Stopped-flow spectroscopy

2000 ◽  
Author(s):  
M. T. Wilson ◽  
J. Torres
Keyword(s):  
Large Data ◽  
Kinetic Studies ◽  
Point Of View ◽  
Quality Data ◽  
Stopped Flow ◽  
Light Sources ◽  
Data Sets ◽  
Kinetic Measurements ◽  
Signal Average ◽  
Short Time

There was a time, fortunately some years ago now, when to undertake rapid kinetic measurements using a stopped-flow spectrophotometer verged on the heroic. One needed to be armed with knowledge of amplifiers, light sources, oscilloscopes etc. and ideally one’s credibility was greatly enhanced were one to build one’s own instrument. Analysis of the data was similarly difficult. To obtain a single rate constant might involve a wide range of skills in addition to those required for the chemical/biochemical manipulation of the system and could easily include photography, developing prints and considerable mathematical agility. Now all this has changed and, from the point of view of the scientist attempting to solve problems through transient kinetic studies, a good thing too! Very high quality data can readily be obtained by anyone with a few hours training and the ability to use a mouse and ‘point and click’ programs. Excellent stopped -flow spectrophotometers can be bought which are reliable, stable, sensitive and which are controlled by computers able to signal-average and to analyse, in seconds, kinetic progress curves in a number of ways yielding rate constants, amplitudes, residuals and statistics. Because it is now so easy, from the technical point of view, to make measurement and to do so without an apprenticeship in kinetic methods, it becomes important to make sure that one collects data that are meaningful and open to sensible interpretation. There are a number of pitfalls to avoid. The emphasis of this article is, therefore, somewhat different to that written by Eccleston (1) in an earlier volume of this series. Less time will be spent on consideration of the hardware, although the general principles are given, but the focus will be on making sure that the data collected means what one thinks it means and then how to be sure one is extracting kinetic parameters from this in a sensible way. With the advent of powerful, fast computers it has now become possible to process very large data sets quickly and this has paved the way for the application of ‘rapid scan’ devices (usually, but not exclusively, diode arrays), which allow complete spectra to be collected at very short time intervals during a reaction.

Folksonomy

2011 ◽  
pp. 877-891
Author(s):  
Katrin Weller ◽  
Isabella Peters ◽  
Wolfgang G. Stock
Keyword(s):  
Large Data ◽  
Document Retrieval ◽  
Point Of View ◽  
Knowledge Organization ◽  
Data Sets ◽  
Document Collections ◽  
User Input ◽  
Indexing Method ◽  
Indexing And Retrieval ◽  
Locating Information

This chapter discusses folksonomies as a novel way of indexing documents and locating information based on user generated keywords. Folksonomies are considered from the point of view of knowledge organization and representation in the context of user collaboration within the Web 2.0 environments. Folksonomies provide multiple benefits which make them a useful indexing method in various contexts; however, they also have a number of shortcomings that may hamper precise or exhaustive document retrieval. The position maintained is that folksonomies are a valuable addition to the traditional spectrum of knowledge organization methods since they facilitate user input, stimulate active language use and timeliness, create opportunities for processing large data sets, and allow new ways of social navigation within document collections. Applications of folksonomies as well as recommendations for effective information indexing and retrieval are discussed.

scholarly journals Characterizing water quality monitoring visualization with Hadoop and Google Maps

2017 ◽  
Vol 12 (4) ◽  
pp. 882-893 ◽  
Author(s):  
Weijian Huang ◽  
Xinfei Zhao ◽  
Yuanbin Han ◽  
Wei Du ◽  
Yao Cheng
Keyword(s):  
Water Quality ◽  
Water Environment ◽  
Large Data ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Data Sets ◽  
Google Maps ◽  
Water Quality Data ◽  
Computer Clusters

Abstract In water quality monitoring, the complexity and abstraction of water environment data make it difficult for staff to monitor the data efficiently and intuitively. Visualization of water quality data is an important part of the monitoring and analysis of water quality. Because water quality data have geographic features, their visualization can be realized using maps, which not only provide intuitive visualization, but also reflect the relationship between water quality and geographical position. For this study, the heat map provided by Google Maps was used for water quality data visualization. However, as the amount of data increases, the computational efficiency of traditional development models cannot meet the computing task needs quickly. Effective storage, extraction and analysis of large water data sets becomes a problem that needs urgent solution. Hadoop is an open source software framework running on computer clusters that can store and process large data sets efficiently, and it was used in this study to store and process water quality data. Through reasonable analysis and experiment, an efficient and convenient information platform can be provided for water quality monitoring.

Folksonomy

2010 ◽  
pp. 132-146 ◽  
Author(s):  
Katrin Weller ◽  
Isabella Peters ◽  
Wolfgang G. Stock
Keyword(s):  
Large Data ◽  
Document Retrieval ◽  
Point Of View ◽  
Knowledge Organization ◽  
Data Sets ◽  
Document Collections ◽  
User Input ◽  
Indexing Method ◽  
Indexing And Retrieval ◽  
Locating Information

This chapter discusses folksonomies as a novel way of indexing documents and locating information based on user generated keywords. Folksonomies are considered from the point of view of knowledge organization and representation in the context of user collaboration within the Web 2.0 environments. Folksonomies provide multiple benefits which make them a useful indexing method in various contexts; however, they also have a number of shortcomings that may hamper precise or exhaustive document retrieval. The position maintained is that folksonomies are a valuable addition to the traditional spectrum of knowledge organization methods since they facilitate user input, stimulate active language use and timeliness, create opportunities for processing large data sets, and allow new ways of social navigation within document collections. Applications of folksonomies as well as recommendations for effective information indexing and retrieval are discussed.

Feminization of Agriculture in China? Myths Surrounding Women's Participation in Farming

2008 ◽  
Vol 194 ◽  
pp. 327-348 ◽  
Author(s):  
Alan de Brauw ◽  
Qiang Li ◽  
Chengfang Liu ◽  
Scott Rozelle ◽  
Linxiu Zhang
Keyword(s):  
Agricultural Sector ◽  
Point Of View ◽  
Women Farmers ◽  
Quality Data ◽  
Data Sets ◽  
Access To Credit ◽  
Women's Participation ◽  
Economic Development Strategies ◽  
Feminization Of Agriculture

AbstractThe goals of this article are to help build a clear picture of the role of women in China's agriculture, to assess whether or not agricultural feminization has been occurring, and if so, to measure its impact on labour use, productivity and welfare. The article uses two high quality data sets to explore who is working on China's farms and the effects of the labour allocation decisions of rural households on labour use, productivity and welfare. It makes three main contributions. First, we establish a conceptual framework within which to define the different dimensions of agricultural feminization and its expected consequences. Second, as a contribution to the China literature and contrary to popular perceptions, we believe we have mostly debunked the myth that China's agriculture is becoming feminized; it is not. We also find that even if women were taking over farms, the consequences in China would be mostly positive – from a labour supply, productivity and income point of view. Finally, there may be some lessons for the rest of the world on what policies and institutions help make women productive when they work on and manage a nation's agricultural sector. Policies that ensure equal access to land, regulations that dictate open access to credit, and economic development strategies that encourage competitive and efficient markets all contribute to an environment in which women farmers can succeed.

scholarly journals A search-engine for single-cell epigenome profiles for multi-purpose applications

2021 ◽  
Author(s):  
Shreya Mishra ◽  
Smriti Chawla ◽  
Neetesh Pandey ◽  
Debarka SenGupta ◽  
Vibhor Kumar
Keyword(s):  
Single Cell ◽  
Large Data ◽  
Open Chromatin ◽  
Data Sets ◽  
Large Pool ◽  
Mouse Cells ◽  
Low Dimensional ◽  
Cell Expression ◽  
Short Time ◽  
Multiple Query

AbstractThe true benefits of large data-sets of single-cell epigenome and transcriptome profiles can be availed only when they are searchable to annotate individual unannotated cells. Matching a single-cell epigenome profile to a large pool of reference cells remains as a challenge and largely unexplored. Here, we introduce scEpiSearch, which enables a user to query single-cell open-chromatin read-count matrices for comparison against a large pool of single-cell expression and open-chromatin profiles from human and mouse cells (∼ 3.5 million cells). Besides providing accurate search in a short time and scalable visualization of results for multiple query cells, scEpisearch also provides a low-dimensional representation of single-cell open-chromatin profiles. It outperformed many other methods in terms of correct low-dimensional embedding of single-cell open-chromatin profiles originating from different platforms and species. Here we show how scEpiSearch is unique in providing several facilities to assist researchers in the analysis of single-cell open-chromatin profiles to infer cellular state, lineage, potency and representative genes.

A global approach to the n-dimensional traveling salesman problem: application to the optimization of crystallographic data collection

1987 ◽  
Vol 20 (6) ◽  
pp. 507-511
Author(s):  
J. B. Weinrach ◽  
D. W. Bennett
Keyword(s):  
Data Collection ◽  
Traveling Salesman Problem ◽  
Large Data ◽  
Traveling Salesman ◽  
Large Data Sets ◽  
Three Dimensions ◽  
Data Sets ◽  
Time Savings ◽  
The Traveling Salesman Problem ◽  
Short Time

An algorithm for the optimization of data collection time has been written and a subsequent computer program tested for diffractometer systems. The program, which utilizes a global statistical approach to the traveling salesman problem, yields reasonable solutions in a relatively short time. The algorithm has been successful in treating very large data sets (up to 4000 points) in three dimensions with subsequent time savings of ca 30%.

scholarly journals Challenges in Data Quality and Complexity of Managing Data Quality Assessment in Big Data

2020 ◽  
Vol 9 (3) ◽  
pp. 589-593
Keyword(s):  
Data Quality ◽  
Large Data ◽  
Point Of View ◽  
Quality Data ◽  
Quality Information ◽  
High Quality ◽  
High Quality Data ◽  
Equal Time ◽  
The Troubles ◽  
Data Quality Assessment

High Quality Data are the precondition for examining and making use of enormous facts and for making sure the estimation of the facts. As of now, far reaching exam and research of price gauges and satisfactory appraisal strategies for massive records are inadequate. To begin with, this paper abridges audits of Data excellent studies. Second, this paper examines the records attributes of the enormous records condition, presents high-quality difficulties appeared by large data, and defines a progressive facts exceptional shape from the point of view of records clients. This system accommodates of big records best measurements, best attributes, and best files. At long last, primarily based on this system, this paper builds a dynamic appraisal technique for records exceptional. This technique has excellent expansibility and versatility and can address the troubles of enormous facts fine appraisal. A few explores have verified that preserving up the character of statistics is regularly recognized as hazardous, however at the equal time is considered as simple to effective basic leadership in building aid the executives. Enormous data sources are exceptionally wide and statistics structures are thoughts boggling. The facts got may additionally have satisfactory troubles, for example, facts mistakes, lacking data, irregularities, commotion, and so forth. The motivation behind facts cleansing (facts scouring) is to pick out and expel mistakes and irregularities from facts so as to decorate their quality. Information cleansing may be separated into 4 examples dependent on usage techniques and degrees manual execution, composing of splendid software programs, records cleaning inconsequential to specific software fields, and taking care of the difficulty of a kind of explicit software area. In these 4 methodologies, the 1/3 has terrific down to earth esteem and may be connected effectively.

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.

Confidentiality of Statistical Records: A Threat-Monitoring Scheme for On Line Dialogue

1976 ◽  
Vol 15 (01) ◽  
pp. 36-42 ◽  
Author(s):  
J. Schlörer
Keyword(s):  
Statistical Data ◽  
Cost Benefit ◽  
Data Bank ◽  
High Ratio ◽  
Point Of View ◽  
Data Sets ◽  
Monitoring Scheme ◽  
Access Controls ◽  
On Line ◽  
Bona Fide

From a statistical data bank containing only anonymous records, the records sometimes may be identified and then retrieved, as personal records, by on line dialogue. The risk mainly applies to statistical data sets representing populations, or samples with a high ratio n/N. On the other hand, access controls are unsatisfactory as a general means of protection for statistical data banks, which should be open to large user communities. A threat monitoring scheme is proposed, which will largely block the techniques for retrieval of complete records. If combined with additional measures (e.g., slight modifications of output), it may be expected to render, from a cost-benefit point of view, intrusion attempts by dialogue valueless, if not absolutely impossible. The bona fide user has to pay by some loss of information, but considerable flexibility in evaluation is retained. The proposal of controlled classification included in the scheme may also be useful for off line dialogue systems.

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