Relevant bibliographies by topics / Scientific Array Data

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Scientific Array Data'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Scientific Array Data"

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Harris, Charles R., K. Jarrod Millman, Stéfan J. van der Walt, et al. "Array programming with NumPy." Nature 585, no. 7825 (2020): 357–62. http://dx.doi.org/10.1038/s41586-020-2649-2.

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AbstractArray programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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Lee, Jun-Yeong, Moon-Hyun Kim, Syed Asif Raza Shah, Sang-Un Ahn, Heejun Yoon, and Seo-Young Noh. "Performance Evaluations of Distributed File Systems for Scientific Big Data in FUSE Environment." Electronics 10, no. 12 (2021): 1471. http://dx.doi.org/10.3390/electronics10121471.

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Data are important and ever growing in data-intensive scientific environments. Such research data growth requires data storage systems that play pivotal roles in data management and analysis for scientific discoveries. Redundant Array of Independent Disks (RAID), a well-known storage technology combining multiple disks into a single large logical volume, has been widely used for the purpose of data redundancy and performance improvement. However, this requires RAID-capable hardware or software to build up a RAID-enabled disk array. In addition, it is difficult to scale up the RAID-based storage. In order to mitigate such a problem, many distributed file systems have been developed and are being actively used in various environments, especially in data-intensive computing facilities, where a tremendous amount of data have to be handled. In this study, we investigated and benchmarked various distributed file systems, such as Ceph, GlusterFS, Lustre and EOS for data-intensive environments. In our experiment, we configured the distributed file systems under a Reliable Array of Independent Nodes (RAIN) structure and a Filesystem in Userspace (FUSE) environment. Our results identify the characteristics of each file system that affect the read and write performance depending on the features of data, which have to be considered in data-intensive computing environments.
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Glushanovskiy, A. V. "Bibliometric analysis of Russian publications’ quality in physical area, included to the Web of Science Core Collection Data Base." Bibliosphere, no. 2 (July 21, 2020): 49–60. http://dx.doi.org/10.20913/1815-3186-2020-2-49-60.

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The article analyzes the changes in the bibliometric characteristics of the array of Russian publications reflected in the Web of Science Core Collection (WoS CC database) in the field of physics in 2018, compared to the same characteristics in 2010. The main parameter to assess the quality (research level) of arrays with bibliometric point of view was “Comprehensive index of quality” (CIQ) for the array of publications, calculated on the basis of one of the parameters in “Method for calculating the qualitative indicator of the state task “the Comprehensive performance score publication”...”, used by the Ministry of science and higher education of the Russian Federation. It was found that with an almost twofold increase in the volume of the array, there was a slight decrease in its quality in terms of CIQ in 2018 in comparison with 2010. The author also compared the characteristics of the array of Russian publications in 2018 with similar ones of the arrays on physical publications in Germany, India and Great Britain, located close to Russia in the ranking by the number of publications included in the WoS array (in this ranking, Russia was on the fourth place in 2018). In the ranking based on the CIQ indicator, the arrays of these countries are significantly ahead of the Russian one, and our country is only on the sixth place. The main reasons for this lag in the Russian publications array are identified. They are: a lower percentage of Russian publications in high-quartile journals and a greater number of publications from conference proceedings. The conclusion is made about the applicability of bibliometric analysis to identify trends in publishing activities in the scientific field.
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NARAYANAN, P. J., and LARRY S. DAVIS. "REPLICATED IMAGE ALGORITHMS AND THEIR ANALYSES ON SIMD MACHINES." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 02n03 (1992): 335–52. http://dx.doi.org/10.1142/s0218001492000217.

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Data parallel processing on processor array architectures has gained popularity in data intensive applications, such as image processing and scientific computing, as massively parallel processor array machines became feasible commercially. The data parallel paradigm of assigning one processing element to each data element results in an inefficient utilization of a large processor array when a relatively small data structure is processed on it. The large degree of parallelism of a massively parallel processor array machine does not result in a faster solution to a problem involving relatively small data structures than the modest degree of parallelism of a machine that is just as large as the data structure. We presented data replication technique to speed up the processing of small data structures on large processor arrays. In this paper, we present replicated data algorithms for digital image convolutions and median filtering, and compare their performance with conventional data parallel algorithms for the same on three popular array interconnection networks, namely, the 2-D mesh, the 3-D mesh, and the hypercube.
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Silver, Jeremy D., and Charles S. Zender. "The compression–error trade-off for large gridded data sets." Geoscientific Model Development 10, no. 1 (2017): 413–23. http://dx.doi.org/10.5194/gmd-10-413-2017.

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Abstract. The netCDF-4 format is widely used for large gridded scientific data sets and includes several compression methods: lossy linear scaling and the non-lossy deflate and shuffle algorithms. Many multidimensional geoscientific data sets exhibit considerable variation over one or several spatial dimensions (e.g., vertically) with less variation in the remaining dimensions (e.g., horizontally). On such data sets, linear scaling with a single pair of scale and offset parameters often entails considerable loss of precision. We introduce an alternative compression method called "layer-packing" that simultaneously exploits lossy linear scaling and lossless compression. Layer-packing stores arrays (instead of a scalar pair) of scale and offset parameters. An implementation of this method is compared with lossless compression, storing data at fixed relative precision (bit-grooming) and scalar linear packing in terms of compression ratio, accuracy and speed. When viewed as a trade-off between compression and error, layer-packing yields similar results to bit-grooming (storing between 3 and 4 significant figures). Bit-grooming and layer-packing offer significantly better control of precision than scalar linear packing. Relative performance, in terms of compression and errors, of bit-groomed and layer-packed data were strongly predicted by the entropy of the exponent array, and lossless compression was well predicted by entropy of the original data array. Layer-packed data files must be "unpacked" to be readily usable. The compression and precision characteristics make layer-packing a competitive archive format for many scientific data sets.
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Mokhnacheva, Yu V., and V. A. Tsvetkova. "Russia in the world array of scientific publications." Вестник Российской академии наук 89, no. 8 (2019): 820–30. http://dx.doi.org/10.31857/s0869-5873898820-830.

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This article presents the results of a study on the representation of Russian publications in the global array of publications that discuss narrow thematic areas within the entire post-Soviet period, using ranking distributions from the Web of Science Core Collection (WoS CC). For extraction and analysis, the classifiers provided by the WoS categories were used. Of the 252 subject categories in the WoS CC, the share of Russian publications for 2010–2017 was not less than 0.4% of the global flow in 132 scientific areas. Therefore, in the period 1993–2000, a gradual recovery was found of Russia’s lost position in the world ranking of countries by number of publications in the WoS CC. Currently, positive changes have been observed both for the entire array of Russian publications in particular in narrow scientific topics. The highest-ranking position for Russian publications fell in 1993–1999, and the greatest decline, when the share of Russian publications fell to their minimum values, was in 2011–2014. Data on the scientific areas in which Russia managed to stay in the top 10 leading countries during its recent history, according to share of publications in the global array, are presented. This list slightly expanded from 2010 to 2017, and today, it includes 39 areas in which Russia is in the top 10 countries, and it is among the five leading countries in eight areas of knowledge.
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Bonaldi, A., T. An, M. Brüggen, et al. "Square Kilometre Array Science Data Challenge 1: analysis and results." Monthly Notices of the Royal Astronomical Society 500, no. 3 (2020): 3821–37. http://dx.doi.org/10.1093/mnras/staa3023.

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ABSTRACT As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the telescope array will be matched only by the techniques developed to exploit the rich scientific value of the data. To drive forward the development of efficient and accurate analysis methods, we are designing a series of data challenges that will provide the scientific community with high-quality data sets for testing and evaluating new techniques. In this paper, we present a description and results from the first such Science Data Challenge 1 (SDC1). Based on SKA MID continuum simulated observations and covering three frequencies (560, 1400, and 9200 MHz) at three depths (8, 100, and 1000 h), SDC1 asked participants to apply source detection, characterization, and classification methods to simulated data. The challenge opened in 2018 November, with nine teams submitting results by the deadline of 2019 April. In this work, we analyse the results for eight of those teams, showcasing the variety of approaches that can be successfully used to find, characterize, and classify sources in a deep, crowded field. The results also demonstrate the importance of building domain knowledge and expertise on this kind of analysis to obtain the best performance. As high-resolution observations begin revealing the true complexity of the sky, one of the outstanding challenges emerging from this analysis is the ability to deal with highly resolved and complex sources as effectively as the unresolved source population.
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Edwards, H. Carter, Daniel Sunderland, Vicki Porter, Chris Amsler, and Sam Mish. "Manycore Performance-Portability: Kokkos Multidimensional Array Library." Scientific Programming 20, no. 2 (2012): 89–114. http://dx.doi.org/10.1155/2012/917630.

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Large, complex scientific and engineering application code have a significant investment in computational kernels to implement their mathematical models. Porting these computational kernels to the collection of modern manycore accelerator devices is a major challenge in that these devices have diverse programming models, application programming interfaces (APIs), and performance requirements. The Kokkos Array programming model provides library-based approach to implement computational kernels that are performance-portable to CPU-multicore and GPGPU accelerator devices. This programming model is based upon three fundamental concepts: (1) manycore compute devices each with its own memory space, (2) data parallel kernels and (3) multidimensional arrays. Kernel execution performance is, especially for NVIDIA® devices, extremely dependent on data access patterns. Optimal data access pattern can be different for different manycore devices – potentially leading to different implementations of computational kernels specialized for different devices. The Kokkos Array programming model supports performance-portable kernels by (1) separating data access patterns from computational kernels through a multidimensional array API and (2) introduce device-specific data access mappings when a kernel is compiled. An implementation of Kokkos Array is available through Trilinos [Trilinos website, http://trilinos.sandia.gov/, August 2011].
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Federici, Memmo, Bruno Luigi Martino, and Pietro Ubertini. "AVES: A high performance computer cluster array for the INTEGRAL satellite scientific data analysis." Experimental Astronomy 34, no. 1 (2012): 105–21. http://dx.doi.org/10.1007/s10686-012-9301-6.

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Labarta, J., E. Ayguadé, J. Oliver, and D. S. Henty. "New OpenMP Directives for Irregular Data Access Loops." Scientific Programming 9, no. 2-3 (2001): 175–83. http://dx.doi.org/10.1155/2001/798505.

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Many scientific applications involve array operations that are sparse in nature, ie array elements depend on the values of relatively few elements of the same or another array. When parallelised in the shared-memory model, there are often inter-thread dependencies which require that the individual array updates are protected in some way. Possible strategies include protecting all the updates, or having each thread compute local temporary results which are then combined globally across threads. However, for the extremely common situation of sparse array access, neither of these approaches is particularly efficient. The key point is that data access patterns usually remain constant for a long time, so it is possible to use an inspector/executor approach. When the sparse operation is first encountered, the access pattern is inspected to identify those updates which have potential inter-thread dependencies. Whenever the code is actually executed, only these selected updates are protected. We propose a new OpenMP clause, indirect, for parallel loops that have irregular data access patterns. This is trivial to implement in a conforming way by protecting every array update, but also allows for an inspector/executor compiler implementation which will be more efficient in sparse cases. We describe efficient compiler implementation strategies for the new directive. We also present timings from the kernels of a Discrete Element Modelling application and a Finite Element code where the inspector/executor approach is used. The results demonstrate that the method can be extremely efficient in practice.
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Dissertations / Theses on the topic "Scientific Array Data"

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Ebenstein, Roee A. "Supporting Advanced Queries on Scientific Array Data." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531322027770129.

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Wang, Yi. "Data Management and Data Processing Support on Array-Based Scientific Data." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1436157356.

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Andrejev, Andrej. "Semantic Web Queries over Scientific Data." Doctoral thesis, Uppsala universitet, Datalogi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-274856.

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Semantic Web and Linked Open Data provide a potential platform for interoperability of scientific data, offering a flexible model for providing machine-readable and queryable metadata. However, RDF and SPARQL gained limited adoption within the scientific community, mainly due to the lack of support for managing massive numeric data, along with certain other important features – such as extensibility with user-defined functions, query modularity, and integration with existing environments and workflows. We present the design, implementation and evaluation of Scientific SPARQL – a language for querying data and metadata combined, represented using the RDF graph model extended with numeric multidimensional arrays as node values – RDF with Arrays. The techniques used to store RDF with Arrays in a scalable way and process Scientific SPARQL queries and updates are implemented in our prototype software – Scientific SPARQL Database Manager, SSDM, and its integrations with data storage systems and computational frameworks. This includes scalable storage solutions for numeric multidimensional arrays and an efficient implementation of array operations. The arrays can be physically stored in a variety of external storage systems, including files, relational databases, and specialized array data stores, using our Array Storage Extensibility Interface. Whenever possible SSDM accumulates array operations and accesses array contents in a lazy fashion. In scientific applications numeric computations are often used for filtering or post-processing the retrieved data, which can be expressed in a functional way. Scientific SPARQL allows expressing common query sub-tasks with functions defined as parameterized queries. This becomes especially useful along with functional language abstractions such as lexical closures and second-order functions, e.g. array mappers. Existing computational libraries can be interfaced and invoked from Scientific SPARQL queries as foreign functions. Cost estimates and alternative evaluation directions may be specified, aiding the construction of better execution plans. Costly array processing, e.g. filtering and aggregation, is thus preformed on the server, saving the amount of communication. Furthermore, common supported operations are delegated to the array storage back-ends, according to their capabilities. Both expressivity and performance of Scientific SPARQL are evaluated on a real-world example, and further performance tests are run using our mini-benchmark for array queries.
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Tirukkovalur, Sravya. "A Global Address Space Approach to Automated Data Management for Parallel Quantum Monte Carlo Applications." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1307464186.

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"Simulating Atmosphere and the TolTEC Detector Array for Data Reduction Pipeline Evaluation." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.55606.

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abstract: TolTEC is a three-color millimeter wavelength camera currently being developed for the Large Millimeter Telescope (LMT) in Mexico. Synthesizing data from previous astronomy cameras as well as knowledge of atmospheric physics, I have developed a simulation of the data collection of TolTEC on the LMT. The simulation was built off smaller sub-projects that informed the development with an understanding of the detector array, the time streams for astronomical mapping, and the science behind Lumped Element Kinetic Inductance Detectors (LEKIDs). Additionally, key aspects of software development processes were integrated into the scientific development process to streamline collaboration across multiple universities and plan for integration on the servers at LMT. The work I have done benefits the data reduction pipeline team by enabling them to efficiently develop their software and test it on simulated data.<br>Dissertation/Thesis<br>Masters Thesis Software Engineering 2019
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Books on the topic "Scientific Array Data"

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Len'kov, Roman. Higher education as a resource management socio-cultural modernization of regions. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1084388.

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The monograph examines the actual theoretical and applied aspects of institutional and regulatory support for social and cultural modernization of regions, including the specification of higher education as a resource and the conditions of modernization, periodization and content of practice of management reform of higher education, the evolution of the role of forecasting in public administration the graduate school. The analysis of resource and policy support socio-cultural modernization of the regions population with higher education through the explication of the problem of the "Assembly" of the future intelligentsia as a socio-cultural potential of modernization, the role of the Institute of higher education in the solution of problems of modernization. The empirical study support a highly skilled regional population policy of modernization in four regions: Moscow region, Bashkortostan, Belgorod region and the Republic of Kalmykia. Proposals for processing of the array data of the respondents with higher education, complemented by social and cultural portraits and information card regions.&#x0D; For professionals and experts on issues of science, higher education and public administration. Will be sought after by post-graduate students, teachers and scientific employees of educational and academic institutions.
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A, Chren William, and Goddard Space Flight Center, eds. Controller for the electronically scanned thinned array radiometer (ESTAR) instrument. National Aeronautics and Space Administration, Goddard Space Flight Center, 1994.

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A, Chren William, and Goddard Space Flight Center, eds. Controller for the electronically scanned thinned array radiometer (ESTAR) instrument. National Aeronautics and Space Administration, Goddard Space Flight Center, 1994.

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Satcher, David. Integrative Medicine and the Social Determinants of Health. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190241254.003.0002.

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This chapter explores the overlap and potential synergies of integrative medicine and preventive medicine in the context of levels of prevention, acknowledging the relative deficiency of research on the effectiveness of practice-based integrative care. The goal of integrative medicine should be to make the widest array of appropriate options available to patients, ultimately blurring the boundaries between conventional care and CAM. Both disciplines should be subject to rigorous scientific inquiry so that interventions that work are systematically distinguished from those that do not. The case is made that responsible use of science and responsiveness to the needs of patients that persist when the data from randomized controlled trials have been exhausted can be reconciled. Integrative medicine is a framework for this reconciliation and, practiced judiciously, offers the promise of better patient outcomes.
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Havstad, Kris M., Laura F. Huenneke, and William H. Schlesinger, eds. Structure and Function of a Chihuahuan Desert Ecosystem. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780195117769.001.0001.

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The Jornada Basin LTER is located in the Chihuahuan Desert, the largest in North America. This region of south central New Mexico has a history of nearly 100 years as the basis for scientific research. This work gives a thorough, encompassing review of the tremendous array of observations resulting from experiments conducted in this ecosystem. Beginning with thorough descriptions of the most salient features of the region, the book then reviews a wide range of archived and active data sets on a diversity of biotic and abiotic features. It next presents a syntheses of important topics including livestock grazing and remediation efforts. A concluding chapter provides a synthesis of the principles that have emerged from this body of work, and how these relate to the broader fields of ecology and natural resource management. It concludes with recommendations for future research directions. The insightful views expressed in this volume should guide management of arid landscapes globally. This is the sixth volume in the Long Term Ecological Network Series.
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Book chapters on the topic "Scientific Array Data"

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Muñiz Fernandez, Fernando, Angel Carreño Torres, Carlos Morcillo-Suarez, and Arcadi Navarro. "Application of Array-Oriented Scientific Data Formats (NetCDF) to Genotype Data, GWASpi as an Example." In Bioinformatics for Personalized Medicine. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28062-7_2.

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Li, Hui, Nengjun Qiu, Mei Chen, et al. "FASTDB: An Array Database System for Efficient Storing and Analyzing Massive Scientific Data." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27161-3_55.

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Berry, Simon T. "The SKA Approach to Sustainable Research." In The Economics of Big Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52391-6_4.

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Abstract The Square Kilometre Array (SKA) is an ambitious project to build a research infrastructure that will enable breakthrough science and discoveries not otherwise possible with current or planned astronomy facilities. Comprising two radio telescopes it will ultimately be the largest scientific instrument on Earth, both in physical scale and in terms of the volume of data it will generate. Like all infrastructures, there is a requirement for SKA to demonstrate where benefits have been realised from past investment, and the potential for future ongoing benefits from technology and knowledge transfer and innovations. The essay discusses the range of non-science benefits for the funders and for wider society going beyond SKA’s core scientific mission and the challenges of thinking about regional impacts when designing a global research infrastructure.
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Riopel, Martin, René Doyon, Daniel Nadeau, and Christian Marois. "An Optimized Data Acquisition System Without Reset Anomaly for the Hawaii and Hawaii-2 Arrays." In Scientific Detectors for Astronomy. Springer Netherlands, 2004. http://dx.doi.org/10.1007/1-4020-2527-0_59.

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Raychaudhuri, Soumya. "Text-Based Analysis of a Single Series of Gene Expression Measurements." In Computational Text Analysis. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780198567400.003.0012.

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In this chapter we begin to address the issue of the analysis of gene expression data with the scientific literature. Here we describe methods for the analysis of a single experiment—one where a single expression measurement has been made for many genes within the same organism. In Chapter 7 we will address the analysis of larger data sets with multiple expression measurements for each of the genes; the questions that occur in that setting are often more complex and utilization of scientific text in that setting can be more useful. But focusing on a single series of expression measurements is an effective starting point in understanding the scientific literature and how it can be used with experimental data. The lessons here can be applied to a wide array of genomic assays besides gene arrays. These methods can be applied to any assay that assigns a single value to each gene In addition, many investigators generate single-condition expression data sets, and these methods are widely applicable. One of the great difficulties in analyzing a single expression series is that context is lacking. That is, we have a large set of isolated measurements. Each measurement corresponds to the log of the relative ratio of a single gene’s expression in an experimental condition compared to its expression in a control condition. These measurements represent a single snapshot of a cell’s physiologic status. One of the great challenges is sorting out the physiologically important expression changes compared to random experimental and physiologic aberrations and fluctuations. Gene expression measurements are subject to a great amount of noise and distinguishing true positives from genes that are not truly induced or repressed is a great challenge. Typically, investigators use their knowledge of biology to prioritize likely positives. In this chapter we argue that text-mining approaches can be used to help prioritize these genes instead. Another equally important challenge is to discern broadly what biological functions are active in a given experiment.
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Kozyreva, Polina M., and Aleksander I. Smirnov. "Fulfilling Life Goals: Subjective Evaluation." In Russia in Reform: Year-Book [collection of scientific articles]. Federal Center of Theoretical and Applied Sociology of the Russian Academy of Sciences, Moscow, Russian Federation, 2020. http://dx.doi.org/10.19181/ezheg.2020.8.

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This article features the results of a study which examines the factors of success or lack thereof when it comes to Russians achieving their life goals. The analysis is based on data from the Russian Longitudinal Monitoring Survey (RLMS-HSE). The study revealed that diminishing confidence in their abilities and in their chance to attain their life goals has over the last few years become a significant component in the attitudes of a considerable amount of Russian people. Achieving them at one stage of life or another does not always make people satisfied with life in general or with its specific aspects. How people assess their chances for attaining success in life is negatively affected during a time of crisis by a decrease in living standards and quality of life in Russia, an increase in anxiety and negative expectations, which in turn are caused by increasingly uncertain life circumstances. However, success in achieving life goals is linked primarily to personal resources rather than external factors. Opportunities for success grow considerably with an increase in age and professional status. In the process of fulfilling one’s goals, health condition, professional qualification, aid from relatives and friends, as well as helpful connections — these are all much more substantial determinants than one’s material security. On the other hand, the importance of such a factor as material prosperity rises from the utmost minimum for young people to near the top in regards to the elderly. Success in life is more often achieved by determined, persistent people who are confident in their own abilities, who possess a wide array of adaptation resources, and who are able to plan ahead.
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Hodes, Martha. "Utter Confusion and Contradiction: Franz Boas and the Problem of Human Complexion." In Indigenous Visions. Yale University Press, 2018. http://dx.doi.org/10.12987/yale/9780300196511.003.0009.

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In the late nineteenth century, Franz Boas joined other men of science who sought to establish a rational method of labeling and organizing variations in human complexion. For Boas, it was imperative to include Native Americans, especially given the prevailing notion that, in collision with white civilization, they were soon to become extinct. Thus, Boas undertook the massive endeavor of calculation and quantification of Indian bodies. This chapter argues that when Boas published his key work, The Mind of Primitive Man in 1911, he had taken important, if tentative, steps toward disrupting the scientific drive toward racial classification. By reading Boas's raw data sheets from the early 1890s along with his writings on an array of topics, one can speculate on the significance of this little-studied aspect of his progressive assertions about race in The Mind of Primitive Man.
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Raychaudhuri, Soumya. "Textual Profiles of Genes." In Computational Text Analysis. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780198567400.003.0010.

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Using algorithms to analyze natural language text is a challenging task. Recent advances in algorithms, and increased availability of computational power and online text has resulted in incremental progress in text analysis (Rosenfeld 2000). For certain specific applications natural language processing algorithms can rival human performance. Even the simplest algorithms and approaches can glean information from the text and do it at a rate much faster than humans. In the case of functional genomics, where an individual assay might include thousands of genes, and tens of thousands of documents pertinent to those genes, the speed of text mining approaches offers a great advantage to investigators trying to understand the data. In this chapter, we will focus on techniques to convert text into simple numerical vectors to facilitate computation. Then we will go on to discuss how these vectors can be combined into textual profiles for genes; these profiles offer additional biologically meaningful information that can complement available genomics data sets. The previous chapter introduced methods to analyze gene expression data and sequence data. The focus of many analytical methods was comparing and grouping genes by similarity. Some sequence analysis methods like dynamic programming and BLAST offer opportunities to compare two sequences, while multiple sequence alignment and weight matrices provide a means to compare families of sequences. Similarly, gene expression array analysis approaches are mostly contingent on distance metrics that compare gene expression profiles to each other; clustering and classification algorithms provide a means to group similar genes. The primary goal of applying these methods was to transfer knowledge between similar genes. We can think of the scientific literature as yet another data type and define document similarity metrics. Algorithms that tap the knowledge locked in the scientific literature require sophisticated natural language processing approaches. On the other hand, assessing document similarity is a comparatively easier task. A measure of document similarity that corresponds to semantic similarity between documents can also be powerful. For example, we might conclude that two genes are related if documents that refer to them are semantically similar.
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Raychaudhuri, Soumya. "Analyzing Groups of Genes." In Computational Text Analysis. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780198567400.003.0013.

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The analysis of large-scale genomic data (such as sequences or expression patterns) frequently involves grouping genes based on common experimental features. The goal of manual or automated analysis of genomics data is to define groups of genes that have shared features within the data, and also have a common biological basis that can account for those commonalities. In utilizing algorithms that define groups of genes based on patterns in data it is critical to be able to assess whether the groups also share a common biological function. In practice, this goal is met by relying on biologists with an extensive understanding of diverse genes that decipher the biology accounting for genes with correlated patterns. They identify the relevant functions that account for experimental results. For example, experts routinely scan large numbers of gene expression clusters to see if any of the clusters are explained by a known biological function. Efficient definition and interpretation of these groups of genes is challenging because the number and diversity of genes exceed the ability of any single investigator to master. Here, we argue that computational methods can utilize the scientific literature to effectively assess groups of genes. Such methods can then be used to analyze groups of genes created by other bioinformatics algorithms, or actually assist in the definition of gene groups. In this chapter we explore statistical scoring methods that score the ‘‘coherence’’ of a gene group using only the scientific literature about the genes—that is whether or not a common function is shared between the genes in the group. We propose and evaluate such a method, and compare it to some other possible methods. In the subsequent chapter, we apply these concepts to gene expression analysis. The major concepts of this chapter are described in the frame box. We begin by introducing the concept of functional coherence. We describe four different strategies to assess the functional coherence of a group of genes. The final part of the chapter emphasizes the most effective of these methods, the neighbor divergence per gene. We present a discussion of its performance properties in general and on its robustness given imperfect groups. Finally we present an example of an application to gene expression array data.
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O’Brien, Margaret. "Evolution of an Information Manager." In Long-Term Ecological Research. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780199380213.003.0055.

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I work in the realm between environmental research and information technology. My academic training provided background in environmental and ecological research. The Long-Term Ecological Research (LTER) program’s culture of data sharing necessitates understanding formats, structures, and vocabularies required to effectively manage research products. I do not “educate” per se, but I advance the field of data management by mentoring and by training student assistants. Because of the LTER program’s early adoption of data management plans, I am able to review and help write these for researchers. The diverse data of the LTER program are widely regarded, with standardized, complete metadata records, so they are sought after by programmers creating sophisticated software tools. I provide an example of real-world data management for those in informatics research and translate concepts between programmers and environmental researchers. The broad array of tasks means that collaboration among information managers is essential. Collaboration works best when groups are small, problems well defined, and when we already have a similar approach and technology. When I started in the LTER program, cataloging and publishing data represented a novel concept. Now, data management is no longer an afterthought. The LTER program has fostered that. I am not a research scientist; I am a data manager. The role is a bridge between research and information technology, and my role in the LTER program is to increase the visibility and awareness of data science for ecology. For most of the last decade, I have been the information manager for the Santa Barbara Coastal site (SBC) at the University of California (UC) Santa Barbara (Figure 34.1). My primary responsibilities are to facilitate research at SBC and to assemble data packages for cataloging. This means that I work with system administrators on hardware needs and user accounts and with scientific staff to keep time series data up to date. I design data products and metadata systems for sharing within and outside the group, design and maintain dynamic websites, and devise the software to hold the “information management system” together. SBC shares scientists and technical personnel with the Moorea Coral Reef site (MCR).
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Conference papers on the topic "Scientific Array Data"

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Marcin, Simon, and Andre Csillaghy. "Accelerating scientific algorithms in array databases with GPUs." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258217.

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Seering, Adam, Philippe Cudre-Mauroux, Samuel Madden, and Michael Stonebraker. "Efficient Versioning for Scientific Array Databases." In 2012 IEEE International Conference on Data Engineering (ICDE 2012). IEEE, 2012. http://dx.doi.org/10.1109/icde.2012.102.

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Soroush, E., and M. Balazinska. "Time travel in a scientific array database." In 2013 29th IEEE International Conference on Data Engineering (ICDE 2013). IEEE, 2013. http://dx.doi.org/10.1109/icde.2013.6544817.

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Marcin, Simon, and Andre Csillaghy. "Running scientific algorithms as array database operators: Bringing the processing power to the data." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840974.

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Wang, Wenjuan, Taoying Liu, Dixin Tang, Hong Liu, Wei Li, and Rubao Lee. "SparkArray: An Array-Based Scientific Data Management System Built on Apache Spark." In 2016 IEEE International Conference on Networking, Architecture and Storage (NAS). IEEE, 2016. http://dx.doi.org/10.1109/nas.2016.7549422.

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Han, Donghyoung, Yoon-Min Nam, Min-Soo Kim, Kyongseok Park, and Sunggeun Han. "SciDFS: An In-Situ Processing System for Scientific Array Data Based on Distributed File System." In 2018 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2018. http://dx.doi.org/10.1109/bigcomp.2018.00062.

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Lustosa, Hermano, Fábio Porto, and Patrick Valduriez. "SAVIME: A Database Management System for Simulation Data Analysis and Visualization." In XXXIV Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbbd.2019.8810.

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Limitations in current DBMSs prevent their wide adoption in scientific applications. In order to make scientific applications benefit from DBMS support, enabling declarative data analysis and visualization over scientific data, we present an in-memory array DBMS system called SAVIME. In this work we describe the system SAVIME, along with its data model. Our preliminary evaluation shows how SAVIME, by using a simple storage definition language (SDL) can outperform the state-of-the-art array database system, SciDB, during the process of data ingestion. We also show that is possible to use SAVIME as a storage alternative for a numerical solver without affecting its scalability.
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Lombardi, Saverio, Lucio A. Antonelli, Denis Bastieri, et al. "ASTRI SST-2M prototype and mini-array data reconstruction and scientific analysis software in the framework of the Cherenkov Telescope Array." In SPIE Astronomical Telescopes + Instrumentation, edited by Gianluca Chiozzi and Juan C. Guzman. SPIE, 2016. http://dx.doi.org/10.1117/12.2231656.

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Kmiec, Mateusz. "MICROSEISMIC EVENT DETECTION USING DIFFERENT ALGORITHMS ON REAL DATA FROM PATCH ARRAY GEOPHONE GRID FROM EASTERN POMERANIA FRACTURING JOB." In 17th International Multidisciplinary Scientific GeoConference SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgem2017/14/s05.032.

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Niu, Ye, Abdullah Al-Mamun, Hui Lin, Tonglin Li, Yi Zhao, and Dongfang Zhao. "Toward Scalable Analysis of Multidimensional Scientific Data: A Case Study of Electrode Arrays." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622423.

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Reports on the topic "Scientific Array Data"

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Daniels, Matthew, Autumn Toney, Melissa Flagg, and Charles Yang. Machine Intelligence for Scientific Discovery and Engineering Invention. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200099.

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The advantages of nations depend in part on their access to new inventions—and modern applications of artificial intelligence can help accelerate the creation of new inventions in the years ahead. This data brief is a first step toward understanding how modern AI and machine learning have begun accelerating growth across a wide array of science and engineering disciplines in recent years.
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