scholarly journals The Importance of High–Quality Data for REDD+ Monitoring and Reporting

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 99
Author(s):  
Marieke Sandker ◽  
Oswaldo Carrillo ◽  
Chivin Leng ◽  
Donna Lee ◽  
Rémi d’Annunzio ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Data Quality ◽  
Reliable Data ◽  
Paris Agreement ◽  
Tree Cover ◽  
Quality Data ◽  
Efficient Allocation ◽  
High Quality ◽  
High Quality Data

This article discusses the importance of quality deforestation area estimates for reliable and credible REDD+ monitoring and reporting. It discusses how countries can make use of global spatial tree cover change assessments, but how considerable additional efforts are required to translate these into national deforestation estimates. The article illustrates the relevance of countries’ continued efforts on improving data quality for REDD+ monitoring by looking at Mexico, Cambodia, and Ghana. The experience in these countries show differences between deforestation areas assessed directly from maps and improved sample-based deforestation area estimates, highlighting significant changes in both magnitude and trend of assessed deforestation from both methods. Forests play an important role in achieving the goals of the Paris Agreement, and therefore the ability of countries to accurately measure greenhouse gases from forests is critical. Continued efforts by countries are needed to produce credible and reliable data. Supporting countries to continually increase the quality of deforestation area estimates will also support more efficient allocation of finance that rewards REDD+ results-based payments.

Improving Data Quality Using Amazon Mechanical Turk Through Platform Setup

2021 ◽  
pp. 193896552110254
Author(s):  
Lu Lu ◽  
Nathan Neale ◽  
Nathaniel D. Line ◽  
Mark Bonn
Keyword(s):  
Best Practices ◽  
Data Quality ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Amazon's Mechanical Turk ◽  
Associated Data

As the use of Amazon’s Mechanical Turk (MTurk) has increased among social science researchers, so, too, has research into the merits and drawbacks of the platform. However, while many endeavors have sought to address issues such as generalizability, the attentiveness of workers, and the quality of the associated data, there has been relatively less effort concentrated on integrating the various strategies that can be used to generate high-quality data using MTurk samples. Accordingly, the purpose of this research is twofold. First, existing studies are integrated into a set of strategies/best practices that can be used to maximize MTurk data quality. Second, focusing on task setup, selected platform-level strategies that have received relatively less attention in previous research are empirically tested to further enhance the contribution of the proposed best practices for MTurk usage.

scholarly journals IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1978 ◽  
Author(s):  
Argyro Mavrogiorgou ◽  
Athanasios Kiourtis ◽  
Konstantinos Perakis ◽  
Stamatios Pitsios ◽  
Dimosthenis Kyriazis
Keyword(s):  
Data Quality ◽  
Medical Devices ◽  
Holistic Approach ◽  
Quality Data ◽  
Quality Estimation ◽  
High Quality ◽  
Data Interoperability ◽  
High Quality Data ◽  
Continuous Growth

It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.

scholarly journals Answering the Min-Cost Quality-Aware Query on Multi-Sources in Sensor-Cloud Systems

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4486 ◽  
Author(s):  
Mohan Li ◽  
Yanbin Sun ◽  
Yu Jiang ◽  
Zhihong Tian
Keyword(s):  
Low Cost ◽  
Real Life ◽  
Quality Data ◽  
Parameter Setting ◽  
Multiple Sources ◽  
High Quality ◽  
High Quality Data ◽  
Real Life Data ◽  
Sensor Cloud

In sensor-based systems, the data of an object is often provided by multiple sources. Since the data quality of these sources might be different, when querying the observations, it is necessary to carefully select the sources to make sure that high quality data is accessed. A solution is to perform a quality evaluation in the cloud and select a set of high-quality, low-cost data sources (i.e., sensors or small sensor networks) that can answer queries. This paper studies the problem of min-cost quality-aware query which aims to find high quality results from multi-sources with the minimized cost. The measurement of the query results is provided, and two methods for answering min-cost quality-aware query are proposed. How to get a reasonable parameter setting is also discussed. Experiments on real-life data verify that the proposed techniques are efficient and effective.

The New Data Quality Task Group (DQTG): ensuring high quality data today and in the future

Metabolomics ◽  
2014 ◽  
Vol 10 (4) ◽  
pp. 539-540 ◽  
Author(s):  
Daniel W. Bearden ◽  
Richard D. Beger ◽  
David Broadhurst ◽  
Warwick Dunn ◽  
Arthur Edison ◽  
...  
Keyword(s):  
Data Quality ◽  
Task Group ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
The Future

scholarly journals A Data Quality Control Program for Computer-Assisted Personal Interviews

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Janet E. Squires ◽  
Alison M. Hutchinson ◽  
Anne-Marie Bostrom ◽  
Kelly Deis ◽  
Peter G. Norton ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Control Program ◽  
Quality Data ◽  
Computer Assisted ◽  
Data Quality Control ◽  
Process Data ◽  
Quality Control Program ◽  
High Quality Data

Researchers strive to optimize data quality in order to ensure that study findings are valid and reliable. In this paper, we describe a data quality control program designed to maximize quality of survey data collected using computer-assisted personal interviews. The quality control program comprised three phases: (1) software development, (2) an interviewer quality control protocol, and (3) a data cleaning and processing protocol. To illustrate the value of the program, we assess its use in the Translating Research in Elder Care Study. We utilize data collected annually for two years from computer-assisted personal interviews with 3004 healthcare aides. Data quality was assessed using both survey and process data. Missing data and data errors were minimal. Mean and median values and standard deviations were within acceptable limits. Process data indicated that in only 3.4% and 4.0% of cases was the interviewer unable to conduct interviews in accordance with the details of the program. Interviewers’ perceptions of interview quality also significantly improved between Years 1 and 2. While this data quality control program was demanding in terms of time and resources, we found that the benefits clearly outweighed the effort required to achieve high-quality data.

scholarly journals A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression (Preprint)

2020 ◽  
Author(s):  
Maryam Zolnoori ◽  
Mark D Williams ◽  
William B Leasure ◽  
Kurt B Angstman ◽  
Che Ngufor
Keyword(s):  
Data Quality ◽  
Quality Data ◽  
Patient Centered ◽  
High Quality ◽  
Data Set ◽  
High Quality Data ◽  
Research Questions ◽  
Quality Issues ◽  
The Impact ◽  
Systematic Framework

BACKGROUND Patient-centered registries are essential in population-based clinical care for patient identification and monitoring of outcomes. Although registry data may be used in real time for patient care, the same data may further be used for secondary analysis to assess disease burden, evaluation of disease management and health care services, and research. The design of a registry has major implications for the ability to effectively use these clinical data in research. OBJECTIVE This study aims to develop a systematic framework to address the data and methodological issues involved in analyzing data in clinically designed patient-centered registries. METHODS The systematic framework was composed of 3 major components: visualizing the multifaceted and heterogeneous patient-centered registries using a data flow diagram, assessing and managing data quality issues, and identifying patient cohorts for addressing specific research questions. RESULTS Using a clinical registry designed as a part of a collaborative care program for adults with depression at Mayo Clinic, we were able to demonstrate the impact of the proposed framework on data integrity. By following the data cleaning and refining procedures of the framework, we were able to generate high-quality data that were available for research questions about the coordination and management of depression in a primary care setting. We describe the steps involved in converting clinically collected data into a viable research data set using registry cohorts of depressed adults to assess the impact on high-cost service use. CONCLUSIONS The systematic framework discussed in this study sheds light on the existing inconsistency and data quality issues in patient-centered registries. This study provided a step-by-step procedure for addressing these challenges and for generating high-quality data for both quality improvement and research that may enhance care and outcomes for patients. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/18366

scholarly journals A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression

10.2196/18366 ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. e18366
Author(s):  
Maryam Zolnoori ◽  
Mark D Williams ◽  
William B Leasure ◽  
Kurt B Angstman ◽  
Che Ngufor
Keyword(s):  
Data Quality ◽  
Quality Data ◽  
Patient Centered ◽  
High Quality ◽  
Data Set ◽  
High Quality Data ◽  
Research Questions ◽  
Quality Issues ◽  
The Impact ◽  
Systematic Framework

Background Patient-centered registries are essential in population-based clinical care for patient identification and monitoring of outcomes. Although registry data may be used in real time for patient care, the same data may further be used for secondary analysis to assess disease burden, evaluation of disease management and health care services, and research. The design of a registry has major implications for the ability to effectively use these clinical data in research. Objective This study aims to develop a systematic framework to address the data and methodological issues involved in analyzing data in clinically designed patient-centered registries. Methods The systematic framework was composed of 3 major components: visualizing the multifaceted and heterogeneous patient-centered registries using a data flow diagram, assessing and managing data quality issues, and identifying patient cohorts for addressing specific research questions. Results Using a clinical registry designed as a part of a collaborative care program for adults with depression at Mayo Clinic, we were able to demonstrate the impact of the proposed framework on data integrity. By following the data cleaning and refining procedures of the framework, we were able to generate high-quality data that were available for research questions about the coordination and management of depression in a primary care setting. We describe the steps involved in converting clinically collected data into a viable research data set using registry cohorts of depressed adults to assess the impact on high-cost service use. Conclusions The systematic framework discussed in this study sheds light on the existing inconsistency and data quality issues in patient-centered registries. This study provided a step-by-step procedure for addressing these challenges and for generating high-quality data for both quality improvement and research that may enhance care and outcomes for patients. International Registered Report Identifier (IRRID) DERR1-10.2196/18366

eMaintenance ontologies for data quality support

2015 ◽  
Vol 21 (3) ◽  
pp. 358-374 ◽  
Author(s):  
Mustafa Aljumaili ◽  
Karina Wandt ◽  
Ramin Karim ◽  
Phillip Tretten
Keyword(s):  
Data Quality ◽  
Production Process ◽  
Quality Data ◽  
Literature Study ◽  
High Quality ◽  
Control Data ◽  
Management Tools ◽  
High Quality Data ◽  
Content Type ◽  
Data Production

Purpose – The purpose of this paper is to explore the main ontologies related to eMaintenance solutions and to study their application area. The advantages of using these ontologies to improve and control data quality will be investigated. Design/methodology/approach – A literature study has been done to explore the eMaintenance ontologies in the different areas. These ontologies are mainly related to content structure and communication interface. Then, ontologies will be linked to each step of the data production process in maintenance. Findings – The findings suggest that eMaintenance ontologies can help to produce a high-quality data in maintenance. The suggested maintenance data production process may help to control data quality. Using these ontologies in every step of the process may help to provide management tools to provide high-quality data. Research limitations/implications – Based on this study, it can be concluded that further research could broaden the investigation to identify more eMaintenance ontologies. Moreover, studying these ontologies in more technical details may help to increase the understandability and the use of these standards. Practical implications – It has been concluded in this study that applying eMaintenance ontologies by companies needs additional cost and time. Also the lack or the ineffective use of eMaintenance tools in many enterprises is one of the limitations for using these ontologies. Originality/value – Investigating eMaintenance ontologies and connecting them to maintenance data production is important to control and manage the data quality in maintenance.

scholarly journals Vegetation Horizontal Occlusion Index (VHOI) from TLS and UAV Image to Better Measure Mangrove LAI

2018 ◽  
Vol 10 (11) ◽  
pp. 1739 ◽  
Author(s):  
Xianxian Guo ◽  
Le Wang ◽  
Jinyan Tian ◽  
Dameng Yin ◽  
Chen Shi ◽  
...  
Keyword(s):  
Data Quality ◽  
Laser Scanning ◽  
Structural Information ◽  
Forest Growth ◽  
Quality Data ◽  
Estimation Accuracy ◽  
High Quality ◽  
High Quality Data ◽  
Area Index ◽  
Uav Image

Accurate measurement of the field leaf area index (LAI) is crucial for assessing forest growth and health status. Three-dimensional (3-D) structural information of trees from terrestrial laser scanning (TLS) have information loss to various extents because of the occlusion by canopy parts. The data with higher loss, regarded as poor-quality data, heavily hampers the estimation accuracy of LAI. Multi-location scanning, which proved effective in reducing the occlusion effects in other forests, is hard to carry out in the mangrove forest due to the difficulty of moving between mangrove trees. As a result, the quality of point cloud data (PCD) varies among plots in mangrove forests. To improve retrieval accuracy of mangrove LAI, it is essential to select only the high-quality data. Several previous studies have evaluated the regions of occlusion through the consideration of laser pulses trajectories. However, the model is highly susceptible to the indeterminate profile of complete vegetation object and computationally intensive. Therefore, this study developed a new index (vegetation horizontal occlusion index, VHOI) by combining unmanned aerial vehicle (UAV) imagery and TLS data to quantify TLS data quality. VHOI is asymptotic to 0.0 with increasing data quality. In order to test our new index, the VHOI values of 102 plots with a radius of 5 m were calculated with TLS data and UAV image. The results showed that VHOI had a strong linear relationship with estimation accuracy of LAI (R2 = 0.72, RMSE = 0.137). In addition, as TLS data were selected by VHOI less than different thresholds (1.0, 0.9, …, 0.1), the number of remaining plots decreased while the agreement between LAI derived from TLS and field-measured LAI was improved. When the VHOI threshold is 0.3, the optimal trade-off is reached between the number of plots and LAI measurement accuracy (R2 = 0.67). To sum up, VHOI can be used as an index to select high-quality data for accurately measuring mangrove LAI and the suggested threshold is 0.30.

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