scholarly journals The state of the art in helioseismic ground-based experiments

1997 ◽  
Vol 181 ◽  
pp. 15-29
Author(s):  
Pere L. Pallé
Keyword(s):  
Solar Cycle ◽  
State Of The Art ◽  
Quality Data ◽  
Direct Impact ◽  
High Quality ◽  
High Quality Data ◽  
The Past ◽  
Long Time ◽  
Inversion Techniques ◽  
And Inversion

The new results obtained from the observation of solar oscillations over the past decade, have a direct impact on our knowledge of the Sun's interior. As a consequence, a great interest in helioseismology has arisen and is reflected in the development of new observational projects as well as new analyse and inversion techniques. In this review we will describe the present ground-based observational programmes, which, unlike the space ones, are mostly designed to produce high quality data over very long time spans (up to solar cycle time scales). The characteristics of the various observational programmes, single-site and network, will be described together with their performances, the main results obtained up to now, and some other logistical aspects.

A seminal paper on the epidemiology of cancer pain

2018 ◽  
Author(s):  
Paul Farquhar-Smith
Keyword(s):  
Cancer Pain ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Seminal Paper ◽  
Patients With Cancer ◽  
The Past ◽  
Pain Prevalence ◽  
Prevalence Of Pain ◽  
Interest In Research

The landmark paper discussed in this chapter is ‘Prevalence of pain in patients with cancer: A systematic review of the past 40 years’, published by van den Beuken et al. in 2007. It is not surprising that this definitive study on cancer pain prevalence is one of the most cited papers in cancer pain. Despite the extent of cancer pain literature, this paper’s 2007 publication is surprisingly recent for the first methodologically sound and major study of cancer pain prevalence. Many previous estimates lacked accuracy, and were prone to bias. What was known was that, despite apparent increasing interest in, research in, and recognition of pain in cancer patients, the prevalence of such pain was still high, even after treatment. This paper attempted to accurately quantify just how high by statistically pooling available high-quality data while avoiding the pitfalls of combining heterogeneous studies, as had plagued previous reports.

scholarly journals Dialog State Tracking with Reinforced Data Augmentation

2020 ◽  
Vol 34 (05) ◽  
pp. 9474-9481
Author(s):  
Yichun Yin ◽  
Lifeng Shang ◽  
Xin Jiang ◽  
Xiao Chen ◽  
Qun Liu
Keyword(s):  
Data Augmentation ◽  
State Of The Art ◽  
The State ◽  
Training Data ◽  
Quality Data ◽  
Specific Context ◽  
High Quality ◽  
High Quality Data ◽  
Fine Grained ◽  
State Tracking

Neural dialog state trackers are generally limited due to the lack of quantity and diversity of annotated training data. In this paper, we address this difficulty by proposing a reinforcement learning (RL) based framework for data augmentation that can generate high-quality data to improve the neural state tracker. Specifically, we introduce a novel contextual bandit generator to learn fine-grained augmentation policies that can generate new effective instances by choosing suitable replacements for specific context. Moreover, by alternately learning between the generator and the state tracker, we can keep refining the generative policies to generate more high-quality training data for neural state tracker. Experimental results on the WoZ and MultiWoZ (restaurant) datasets demonstrate that the proposed framework significantly improves the performance over the state-of-the-art models, especially with limited training data.

scholarly journals Selective Editing: A Quest for Efficiency and Data Quality

2013 ◽  
Vol 29 (4) ◽  
pp. 473-488 ◽  
Author(s):  
Ton de Waal
Keyword(s):  
Statistical Data ◽  
Quality Data ◽  
Future Research ◽  
High Quality ◽  
High Quality Data ◽  
The Past ◽  
Usual Form ◽  
Data Editing ◽  
Data Record ◽  
Editing Process

Abstract National statistical institutes are responsible for publishing high quality statistical information on many different aspects of society. This task is complicated considerably by the fact that data collected by statistical offices often contain errors. The process of correcting errors is referred to as statistical data editing. For many years this has been a purely manual process, with people checking the collected data record by record and correcting them if necessary. For this reason the data editing process has been both expensive and time-consuming. This article sketches some of the important methodological developments aiming to improve the efficiency of the data editing process that have occurred during the past few decades. The article focuses on selective editing, which is based on an idea rather shocking for people working in the production of high-quality data: that it is not necessary to find and correct all errors. Instead of trying to correct all errors, it generally suffices to correct only those errors where data editing has substantial influence on publication figures. This overview article sketches the background of selective editing, describes the most usual form of selective editing up to now, and discusses the contributions to this special issue of the Journal of Official Statistics on selective editing. The article concludes with describing some possible directions for future research on selective editing and statistical data editing in general.

scholarly journals Time for Better Access to High-Quality Abortion Data in the United States

2020 ◽  
Vol 189 (7) ◽  
pp. 640-647
Author(s):  
Katherine A Ahrens ◽  
Jennifer A Hutcheon
Keyword(s):  
United States ◽  
Health Outcomes ◽  
The United States ◽  
Social Consequences ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Individual Level ◽  
The Past ◽  
Research Activities

Abstract Despite considerable lay attention on the regulation and legislation of abortion in the United States, important gaps remain in our understanding of its incidence and health and social consequences since its legalization in 1973. Many of these gaps in knowledge can be attributed to a lack of access to high-quality, individual-level abortion data over the past 46 years. Herein, we review the strengths and limitations of different, currently available methods for enumerating abortions in the United States and discuss how lack of access to high-quality data limits our surveillance and research activities of not only abortion but other important reproductive and perinatal health outcomes. We conclude by discussing some potential opportunities for improved access to high-quality abortion data in the United States.

Interpretation of Quasar Colours in the Near IR

1977 ◽  
Vol 3 (2) ◽  
pp. 137-140 ◽  
Author(s):  
A. R. Hyland ◽  
M. P. Schwarz
Keyword(s):  
High Sensitivity ◽  
Quality Data ◽  
Published Data ◽  
High Quality ◽  
High Quality Data ◽  
The Past ◽  
Recent Introduction ◽  
Near Ir ◽  
Infrared Data

Over the past ten years, there has been considerable interest in the infrared continua of quasars, however few published colours exist in the literature. The only major compilation of infrared data out to 2.2 μ is that of Oke et al. (1970). The intrinsic faintness of the sources prevented the acquisition of significant high quality data. This situation is not expected to remain static for very long. The recent introduction of new high sensitivity InSb detectors has made it possible for a large number of sources to be measured, and published data is expected to increase significantly over the next few years.

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

Developmental Impacts of Remittances on Migrant-Sending Households: Micro-Level Evidence from Punjab, Pakistan

2019 ◽  
Vol 14 (3) ◽  
pp. 338-366
Author(s):  
Kashif Imran ◽  
Evelyn S. Devadason ◽  
Cheong Kee Cheok
Keyword(s):  
Human Development ◽  
Human Development Index ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Development Index ◽  
Policy Interventions ◽  
Education And Health ◽  
Micro Level ◽  
The Government

This article analyzes the overall and type of developmental impacts of remittances for migrant-sending households (HHs) in districts of Punjab, Pakistan. For this purpose, an HH-based human development index is constructed based on the dimensions of education, health and housing, with a view to enrich insights into interactions between remittances and HH development. Using high-quality data from a HH micro-survey for Punjab, the study finds that most migrant-sending HHs are better off than the HHs without this stream of income. More importantly, migrant HHs have significantly higher development in terms of housing in most districts of Punjab relative to non-migrant HHs. Thus, the government would need policy interventions focusing on housing to address inequalities in human development at the district-HH level, and subsequently balance its current focus on the provision of education and health.

Capturing sexual assault data: An information system designed by forensic clinicians and healthcare researchers

2017 ◽  
Vol 47 (1) ◽  
pp. 46-55 ◽  
Author(s):  
S Aqif Mukhtar ◽  
Debbie A Smith ◽  
Maureen A Phillips ◽  
Maire C Kelly ◽  
Renate R Zilkens ◽  
...  
Keyword(s):  
Information System ◽  
Sexual Assault ◽  
Data Management ◽  
Sexual Violence ◽  
Western Australia ◽  
Management System ◽  
Data Management System ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data

Background: The Sexual Assault Resource Center (SARC) in Perth, Western Australia provides free 24-hour medical, forensic, and counseling services to persons aged over 13 years following sexual assault. Objective: The aim of this research was to design a data management system that maintains accurate quality information on all sexual assault cases referred to SARC, facilitating audit and peer-reviewed research. Methods: The work to develop SARC Medical Services Clinical Information System (SARC-MSCIS) took place during 2007–2009 as a collaboration between SARC and Curtin University, Perth, Western Australia. Patient demographics, assault details, including injury documentation, and counseling sessions were identified as core data sections. A user authentication system was set up for data security. Data quality checks were incorporated to ensure high-quality data. Results: An SARC-MSCIS was developed containing three core data sections having 427 data elements to capture patient’s data. Development of the SARC-MSCIS has resulted in comprehensive capacity to support sexual assault research. Four additional projects are underway to explore both the public health and criminal justice considerations in responding to sexual violence. The data showed that 1,933 sexual assault episodes had occurred among 1881 patients between January 1, 2009 and December 31, 2015. Sexual assault patients knew the assailant as a friend, carer, acquaintance, relative, partner, or ex-partner in 70% of cases, with 16% assailants being a stranger to the patient. Conclusion: This project has resulted in the development of a high-quality data management system to maintain information for medical and forensic services offered by SARC. This system has also proven to be a reliable resource enabling research in the area of sexual violence.

scholarly journals Fooled by Performance Randomness: Overrewarding Luck

2019 ◽  
Vol 101 (4) ◽  
pp. 658-666 ◽  
Author(s):  
Romain Gauriot ◽  
Lionel Page
Keyword(s):  
Causal Effect ◽  
Experimental Situation ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Economic Organizations ◽  
Quasi Experimental ◽  
Similar Location ◽  
Do So

We provide evidence of a violation of the informativeness principle whereby lucky successes are overly rewarded. We isolate a quasi-experimental situation where the success of an agent is as good as random. To do so, we use high-quality data on football (soccer) matches and select shots on goal that landed on the goal posts. Using nonscoring shots, taken from a similar location on the pitch, as counterfactuals to scoring shots, we estimate the causal effect of a lucky success (goal) on the evaluation of the player's performance. We find clear evidence that luck is overly influencing managers' decisions and evaluators' ratings. Our results suggest that this phenomenon is likely to be widespread in economic organizations.

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