Novel LC-MS2 Product Dependent Parallel Data Acquisition Function and Data Analysis Workflow for Sequencing and Identification of Intact Glycopeptides

2014 ◽  
Vol 86 (11) ◽  
pp. 5478-5486 ◽  
Author(s):  
Sz-Wei Wu ◽  
Tsung-Hsien Pu ◽  
Rosa Viner ◽  
Kay-Hooi Khoo
Keyword(s):  
Data Analysis ◽  
Data Acquisition ◽  
Parallel Data ◽  
Analysis Workflow

Installation and preliminary data analysis of Penang magnetic data Acquisition system (MAGDAS) in Malaysia

2021 ◽  
Vol 67 (7) ◽  
pp. 2199-2206
Author(s):  
N.A. Zakaria ◽  
S.H.M. Yusoff ◽  
N.A.M. Rizal ◽  
N.S.A. Hamid ◽  
M.H. Hashim ◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Acquisition ◽  
Preliminary Data ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Magnetic Data

Improvement of web-based data acquisition and management system for GOSAT validation lidar data analysis

2013 ◽  
Author(s):  
Hiroshi Okumura ◽  
Shoichiro Takubo ◽  
Takeru Kawasaki ◽  
Indra Nugraha Abdullah ◽  
Osamu Uchino ◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Acquisition ◽  
Management System ◽  
Lidar Data ◽  
Web Based

Magnetic Data Acquisition System (MAGDAS) Malaysia: installation and preliminary data analysis at ESERI, UNISZA

2018 ◽  
Vol 93 (5) ◽  
pp. 553-564 ◽  
Author(s):  
R. Umar ◽  
◽  
S. F. Natasha ◽  
S. S. N. Aminah ◽  
K. N. Juhari ◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Acquisition ◽  
Preliminary Data ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Magnetic Data

scholarly journals Effect of Community Reception to Sustainable Ecotourism Development

2018 ◽  
Vol 1 (1) ◽  
pp. 258
Author(s):  
Ainur Rochmaniah
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Data Acquisition ◽  
Significant Influence ◽  
Simple Linear Regression ◽  
Tourism Destination ◽  
District Government ◽  
The Government ◽  
Marine Ecotourism ◽  
Acquisition Technique

Tourism has been an icon of countless regions in Indonesia since the founding of "Visit to Indonesia" in 2009 by the Government. All efforts were made by stakeholders (Government, managers of tourism destination, hotels and surrounding communities) in order to increase the visit of local tourist (Wiscal), archipelagic tourist (Wisnu) and foreign tourists (Wisman), one of the method is by implementing Saptapesona. The goal of this research is to distinguish the influence of reception of society of Sidoarjo toward marine ecotourism development through the implementation of Saptapesona. The type of this study is quantitative with data acquisition technique through observation and questionnaire, distributed to tourism managers, village and district government staff, and tourists in three different locations namely Sedati, Candi, and Jabon respectively for about 144 respondents. The data analysis was using simple linear regression. The results showed that there was a significant influence of community receptions on the development of marine ecotourism.

Seamless Data Analysis, Visualizations and Sharing: Perspective from IRRI’s Rainfed Rice Breeding Program

2020 ◽  
Author(s):  
Waseem Hussain ◽  
Sankalp Bhosale ◽  
Margaret Catolos ◽  
Mahender Anumalla ◽  
Apurva Khanna ◽  
...  
Keyword(s):  
Data Analysis ◽  
Agricultural Research ◽  
Rice Breeding ◽  
Phenotypic Data ◽  
Data Set ◽  
Rainfed Rice ◽  
Daunting Task ◽  
Analysis Workflow ◽  
Interactive Visualizations ◽  
Network Partner

Abstract Phenotypic data analysis is a key component in crop breeding to extract meaningful insights from data in making better breeding decisions. Each year the rainfed rice breeding (RRB) program at IRRI conducts trials in the national agricultural research and extension systems (NARES) network-partner sites across South Asia, Southeast Asia and Africa. Analyzing the data from the network trials and sharing the results with the partners in the best possible format is a daunting task. It is crucial to demystify data analysis to the NARES partners for making better breeding decisions. Here, we provide an overview of how RRB program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly , LaTeX and HTML to develop a unique data analysis workflow and redesigned it to a reproducible document for better interpretation, visualization and seamlessly sharing with partners. The generated report is the state-of-the-art implementation of analysis workflow and outputs either in text, tables or graphics in a unified way as one document. The analysis is highly reproducible and can be regenerated based at any time. The plots are built with enhanced dynamic and interactive visualizations to aid in better understanding and extract information with ease. Tables are highly interactive and manageable rendering liberty to be exported within the document in numerous formats. The source code and demo data set for download and use is available at https://github.com/whussain2/Analysis-pipeline . Conclusively, the analysis workflow and document we presented is not limited to IRRI’s RRB program but is applicable to any organization or institute with full-fledged breeding programs.

Discovering Concepts in Structural Data

1999 ◽  
Author(s):  
Diane J. Cook ◽  
Lawrence B. Holder
Keyword(s):  
Data Analysis ◽  
Missing Data ◽  
Data Acquisition ◽  
Domain Knowledge ◽  
Minimum Description Length ◽  
Structural Data ◽  
Original Data ◽  
Graph Match ◽  
Replacement Process ◽  
Data Objects

The large amount of data collected today is quickly overwhelming researchers’ abilities to interpret the data and discover interesting patterns. In response to this problem, a number of researchers have developed techniques for discovering concepts in databases. These techniques work well for data expressed in a nonstructural, attribute-value representation and address issues of data relevance, missing data, noise and uncertainty, and utilization of domain knowledge (Fisher, 1987; Cheeseman and Stutz, 1996). However, recent data acquisition projects are collecting structural data describing the relationships among the data objects. Correspondingly, there exists a need for techniques to analyze and discover concepts in structural databases (Fayyad et al., 1996b). One method for discovering knowledge in structural data is the identification of common substructures. The goal is to find substructures capable of compressing the data and to identify conceptually interesting substructures that enhance the interpretation of the data. Substructure discovery is the process of identifying concepts describing interesting and repetitive substructures within structural data. Once discovered, the substructure concept can be used to simplify the data by replacing instances of the substructure with a pointer to the newly discovered concept. The discovered substructure concepts allow abstraction over detailed structure in the original data and provide new, relevant attributes for interpreting the data. Iteration of the substructure discovery and replacement process constructs a hierarchical description of the structural data in terms of the discovered substructures. This hierarchy provides varying levels of interpretation that can be accessed based on the goals of the data analysis. We describe a system called Subdue that discovers interesting substructures in structural data based on the minimum description length (MDL) principle. The Subdue system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously discovered substructures, multiple passes of Subdue produce a hierarchical description of the structural regularities in the data. Subdue uses a computationally bounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints.

Proving the Preclusion of Data Manipulation Using Parallel Data Acquisition in Chromatography

2018 ◽  
Vol 941 ◽  
pp. 2390-2394
Author(s):  
David Thomas Marehn ◽  
Detlef Wilhelm ◽  
Heike Pospisil ◽  
Roberto Pizzoferrato
Keyword(s):  
Data Acquisition ◽  
Best Practice ◽  
Sampling Rate ◽  
Good Manufacturing Practice ◽  
Special Role ◽  
Analytical Instruments ◽  
Data Collector ◽  
Parallel Data ◽  
Time Variations ◽  
Hplc Detector

Traceability has an enormous value for companies, but especially for those working in the regulated environment. It plays a special role in the field of pharmacy with respect to manufacturing, controlling and distributing batches of drugs. Through the guidance of Good Manufacturing Practice (GMP) traceability should be ensured. An increasing number of pharmaceutical companies are member of one of the global pharmacopoeias (United States Pharmacopeia, European Pharmacopeia and Japanese Pharmacopeia). The specifications of these pharmacopoeias describe the best practice in documentation, control, qualification and risk management. But however, the pharmacopoeias are written very generally and do not distinguish between the vendors of the analytical instruments. Here, we analyze how chromatographic analyses and data acquisition rely on a specific vendor of the device and the chromatography data system (CDS), the controlling software. We present a way to compare the data acquisition of different CDSs communicating with HPLC instruments. A newly developed software called Data Collector allows the acquisition of data from a HPLC detector parallel to the controlling CDS in the same run. Two HPLC systems and two different CDSs using a well defined sample standard have been tested. The direct comparison of the acquired data precludes unexpected data manipulations of both tested CDSs and shows that there are primarily deviations between the CDSs due to time variations only which depend on the sampling rate. All in all the Data Collector can be used for the traceability of data acquisition.

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