Abstract Chapter 3 addresses data processing techniques for magnetic resonance spectroscopy, guiding the reader from raw data to a fully processed spectrum suitable for analysis. The chapter describes how to do this to maximize data quality: achieving minimal linewidth, high signal-to-noise ratio, and reduced artefacts. The chapter discusses data export options, file formats from different scanner vendors, and the complexities involved in handling MRS data, including methods for visual inspection and quality control. Processing steps cover single-voxel data techniques like coil combination, phase and frequency alignment, and residual water removal. Special techniques are included for handling edited spectroscopy and MRS imaging (MRSI), with focus on correcting frequency offsets, phase errors, and removing extracranial lipid signals. The chapter also touches on denoising methods and their limitations, recommending caution due to potential bias. Examples are given throughout, linking to online practical resources.

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Data Processing

P R Raggatt · Palgrave Macmillan UK eBooks · 1991

It is self-evident that computerised processing of immunoassay data is advantageous. What is not self-evident is whether any particular computer method gives the correct result in all circumstances. The objective of this chapter is to provide the information on which a judgement can be formed about the correctness of immunoassay results calculated by computer.

Cited by 2

Data Processing

Rosa Rodriguez-Bailon, Efrain Garcia-Sanchez, Mar Montoya et al. · OSF Preprints (OSF Preprints) · 2021

Cited by 0Open access

Data processing

W. G. Rees · Cambridge University Press eBooks · 2001

The general direction of this book has been to follow approximately the flow of information, from the thermal or other mechanism for the generation of electromagnetic radiation, to its interaction with the surface to be sensed, thence to its interaction with the atmosphere, and finally to its detection by the sensor. It is clear that that the information has not yet reached its final destination. Firstly, it is still at the sensor and not with the data user. Secondly, the ‘raw’ data will in general require a significant amount of processing before they can be applied to the task for which they were acquired.

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Data processing

United Nations Department of Economic and Social Affairs · Studies in methods. Series F · 2021

5.1. The strategic directions for the data-processing phase need to be established early in the census cycle. The single most important decision regarding the processing phase is deciding on the processing system to be used and the technology that will be adopted.

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Data processing.

Ryanne J. M. Lemmens (563125), Annick A. A. Timmermans (563126), Yvonne J. M. Janssen-Potten (563127) et al. · Figshare · 2015

A) Raw activity count data (solid) and filtered data (dashed) of the left hand; B) Raw activity count data (solid) and filtered data (dashed) of the right hand; C) Filtered data of both hands and summation of filtered data, including start points (+) and endpoints (O) of uptime; D) Raw activity count data of both hands including start points (+) and endpoints (O) of uptime.

Cited by 0Open access

Data processing.

Feng Yuxin (18275639), Tian Yunxia (18275642), Lv Xiaoyu (18275645) · Figshare · 2024

<div>Red tourism is a distinctive form of tourism in China. Its network attention serves as a typical indicator to measure the level of promotion and publicity for red tourism, as well as an important reflection of its influence. Understanding the network structure of red tourism is of significant importance for optimizing the spatial pattern of tourism and promoting the development of the tourism industry. Based on this, this study takes the classic red tourism attractions in Shaanxi province, China as an example and constructs a multi-source data network attention evaluation index. Additionally, it employs social network theory to explore the network attention and tourist flow characteristics of the case study area. Research shows that: (1) Overall, the network attention to case-based destinations is relatively low, and there are significant differences in network attention among different attractions. Spatially, the distribution of network attention is uneven. This is manifested by higher network attention to attractions in Yan’an city and lower network attention to attractions in other regions. (2) There are differences in the network attention of different types of attractions. High-level attractions have a higher level of online attention, while low-level attractions have a lower level of network attention. Additionally, archaeological sites tend to receive a higher level of online attention. (3) The network density of tourist flow is low, and the tourism connections between nodes are not closely linked. The linkage between core nodes and edge nodes in tourism is poor. Developed tourism routes only exist in core nodes. (4) Nodes such as Zaoyuan revolution site, Yangjialing revolution site, and Wangjiaping revolution site have a significant influence in the network structure. In addition, the integration and development between red nodes and non-red nodes have been achieved. (5) There is a correlation between network attention and tourist flow, as well as a ‘misplacement’ feature. Based on the characteristics of attractions, they can be divided into four types: bright-star attractions, cash-cow attractions, thin-dog attractions, and question attractions. Based on the above conclusions, this study proposes targeted development recommendations.</div>

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Data processing.

Paul S. Pridham (14684632), Leia Stirling (4598134) · Figshare · 2023

This compressed folder contains README.md, which contains instructions for running the code; UltrasoundModeling.m, a Matlab/Octave file which contains the code used to generate the plots and values found in this paper; a Digitize folder which contains comma separated variable (csv) files of the data from Lai et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0281944#pone.0281944.ref030" target="_blank">30</a>] digitized using WebPlotDigitizer [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0281944#pone.0281944.ref058" target="_blank">58</a>]; and an empty Figures folder to store the generated plots. (ZIP)

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