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Journal articles on the topic 'Data editing'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

van de Pol, Frank, and Jelke Bethlehem. "Data editing perspectives." Statistical Journal of the United Nations Economic Commission for Europe 14, no. 2 (April 1, 1997): 153–71. http://dx.doi.org/10.3233/sju-1997-14203.

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2

Tsukamoto, Kaoru. "Play data editing device and method of editing play data." Journal of the Acoustical Society of America 123, no. 3 (2008): 1235. http://dx.doi.org/10.1121/1.2901368.

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3

Esmail, Whaj Muneer. "A Critical Analysis of the Intentional Deviation in News Editing." Al-Adab Journal 1, no. 137 (June 15, 2021): 47–72. http://dx.doi.org/10.31973/aj.v1i137.1087.

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News Editing is the clearest coding, which reflects the writer's behavior of the editor who linguistically, socially, or culturally edits and deviates some of the source language aspects. It, furthermore, refers to the writer’s competence of using an influential linguistic style and preserving the SL norms and policies. At the same time, editing news presents a new horizon within a different political framework into TL. The problem of news editing of the same TV in Arabic and English editions lies in discrepancies in meanings; intentional deviation and politics. For instance, BBC, which broadcasts in Arabic, has a different editing from its English edition. This study ascribes such differences to the different socio-cultural and political strategies adopted by the writer. The primary objectives of the study are: Finding out the political reasons behind the discrepancy and the intentional deviation in news editing. Identifying the political attitude of the original editor and the political attitude of the editor. The data set in this study consisted of “TWO” edited news editing (1 from English into Arabic) and (1 from Arabic into English). These two news writings have been broadcasted on BBC English & Arabic editions. A critical-stylistic analysis has been conducted by applying House’s (2001) model of TQA.

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4

Persch, G. "Editing IDL data structures." ACM SIGPLAN Notices 22, no. 11 (November 1987): 79–86. http://dx.doi.org/10.1145/39305.39313.

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5

de Waal, Ton. "Selective Editing: A Quest for Efficiency and Data Quality." Journal of Official Statistics 29, no. 4 (December 1, 2013): 473–88. http://dx.doi.org/10.2478/jos-2013-0036.

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Abstract National statistical institutes are responsible for publishing high quality statistical information on many different aspects of society. This task is complicated considerably by the fact that data collected by statistical offices often contain errors. The process of correcting errors is referred to as statistical data editing. For many years this has been a purely manual process, with people checking the collected data record by record and correcting them if necessary. For this reason the data editing process has been both expensive and time-consuming. This article sketches some of the important methodological developments aiming to improve the efficiency of the data editing process that have occurred during the past few decades. The article focuses on selective editing, which is based on an idea rather shocking for people working in the production of high-quality data: that it is not necessary to find and correct all errors. Instead of trying to correct all errors, it generally suffices to correct only those errors where data editing has substantial influence on publication figures. This overview article sketches the background of selective editing, describes the most usual form of selective editing up to now, and discusses the contributions to this special issue of the Journal of Official Statistics on selective editing. The article concludes with describing some possible directions for future research on selective editing and statistical data editing in general.

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6

Jing, Changfeng, Yanli Zhu, Jiayun Fu, and Meng Dong. "A Lightweight Collaborative GIS Data Editing Approach to Support Urban Planning." Sustainability 11, no. 16 (August 16, 2019): 4437. http://dx.doi.org/10.3390/su11164437.

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Collaborative geospatial data editing is different from other collaborative editing systems, such as textual editing, owing to its geospatial nature. This paper presents a version-based lightweight collaborative geospatial editing method for urban planning. This method extracts editing data and generates a version for collaborative editing, which reduces the data size and thus allows for a high feedback speed. A replication mechanism is engaged to replicate a version for the client to freely edit, which ensures constraint-free editing in collaboration. Based on this method, realizing the fact that heterogeneous geospatial data and non-professional users are involved, a lightweight architecture, integrating web services, and component technologies, was proposed. This architecture provides a unified data access interface and powerful editing ability and ensures a high feedback speed and constraint-free editing. The result of the application of the proposed approach in a practical project demonstrates the usability of collaborative geospatial editing in urban planning. While this approach has been designed for urban planning, it can be modified for use in other domains.

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7

Sengupta, Binanda, Yingjiu Li, Yangguang Tian, and Robert H. Deng. "Editing-Enabled Signatures: A New Tool for Editing Authenticated Data." IEEE Internet of Things Journal 7, no. 6 (June 2020): 4997–5007. http://dx.doi.org/10.1109/jiot.2020.2972741.

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8

Pannekoek, Jeroen, Sander Scholtus, and Mark Van der Loo. "Automated and Manual Data Editing: A View on Process Design and Methodology." Journal of Official Statistics 29, no. 4 (December 1, 2013): 511–37. http://dx.doi.org/10.2478/jos-2013-0038.

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Abstract Data editing is arguably one of the most resource-intensive processes at NSIs. Forced by everincreasing budget pressure, NSIs keep searching for more efficient forms of data editing. Efficiency gains can be obtained by selective editing, that is, limiting the manual editing to influential errors, and by automating the editing process as much as possible. In our view, an optimal mix of these two strategies should be aimed for. In this article we present a decomposition of the overall editing process into a number of different tasks and give an upto- date overview of all the possibilities of automatic editing in terms of these tasks. During the design of an editing process, this decomposition may be helpful in deciding which tasks can be done automatically and for which tasks (additional) manual editing is required. Such decisions can be made a priori, based on the specific nature of the task, or by empirical evaluation, which is illustrated by examples. The decomposition in tasks, or statistical functions, also naturally leads to reuseable components, resulting in efficiency gains in process design.

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9

Ferguson, Dania P. "SAS use in data editing." Statistical Journal of the United Nations Economic Commission for Europe 8, no. 2 (October 1, 1991): 167–74. http://dx.doi.org/10.3233/sju-1991-8205.

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10

Riera-Ledesma, Jorge, and Juan-José Salazar-González. "Algorithms for automatic data editing." Statistical Journal of the United Nations Economic Commission for Europe 20, no. 3-4 (August 17, 2004): 255–64. http://dx.doi.org/10.3233/sju-2003-203-405.

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11

Revilla, Pedro, Manuel González, Margarita González, and José Quesada. "Data editing by reporting enterprises." Statistical Journal of the United Nations Economic Commission for Europe 21, no. 1 (November 23, 2004): 67–74. http://dx.doi.org/10.3233/sju-2004-21107.

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12

Daalmans, Jacco. "Constraint Simplification for Data Editing of Numerical Variables." Journal of Official Statistics 34, no. 1 (March 1, 2018): 27–39. http://dx.doi.org/10.1515/jos-2018-0002.

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Abstract Data editing is the process of checking and correcting data. In practise, these processes are often automated. A large number of constraints needs to be handled in many applications. This article shows that data editing can benefit from automated constraint simplification techniques. Performance can be improved, which broadens the scope of applicability of automatic data editing. Flaws in edit rule formulation may be detected, which improves the quality of automatic edited data.

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13

Wang, Yulan, Xiaofeng Song, and Tianyi Xu. "Identification and Analysis of RNA Editing Events in Ovarian Serous Cystadenoma Using RNA-seq Data." Current Gene Therapy 21, no. 3 (June 8, 2021): 258–69. http://dx.doi.org/10.2174/1566523221666210211111324.

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Background: Recent studies have revealed thousands of A-to-I RNA editing events in primates. These events are closely related to the occurrence and development of multiple cancers, but the origination and general functions of these events in ovarian cancer remain incompletely understood. Objective: To further the determination of molecular mechanisms of ovarian cancer from the perspective of RNA editing. Methods : Here, we used the SNP-free RNA editing Identification Toolkit (SPRINT) to detect RNA editing sites. These editing sites were then annotated, and related functional analysis was performed. Results: In this study, about 1.7 million RES were detected in each sample, and 98% of these sites were due to A-to-G editing and were mainly distributed in non-coding regions. More than 1,000 A-- to-G RES were detected in CDS regions, and nearly 700 could lead to amino acid changes. Our results also showed that editing in the 3′UTR regions could influence miRNA-target binding. We predicted the network of changed miRNA-mRNA interaction caused by the A-to-I RNA editing sites. We also screened the differential RNA editing sites between ovarian cancer and adjacent normal tissues. We then performed GO and KEGG pathway enrichment analysis on the genes that contained these differential RNA editing sites. Finally, we identified the potential dysregulated RNA editing events in ovarian cancer samples. Conclusion: This study systematically identified and analyzed RNA editing events in ovarian cancer and laid a foundation to explore the regulatory mechanism of RNA editing and its function in ovarian cancer.

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Rabeshko, Yurii, and Yurii Turbal. "Review of joint text editing algorithms Conflict-free Replicated Data Types (CRDT)." Вісник Черкаського державного технологічного університету 28, no. 4 (November 10, 2023): 10–18. http://dx.doi.org/10.62660/2306-4412.4.2023.10-18.

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Analysing and selecting algorithms for collaborative text editing, especially implementing conflict-free replicated data types, is critical to understanding how modern systems can achieve real-time collaboration while ensuring data integrity. The purpose of the study is to review various collaborative editing algorithms and conduct a comparative analysis to understand their advantages, disadvantages, and applications. Statistical methods, methods for analysing algorithms and their use in real-world scenarios are used. The study results showed that the use of collaborative text editing algorithms contributes to solving important tasks and challenges in the modern world of information technology. Collaborative editing algorithms are determined to facilitate realtime communication and information exchange. This is especially important in the context of remote work and communication, which have become the standard for many organisations. Collaborative text editing is widely used in various fields and industries where teamwork, real-time collaboration, and document sharing are essential, such as scientific research, education, software development, book and manuscript editing, legal cooperation, contract draughting, medical reports, etc. In addition, using optimised collaborative editing algorithms helps reduce the time required to process data and create text materials. Collaborative editing algorithms have a wide range of applications in research, business, and education. They allow teams and individual users to solve problems more efficiently and work together on projects. The ability to use the results obtained in practical activities will allow using algorithms for joint text editing for further innovation and development of information technologies, which will allow working together and exchanging information with the whole world in real-time

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15

Van den Broeck, Jan, Solveig Argeseanu Cunningham, Roger Eeckels, and Kobus Herbst. "Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities." PLoS Medicine 2, no. 10 (September 6, 2005): e267. http://dx.doi.org/10.1371/journal.pmed.0020267.

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16

Soedarso, Nick. "Mengolah Data Video Analog Menjadi Video Digital Sederhana." Humaniora 1, no. 2 (October 31, 2010): 569. http://dx.doi.org/10.21512/humaniora.v1i2.2897.

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Nowadays, editing technology has entered the digital age. Technology will demonstrate the evidence of processing analog to digital data has become simpler since editing technology has been integrated in the society in all aspects. Understanding the technique of processing analog to digital data is important in producing a video. To utilize this technology, the introduction of equipments is fundamental to understand the features. The next phase is the capturing process that supports the preparation in editing process from scene to scene; therefore, it will become a watchable video.

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17

Behroozi, Javad, Shirin Shahbazi, Mohammad Reza Bakhtiarizadeh, and Habibollah Mahmoodzadeh. "Genome-Wide Characterization of RNA Editing Sites in Primary Gastric Adenocarcinoma through RNA-seq Data Analysis." International Journal of Genomics 2020 (December 18, 2020): 1–16. http://dx.doi.org/10.1155/2020/6493963.

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RNA editing is a posttranscriptional nucleotide modification in humans. Of the various types of RNA editing, the adenosine to inosine substitution is the most widespread in higher eukaryotes, which is mediated by the ADAR family enzymes. Inosine is recognized by the biological machinery as guanosine; therefore, editing could have substantial functional effects throughout the genome. RNA editing could contribute to cancer either by exclusive editing of tumor suppressor/promoting genes or by introducing transcriptomic diversity to promote cancer progression. Here, we provided a comprehensive overview of the RNA editing sites in gastric adenocarcinoma and highlighted some of their possible contributions to gastric cancer. RNA-seq data corresponding to 8 gastric adenocarcinoma and their paired nontumor counterparts were retrieved from the GEO database. After preprocessing and variant calling steps, a stringent filtering pipeline was employed to distinguish potential RNA editing sites from SNPs. The identified potential editing sites were annotated and compared with those in the DARNED database. Totally, 12362 high-confidence adenosine to inosine RNA editing sites were detected across all samples. Of these, 12105 and 257 were known and novel editing events, respectively. These editing sites were unevenly distributed across genomic regions, and nearly half of them were located in 3 ′ UTR. Our results revealed that 4868 editing sites were common in both normal and cancer tissues. From the remaining sites, 3985 and 3509 were exclusive to normal and cancer tissues, respectively. Further analysis revealed a significant number of differentially edited events among these sites, which were located in protein coding genes and microRNAs. Given the distinct pattern of RNA editing in gastric adenocarcinoma and adjacent normal tissue, edited sites have the potential to serve as the diagnostic biomarkers and therapeutic targets in gastric cancer.

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18

Ingram, Kelly, Ferenc Bunta, and David Ingram. "Digital Data Collection and Analysis." Language, Speech, and Hearing Services in Schools 35, no. 2 (April 2004): 112–21. http://dx.doi.org/10.1044/0161-1461(2004/013).

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Technology for digital speech recording and speech analysis is now readily available for all clinicians who use a computer. This article discusses some advantages of moving from analog to digital recordings and outlines basic recording procedures. The purpose of this article is to familiarize speech-language pathologists with computerized audio files and the benefits of working with those sound files as opposed to using analog recordings. This article addresses transcription issues and offers practical examples of various functions, such as playback, editing sound files, using waveform displays, and extracting utterances. An appendix is provided that describes step-by-step how digital recording can be done. It also provides some editing examples and a list of useful computer programs for audio editing and speech analyses. In addition, this article includes suggestions for clinical uses in both the assessment and the treatment of various speech and language disorders.

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19

Habibzadeh, Farrokh. "Statistical Data Editing in Scientific Articles." Journal of Korean Medical Science 32, no. 7 (2017): 1072. http://dx.doi.org/10.3346/jkms.2017.32.7.1072.

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20

Kim, Kihong. "Open data policy of Science Editing." Science Editing 5, no. 2 (August 20, 2018): 91. http://dx.doi.org/10.6087/kcse.132.

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21

Luzi, Orietta, and Natalie Shlomo. "Editing by respondents and data suppliers." Statistical Journal of the United Nations Economic Commission for Europe 21, no. 1 (November 23, 2004): 53–65. http://dx.doi.org/10.3233/sju-2004-21106.

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22

Bolstein, Richard. "Data Editing in Federal Statistical Agencies." Journal of the American Statistical Association 86, no. 414 (June 1991): 551. http://dx.doi.org/10.2307/2290618.

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23

Aguilar, Jesús S., José C. Riquelme, and Miguel Toro. "Data set Editing by Ordered Projection." Intelligent Data Analysis 5, no. 5 (November 19, 2001): 405–17. http://dx.doi.org/10.3233/ida-2001-5504.

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24

Ho, Calvin W. L., and Adrienne Hunt. "Biodiversity, Big Data and Genome Editing." Asian Bioethics Review 11, no. 2 (June 2019): 129–32. http://dx.doi.org/10.1007/s41649-019-00091-5.

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25

Karabelas, Susan, and Frances LoPresti. "Computer models for editing study data." Controlled Clinical Trials 13, no. 5 (October 1992): 415. http://dx.doi.org/10.1016/0197-2456(92)90133-k.

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Barton, Bruce, Barbara Schumann, Richard Gross, and Ellis Clarke. "Techniques for editing longitudinal growth data." Controlled Clinical Trials 13, no. 5 (October 1992): 438. http://dx.doi.org/10.1016/0197-2456(92)90189-7.

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27

da Costa, Bruna Lopes, Masha Kolesnikova, Sarah R. Levi, Thiago Cabral, Stephen H. Tsang, Irene H. Maumenee, and Peter M. J. Quinn. "Clinical and Therapeutic Evaluation of the Ten Most Prevalent CRB1 Mutations." Biomedicines 11, no. 2 (January 27, 2023): 385. http://dx.doi.org/10.3390/biomedicines11020385.

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Mutations in the Crumbs homolog 1 (CRB1) gene lead to severe inherited retinal dystrophies (IRDs), accounting for nearly 80,000 cases worldwide. To date, there is no therapeutic option for patients suffering from CRB1-IRDs. Therefore, it is of great interest to evaluate gene editing strategies capable of correcting CRB1 mutations. A retrospective chart review was conducted on ten patients demonstrating one or two of the top ten most prevalent CRB1 mutations and receiving care at Columbia University Irving Medical Center, New York, NY. Patient phenotypes were consistent with previously published data for individual CRB1 mutations. To identify the optimal gene editing strategy for these ten mutations, base and prime editing designs were evaluated. For base editing, we adopted the use of a near-PAMless Cas9 (SpRY Cas9), whereas for prime editing, we evaluated the canonical NGG and NGA prime editors. We demonstrate that for the correction of c.2843G>A, p.(Cys948Tyr), the most prevalent CRB1 mutation, base editing has the potential to generate harmful bystanders. Prime editing, however, avoids these bystanders, highlighting its future potential to halt CRB1-mediated disease progression. Additional studies investigating prime editing for CRB1-IRDs are needed, as well as a thorough analysis of prime editing’s application, efficiency, and safety in the retina.

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Dukhanina, Alexandra V. "The Life of St. Stephen of Perm in the Printed Prologue: Textual Criticism and Codicological Value." Труды Отдела древнерусской литературы 68 (2020): 135–74. http://dx.doi.org/10.31860/0130-464x-2020-67-135-174.

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The Life of St. Stephen of Perm in a specific redaction was included in the second edition of the Prologue of 1642—1643 and reprinted in all subsequent editions of the Prologue in the 17th—18th centuries. Eight handwritten copies of the text belonging to this redaction have been found in 17th- and 18th-century manuscripts. In most editions of the Prologue the text reveals minor linguistic and stylistic changes that provide material for the history of editing of the Prologue, as well as for the history of the Russian literary language. They also allow determining which particular edition served as a model for this or that manuscript copy of the Life. Knowing the publication year of the editions has helped to clarify the dating of some manuscripts of this redaction of the Life and even to correct some data from an album of seventeenth-century watermarks

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Jiang, Jiao. "Application of gene editing technology to DNA digital data storage." Highlights in Science, Engineering and Technology 73 (November 29, 2023): 452–58. http://dx.doi.org/10.54097/hset.v73i.14051.

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While the archival digital storage industry is approaching its physical limits, demand is increasing significantly, so alternatives are emerging. The modern world is in dire need of durable, scalable and economical alternative storage media. Deoxyribonucleic acid (DNA), a promising storage medium, offers superior information durability, capacity and energy consumption, making it a promising candidate for long-term data storage. However, the design and realization of DNA digital data storage face many problems, but gene editing technology, as a technology that makes modifications to genes directly from the molecular level, provides a breakthrough in solving these problems. In this paper, I show some methods for designing DNA digital data storage based on gene editing technology. The method utilizes gene editing technology to modify DNA molecules to improve their storage capacity and stability. At the same time, this paper also introduces the application cases of gene editing technology in DNA bio storage devices and looks forward to its future development.

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30

Zhang, Xue, and Wangxin Xiao. "Active semi-supervised framework with data editing." Computer Science and Information Systems 9, no. 4 (2012): 1513–32. http://dx.doi.org/10.2298/csis120202045z.

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In order to address the insufficient training data problem, many active semi-supervised algorithms have been proposed. The self-labeled training data in semi-supervised learning may contain much noise due to the insufficient training data. Such noise may snowball themselves in the following learning process and thus hurt the generalization ability of the final hypothesis. Extremely few labeled training data in sparsely labeled text classification aggravate such situation. If such noise could be identified and removed by some strategy, the performance of the active semi-supervised algorithms should be improved. However, such useful techniques of identifying and removing noise have been seldom explored in existing active semi-supervised algorithms. In this paper, we propose an active semi-supervised framework with data editing (we call it ASSDE) to improve sparsely labeled text classification. A data editing technique is used to identify and remove noise introduced by semi-supervised labeling. We carry out the data editing technique by fully utilizing the advantage of active learning, which is novel according to our knowledge. The fusion of active learning with data editing makes ASSDE more robust to the sparsity and the distribution bias of the training data. It further simplifies the design of semi-supervised learning which makes ASSDE more efficient. Extensive experimental study on several real-world text data sets shows the encouraging results of the proposed framework for sparsely labeled text classification, compared with several state-of-the-art methods.

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Osman, Mohd Haniff, Zulkifli Mohd Nopiah, Shahrum Abdullah, and Izamarlina Asshaari. "Genetic Algorithm-Based Fatigue Data Editing Technique." Applied Mechanics and Materials 663 (October 2014): 431–36. http://dx.doi.org/10.4028/www.scientific.net/amm.663.431.

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Durability testing is an essential process node in automotive component design analysis. The test associated with loading history can be accelerated if the fatigue data editing approach is considered for simplifying the given history. Even though they have been proven, unfortunately, the existing editing techniques involve complex mechanisms (e.g. abrupt detection, Fourier transformation and wavelet analysis), which are complicated in nature and which demand high computational costs. Therefore, this paper is a proposal of a simple technique that makes use of the rule-based fatigue segment classifier when deciding which parts of the history need to be removed. Rules representing the new labelling practice have been generated based on the classification data mining framework. In the context of this study, a rule set represents a group of undiscovered relationship between time domain statistical parameters and damage level. A dataset consisting of an equal length of fatigue segments trained using a multi-objective approach called the Elitist Non-dominated Sorting in Genetic Algorithm (NSGA-II) for seeking several optimal sets of rules (i.e., classifiers) by maximizing predictive accuracy and comprehensibility. The number of attributes underlying the rule set is referred to for final classifier selection where the fitter solution serves as the proposed editing technique. Comparison results on strain-stress cycle properties for the edited history and the full-length version shows that the proposed technique is suitable for fatigue data editing. Moreover, it has an additional benefit that no prior requirement on the frequency or time-frequency analysis is needed, providing the damage level of fatigue segments rapidly and the discovering of linguistic knowledge as a novelty.

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32

Herbrechter, Robin, Nadine Hube, Raoul Buchholz, and Andreas Reiner. "Splicing and editing of ionotropic glutamate receptors: a comprehensive analysis based on human RNA-Seq data." Cellular and Molecular Life Sciences 78, no. 14 (June 8, 2021): 5605–30. http://dx.doi.org/10.1007/s00018-021-03865-z.

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AbstractIonotropic glutamate receptors (iGluRs) play key roles for signaling in the central nervous system. Alternative splicing and RNA editing are well-known mechanisms to increase iGluR diversity and to provide context-dependent regulation. Earlier work on isoform identification has focused on the analysis of cloned transcripts, mostly from rodents. We here set out to obtain a systematic overview of iGluR splicing and editing in human brain based on RNA-Seq data. Using data from two large-scale transcriptome studies, we established a workflow for the de novo identification and quantification of alternative splice and editing events. We detected all canonical iGluR splice junctions, assessed the abundance of alternative events described in the literature, and identified new splice events in AMPA, kainate, delta, and NMDA receptor subunits. Notable events include an abundant transcript encoding the GluA4 amino-terminal domain, GluA4-ATD, a novel C-terminal GluD1 (delta receptor 1) isoform, GluD1-b, and potentially new GluK4 and GluN2C isoforms. C-terminal GluN1 splicing may be controlled by inclusion of a cassette exon, which shows preference for one of the two acceptor sites in the last exon. Moreover, we identified alternative untranslated regions (UTRs) and species-specific differences in splicing. In contrast, editing in exonic iGluR regions appears to be mostly limited to ten previously described sites, two of which result in silent amino acid changes. Coupling of proximal editing/editing and editing/splice events occurs to variable degree. Overall, this analysis provides the first inventory of alternative splicing and editing in human brain iGluRs and provides the impetus for further transcriptome-based and functional investigations.

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33

Tran, Stephen S., Qing Zhou, and Xinshu Xiao. "Statistical inference of differential RNA-editing sites from RNA-sequencing data by hierarchical modeling." Bioinformatics 36, no. 9 (January 31, 2020): 2796–804. http://dx.doi.org/10.1093/bioinformatics/btaa066.

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Abstract Motivation RNA-sequencing (RNA-seq) enables global identification of RNA-editing sites in biological systems and disease. A salient step in many studies is to identify editing sites that statistically associate with treatment (e.g. case versus control) or covary with biological factors, such as age. However, RNA-seq has technical features that incumbent tests (e.g. t-test and linear regression) do not consider, which can lead to false positives and false negatives. Results In this study, we demonstrate the limitations of currently used tests and introduce the method, RNA-editing tests (REDITs), a suite of tests that employ beta-binomial models to identify differential RNA editing. The tests in REDITs have higher sensitivity than other tests, while also maintaining the type I error (false positive) rate at the nominal level. Applied to the GTEx dataset, we unveil RNA-editing changes associated with age and gender, and differential recoding profiles between brain regions. Availability and implementation REDITs are implemented as functions in R and freely available for download at https://github.com/gxiaolab/REDITs. The repository also provides a code example for leveraging parallelization using multiple cores.

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34

Cox, N. W. P., and D. A. Croot. "Data editing in a mixed DBMS environment." Statistical Journal of the United Nations Economic Commission for Europe 8, no. 2 (October 1, 1991): 117–36. http://dx.doi.org/10.3233/sju-1991-8202.

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35

Kyriazis, Ioannis, and Ioannis Fudos. "Editing Operators for Cross-Sectional Data-Sets." Computer-Aided Design and Applications 16, no. 6 (March 13, 2019): 1112–26. http://dx.doi.org/10.14733/cadaps.2019.1112-1126.

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36

Blewitt, Geoffrey. "An Automatic Editing Algorithm for GPS data." Geophysical Research Letters 17, no. 3 (March 1990): 199–202. http://dx.doi.org/10.1029/gl017i003p00199.

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37

Smallwood, David O. "Pyroshock data analysis—Analysis, editing, and validation." Journal of the Acoustical Society of America 111, no. 5 (2002): 2360. http://dx.doi.org/10.1121/1.4777951.

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38

Little, Roderick J. A., and Philip J. Smith. "Editing and Imputation for Quantitative Survey Data." Journal of the American Statistical Association 82, no. 397 (March 1987): 58–68. http://dx.doi.org/10.1080/01621459.1987.10478391.

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39

Min-Ling Zhang and Zhi-Hua Zhou. "CoTrade: Confident Co-Training With Data Editing." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41, no. 6 (December 2011): 1612–26. http://dx.doi.org/10.1109/tsmcb.2011.2157998.

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Gao, Lin, Shu-Yu Chen, Yu-Kun Lai, and Shihong Xia. "Data-Driven Shape Interpolation and Morphing Editing." Computer Graphics Forum 36, no. 8 (September 27, 2016): 19–31. http://dx.doi.org/10.1111/cgf.12991.

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Kok, A. L., and J. A. R. Blais. "Editing strategies for digitally correlated gestalt data." Photogrammetria 42, no. 4 (March 1988): 143–56. http://dx.doi.org/10.1016/0031-8663(88)90045-2.

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Guan, Donghai, Weiwei Yuan, Young-Koo Lee, and Sungyoung Lee. "Nearest neighbor editing aided by unlabeled data." Information Sciences 179, no. 13 (June 2009): 2273–82. http://dx.doi.org/10.1016/j.ins.2009.02.011.

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HUDEC, P. P. "Data Handling by Microcomputer: Acquisition, Editing, Processing." Environmental & Engineering Geoscience xxiii, no. 3 (August 1, 1986): 279–85. http://dx.doi.org/10.2113/gseegeosci.xxiii.3.279.

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Franco, Annalisa, Davide Maltoni, and Loris Nanni. "Data pre-processing through reward–punishment editing." Pattern Analysis and Applications 13, no. 4 (September 3, 2010): 367–81. http://dx.doi.org/10.1007/s10044-010-0182-x.

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Ragsdale, Cliff T., and Patrick G. McKeown. "On solving the continuous data editing problem." Computers & Operations Research 23, no. 3 (March 1996): 263–73. http://dx.doi.org/10.1016/0305-0548(96)81769-2.

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Sy, Alexander. "An alternative editing technique for oceanographic data." Deep Sea Research Part A. Oceanographic Research Papers 32, no. 12 (December 1985): 1591–99. http://dx.doi.org/10.1016/0198-0149(85)90105-0.

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Mielczarek, Magda, Bartosz Czech, Jarosław Stańczyk, Joanna Szyda, and Bernt Guldbrandtsen. "Extraordinary Command Line: Basic Data Editing Tools for Biologists Dealing with Sequence Data." Open Bioinformatics Journal 13, no. 1 (December 31, 2020): 137–45. http://dx.doi.org/10.2174/1875036202013010137.

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Abstract:

The command line is a standard way of using the Linux operating system. It contains many features essential for efficiently handling data editing and analysis processes. Therefore, it is very useful in bioinformatics applications. Commands allow for rapid manipulation of large ASCII files or very numerous files, making basic command line programming skills a critical component in modern life science research. The following article is not a guide to Linux commands. In this manuscript, in contrast to many various Linux manuals, we aim to present basic command line tools helpful in handling biological sequence data. This manuscript provides a collection of simple and popular hacks dedicated to users with very basic experience in the area of the Linux command line. It includes a description of data formats and examples of editing of four types of data formats popular in bioinformatics applications.

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WU, Shuai. "Research on the innovation of book collection and editing under the environment of big data." Region - Educational Research and Reviews 5, no. 4 (November 24, 2023): 130. http://dx.doi.org/10.32629/rerr.v5i4.1407.

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With the comprehensive development of science and technology in China and the gradual rise of the data era, it has had a profound impact on the work of library collection and editing. However, in the past work, many practitioners have not fully realized how to innovate the library collection and editing work in the era of big data. This paper mainly discusses the innovative research problems and coping strategies of book collection and editing under the big data environment, hoping to improve the work efficiency and quality.

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Yamamoto, T., and M. Nakagawa. "MERGING AIRBORNE LIDAR DATA AND SATELLITE SAR DATA FOR BUILDING CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W5 (May 13, 2015): 227–32. http://dx.doi.org/10.5194/isprsarchives-xl-4-w5-227-2015.

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A frequent map revision is required in GIS applications, such as disaster prevention and urban planning. In general, airborne photogrammetry and LIDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, attribute data acquisition and classification depend on manual editing works including ground surveys. In general, airborne photogrammetry and LiDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, these approaches classify geometrical attributes. Moreover, ground survey and manual editing works are finally required in attribute data classification. On the other hand, although geometrical data extraction is difficult, SAR data have a possibility to automate the attribute data acquisition and classification. The SAR data represent microwave reflections on various surfaces of ground and buildings. There are many researches related to monitoring activities of disaster, vegetation, and urban. Moreover, we have an opportunity to acquire higher resolution data in urban areas with new sensors, such as ALOS2 PALSAR2. Therefore, in this study, we focus on an integration of airborne LIDAR data and satellite SAR data for building extraction and classification.

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Granquist, Leopold. "Macro-editing – A review of some methods for rationalizing the editing of survey data." Statistical Journal of the United Nations Economic Commission for Europe 8, no. 2 (October 1, 1991): 137–54. http://dx.doi.org/10.3233/sju-1991-8203.

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