Living standards in the past: new perspectives on well-being in Asia and Europe By Robert C. Allen, Tommy Bengtsson and Martin Dribe, eds. Oxford: Oxford University Press, 2005. Pp. xxii + 472. ISBN 0-19-928068-1

2006 ◽  
Vol 1 (2) ◽  
pp. 289-292 ◽  
Author(s):  
Anne Booth
Keyword(s):  
Labour Force ◽  
Large Data ◽  
Well Being ◽  
Living Standards ◽  
Large Data Sets ◽  
Data Sets ◽  
Household Surveys ◽  
Absolute Poverty ◽  
Oxford University ◽  
The Past

We live in an age of increasingly abundant statistical information. The advent of more large data sets obtained from household surveys, as well as from population censuses, labour force surveys, economic censuses and so on, has facilitated reasonably accurate estimates of income and expenditures for households in many parts of the world. These estimates can in turn be used to estimate a number of distributional indicators, as well as estimates of relative and absolute poverty. In addition better census coverage has permitted estimates of infant and child mortality rates, life expectancies, literacy rates and indicators of educational attainment. Such data have in turn been used to estimate composite indicators of wellbeing such as the Human Development Index, not just for entire countries but often for regions within countries as well.

Not all written in stone: interdisciplinary syntheses in echinoderm paleontology

2001 ◽  
Vol 79 (7) ◽  
pp. 1209-1231 ◽  
Author(s):  
Rich Mooi
Keyword(s):  
Evolutionary History ◽  
Large Scale ◽  
Body Wall ◽  
Developmental Trajectories ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Primary Target ◽  
The Past ◽  
Broad Scale

The fossil record of the Echinodermata is relatively complete, and is represented by specimens retaining an abundance of features comparable to that found in extant forms. This yields a half-billion-year record of evolutionary novelties unmatched in any other major group, making the Echinodermata a primary target for studies of biological change. Not all of this change can be understood by studying the rocks alone, leading to synthetic research programs. Study of literature from the past 20 years indicates that over 1400 papers on echinoderm paleontology appeared in that time, and that overall productivity has remained almost constant. Analysis of papers appearing since 1990 shows that research is driven by new finds including, but not restricted to, possible Precambrian echinoderms, bizarre new edrioasteroids, early crinoids, exquisitely preserved homalozoans, echinoids at the K-T boundary, and Antarctic echinoids, stelleroids, and crinoids. New interpretations of echinoderm body wall homologies, broad-scale syntheses of embryological information, the study of developmental trajectories through molecular markers, and the large-scale ecological and phenotypic shifts being explored through morphometry and analyses of large data sets are integrated with study of the fossils themselves. Therefore, recent advances reveal a remarkable and continuing synergistic expansion in our understanding of echinoderm evolutionary history.

First Nations Community Well-Being Research and Large Data Sets: A Respectful Caution

2017 ◽  
Vol 12 (2) ◽  
pp. 15-24 ◽  
Author(s):  
Alexandra S. Drawson ◽  
Aislin R. Mushquash ◽  
Christopher J. Mushquash
Keyword(s):  
First Nations ◽  
Social Determinants ◽  
Large Data ◽  
Well Being ◽  
Large Data Sets ◽  
Data Sets ◽  
Nations People ◽  
Level Data ◽  
Traditional Language ◽  
First Nations People

Health researchers are increasingly encouraged to use large, community-level data sets to examine factors that promote or diminish health, including social determinants. First Nations people in Canada experience disparity in a range of social determinants of health that result in relatively low community well-being scores, when compared to non-First Nations people. However, First Nations people also possess unique protective factors that enhance well-being, such as traditional language usage. Large data sets offer First Nations a new avenue for advocating for supports and services to decrease health inequity while developing culture-based evidence. However, care must be taken to ensure that these data are interpreted appropriately. In this paper, we respectfully offer a cautionary note on the importance of understanding culture and context when conducting First Nations health research with large data sets. We have framed this caution through a narrative presentation of a simple and concrete example. We then outline some approaches to research that can ensure appropriate development of research questions and interpretation of research findings.

A Review of Data Mining Techniques and Applications

2017 ◽  
Vol 21 (1) ◽  
pp. 31-48 ◽  
Author(s):  
Ratchakoon Pruengkarn ◽  
◽  
Kok Wai Wong ◽  
Chun Che Fung
Keyword(s):  
Data Mining ◽  
Health Informatics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Huge Number ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
The Past ◽  
Number Of Publications

Data mining is the analytics and knowledge discovery process of analyzing large volumes of data from various sources and transforming the data into useful information. Various disciplines have contributed to its development and is becoming increasingly important in the scientific and industrial world. This article presents a review of data mining techniques and applications from 1996 to 2016. Techniques are divided into two main categories: predictive methods and descriptive methods. Due to the huge number of publications available on this topic, only a selected number are used in this review to highlight the developments of the past 20 years. Applications are included to provide some insights into how each data mining technique has evolved over the last two decades. Recent research trends focus more on large data sets and big data. Recently there have also been more applications in area of health informatics with the advent of newer algorithms.

scholarly journals An Efficient Data Retrieval Parallel Reeb Graph Algorithm

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 258
Author(s):  
Mustafa Hajij ◽  
Paul Rosen
Keyword(s):  
Large Data ◽  
Graph Algorithm ◽  
Data Retrieval ◽  
Scalar Function ◽  
Large Data Sets ◽  
Data Sets ◽  
Reeb Graph ◽  
The Past ◽  
Segmentation Algorithms ◽  
Efficient Data

The Reeb graph of a scalar function that is defined on a domain gives a topologically meaningful summary of that domain. Reeb graphs have been shown in the past decade to be of great importance in geometric processing, image processing, computer graphics, and computational topology. The demand for analyzing large data sets has increased in the last decade. Hence, the parallelization of topological computations needs to be more fully considered. We propose a parallel augmented Reeb graph algorithm on triangulated meshes with and without a boundary. That is, in addition to our parallel algorithm for computing a Reeb graph, we describe a method for extracting the original manifold data from the Reeb graph structure. We demonstrate the running time of our algorithm on standard datasets. As an application, we show how our algorithm can be utilized in mesh segmentation algorithms.

Considering All of the Data on Digital-Media Use and Depressive Symptoms: Response to Ophir, Lipshits-Braziler, and Rosenberg (2020)

2020 ◽  
Vol 8 (2) ◽  
pp. 379-383
Author(s):  
Jean M. Twenge ◽  
Thomas E. Joiner ◽  
Megan L. Rogers ◽  
Gabrielle N. Martin
Keyword(s):  
Depressive Symptoms ◽  
Digital Media ◽  
Media Use ◽  
Large Data ◽  
Well Being ◽  
Large Data Sets ◽  
Data Sets ◽  
Individual Level ◽  
Monitoring The Future ◽  
The Individual

We have documented increases since 2012 in depressive symptoms, suicide-related outcomes, and suicide and identified associations between digital-media use and depressive symptoms and suicide-related outcomes across two data sets: Monitoring the Future (MtF) and the Youth Risk Behavior Surveillance System (YRBSS). Ophir, Lipshits-Braziler, and Rosenberg’s criticisms of the MtF data (this issue; pp. 374–378) are addressed by the YRBSS data, which included a measure of digital-media use in hours. Ophir et al. assumed that the displacement of nonscreen activities by screen activities occurs only at the individual level, whereas in fact, time displacement at the group or cohort level may be more important. Some discrepancies in the literature can be traced to the use of percentage variance explained; in fact, heavy (vs. light) digital-media users are considerably more likely (often twice as likely) to be depressed or low in well-being across several large data sets.

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