ADCPs on Surface Buoys: A Brief Look at Legacy Systems and Moving Forward

2018 ◽  
Vol 52 (3) ◽  
pp. 55-63
Author(s):  
Rick Cole
Keyword(s):  
Data Streams ◽  
Quality Data ◽  
Legacy Systems ◽  
Data Sets ◽  
High Quality ◽  
High Quality Data ◽  
Multiple Systems ◽  
Wave Measurements ◽  
Acoustic Doppler Current Profilers ◽  
Ocean Observing

AbstractThe discussion constantly comes up when planning an observing project, whether one system or multiple systems in an array: “Full water column current velocity and direction included in with all data streams is required,” usually surface meteorology, density, and perhaps wave measurements are needed as well (a complete MetOcean sensor suite). Measuring currents means using acoustic Doppler current profilers (ADCP) in today's ocean observing world. Bottom-mounted systems are now standard and straightforward and the best approach for the cleanest data sets. Surface-mounted ADCPs, on moving buoys, take some thought. Hardware is relatively easy; data processing is not so easy, at least not at first, until all that is involved below the waterline of the buoy is understood. The main reason for placing the buoy in the water in the first place is to collect “good,” high-quality data. Here we look at some early applications where ADCPs (RD Instruments/Teledyne RD Instruments) were mounted on surface buoys and discuss some results and ideas on options moving forward.

scholarly journals Pulsar Timing Arrays: Status and Techniques

2012 ◽  
Vol 8 (S291) ◽  
pp. 165-170 ◽  
Author(s):  
George Hobbs
Keyword(s):  
Solar System ◽  
Gravitational Waves ◽  
Quality Data ◽  
Data Sets ◽  
High Quality ◽  
High Quality Data ◽  
Pulsar Timing ◽  
Time Standard ◽  
Near Future

AbstractThree pulsar timing arrays are now producing high quality data sets. As reviewed in this paper, these data sets are been processed to 1) develop a pulsar-based time standard, 2) search for errors in the solar system planetary ephemeris and 3) detect gravitational waves. It is expected that the data sets will significantly improve in the near future by combining existing observations and by using new telescopes.

scholarly journals Multi-Objective Optimization to Identify High Quality Clusters with Close Referential Point using Evolutionary Clustering Techniques

2018 ◽  
Vol 7 (3) ◽  
pp. 68-71
Author(s):  
M. Anusha
Keyword(s):  
Optimization Problems ◽  
Quality Data ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
High Quality ◽  
High Quality Data ◽  
Clustering Techniques ◽  
Multi Objective ◽  
Evolutionary Clustering ◽  
Data Clusters

Most of the real-world optimization problems have multiple objectives to deal with. Satisfying one objective at a time may lead to the huge deviation in other. This paper uses criterion knowledge ranking algorithm solving multi-objective optimization problems. The aim of this research paper is to solve a multi-objective optimization algorithm with close reference point learning method to identify high quality data clusters. A Simple crossover measure is used to quantify the diversity of the whole set, by considering all patterns as a complete entity. In this paper, the task of identifying high quality data clusters using close reference points is proposed to solve multi-objective optimization problem using evolutionary clustering techniques. The proposed algorithm finds the closest feature from the selected features of the data sets that also minimizes the cost while maintains the quality of the solution by producing better convergence. The resultant clusters were analysed and validated using cluster validity indexes. The proposed algorithm is tested with several UCI real-life data sets. The experimental results substantiates that the algorithm is efficient and robust.

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.

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