden)

Claudia Aradau

2015-10-01

Full Text Available The Snowden revelations and the emergence of ‘Big Data’ have rekindled questions about how security practices are deployed in a digital age and with what political effects. While critical scholars have drawn attention to the social, political and legal challenges to these practices, the debates in computer and information science have received less analytical attention. This paper proposes to take seriously the critical knowledge developed in information and computer science and reinterpret their debates to develop a critical intervention into the public controversies concerning data-driven security and digital surveillance. The paper offers a two-pronged contribution: on the one hand, we challenge the credibility of security professionals’ discourses in light of the knowledge that they supposedly mobilize; on the other, we argue for a series of conceptual moves around data, human–computer relations, and algorithms to address some of the limitations of existing engagements with the Big Data-security assemblage.

Science & Society seminar: Evolution is not only a story of genes

CERN Multimedia

2002-01-01

Memes are behaviours and ideas copied from person to person by imitation. These include songs, habits, skills, inventions and ways of doing things. Darwinian evolutionary theory, which holds that genes control the traits of organisms, has traditionally explained human nature. Susan Blackmore offers a new look at evolution, and considers evolving memes as well as genes. This will be the subject of the next Science and Society seminar, 'The evolution of Meme machines', that will take place on Thursday 24 October. According to the meme idea, everything changed in human evolution when imitation first appeared because imitation let loose a new replicator, the meme. Since that time, two replicators have been driving human evolution, not one. This is why humans have such big brains, and why they alone produce and understand grammatical language, sing, dance, wear clothes and have complex cumulative cultures. Unlike other brains, human brains had to solve the problem of choosing which memes to imitate. In other wor...

Research on the thinking path of enterprise management in the era of big data

Directory of Open Access Journals (Sweden)

Guo Ying

2016-01-01

Full Text Available With the acceleration of economic globalization, enterprise internationalization deepen and capital supply chain extension, Chinese enterprises will inevitably was involved in the economic tide to, the business was information and data of the high-frequency explosive growth. At the same time, the rapid expansion of the management network and the management functions of the cross redundancy is serious, so that the enterprise management, decision-making, execution are subjected to tremendous impact. Big data era has arrived, companies need new management ideas and solutions to cope with the challenges of the new situation. This article embarks from the enterprise management under the background of big data, according to three aspects: production, marketing, transportation, a full range analysis to in the era of big data for scientific and effective management planning, provide strong management support for the development of enterprises.

Vectors into the Future of Mass and Interpersonal Communication Research: Big Data, Social Media, and Computational Social Science.

Science.gov (United States)

Cappella, Joseph N

2017-10-01

Simultaneous developments in big data, social media, and computational social science have set the stage for how we think about and understand interpersonal and mass communication. This article explores some of the ways that these developments generate 4 hypothetical "vectors" - directions - into the next generation of communication research. These vectors include developments in network analysis, modeling interpersonal and social influence, recommendation systems, and the blurring of distinctions between interpersonal and mass audiences through narrowcasting and broadcasting. The methods and research in these arenas are occurring in areas outside the typical boundaries of the communication discipline but engage classic, substantive questions in mass and interpersonal communication.

How Big Is Too Big?

Science.gov (United States)

Cibes, Margaret; Greenwood, James

2016-01-01

Media Clips appears in every issue of Mathematics Teacher, offering readers contemporary, authentic applications of quantitative reasoning based on print or electronic media. This issue features "How Big is Too Big?" (Margaret Cibes and James Greenwood) in which students are asked to analyze the data and tables provided and answer a…

The SAMI Galaxy Survey: A prototype data archive for Big Science exploration

Science.gov (United States)

Konstantopoulos, I. S.; Green, A. W.; Foster, C.; Scott, N.; Allen, J. T.; Fogarty, L. M. R.; Lorente, N. P. F.; Sweet, S. M.; Hopkins, A. M.; Bland-Hawthorn, J.; Bryant, J. J.; Croom, S. M.; Goodwin, M.; Lawrence, J. S.; Owers, M. S.; Richards, S. N.

2015-11-01

We describe the data archive and database for the SAMI Galaxy Survey, an ongoing observational program that will cover ≈3400 galaxies with integral-field (spatially-resolved) spectroscopy. Amounting to some three million spectra, this is the largest sample of its kind to date. The data archive and built-in query engine use the versatile Hierarchical Data Format (HDF5), which precludes the need for external metadata tables and hence the setup and maintenance overhead those carry. The code produces simple outputs that can easily be translated to plots and tables, and the combination of these tools makes for a light system that can handle heavy data. This article acts as a contextual companion to the SAMI Survey Database source code repository, samiDB, which is freely available online and written entirely in Python. We also discuss the decisions related to the selection of tools and the creation of data visualisation modules. It is our aim that the work presented in this article-descriptions, rationale, and source code-will be of use to scientists looking to set up a maintenance-light data archive for a Big Science data load.

Framing Big Data: The discursive construction of a radio cell query in Germany

Directory of Open Access Journals (Sweden)

Christian Pentzold

2017-11-01

Full Text Available The article examines the construction of “Big Data” in media discourse. Rather than asking what Big Data really is or is not, it deals with the discursive work that goes into making Big Data a socially relevant phenomenon and problem in the first place. It starts from the idea that in modern societies the public understanding of technology is largely driven by a media-based discourse, which is a key arena for circulating collectively shared meanings. This largely ignored dimension invites us to appreciate what matters to journalists and the wider public when discussing the collection and use of data. To this end, our study looks at how Big Data is framed in terms of the governmental use of large datasets as a contentious area of data application. It reconstructs the perspectives surrounding the so-called “Handygate” affair in Germany based on broadcast news and social media conversations. In this incident, state authorities collected and analyzed mobile phone data through a radio cell query during events to commemorate the Dresden bombing in February 2011. We employ a qualitative discourse analysis that allows us to reconstruct the conceptualizations of Big Data as a proper instrument for criminal prosecution or an unjustified infringement of constitutional rights.

Accelerators: Sparking Innovation and Transdisciplinary Team Science in Disparities Research

Directory of Open Access Journals (Sweden)

Carol R. Horowitz

2017-02-01

Full Text Available Development and implementation of effective, sustainable, and scalable interventions that advance equity could be propelled by innovative and inclusive partnerships. Readied catalytic frameworks that foster communication, collaboration, a shared vision, and transformative translational research across scientific and non-scientific divides are needed to foster rapid generation of novel solutions to address and ultimately eliminate disparities. To achieve this, we transformed and expanded a community-academic board into a translational science board with members from public, academic and private sectors. Rooted in team science, diverse board experts formed topic-specific “accelerators”, tasked with collaborating to rapidly generate new ideas, questions, approaches, and projects comprising patients, advocates, clinicians, researchers, funders, public health and industry leaders. We began with four accelerators—digital health, big data, genomics and environmental health—and were rapidly able to respond to funding opportunities, transform new ideas into clinical and community programs, generate new, accessible, actionable data, and more efficiently and effectively conduct research. This innovative model has the power to maximize research quality and efficiency, improve patient care and engagement, optimize data democratization and dissemination among target populations, contribute to policy, and lead to systems changes needed to address the root causes of disparities.

Challenges in data science: a complex systems perspective

International Nuclear Information System (INIS)

Carbone, Anna; Jensen, Meiko; Sato, Aki-Hiro

2016-01-01

The ability to process and manage large data volumes has been proven to be not enough to tackle the current challenges presented by “Big Data”. Deep insight is required for understanding interactions among connected systems, space- and time- dependent heterogeneous data structures. Emergence of global properties from locally interacting data entities and clustering phenomena demand suitable approaches and methodologies recently developed in the foundational area of Data Science by taking a Complex Systems standpoint. Here, we deal with challenges that can be summarized by the question: “What can Complex Systems Science contribute to Big Data? ”. Such question can be reversed and brought to a superior level of abstraction by asking “What Knowledge can be drawn from Big Data?” These aspects constitute the main motivation behind this article to introduce a volume containing a collection of papers presenting interdisciplinary advances in the Big Data area by methodologies and approaches typical of the Complex Systems Science, Nonlinear Systems Science and Statistical Physics.

Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science.

Science.gov (United States)

Rein, Robert; Memmert, Daniel

2016-01-01

Until recently tactical analysis in elite soccer were based on observational data using variables which discard most contextual information. Analyses of team tactics require however detailed data from various sources including technical skill, individual physiological performance, and team formations among others to represent the complex processes underlying team tactical behavior. Accordingly, little is known about how these different factors influence team tactical behavior in elite soccer. In parts, this has also been due to the lack of available data. Increasingly however, detailed game logs obtained through next-generation tracking technologies in addition to physiological training data collected through novel miniature sensor technologies have become available for research. This leads however to the opposite problem where the shear amount of data becomes an obstacle in itself as methodological guidelines as well as theoretical modelling of tactical decision making in team sports is lacking. The present paper discusses how big data and modern machine learning technologies may help to address these issues and aid in developing a theoretical model for tactical decision making in team sports. As experience from medical applications show, significant organizational obstacles regarding data governance and access to technologies must be overcome first. The present work discusses these issues with respect to tactical analyses in elite soccer and propose a technological stack which aims to introduce big data technologies into elite soccer research. The proposed approach could also serve as a guideline for other sports science domains as increasing data size is becoming a wide-spread phenomenon.

What Difference Does Quantity Make? On the Epistemology of Big Data in Biology

Science.gov (United States)

Leonelli, Sabina

2015-01-01

Is big data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of big data science does not lie in the sheer quantity of data involved, but rather in (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community; and (2) the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this paper reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data; and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which big data need to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analyzing data given these developments, and the opportunities and worries associated to big data discourse and methods. PMID:25729586

What Difference Does Quantity Make? On the Epistemology of Big Data in Biology.

Science.gov (United States)

Leonelli, Sabina

2014-06-01

Is big data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of big data science does not lie in the sheer quantity of data involved, but rather in (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community; and (2) the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this paper reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data; and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which big data need to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analyzing data given these developments, and the opportunities and worries associated to big data discourse and methods.

What difference does quantity make? On the epistemology of Big Data in biology

Directory of Open Access Journals (Sweden)

S Leonelli

2014-07-01

Full Text Available Is Big Data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of Big Data science does not lie in the sheer quantity of data involved, but rather in (1 the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community and (2 the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this article reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which Big Data needs to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analysing data given these developments, and the opportunities and worries associated with Big Data discourse and methods.

Air Toxics Under the Big Sky: Examining the Effectiveness of Authentic Scientific Research on High School Students' Science Skills and Interest.

Science.gov (United States)

Ward, Tony J; Delaloye, Naomi; Adams, Earle Raymond; Ware, Desirae; Vanek, Diana; Knuth, Randy; Hester, Carolyn Laurie; Marra, Nancy Noel; Holian, Andrij

2016-01-01

Air Toxics Under the Big Sky is an environmental science outreach/education program that incorporates the Next Generation Science Standards (NGSS) 8 Practices with the goal of promoting knowledge and understanding of authentic scientific research in high school classrooms through air quality research. A quasi-experimental design was used in order to understand: 1) how the program affects student understanding of scientific inquiry and research and 2) how the open inquiry learning opportunities provided by the program increase student interest in science as a career path . Treatment students received instruction related to air pollution (airborne particulate matter), associated health concerns, and training on how to operate air quality testing equipment. They then participated in a yearlong scientific research project in which they developed and tested hypotheses through research of their own design regarding the sources and concentrations of air pollution in their homes and communities. Results from an external evaluation revealed that treatment students developed a deeper understanding of scientific research than did comparison students, as measured by their ability to generate good hypotheses and research designs, and equally expressed an increased interest in pursuing a career in science. These results emphasize the value of and need for authentic science learning opportunities in the modern science classroom.

Air Toxics Under the Big Sky: Examining the Effectiveness of Authentic Scientific Research on High School Students’ Science Skills and Interest

Science.gov (United States)

Delaloye, Naomi; Adams, Earle Raymond; Ware, Desirae; Vanek, Diana; Knuth, Randy; Hester, Carolyn Laurie; Marra, Nancy Noel; Holian, Andrij

2016-01-01

Air Toxics Under the Big Sky is an environmental science outreach/education program that incorporates the Next Generation Science Standards (NGSS) 8 Practices with the goal of promoting knowledge and understanding of authentic scientific research in high school classrooms through air quality research. A quasi-experimental design was used in order to understand: 1) how the program affects student understanding of scientific inquiry and research and 2) how the open inquiry learning opportunities provided by the program increase student interest in science as a career path. Treatment students received instruction related to air pollution (airborne particulate matter), associated health concerns, and training on how to operate air quality testing equipment. They then participated in a yearlong scientific research project in which they developed and tested hypotheses through research of their own design regarding the sources and concentrations of air pollution in their homes and communities. Results from an external evaluation revealed that treatment students developed a deeper understanding of scientific research than did comparison students, as measured by their ability to generate good hypotheses and research designs, and equally expressed an increased interest in pursuing a career in science. These results emphasize the value of and need for authentic science learning opportunities in the modern science classroom. PMID:28286375

Air Toxics Under the Big Sky: examining the effectiveness of authentic scientific research on high school students' science skills and interest

Science.gov (United States)

Ward, Tony J.; Delaloye, Naomi; Adams, Earle Raymond; Ware, Desirae; Vanek, Diana; Knuth, Randy; Hester, Carolyn Laurie; Marra, Nancy Noel; Holian, Andrij

2016-04-01

Air Toxics Under the Big Sky is an environmental science outreach/education program that incorporates the Next Generation Science Standards (NGSS) 8 Practices with the goal of promoting knowledge and understanding of authentic scientific research in high school classrooms through air quality research. This research explored: (1) how the program affects student understanding of scientific inquiry and research and (2) how the open-inquiry learning opportunities provided by the program increase student interest in science as a career path. Treatment students received instruction related to air pollution (airborne particulate matter), associated health concerns, and training on how to operate air quality testing equipment. They then participated in a yearlong scientific research project in which they developed and tested hypotheses through research of their own design regarding the sources and concentrations of air pollution in their homes and communities. Results from an external evaluation revealed that treatment students developed a deeper understanding of scientific research than did comparison students, as measured by their ability to generate good hypotheses and research designs, and equally expressed an increased interest in pursuing a career in science. These results emphasize the value of and need for authentic science learning opportunities in the modern science classroom.

Primary Science Interview: Science Sparks

Science.gov (United States)

Bianchi, Lynne

2016-01-01

In this "Primary Science" interview, Lynne Bianchi talks with Emma Vanstone about "Science Sparks," which is a website full of creative, fun, and exciting science activity ideas for children of primary-school age. "Science Sparks" started with the aim of inspiring more parents to do science at home with their…

BIG Data - BIG Gains? Understanding the Link Between Big Data Analytics and Innovation

OpenAIRE

Niebel, Thomas; Rasel, Fabienne; Viete, Steffen

2017-01-01

This paper analyzes the relationship between firms’ use of big data analytics and their innovative performance for product innovations. Since big data technologies provide new data information practices, they create new decision-making possibilities, which firms can use to realize innovations. Applying German firm-level data we find suggestive evidence that big data analytics matters for the likelihood of becoming a product innovator as well as the market success of the firms’ product innovat...

Networking for big data

CERN Document Server

Yu, Shui; Misic, Jelena; Shen, Xuemin (Sherman)

2015-01-01

Networking for Big Data supplies an unprecedented look at cutting-edge research on the networking and communication aspects of Big Data. Starting with a comprehensive introduction to Big Data and its networking issues, it offers deep technical coverage of both theory and applications.The book is divided into four sections: introduction to Big Data, networking theory and design for Big Data, networking security for Big Data, and platforms and systems for Big Data applications. Focusing on key networking issues in Big Data, the book explains network design and implementation for Big Data. It exa

Fixing the Big Bang Theory's Lithium Problem

Science.gov (United States)

Kohler, Susanna

2017-02-01

How did our universe come into being? The Big Bang theory is a widely accepted and highly successful cosmological model of the universe, but it does introduce one puzzle: the cosmological lithium problem. Have scientists now found a solution?Too Much LithiumIn the Big Bang theory, the universe expanded rapidly from a very high-density and high-temperature state dominated by radiation. This theory has been validated again and again: the discovery of the cosmic microwave background radiation and observations of the large-scale structure of the universe both beautifully support the Big Bang theory, for instance. But one pesky trouble-spot remains: the abundance of lithium.The arrows show the primary reactions involved in Big Bang nucleosynthesis, and their flux ratios, as predicted by the authors model, are given on the right. Synthesizing primordial elements is complicated! [Hou et al. 2017]According to Big Bang nucleosynthesis theory, primordial nucleosynthesis ran wild during the first half hour of the universes existence. This produced most of the universes helium and small amounts of other light nuclides, including deuterium and lithium.But while predictions match the observed primordial deuterium and helium abundances, Big Bang nucleosynthesis theory overpredicts the abundance of primordial lithium by about a factor of three. This inconsistency is known as the cosmological lithium problem and attempts to resolve it using conventional astrophysics and nuclear physics over the past few decades have not been successful.In a recent publicationled by Suqing Hou (Institute of Modern Physics, Chinese Academy of Sciences) and advisorJianjun He (Institute of Modern Physics National Astronomical Observatories, Chinese Academy of Sciences), however, a team of scientists has proposed an elegant solution to this problem.Time and temperature evolution of the abundances of primordial light elements during the beginning of the universe. The authors model (dotted lines

Analyzing Big Data in Medicine with Virtual Research Environments and Microservices

OpenAIRE

Ola, Spjuth

2016-01-01

Presentation by Ola Spjuth, Deputy director at Department of Information Technology, Uppsala Multidisciplinary Centre for Advanced Computational Science, at Big Data in Medicine, Uppsala, Sweden.

Global fluctuation spectra in big-crunch-big-bang string vacua

International Nuclear Information System (INIS)

Craps, Ben; Ovrut, Burt A.

2004-01-01

We study big-crunch-big-bang cosmologies that correspond to exact world-sheet superconformal field theories of type II strings. The string theory spacetime contains a big crunch and a big bang cosmology, as well as additional 'whisker' asymptotic and intermediate regions. Within the context of free string theory, we compute, unambiguously, the scalar fluctuation spectrum in all regions of spacetime. Generically, the big crunch fluctuation spectrum is altered while passing through the bounce singularity. The change in the spectrum is characterized by a function Δ, which is momentum and time dependent. We compute Δ explicitly and demonstrate that it arises from the whisker regions. The whiskers are also shown to lead to 'entanglement' entropy in the big bang region. Finally, in the Milne orbifold limit of our superconformal vacua, we show that Δ→1 and, hence, the fluctuation spectrum is unaltered by the big-crunch-big-bang singularity. We comment on, but do not attempt to resolve, subtleties related to gravitational back reaction and light winding modes when interactions are taken into account

Steering with big words: articulating ideographs in research programs

NARCIS (Netherlands)

Bos, Colette; Walhout, Bart; Walhout, Bart; Peine, Alexander; van Lente, Harro

2014-01-01

Nowadays, science should address societal challenges, such as ‘sustainability’, or ‘responsible research and innovation’. This emerging form of steering toward broad and generic goals involves the use of ‘big words’: encompassing concepts that are uncontested themselves, but that allow for multiple

Advanced Research and Data Methods in Women's Health: Big Data Analytics, Adaptive Studies, and the Road Ahead.

Science.gov (United States)

Macedonia, Christian R; Johnson, Clark T; Rajapakse, Indika

2017-02-01

Technical advances in science have had broad implications in reproductive and women's health care. Recent innovations in population-level data collection and storage have made available an unprecedented amount of data for analysis while computational technology has evolved to permit processing of data previously thought too dense to study. "Big data" is a term used to describe data that are a combination of dramatically greater volume, complexity, and scale. The number of variables in typical big data research can readily be in the thousands, challenging the limits of traditional research methodologies. Regardless of what it is called, advanced data methods, predictive analytics, or big data, this unprecedented revolution in scientific exploration has the potential to dramatically assist research in obstetrics and gynecology broadly across subject matter. Before implementation of big data research methodologies, however, potential researchers and reviewers should be aware of strengths, strategies, study design methods, and potential pitfalls. Examination of big data research examples contained in this article provides insight into the potential and the limitations of this data science revolution and practical pathways for its useful implementation.

IdeaSquare - finding and creating new ways to collaborate

CERN Multimedia

CERN. Geneva

2014-01-01

IdeaSquare is a new pilot project meant to connect people inside and outside CERN to work together and helping the CERN-inspired innovations to create positive impact on society. We started our work last October with a five-month student project, Challenge Based Innovation (CBI) that has gathered some quite nice feedback along the way (http://cern.ch/go/wmM7), but is only one of our activities. Our big goal is scaling this collaboration up for different kinds of people all around the world to participate easily. We want to start by providing the student engineers, industrial designers and economists in the next round of CBI-course with better tools and services for working together and sharing their ideas. And in the long run, we want to create a scalable system that would allow a lot more people to work together and learn in similar constructive projects in the future. What are the tools at CERN we should use during the next round of CBI - Sharepoint, Vidyo, Owncloud, social.cern.ch... and something else? I...

Big data and information management: modeling the context decisional supported by sistemography

Directory of Open Access Journals (Sweden)

William Barbosa Vianna

2016-04-01

Full Text Available Introduction: The study justified by the scarcity of studies in the field of information science that addressing the phenomenon of big data from the perspective of information management. that will allow further development of computer simulation. Objective: The objective is to identify and represent the general elements of the decision-making process in the context of big data. Methodology: It is an exploratory study and theoretical and deductive nature. Results: It resulted in the identification of the main elements involved in decision-making on big data environment and its sistemografic representation. Conclusions: It was possible to develop a representation which will allow further development of computer simulation.

Data science, learning, and applications to biomedical and health sciences.

Science.gov (United States)

Adam, Nabil R; Wieder, Robert; Ghosh, Debopriya

2017-01-01

The last decade has seen an unprecedented increase in the volume and variety of electronic data related to research and development, health records, and patient self-tracking, collectively referred to as Big Data. Properly harnessed, Big Data can provide insights and drive discovery that will accelerate biomedical advances, improve patient outcomes, and reduce costs. However, the considerable potential of Big Data remains unrealized owing to obstacles including a limited ability to standardize and consolidate data and challenges in sharing data, among a variety of sources, providers, and facilities. Here, we discuss some of these challenges and potential solutions, as well as initiatives that are already underway to take advantage of Big Data. © 2017 New York Academy of Sciences.

Big Argumentation?

Directory of Open Access Journals (Sweden)

Daniel Faltesek

2013-08-01

Full Text Available Big Data is nothing new. Public concern regarding the mass diffusion of data has appeared repeatedly with computing innovations, in the formation before Big Data it was most recently referred to as the information explosion. In this essay, I argue that the appeal of Big Data is not a function of computational power, but of a synergistic relationship between aesthetic order and a politics evacuated of a meaningful public deliberation. Understanding, and challenging, Big Data requires an attention to the aesthetics of data visualization and the ways in which those aesthetics would seem to depoliticize information. The conclusion proposes an alternative argumentative aesthetic as the appropriate response to the depoliticization posed by the popular imaginary of Big Data.

Understanding Big Data for Industrial Innovation and Design: The Missing Information Systems Perspective

Directory of Open Access Journals (Sweden)

Miguel Baptista Nunes

2017-12-01

Full Text Available This paper identifies a need to complement the current rich technical and mathematical research agenda on big data with a more information systems and information science strand, which focuses on the business value of big data. An agenda of research for information systems would explore motives for using big data in real organizational contexts, and consider proposed benefits, such as increased effectiveness and efficiency, production of high-quality products/services, creation of added business value, and stimulation of innovation and design. Impacts of such research on the academic community, the industrial and business world, and policy-makers are discussed.

Drawings as imaginative expressions of philosophical ideas in a Grade 2 South African literacy classroom

Directory of Open Access Journals (Sweden)

Karin S. Murris

2016-07-01

Full Text Available This article reports on a philosophy for children (P4C literacy project in a South African foundation phase classroom that introduces an important new focus in the P4C classroom: the visualisation of philosophical ideas provoked by the picture book The Big Ugly Monster and the Little Stone Rabbit (2004 by Chris Wormell, giving voice to young children’s own imaginative ideas and beliefs (in this case about death. This research shows how a particular use of the community of philosophical enquiry pedagogy combined with the making of drawings necessitates a rethinking of what ‘voice’ means. We conclude that the children’s drawings bring something new into existence, thereby offering unique material and discursive opportunities for all children, including those who otherwise might not have expressed their ideas. Keywords: Comprehension; emergent literacy; visual research; community of enquiry; philosophy with children; picturebooks; death; voice; inclusion; participation

Proposition of a method to formulate idea and innovation problems

Directory of Open Access Journals (Sweden)

Stéphane GORIA

2010-01-01

Full Text Available Nowadays, innovation is a major stake for development and durability of many companies and institution’s activities. In our work, we are interested in interpersonal communication problem in innovation context. We focus on the upstream of innovation process and we consider the means that can be used. First to leverage organisation’s innovation perspective and second to solve innovation problem proposed by a strategic decision maker. To do this, we believe that people charge with solving an innovation problem should manage some difficulties: to identify what can be an innovation for the decision maker, to define the objects with a big innovation potential, communicate to his possible partners the innovation field to investigate and to find ideas to innovate etc. Then, we try to identify and to present these ideas to contribute to decision making process. At this time when the Web is really the universal source with access to formidable information quantities, we use it to establish some creativity and idea sources for innovation. This paper presents a method to solve these communication problems. It presents a tool box which potentialities are illustrated by an example of the management of a “new chair” development problem.

50 quantum physics ideas you really need to know

CERN Document Server

Baker, Joanne

2013-01-01

Following on from the highly successful 50 Physics Ideas You Really Need to Know, author Joanne Baker consolidates the foundation concepts of physics and moves on to present clear explanations of the most cutting-edge area of science: quantum physics. With 50 concise chapters covering complex theories and their advanced applications - from string theory to black holes, and quarks to quantum computing - alongside informative two-colour illustrations, this book presents key ideas in straightforward, bite-sized chunks. Ideal for the layperson, this book will challenge the way you understand the world. The ideas explored include: Theory of relativity; Schrodinger's cat; Nuclear forces: fission and fusion; Antimatter; Superconductivity.

Evaluation of Big Data Containers for Popular Storage, Retrieval, and Computation Primitives in Earth Science Analysis

Science.gov (United States)

Das, K.; Clune, T.; Kuo, K. S.; Mattmann, C. A.; Huang, T.; Duffy, D.; Yang, C. P.; Habermann, T.

2015-12-01

Data containers are infrastructures that facilitate storage, retrieval, and analysis of data sets. Big data applications in Earth Science require a mix of processing techniques, data sources and storage formats that are supported by different data containers. Some of the most popular data containers used in Earth Science studies are Hadoop, Spark, SciDB, AsterixDB, and RasDaMan. These containers optimize different aspects of the data processing pipeline and are, therefore, suitable for different types of applications. These containers are expected to undergo rapid evolution and the ability to re-test, as they evolve, is very important to ensure the containers are up to date and ready to be deployed to handle large volumes of observational data and model output. Our goal is to develop an evaluation plan for these containers to assess their suitability for Earth Science data processing needs. We have identified a selection of test cases that are relevant to most data processing exercises in Earth Science applications and we aim to evaluate these systems for optimal performance against each of these test cases. The use cases identified as part of this study are (i) data fetching, (ii) data preparation for multivariate analysis, (iii) data normalization, (iv) distance (kernel) computation, and (v) optimization. In this study we develop a set of metrics for performance evaluation, define the specifics of governance, and test the plan on current versions of the data containers. The test plan and the design mechanism are expandable to allow repeated testing with both new containers and upgraded versions of the ones mentioned above, so that we can gauge their utility as they evolve.

Unconventional Ideas for Axion and Dark Matter Experiments

CERN Document Server

Caspers, Fritz

2015-01-01

In this contribution an entirely different way compared to conventional approaches for axion, hidden photon and dark matter (DM) detection is proposed for discussion. The idea is to use living plants which are known to be very sensitive to all kind of environmental parameters, as detectors. A possible observable in such living plants could be the natural bio-photon level, a kind of metabolism related chemoluminescence. Another observable might be morphological changes or systematic leave movements. However a big problem for such kind of experiment would be the availability of a known, controllable and calibrated DM source. The objective of this small paper is primarily to trigger a debate and not so much to present a well-defined and clearly structured proposal.

The Relationship between Big Data and Mathematical Modeling: A Discussion in a Mathematical Education Scenario

Science.gov (United States)

Dalla Vecchia, Rodrigo

2015-01-01

This study discusses aspects of the association between Mathematical Modeling (MM) and Big Data in the scope of mathematical education. We present an example of an activity to discuss two ontological factors that involve MM. The first is linked to the modeling stages. The second involves the idea of pedagogical objectives. The main findings…

Observatories, think tanks, and community models in the hydrologic and environmental sciences: How does it affect me?

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Torgersen, Thomas

2006-06-01

Multiple issues in hydrologic and environmental sciences are now squarely in the public focus and require both government and scientific study. Two facts also emerge: (1) The new approach being touted publicly for advancing the hydrologic and environmental sciences is the establishment of community-operated "big science" (observatories, think tanks, community models, and data repositories). (2) There have been important changes in the business of science over the last 20 years that make it important for the hydrologic and environmental sciences to demonstrate the "value" of public investment in hydrological and environmental science. Given that community-operated big science (observatories, think tanks, community models, and data repositories) could become operational, I argue that such big science should not mean a reduction in the importance of single-investigator science. Rather, specific linkages between the large-scale, team-built, community-operated big science and the single investigator should provide context data, observatory data, and systems models for a continuing stream of hypotheses by discipline-based, specialized research and a strong rationale for continued, single-PI ("discovery-based") research. I also argue that big science can be managed to provide a better means of demonstrating the value of public investment in the hydrologic and environmental sciences. Decisions regarding policy will still be political, but big science could provide an integration of the best scientific understanding as a guide for the best policy.

The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness

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Wong, Ho Ting; Chiang, Vico Chung Lim; Choi, Kup Sze; Loke, Alice Yuen

2016-01-01

The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term “Big Data”, which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size. Having a suitable definition for nurse researchers in their context of research and practice is essential for the advancement of nursing research. In view of the need for a better understanding on what Big Data is, the aim in this paper is to explore and discuss the concept. Furthermore, an example of a Big Data research study on disaster nursing preparedness involving six million patient records is used for discussion. The example demonstrates that a Big Data analysis can be conducted from many more perspectives than would be possible in traditional sampling, and is superior to traditional sampling. Experience gained from the process of using Big Data in this study will shed light on future opportunities for conducting evidence-based nursing research to achieve competence in disaster nursing. PMID:27763525

The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness

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Ho Ting Wong

2016-10-01

Full Text Available The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term “Big Data”, which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size. Having a suitable definition for nurse researchers in their context of research and practice is essential for the advancement of nursing research. In view of the need for a better understanding on what Big Data is, the aim in this paper is to explore and discuss the concept. Furthermore, an example of a Big Data research study on disaster nursing preparedness involving six million patient records is used for discussion. The example demonstrates that a Big Data analysis can be conducted from many more perspectives than would be possible in traditional sampling, and is superior to traditional sampling. Experience gained from the process of using Big Data in this study will shed light on future opportunities for conducting evidence-based nursing research to achieve competence in disaster nursing.

The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness.

Science.gov (United States)

Wong, Ho Ting; Chiang, Vico Chung Lim; Choi, Kup Sze; Loke, Alice Yuen

2016-10-17

The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term "Big Data", which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size. Having a suitable definition for nurse researchers in their context of research and practice is essential for the advancement of nursing research. In view of the need for a better understanding on what Big Data is, the aim in this paper is to explore and discuss the concept. Furthermore, an example of a Big Data research study on disaster nursing preparedness involving six million patient records is used for discussion. The example demonstrates that a Big Data analysis can be conducted from many more perspectives than would be possible in traditional sampling, and is superior to traditional sampling. Experience gained from the process of using Big Data in this study will shed light on future opportunities for conducting evidence-based nursing research to achieve competence in disaster nursing.

Technology for Mining the Big Data of MOOCs

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O'Reilly, Una-May; Veeramachaneni, Kalyan

2014-01-01

Because MOOCs bring big data to the forefront, they confront learning science with technology challenges. We describe an agenda for developing technology that enables MOOC analytics. Such an agenda needs to efficiently address the detailed, low level, high volume nature of MOOC data. It also needs to help exploit the data's capacity to reveal, in…

Eliciting, Identifying, Interpreting, and Responding to Students' Ideas: Teacher Candidates' Growth in Formative Assessment Practices

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Gotwals, Amelia Wenk; Birmingham, Daniel

2016-06-01

With the goal of helping teacher candidates become well-started beginners, it is important that methods courses in teacher education programs focus on high-leverage practices. Using responsive teaching practices, specifically eliciting, identifying, interpreting, and responding to students' science ideas (i.e., formative assessment), can be used to support all students in learning science successfully. This study follows seven secondary science teacher candidates in a yearlong practice-based methods course. Course assignments (i.e., plans for and reflections on teaching) as well as teaching videos were analyzed using a recursive qualitative approach. In this paper, we present themes and patterns in teacher candidates' abilities to elicit, identify, interpret, and respond to students' ideas. Specifically, we found that those teacher candidates who grew in the ways in which they elicited students' ideas from fall to spring were also those who were able to adopt a more balanced reflection approach (considering both teacher and student moves). However, we found that even the teacher candidates who grew in these practices did not move toward seeing students' ideas as nuanced; rather, they saw students' ideas in a dichotomous fashion: right or wrong. We discuss implications for teacher preparation, specifically for how to promote productive reflection and tools for better understanding students' ideas.

Increasing Bellevue School District's elementary teachers' capacity for teaching inquiry-based science: Using ideas from contemporary learning theory to inform professional development

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Maury, Tracy Anne

This Capstone project examined how leaders in the Bellevue School District can increase elementary teachers' capacity for teaching inquiry-based science through the use of professional learning activities that are grounded in ideas from human learning theory. A framework for professional development was constructed and from that framework, a set of professional learning activities were developed as a means to support teacher learning while project participants piloted new curriculum called the Isopod Habitat Challenge. Teachers in the project increased their understanding of the learning theory principles of preconceptions and metacognition. Teachers did not increase their understanding of the principle of learning with understanding, although they did articulate the significance of engaging children in student-led inquiry cycles. Data from the curriculum revision and professional development project coupled with ideas from learning theory, cognition and policy implementation, and learning community literatures suggest Bellevue's leaders can encourage peer-to-peer interaction, link professional development to teachers' daily practice, and capitalize on technology as ways to increase elementary teachers' capacity for teaching inquiry-based science. These lessons also have significance for supporting teacher learning and efficacy in other subject areas and at other levels in the system.

Building Community Consensus for Earth Science Literacy Using an Online Workshop (Invited)

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Wysession, M. E.; Tuddenham, P.; Taber, J.; Ladue, N.

2009-12-01

The Earth Science Literacy Principles, published in the spring of 2009, represented a community consensus about what all Americans should understand about Earth sciences. Central to its creation was a 2-week online workshop that involved participation by 350 Earth scientists and educators. The online workshop, hosted by The College of Exploration, was an excellent medium for incorporating the ideas and concerns of 350 people in near-real time. NSF tasked the Earth Science Literacy Initiative (ESLI) (www.earthscienceliteracy.org) with constructing a set of “Big Ideas” and “Supporting Concepts” that distilled the essential understandings of the GEO-EAR division of NSF. Because of the wide diversity of sub-fields involved (ranging from paleobiology to tectonics), finding a mechanism for incorporating many different views while retaining an organized structure was a challenge. The online workshop turned out to be ideal for this task. Though the 2-week asynchronous workshop was designed to replicate a 2-day in-person workshop, at the drawn-out pace of one hour of requested participation per day, in reality it was much more productive. Many aspects of an in-person workshop were replicated in the the online space. Plenary talks were presented in the main conference room via videos recorded just before or during the 2-week period. The workshop was structured with 150 invited participants and 200 observers. The participants had access to all of the rooms while the observers could see all rooms but could only chat in their own area, the Observation Café. Each breakout room had a moderator who attempted to guide discussion, including suggesting off-topic conversations be moved to the Earth Café. An organizing committee of about a dozen people teleconferenced daily, determining the goals or tasks for the participants for that day. This allowed for a high level of flexibility, with the workshop structure flowing in response to the results up to that point. The first

Uncovering Student Ideas in Astronomy 45 Formative Assessment Probes

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Keeley, Page

2012-01-01

What do your students know-or think they know-about what causes night and day, why days are shorter in winter, and how to tell a planet from a star? Find out with this book on astronomy, the latest in NSTA's popular Uncovering Student Ideas in Science series. The 45 astronomy probes provide situations that will pique your students' interest while helping you understand how your students think about key ideas related to the universe and how it operates.

Investigating undergraduate students’ ideas about the curvature of the Universe

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Kim Coble

2018-06-01

Full Text Available [This paper is part of the Focused Collection on Astronomy Education Research.] As part of a larger project studying undergraduate students’ understanding of cosmology, we explored students’ ideas about the curvature of the Universe. We investigated preinstruction ideas held by introductory astronomy (ASTRO 101 students at three participating universities and postinstruction ideas at one. Through thematic analysis of responses to questions on three survey forms and preinstruction interviews, we found that prior to instruction a significant fraction of students said the Universe is round. Students’ reasoning for this included that the Universe contains round objects, therefore it must also be round, or an incorrect idea that the big bang theory describes an explosion from a central point. We also found that a majority of students think that astronomers use the term curvature to describe properties, such as dimensions, angles, or size, of the Universe or objects in the Universe, or that astronomers use the term curvature to describe the bending of space due to gravity. Students are skeptical that the curvature of the Universe can be measured, to a greater or lesser degree depending on question framing. Postinstruction responses to a multiple-choice exam question and interviews at one university indicate that students are more likely to correctly respond that the Universe as a whole is not curved postinstruction, though the idea that the Universe is round still persists for some students. While we see no evidence that priming with an elliptical or rectangular map of the cosmic microwave background on a postinstruction exam affects responses, students do cite visualizations such as diagrams among the reasons for their responses in preinstruction surveys.

Envisioning the future of 'big data' biomedicine.

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Bui, Alex A T; Van Horn, John Darrell

2017-05-01

Through the increasing availability of more efficient data collection procedures, biomedical scientists are now confronting ever larger sets of data, often finding themselves struggling to process and interpret what they have gathered. This, while still more data continues to accumulate. This torrent of biomedical information necessitates creative thinking about how the data are being generated, how they might be best managed, analyzed, and eventually how they can be transformed into further scientific understanding for improving patient care. Recognizing this as a major challenge, the National Institutes of Health (NIH) has spearheaded the "Big Data to Knowledge" (BD2K) program - the agency's most ambitious biomedical informatics effort ever undertaken to date. In this commentary, we describe how the NIH has taken on "big data" science head-on, how a consortium of leading research centers are developing the means for handling large-scale data, and how such activities are being marshalled for the training of a new generation of biomedical data scientists. All in all, the NIH BD2K program seeks to position data science at the heart of 21 st Century biomedical research. Copyright © 2017 Elsevier Inc. All rights reserved.

Generation of Ideas, Ideation and Idea Management

Directory of Open Access Journals (Sweden)

Patricia Dorow

2015-04-01

Full Text Available Ideas are vital for organizations because they are the source for innovation and this in turn is endlesssource of competitive advantage. The correct definition of concepts not only allows the targeting ofacademic studies, but its future application in everyday life of organizations. The overall objectiveof this article is to clarify the terms related to generation of ideas, ideation and idea management.The method used was a literature review, and later, an analysis of the concepts used by the studiessurveyed, seeking points of convergence and divergence. As a result we propose a clarification inorder to aid understanding of the terms, setting a benchmark for future research. We conclude thatideation and idea generation are the same, they are the process of creating new ideas and ideamanagement comprises the management of ideas throughout the innovation process.

Big data

DEFF Research Database (Denmark)

Madsen, Anders Koed; Flyverbom, Mikkel; Hilbert, Martin

2016-01-01

is to outline a research agenda that can be used to raise a broader set of sociological and practice-oriented questions about the increasing datafication of international relations and politics. First, it proposes a way of conceptualizing big data that is broad enough to open fruitful investigations......The claim that big data can revolutionize strategy and governance in the context of international relations is increasingly hard to ignore. Scholars of international political sociology have mainly discussed this development through the themes of security and surveillance. The aim of this paper...... into the emerging use of big data in these contexts. This conceptualization includes the identification of three moments contained in any big data practice. Second, it suggests a research agenda built around a set of subthemes that each deserve dedicated scrutiny when studying the interplay between big data...

Concurrence of big data analytics and healthcare: A systematic review.

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Mehta, Nishita; Pandit, Anil

2018-06-01

The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges. A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered. Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected. Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare. A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare. This review study unveils that there is a paucity of information on evidence of real-world use of

High Performance Numerical Computing for High Energy Physics: A New Challenge for Big Data Science