scholarly journals Tracing Long-term Value Change in (Energy) Technologies: Opportunities of Probabilistic Topic Models Using Large Data Sets

2021 ◽  
pp. 016224392110544
Author(s):  
T. E. de Wildt ◽  
I. R. van de Poel ◽  
E. J. L. Chappin
Keyword(s):  
Fossil Fuels ◽  
Social Acceptance ◽  
Topic Models ◽  
Large Data ◽  
Data Sets ◽  
Value Change ◽  
Energy Technologies ◽  
Text Corpora ◽  
Probabilistic Topic Models

We propose a new approach for tracing value change. Value change may lead to a mismatch between current value priorities in society and the values for which technologies were designed in the past, such as energy technologies based on fossil fuels, which were developed when sustainability was not considered a very important value. Better anticipating value change is essential to avoid a lack of social acceptance and moral acceptability of technologies. While value change can be studied historically and qualitatively, we propose a more quantitative approach that uses large text corpora. It uses probabilistic topic models, which allow us to trace (new) values that are (still) latent. We demonstrate the approach for five types of value change in technology. Our approach is useful for testing hypotheses about value change, such as verifying whether value change has occurred and identifying patterns of value change. The approach can be used to trace value change for various technologies and text corpora, including scientific articles, newspaper articles, and policy documents.

Ahead of the Curve

2017 ◽  
Author(s):  
Douglas Allchin
Keyword(s):  
Fossil Fuels ◽  
Data Sets ◽  
Hard Data ◽  
Scientific Investigations ◽  
Job Opportunities ◽  
Stereotypical Image ◽  
Carbon Dioxide Co2 ◽  
Term Data ◽  
Mendel’S Laws

Graphs function plainly to summarize data. They hardly seem momentous. They are not like a famous discovery, whose significance is often marked by an eponymous name: Mendel’s laws, the Watson and Crick model of DNA, Darwinian theory. Who would name a mere graph? They seem mundane fragments of science, hardly worth celebrating. A notable exception, however, is the Keeling Curve (Figure 2.1). This simple graph depicts the steady rise in the concentration of carbon dioxide (CO2) in the Earth’s atmosphere over the last half century. It helps document how humans have transformed the atmosphere and, with it, the Earth’s temperature. The Keeling Curve is a linchpin in the evidence that humans have changed the planet’s climate. The Keeling Curve starts in 1958 and continues uninterrupted for over five decades. The scale of the data is extraordinary, an ideal rarely achieved in science. The hard data from real-time measurements show the steady accumulation of CO2 from burning fossil fuels. The results, presented in a simple yet striking visual format, serve to warn an energy-hungry culture of its environmental hubris. Although just a graph, it is monumental in scope and significance. The Keeling Curve, viewed in retrospect, raises an interesting question about how science works. How do such important long-term data sets emerge? Often we assume that scientific investigations find just what they intend to find. That is an implicit lesson of the tidy scientific method, as widely presented (see essay 5). But should we trust this sacred bovine? Could anyone have predicted this curve or its importance in advance? How did these important data originate? What happened before the graph was fully created? What happened, literally, ahead of the Curve? The Keeling Curve is named after its creator, Charles David Keeling. In the 1950s, as a handsome young man frequently enjoying the great outdoors (Figure 2.2), he hardly fit the stereotypical image of a scientist clad in a white coat, isolated in a lab. Indeed, with a fresh degree in chemistry, he turned down many job opportunities because he wanted to be closer to nature on the West Coast.

scholarly journals Bibliography, catalogs, pixel data: Management of heterogeneous Big Data at CDS by the documentalists

2018 ◽  
Vol 186 ◽  
pp. 02001 ◽  
Author(s):  
M. Buga ◽  
P. Fernique ◽  
C. Bot ◽  
M. G. Allen ◽  
F. Bonnarel ◽  
...  
Keyword(s):  
Data Storage ◽  
High Speed ◽  
Three Dimensional ◽  
Numerical Data ◽  
Large Data ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Astronomical Data ◽  
Data Cubes

High speed Internet and the evolution of data storage space in terms of cost-effectiveness has changed the way data are managed today. Large amounts of heterogeneous data can now be visualized easily and rapidly using interactive applications such as “Google Maps”. In this respect, the Hierarchical Progressive Survey (HiPS) method has been developed by the Centre de Données astronomiques de Strasbourg (CDS) since 2009. HiPS uses the hierarchical sky tessellation called HEALPix to describe and organize images, data cubes or source catalogs. These HiPS can be accessed and visualized using applications such as Aladin. We show that structuring the data using HiPS enables easy and quick access to large and complex sets of astronomical data. As with bibliographic and catalog data, full documentation and comprehensive metadata are absolutely required for pertinent usage of these data. Hence the role of documentalists in the process of producing HiPS is essential. We present the interaction between documentalists and other specialists who are all part of the CDS team and support this process. More precisely, we describe the tools used by the documentalists to generate HiPS or to update the Virtual Observatory standardized descriptive information (the “metadata”). We also present the challenges faced by the documentalists processing such heterogeneous data on the scales of megabytes up to petabytes. On one hand, documentalists at CDS manage small size textual or numerical data for one or few astronomical objects. On the other hand, they process large data sets such as big catalogs containing heterogeneous data like spectra, images or data cubes, for millions of astronomical objects. Finally, by participating in the development of an interactive visualization of images or three-dimensional data cubes using the HiPS method, documentalists contribute to a long-term management of complex, large astronomical data.

scholarly journals Bioinformatics and functional magnetic resonance imaging in clinical populations: practical aspects of data collection, analysis, interpretation, and management

2005 ◽  
Vol 19 (4) ◽  
pp. 1-5 ◽  
Author(s):  
Diana J. Vincent ◽  
Mark W. Hurd
Keyword(s):  
Magnetic Resonance ◽  
Data Collection ◽  
Planning Process ◽  
Developmental Stages ◽  
Large Data ◽  
Cortical Activation ◽  
Data Sets ◽  
Functional Magnetic Resonance ◽  
Neurosurgical Planning

In this paper the authors review the issues associated with bioinformatics and functional magnetic resonance (fMR) imaging in the context of neurosurgery. They discuss the practical aspects of data collection, analysis, interpretation, and the management of large data sets, and they consider the challenges involved in the adoption of fMR imaging into clinical neurosurgical practice. Their goal is to provide neurosurgeons and other clinicians with a better understanding of some of the current issues associated with bioinformatics or neuroinformatics and fMR imaging. Thousands to tens of thousands of images are typically acquired during an fMR imaging session. It is essential to follow an activation task paradigm exactly to obtain an accurate representation of cortical activation. These images are then interactively postprocessed offline to produce an activation map, or in some cases a series of maps. The maps may then be viewed and interpreted in consultation with a neurosurgeon and/or other clinicians. After this consultation, long-term archiving of the processed fMR activation maps along with the standard structural MR images is a complex but necessary final step in this process. The fMR modality represents a valuable tool in the neurosurgical planning process that is still in the developmental stages for routine clinical use, but holds exceptional promise for patient care.

Using large data sets in long-term care to measure and improve quality

2004 ◽  
Vol 52 (1) ◽  
pp. 38-44 ◽  
Author(s):  
Judith Ryan ◽  
Robyn I. Stone ◽  
Charissa R. Raynor
Keyword(s):  
Long Term Care ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Improve Quality ◽  
Term Care

Governance for securing feedstock supply of biogas plants

2012 ◽  
Vol 12 (1) ◽  
pp. 67-84 ◽  
Author(s):  
S. Gold
Keyword(s):  
Capital Investment ◽  
Fossil Fuels ◽  
Biogas Plant ◽  
Specific Situation ◽  
Relational Contracts ◽  
Market Prices ◽  
Societal Culture ◽  
Energy Technologies ◽  
Energy Options

As stocks of fossil fuels become harder to access and exploit worldwide, renewable energies as a sustainable option have entered the limelight in recent years. Bio-energy plays an essential role in the current mix of renewable energy technologies. In Germany, the state fosters insistently biogas technology within the concert of bio-energy options. Literature suggests that long-term stable economic ties with supplying farmers are the pivotal managerial challenge of bio-energy plant operators to secure a constant and competitively priced feedstock supply. Biogas plant operators are generally in a vulnerable position vis-à-vis their suppliers due to their substantial specific capital investment and a rather restricted economically viable catchment area. Transactional uncertainty due to volatile agricultural market prices, changing yield quantities, and unpredictable behaviour of suppliers makes biogas plant operators aim for effective governance. The study aims by means of in-depth interviews to explore how biogas plant operators perceive their specific situation of transactional uncertainty and which instruments (concerning formal and relational contracts) they use for securing stable and constant feedstock supply. Findings indicate that mutually reinforcing interaction of formal contracts, business partnerships, and equity participation with relational contracts proves to be successful in establishing long-term stable feedstock supply. While the specific rural societal culture has an ambiguous impact on governance design, local rootedness of biogas plant operators provide high levels of social capital and thus clearly facilitate the search for effective solutions that satisfy all parties.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

Asian Americans and Pacific Islanders: Employment Issues in the United States

2011 ◽  
Vol 9 (1-2) ◽  
pp. 58-69
Author(s):  
Marlene Kim
Keyword(s):  
United States ◽  
Asian Americans ◽  
The United States ◽  
Current Data ◽  
Pacific Islanders ◽  
Native Hawaiians ◽  
Data Sets ◽  
High Poverty ◽  
Unemployment Rates

Asian Americans and Pacific Islanders (AAPIs) in the United States face problems of discrimination, the glass ceiling, and very high long-term unemployment rates. As a diverse population, although some Asian Americans are more successful than average, others, like those from Southeast Asia and Native Hawaiians and Pacific Islanders (NHPIs), work in low-paying jobs and suffer from high poverty rates, high unemployment rates, and low earnings. Collecting more detailed and additional data from employers, oversampling AAPIs in current data sets, making administrative data available to researchers, providing more resources for research on AAPIs, and enforcing nondiscrimination laws and affirmative action mandates would assist this population.

NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

2019 ◽  
Author(s):  
Mykhajlo Klymash ◽  
Olena Hordiichuk — Bublivska ◽  
Ihor Tchaikovskyi ◽  
Oksana Urikova
Keyword(s):  
Distributed Systems ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Decomposition ◽  
Distributed Information ◽  
Software Model ◽  
Computing Performance ◽  
Mapreduce Model ◽  
Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of com­putational efficiency on distributed systems were obtained using the MPI messa­ging protocol and MapReduce node interaction software model. Were analyzed the effici­ency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

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