HADRON SPECTROSCOPY WITH CLEO DATA

2003 ◽  
Vol 18 (03) ◽  
pp. 515-523
Author(s):  
D. Urner
Keyword(s):  
Lattice Qcd ◽  
Perturbative Qcd ◽  
High Precision ◽  
Light Meson ◽  
Data Sets ◽  
Data Set ◽  
Hadron Spectroscopy ◽  
Cleo Collaboration ◽  
Meson Spectroscopy ◽  
Existing Data

The CLEO collaboration will explore the charm sector starting 2003. It is foreseen to collect on the order of 6 million [Formula: see text] pairs, 300000 [Formula: see text] pair at threshold and one billion J/ψ decays. High precision charm data will enable us to validate upcoming lattice QCD calculations that are expected to produce 1-3% errors for some non-perturbative QCD quantities. Virtually background free, they will provide interesting decay mechanisms to explore light meson spectroscopy. The radiative J/ψ decays will be the first high statistics data set well suited for meson spectroscopy between 1600 and 3000 MeV. Together with the already existing data from the ϒ resonances, data sets for a large number of decay mechanisms will be available, which, combined, can be used to extract the nature of many resonances.

scholarly journals Light-Meson Spectroscopy at GlueX

2018 ◽  
Vol 46 ◽  
pp. 1860029 ◽  
Author(s):  
Alexander Austregesilo
Keyword(s):  
Light Meson ◽  
Meson Spectrum ◽  
Amplitude Analysis ◽  
Hydrogen Target ◽  
Data Set ◽  
Early Results ◽  
Meson Spectroscopy ◽  
Beam Asymmetries ◽  
Linearly Polarized ◽  
Pseudo Scalar

GlueX at Jefferson Lab aims to study the light meson spectrum with an emphasis on the search for light hybrid mesons. To this end, a linearly-polarized [Formula: see text]GeV photon beam impinges on a hydrogen target contained within a hermetic detector with near-complete neutral and charged particle coverage. In 2016, the experiment completed its commissioning and subsequently started to take data in its design configuration. With the size of the data set so far, GlueX already exceeds previous experiments for polarized photoproduction in this energy regime. A selection of early results will be presented, focusing on beam asymmetries for pseudo-scalar and vector mesons. The potential to make significant contributions to the field of light-meson spectroscopy is highlighted by the observation of several known meson resonances. Furthermore, the strategy to map the light meson spectrum with amplitude analysis tools will be outlined.

scholarly journals How to Automatically Document Data With the codebook Package to Facilitate Data Reuse

2019 ◽  
Vol 2 (2) ◽  
pp. 169-187 ◽  
Author(s):  
Ruben C. Arslan
Keyword(s):  
R Package ◽  
Data Reuse ◽  
Data Sets ◽  
Data Set ◽  
Psychological Scales ◽  
Rich Data ◽  
Data Documentation ◽  
Machine Readable ◽  
Basic Standards ◽  
Existing Data

Data documentation in psychology lags behind not only many other disciplines, but also basic standards of usefulness. Psychological scientists often prefer to invest the time and effort that would be necessary to document existing data well in other duties, such as writing and collecting more data. Codebooks therefore tend to be unstandardized and stored in proprietary formats, and they are rarely properly indexed in search engines. This means that rich data sets are sometimes used only once—by their creators—and left to disappear into oblivion. Even if they can find an existing data set, researchers are unlikely to publish analyses based on it if they cannot be confident that they understand it well enough. My codebook package makes it easier to generate rich metadata in human- and machine-readable codebooks. It uses metadata from existing sources and automates some tedious tasks, such as documenting psychological scales and reliabilities, summarizing descriptive statistics, and identifying patterns of missingness. The codebook R package and Web app make it possible to generate a rich codebook in a few minutes and just three clicks. Over time, its use could lead to psychological data becoming findable, accessible, interoperable, and reusable, thereby reducing research waste and benefiting both its users and the scientific community as a whole.

scholarly journals Current Topics in Avian Conservation Genetics with Special Reference to the Southwestern Willow Flycatcher

2016 ◽  
Vol 9 (1) ◽  
pp. 60-69
Author(s):  
Robert M. Zink
Keyword(s):  
Analytical Methods ◽  
Scientific Method ◽  
Morphological Data ◽  
Data Sets ◽  
Avian Conservation ◽  
Full Data ◽  
Data Set ◽  
Willow Flycatcher ◽  
Existing Data ◽  
Conservation Decisions

It is sometimes said that scientists are entitled to their own opinions but not their own set of facts. This suggests that application of the scientific method ought to lead to a single conclusion from a given set of data. However, sometimes scientists have conflicting opinions about which analytical methods are most appropriate or which subsets of existing data are most relevant, resulting in different conclusions. Thus, scientists might actually lay claim to different sets of facts. However, if a contrary conclusion is reached by selecting a subset of data, this conclusion should be carefully scrutinized to determine whether consideration of the full data set leads to different conclusions. This is important because conservation agencies are required to consider all of the best available data and make a decision based on them. Therefore, exploring reasons why different conclusions are reached from the same body of data has relevance for management of species. The purpose of this paper was to explore how two groups of researchers can examine the same data and reach opposite conclusions in the case of the taxonomy of the endangered subspecies Southwestern Willow Flycatcher (Empidonax traillii extimus). It was shown that use of subsets of data and characters rather than reliance on entire data sets can explain conflicting conclusions. It was recommend that agencies tasked with making conservation decisions rely on analyses that include all relevant molecular, ecological, behavioral, and morphological data, which in this case show that the subspecies is not valid, and hence its listing is likely not warranted.

scholarly journals Scalable Micro-planned Generation of Discourse from Structured Data

2020 ◽  
Vol 45 (4) ◽  
pp. 737-763 ◽  
Author(s):  
Anirban Laha ◽  
Parag Jain ◽  
Abhijit Mishra ◽  
Karthik Sankaranarayanan
Keyword(s):  
Natural Language ◽  
Structured Data ◽  
Data Sets ◽  
Data Types ◽  
Data Set ◽  
Language Generation ◽  
Parallel Data ◽  
Simple Sentences ◽  
Diverse Data ◽  
Existing Data

We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Modern data-to-text NLG systems typically use end-to-end statistical and neural architectures that learn from a limited amount of task-specific labeled data, and therefore exhibit limited scalability, domain-adaptability, and interpretability. Unlike these systems, ours is a modular, pipeline-based approach, and does not require task-specific parallel data. Rather, it relies on monolingual corpora and basic off-the-shelf NLP tools. This makes our system more scalable and easily adaptable to newer domains. Our system utilizes a three-staged pipeline that: (i) converts entries in the structured data to canonical form, (ii) generates simple sentences for each atomic entry in the canonicalized representation, and (iii) combines the sentences to produce a coherent, fluent, and adequate paragraph description through sentence compounding and co-reference replacement modules. Experiments on a benchmark mixed-domain data set curated for paragraph description from tables reveals the superiority of our system over existing data-to-text approaches. We also demonstrate the robustness of our system in accepting other popular data sets covering diverse data types such as knowledge graphs and key-value maps.

Preparation of collected primary data for statistical analysis

2018 ◽  
pp. 145-154
Author(s):  
Alan J. Silman ◽  
Gary J. Macfarlane ◽  
Tatiana Macfarlane
Keyword(s):  
Statistical Analysis ◽  
Epidemiological Studies ◽  
Primary Data ◽  
Data Sets ◽  
Data Set ◽  
Patient Questionnaires ◽  
Detailed Statistical Analysis ◽  
Study Methodology ◽  
Existing Data

Although epidemiological studies are increasingly based on the analysis of existing data sets (including linked data sets), many studies still require primary data collection. Such data may come from patient questionnaires, interviews, abstraction from records, and/or the results of tests and measures such as weight or blood test results. The next stage is to analyse the data gathered from individual subjects to provide the answers required. Before commencing with the statistical analysis of any data set, the data themselves must be prepared in a format so that the detailed statistical analysis can achieve its goals. Items to be considered include the format the data are initially collected in and how they are transferred to an appropriate electronic form. This chapter explores how errors are minimized and the quality of the data set ensured. These tasks are not trivial and need to be planned as part of a detailed study methodology.

scholarly journals Revealing Structure in Visualizations of Dense 2D and 3D Parallel Coordinates

2006 ◽  
Vol 5 (2) ◽  
pp. 125-136 ◽  
Author(s):  
Jimmy Johansson ◽  
Patric Ljung ◽  
Mikael Jern ◽  
Matthew Cooper
Keyword(s):  
High Precision ◽  
Transfer Functions ◽  
Multivariate Data ◽  
Data Sets ◽  
Parallel Coordinates ◽  
Data Set ◽  
Number Of Clusters ◽  
Analysis Process ◽  
2D And 3D

Parallel coordinates is a well-known technique used for visualization of multivariate data. When the size of the data sets increases the parallel coordinates display results in an image far too cluttered to perceive any structure. We tackle this problem by constructing high-precision textures to represent the data. By using transfer functions that operate on the high-precision textures, it is possible to highlight different aspects of the entire data set or clusters of the data. Our methods are implemented in both standard 2D parallel coordinates and 3D multi-relational parallel coordinates. Furthermore, when visualizing a larger number of clusters, a technique called ‘feature animation’ may be used as guidance by presenting various cluster statistics. A case study is also performed to illustrate the analysis process when analysing large multivariate data sets using our proposed techniques.

scholarly journals Excited light meson spectroscopy from lattice QCD

2012 ◽  
Author(s):  
Christopher E. Thomas ◽  
Hadron Spectrum Collaboration
Keyword(s):  
Lattice Qcd ◽  
Light Meson ◽  
Meson Spectroscopy

scholarly journals Introducing the Targeted Mass Killing Data Set for the Study and Forecasting of Mass Atrocities

2020 ◽  
Vol 64 (7-8) ◽  
pp. 1524-1547
Author(s):  
Charles Butcher ◽  
Benjamin E. Goldsmith ◽  
Sascha Nanlohy ◽  
Arcot Sowmya ◽  
David Muchlinski
Keyword(s):  
Data Sets ◽  
Mass Killing ◽  
Data Set ◽  
General Data ◽  
Research Questions ◽  
Existing Data

This article describes a new data set for the study of genocide, politicide, and similar atrocities. Existing data sets have facilitated advances in understanding and policy-relevant applications such as forecasting but have been criticized for insufficient transparency, replicability, and for omitting failed or prevented attempts at genocide/politicide. More general data sets of mass civilian killing do not typically enable users to isolate situations in which specific groups are deliberately targeted. The Targeted Mass Killing (TMK) data set identifies 201 TMK episodes, 1946 to 2017, with annualized information on perpetrator intent, severity, targeted groups, and new ordinal and binary indicators of genocide/politicide that can serve as alternatives to existing measures. Users are also able to construct their own indicators based on their research questions or preferred definitions. The article discusses the concept and operationalization of TMK, provides comparisons with other data sets, and highlights some of the strengths and new capabilities of the TMK data.

Improving Data Quality: Actors, Incentives, and Capabilities

2007 ◽  
Vol 15 (4) ◽  
pp. 365-386 ◽  
Author(s):  
Yoshiko M. Herrera ◽  
Devesh Kapur
Keyword(s):  
Data Quality ◽  
International Organizations ◽  
Data Sets ◽  
Data Set ◽  
The Core ◽  
Use Of Data ◽  
Private Organizations ◽  
Assess Data Quality ◽  
Existing Data ◽  
Shape Data

This paper examines the construction and use of data sets in political science. We focus on three interrelated questions: How might we assess data quality? What factors shape data quality? and How can these factors be addressed to improve data quality? We first outline some problems with existing data set quality, including issues of validity, coverage, and accuracy, and we discuss some ways of identifying problems as well as some consequences of data quality problems. The core of the paper addresses the second question by analyzing the incentives and capabilities facing four key actors in a data supply chain: respondents, data collection agencies (including state bureaucracies and private organizations), international organizations, and finally, academic scholars. We conclude by making some suggestions for improving the use and construction of data sets.It is a capital mistake, Watson, to theorise before you have all the evidence. It biases the judgment.—Sherlock Holmes in “A Study in Scarlet”Statistics make officials, and officials make statistics.”—Chinese proverb

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