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Data Collection and Analysis

Data Collection and Analysis

• Complete data means that the value of the life time of each item is observed or known. For example, for life data analysis, the data (if complete, which is unusual in field data collection) would comprise the times-to-failure of all units in the field.

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Evaluating a visual tool for systematic data collection and analysis for 
		design students

Evaluating a visual tool for systematic data collection and analysis for design students

Our early investigations also suggested that design students might benefit from a visual and interactive method such as a visual software tool to assist them collecting and manipulating data. This insight was based on the responses given by the design lecturers during the interviews. These suggested that design students might benefit from a visual and interactive method such as a software tool to assist them in collecting and manipulating data. Design students also claimed that they are visually literate and prefer visuals to numeric information. These findings appear similar to Goodman, Clarke, Langdon & Clarks on’s proposal that a design tool should be visual, interactive, concise and relevant to a design context [13]. Based on this feedback, existing visual tools that could potentially be suitable for design students to conduct systematic research were investigated. This sample included Computer Aided Kansei Engineering (CAKE) [14], Visual Research Package [15], Web 2D Analytical tool [16], and other automated visual tools [17, 18]. However, none of these tools addressed the lack of systematic data collection and analysis knowledge and skills among design students, and nor did they encourage design students to use statistics in the early phases of design projects, suggesting the need to design a new tool with appropriate methods for that audience.
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Qualitative Data Collection and Analysis Methods: The INSTINCT Trial

Qualitative Data Collection and Analysis Methods: The INSTINCT Trial

Patient care practices often lag behind current scientific evidence and professional guidelines. The failure of such knowledge translation (KT) efforts may reflect inadequate assessment and management of specific barriers confronting both physicians and patients at the point of treatment level. Effective KT in this setting may benefit from the use of qualitative methods to identify and overcome these barriers. Qualitative meth- odology allows in-depth exploration of the barriers involved in adopting practice change and has been in- frequently used in emergency medicine research. The authors describe the methodology for qualitative analysis within the INcreasing Stroke Treatment through INteractive behavioral Change Tactics (IN- STINCT) trial. This includes processes for valid data collection and reliable analysis of the textual data from focus group and interview transcripts. INSTINCT is a 24-hospital, randomized, controlled study that is designed to evaluate a system-based barrier assessment and interactive educational intervention to increase appropriate tissue plasminogen activator (tPA) use in ischemic stroke. Intervention hospitals undergo baseline barrier assessment using both qualitative as well as quantitative (survey) techniques. In- vestigators obtain data on local barriers to tPA use, as well as information on local attitudes, knowledge, and beliefs regarding acute stroke treatment. Targeted groups at each site include emergency physicians, emergency nurses, neurologists, radiologists, and hospital administrators. Transcript analysis using NVivo7 with a predefined barrier taxonomy is described. This will provide both qualitative insight on thrombolytic use and importance of specific barrier types for each site. The qualitative findings subse- quently direct the form of professional education efforts and system interventions at treatment sites. ACADEMIC EMERGENCY MEDICINE 2007; 14:1064–1071 ª 2007 by the Society for Academic Emergency Medicine
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A review of research process, data collection and analysis

A review of research process, data collection and analysis

In summary, the research process begins with de ining research problems and then review of literatures, formulation of hypothesis, data collection, analysis, interpretation and end in report writing. There are chances of occurrence of many biases in data collection. Importantly, the analysis of research data should be done with very caution. If a researcher use statistical test for signi icance, he/she should show exact p values. It is also better still, to show con idence limits instead. The standard error of the mean should be shown only in case of estimating population parameter. Usually between- subject standard deviation should be presented to convey the spread between subjects. In population studies, this standard deviation helps convey magnitude of differences or changes in the mean. In interventions, show also the within-subject standard deviation (the typical error) to convey precision of measurement. Standard deviation helps convey magnitude of differences or changes in mean performance.
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<p>A narrative review of data collection and analysis guidelines for comparative effectiveness research in chronic pain using patient-reported outcomes and electronic health records</p>

<p>A narrative review of data collection and analysis guidelines for comparative effectiveness research in chronic pain using patient-reported outcomes and electronic health records</p>

and multisite project clinical teams utilize comprehensive age- and disease-specific measures that are implemented as widely as possible and checked regularly for reliability. PBE data are merged into a central study database for analysis and hypothesis testing. Study findings are then implemented into clinical practice for validation testing with the ultimate goal of integration into standard care. PBE studies are designed to improve on traditional observational studies by 1) examining large, diverse patient populations; 2) involving clinicians in the research design and data collection; 3) using carefully selected patient characteristics for analysis to avoid bias; and 4) standardizing data collection and treatment documentation at all research sites. PBE methodology is ideal for conducting “pragmatic” trials that are designed to measure the overall benefit produced by a treatment in a naturalistic clinical setting. 9 Reviews in the pain medicine literature indicate
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Aerosol Particle Measurements: Strategies for Health Relevant Data Collection and Analysis

Aerosol Particle Measurements: Strategies for Health Relevant Data Collection and Analysis

rics for this exposure and for the subsequent lung-deposited particle dose may be relevant to observed health outcomes. The metric most relevant to any given case remains uncertain, and typical data collection rarely possesses the resolution needed to efficiently explore the different options. The purpose of this study is to assess the nature of the health-related information that existing technology can provide and demonstrate the performance criteria that future technology should target. We do this by employing the methodology described in a previous study to evaluate the performance of a range of measurement schemes in estimating particle dose delivered to the human lungs. Our analysis demonstrates the need for, and validity of, a new paradigm for health-related aerosol particle measurements that prioritizes estimation of regional lung dose metrics. It is important to collect enough informa- tion from airborne particle measurements that various physical metrics of particle dose to the human lungs can be inferred. However, the measurement approach must also be accessible enough to encourage widespread use. We explore how the technology of differential electrical mobility classification has great potential for application in a measurement system capable of collecting data more informative than a single metric concentration, yet still simple enough to be used in developing a dense monitoring network.
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A Research Study of Data Collection and Analysis of Semantics of Programming Languages

A Research Study of Data Collection and Analysis of Semantics of Programming Languages

to the data collection and analysis study of programming language. We identify general developments evaluating trainee and specialist programmers, programming awareness and approaches, programmer creations and conceptions, and object oriented versus procedural programming. (We do not cover research relating specially to other programming style). The main focus of the review is on programming trainee and topics relating to data collection and analysis. Various problems practiced trainee are acknowledged, as well as problem raise relating to various programming language which is most useful programming language of entire programming languages, to algorithmic complexity in certain language features, to the “Weakness” of trainees knowledge and so on. We reviews this materializes and give an opinion few practical suggestions for future work. We implicate that the key issue that materialize is difference of powerful and in- powerful trainees.
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Predictive Data Collection and Analysis for Placement Using CodeIgniter Framework

Predictive Data Collection and Analysis for Placement Using CodeIgniter Framework

The purpose of the project "Predictive Data Collection and Analysis for Placement Using CodeIgniter Framework”, manual work makes the process slow and other problems such as inconsistency and ambiguity on operations. In order to avoid this web based placement managed system is proposed, where the student information in the college with regard to placement is managed efficiently. It intends to help faster access procedures in placement related activities and ensures to maintain the details of the student. Students logging should be able to upload their personal and educational information. The key feature of this project is that it is one time registration enabled. Registered students, placement officer, can access the application generated based on login credentials. The TPO of the college can use the
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A traffic data collection and analysis method based on wireless sensor network

A traffic data collection and analysis method based on wireless sensor network

In this paper, a WSN-based traffic data collection scheme is proposed, which is low cost and low power. The scheme can collect vehicle speed and position infor- mation accurately and timely and lays a foundation for traffic flow analysis. Then a method based on incremen- tal noise addition is proposed for the chaotic identifica- tion of traffic flow signal. PPS is used to add noise incrementally to the analyzed signal, and the MD mean value is used as a measurement for the complexity of the signal. It is found that, for different types of signals, the complexity trends of surrogate data under each noise in- tensity are different. For the periodic signal, when the noise intensity is small, the MD mean value of surrogate data is stable; when the noise intensity is larger than a threshold, the MD mean value starts to decrease grad- ually. For the chaotic signal, such as traffic flow, the first MD mean value is obviously smaller than the periodic signal, and as the increase of noise intensity, the MD mean value keeps decreasing monotonously. For the ran- dom signal, the MD mean value keeps at a low value. Therefore, as the noise intensity increases, the trend of MD mean values is an effective criterion for distinguish- ing various types of signals. Of course, although param- eter A is used to try to quantify the criteria presented in this paper, the criterion is more inclined to a qualitative criterion than a quantitative one. Further research is needed to make extensive use of the criterion.
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Data Collection and Analysis in the Air Travel Planning Domain

Data Collection and Analysis in the Air Travel Planning Domain

DATA COLLECTION AND ANALYSIS IN THE AIR TRAVEL PLANNING DOMAIN D A T A C O L L E C T I O N A N D A N A L Y S I S IN T H E AIR T R A V E L P L A N N I N G D O M A I N Jacqueline C Kowtko, Patti J Price[.]

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Data Collection and Analysis in a Pan European Electric Vehicle Fleet

Data Collection and Analysis in a Pan European Electric Vehicle Fleet

Electric vehicles can provide a means of establishing cleaner transport networks and potentially more efficient energy generation systems. Achieving these goals requires in depth knowledge of the effects of EV integration on both systems in order to predict and control energy demand when operating on a massive scale. Considerations such as local energy mix, policies and incentives will influence both the uptake and effects of electric vehicles. This paper presents a preliminary look at the data collection and analysis work that is ongoing in the Green eMotion project. Data is collected from a large European fleet distributed over several distinct regions, with the aim of gaining an understanding of EV users need for infrastructure and associated energy usage levels. Early results indicate that there are considerable differences in energy demand and infrastructure usage patterns depending on the primary use of the EV and charging equipment. This information will be valuable when predicting and planning for future energy demands. Further differences in usage patterns may be identified through regional analysis of EV fleet data, taking into consideration local policy and energy tariff information. This analysis will provide deeper insights into the likely effects of a large EV fleet and also possible methods for regulating EV energy demands through policy and infrastructure planning.
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Data Collection and Data Analysis in Honeypots and Honeynets

Data Collection and Data Analysis in Honeypots and Honeynets

Location of collector is an important issue in data collection. Usage of secure communication channel is sufficient only for low-interaction and medium- interaction honeypots. For high-interaction honeypots it is needed to use another method of data collection. Collector located on virtual honeynet based on operating-system level virtualization is solution for this type of honeypots. Analysis of data from different honeypots is the second important issue. In paper we propose framework, which adds a new abstract layer – layer of event over all data from honeypots. Events are essential parts of data from each honeypots. Sandia’s taxonomy discusses the taxonomy of incident in general. In paper we focus on taxonomy of incident from the perspective of honeypots and honeynets. We propose new honeypot-incident taxonomy, which is modification of already mentioned Sandia’s taxonomy.
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Improving Driver Safety through Naturalistic Data Collection and Analysis Methods

Improving Driver Safety through Naturalistic Data Collection and Analysis Methods

After the 100 car study a number of additional naturalistic studies were performed through partnerships with organizations such as the Federal Motor Carrier Administration [9], the National Highway Safety Administration, the National Institutes of Health (NIH), and the Crash Avoidance Metrics Partnership [5]. This rapid database expansion increased storage demands to over 100 terabytes and highlighted the need to improve the compression and access speed performance. These studies fueled a transition to dedicated standard (video) and high- speed (parametric data) storage server racks attached to database management systems based on the structured query language (SQL). An independent local area network (LAN) protects from outside intruders while the array of independent storage disks (RAID) and routine offsite tape backup protect data from hardware failures. Although a 16 processor windows computing cluster is used for the analysis of large jobs, most analysis is performed on local machines.
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Remote Data Collection and Analysis Tom Worlton Argonne National Laboratory

Remote Data Collection and Analysis Tom Worlton Argonne National Laboratory

In order to enable scientists to more easily collect and analyze data and to allow them to do experiments remotely, we have begun a project to provide remote [r]

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Elbow Patients’ Data Collection and Analysis: An Examination of Electromyography Healing Patterns

Elbow Patients’ Data Collection and Analysis: An Examination of Electromyography Healing Patterns

Metrics in the time domain are fast and easily implemented. This is because they are calculated based on raw EMG time series [65] [68] [69]. Features in this domain have been used widely in both the medical and engineering fields of researches and practices. Although the EMG signal is non-stationary, meaning it changes in statistical properties over time, it is assumed to be stationary in this domain [70]. This is a disadvantage as it can cause variations when dynamic motions are recorded. Additionally, as a lot of the metrics in this domain heavily depend on amplitude values, any interference in the collection process can negatively affect the signal and thus filtering is important. This section outlines the features extracted in the time domain. Formulae for each metric are presented; all of these calculations were done using MATLAB functions.
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6 Methods of data collection and analysis

6 Methods of data collection and analysis

1.5 Differences between quantitative and qualitative research and their application Research is a systematic investigation that aims to generate knowledge about a particular phenomenon. However, the nature of this knowledge varies and reflects your study objectives. Some study objectives seek to make standardised and systematic comparisons, others seek to study a phenomenon or situation in detail. These different intentions require different approaches and methods, which are typically categorised as either quantitative or qualitative. You have probably already made decisions about using qualitative or quantitative data for monitoring and evaluation. Perhaps you have had to choose between using a questionnaire or conducting a focus group discussion in order to gather data for a particular indicator.
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Wind Data Collection and Analysis in Kumasi

Wind Data Collection and Analysis in Kumasi

Wind data in the form of wind speed and direction were sampled every two seconds by the Wind Explorer. These data were then combined with the standard deviations of the wind data and averaged and stored every ten minutes by the NRG Wind Data logger. During these 10 minutes averaging periods a binary file is generated and held on the data plug. These binary files were later combined with a site file and converted into an ASCII text file using the NRG Data Retriever Software. The ASCII text file was subsequently imported into a Notepad and an excel spreadsheet. Statistical analysis software called Stata was used to analyze the seven month in- built wind data stored in the Wind Explorer. Stata was used to create bar graphs which depict mean monthly variations in the wind data for the seven separate months of March to September, 2011. It was also used to create the hourly frequency distributions of wind speeds and wind directions for the first seven-month period of wind measurements. A program called Climate Analyst distributed alongside the main WAsP Software Worldwide by the Wind Energy Department of Risøe DTU of Denmark was used to generate time-series graphs of the wind direction and wind speed using the wind data for the three contiguous months of July, August and September, 2011. This software was also used to produce wind rose, wind speed histogram and some calculations based on the wind data for the third quarter of the year, 2011 for
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The Honeynet Project: Data Collection Tools, Infrastructure, Archives and Analysis

The Honeynet Project: Data Collection Tools, Infrastructure, Archives and Analysis

Honeywall[13] is a bootable CDROM which can be used to quickly build a high-interaction honeynet. Honeywall acts as a transparent layer two network bridge and allows for transparent data control, data capture plus online data analysis. Some of the data collected by Honeywall includes iptables logs, keystroke logs (for Sebek enabled high interaction honeypots), file and I/O data, passive OS fingerprints via p0f, snort alerts, argus/netflow/hflow data and full binary packet dumps. Another function of a Honeywall is to mitigate outbound attacks launched from a compromised honeypot onto the public Internet; snort_inline and connection rate limiting are used for this purpose. A classical generation III honeynet is formed from various honeypot hosts located behind a Honeywall bridge. Secure remote management and online data analysis are provided by the WallEye web based console.
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Big Data Collection: Analysis and Processing of Efficient IoT Based Sensor

Big Data Collection: Analysis and Processing of Efficient IoT Based Sensor

Abstract- Internet of Things (IoT) provides to everyone new types of services in order to improve everyday life. Through this new technology, other recently developed technologies such as Big Data, Cloud Computing, and Monitoring could take part. In this work, we survey the four aforementioned technologies in order to find out their common operations, and combine their functionality, in order to have beneficial scenarios of their use. Despite the boarder concept of a smart city, we will try to investigate new systems for collecting and managing sensors’ data in a smart building which operates in IoT environment. As a bases technology for the proposed sensor management system, a cloud server would be used, collecting the data that produced from each sensor in the smart building. These data are easy to be managed and controlled from distance, by a remote (mobile) device operating on a network set up in IoT technology. As a result, the proposed soluti ons for collecting and managing sensors’ data in a smart building could lead us in an energy efficient smart building, and thus in a Green S mart Building
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Dashboard for Success An Evaluation & Data Collection, Management, Analysis & Reporting System

Dashboard for Success An Evaluation & Data Collection, Management, Analysis & Reporting System

We create our tools in multiple languages, if necessary. This next screen is the system set up for the International Criminal Court of The Hague’s outreach arm. Since they are international and the system collects data and reports to member states in both English and French, they had a need for both languages. This screen shows a French version of the login screen along with a link to the English version and the menu on the left in French.

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