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Artículos de revistas sobre el tema "Data collection Methods"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 9 de junio de 2025

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1

Wilcox, Adam B., Kathleen D. Gallagher, Bernadette Boden-Albala, and Suzanne R. Bakken. "Research Data Collection Methods." Medical Care 50 (July 2012): S68—S73. http://dx.doi.org/10.1097/mlr.0b013e318259c1e7.

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2

Harwood, Eileen M. "Data Collection Methods Series." Journal of Wound, Ostomy and Continence Nursing 36, no. 3 (May 2009): 246–50. http://dx.doi.org/10.1097/won.0b013e3181a1a4d3.

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3

Hutchinson, Elizabeth, and Eileen M. Harwood. "Data Collection Methods Series." Journal of Wound, Ostomy and Continence Nursing 36, no. 4 (July 2009): 371–75. http://dx.doi.org/10.1097/won.0b013e3181aaf65e.

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4

Harwood, Eileen M., and Elizabeth Hutchinson. "Data Collection Methods Series." Journal of Wound, Ostomy and Continence Nursing 36, no. 6 (November 2009): 592–99. http://dx.doi.org/10.1097/won.0b013e3181bd7f77.

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5

Hinojosa, Renee, Mary Erickson Megel, and Judith A. Heermann. "Methods of Data Collection." Plastic Surgical Nursing 14, no. 2 (1994): 109–10. http://dx.doi.org/10.1097/00006527-199406000-00014.

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6

Harwood, Eileen M., and Pakou Vang. "Data Collection Methods Series." Journal of Wound, Ostomy and Continence Nursing 36, no. 1 (January 2009): 15–20. http://dx.doi.org/10.1097/01.won.0000345172.28246.7a.

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7

Harwood, Eileen M., and Elizabeth Hutchinson. "Data Collection Methods Series." Journal of Wound, Ostomy and Continence Nursing 36, no. 2 (March 2009): 129–35. http://dx.doi.org/10.1097/01.won.0000347654.49228.44.

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8

Mazhar, Syeda Ayeman. "Methods of Data Collection: A Fundamental Tool of Research." Journal of Integrated Community Health 10, no. 01 (June 14, 2021): 6–10. http://dx.doi.org/10.24321/2319.9113.202101.

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For conducting research, it is a must to collect data. Data is basically the information that is required for investigating a research problem after proper designing. The importance of data collection lies in the fact that without gathering the particular information the research could not be carried out. The data may be primary or secondary. Usually, the methods of primary data collection in behavioural sciences include observation methods, interviews, questionnaires, and through database. The sources of secondary data include the previously published books, magazines, journals, etc. and unpublished autobiographies and biographies, etc. Thus, data collection is mandatory to accomplish the research process and therefore, it is the fundamental tool of research. This paper reviews, in detail, the various methods and different ways of gathering the information for undertaking research.

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9

Frick, Kevin D. "Microcosting Quantity Data Collection Methods." Medical Care 47, Supplement (July 2009): S76—S81. http://dx.doi.org/10.1097/mlr.0b013e31819bc064.

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10

Aishima, J., P. Gibbons, R. Fearn, A. Ashton, and K. Levik. "UsingGDAfor novel data-collection methods." Acta Crystallographica Section A Foundations of Crystallography 67, a1 (August 22, 2011): C277. http://dx.doi.org/10.1107/s0108767311093068.

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11

Bill, Ralf. "Unmanned Aerial Systems (UAS) - Attractive Extensions to Spatial Data Collection Methods." Photogrammetrie - Fernerkundung - Geoinformation 2014, no. 4 (August 1, 2014): 225–26. http://dx.doi.org/10.1127/1432-8364/2014/0238.

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12

Paradis, Elise, Bridget O'Brien, Laura Nimmon, Glen Bandiera, and Maria Athina (Tina) Martimianakis. "Design: Selection of Data Collection Methods." Journal of Graduate Medical Education 8, no. 2 (May 1, 2016): 263–64. http://dx.doi.org/10.4300/jgme-d-16-00098.1.

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13

Harwoodn, Eileen M., and Elizabeth Hutchinson. "Data Collection Methods Series Part 5." Journal of Wound, Ostomy and Continence Nursing 36, no. 5 (September 2009): 476–81. http://dx.doi.org/10.1097/won.0b013e3181b35248.

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14

Jasperse, Dorice M., and Susan W. Ahmed. "Comparison of two data collection methods." Controlled Clinical Trials 6, no. 3 (September 1985): 241–42. http://dx.doi.org/10.1016/0197-2456(85)90081-9.

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15

Serrone, Giulia Del, Giuseppe Cantisani, and Paolo Peluso. "Speed data collection methods: a review." Transportation Research Procedia 69 (2023): 512–19. http://dx.doi.org/10.1016/j.trpro.2023.02.202.

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16

Fairfield, Tasha. "A Bayesian Perspective on Theory-Blind Data Collection." Qualitative & Multi-Method Research 18, no. 2 (February 8, 2021): 30–35. https://doi.org/10.5281/zenodo.4046614.

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Copestake, Goertz, and Haggard’s (CGH) “Veil of ignorance Process Tracing” (VPT)—which in essence entails placing a firewall between data collection and hypothesis testing1—is an interesting addition to a growing list of proposals made in recent years that aim to address potential sources of bias in qualitative social science. Many of these proposals (e.g., pre-registration, time-logging whether evidence was discovered before or after a hypothesis was devised) import prescriptions from large-N, frequentist, statistical research that, from a Bayesian perspective, are not applicable to qualitative research. Bayesian reasoning provides its own safeguards against the problems of confirmation bias and ad hoc hypothesizing, without imposing procedural constraints that would interfere with the inherently iterative, dynamic, and interactive nature of case-study research—where we go back and forth between hypothesizing, data collection, and analysis (Fairfield and Charman 2019).

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17

Stewart, Marissa C., Lara E. McCormick, Jesse R. Goliath, Paul W. Sciulli, and Sam D. Stout. "A Comparison of Histomorphometric Data Collection Methods*." Journal of Forensic Sciences 58, no. 1 (May 23, 2012): 109–13. http://dx.doi.org/10.1111/j.1556-4029.2012.02195.x.

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18

Shaw, M. C. "Standardised methods of data collection are required." BMJ 310, no. 6993 (June 10, 1995): 1534–35. http://dx.doi.org/10.1136/bmj.310.6993.1534e.

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19

Smith, Eric E., Janet Warner, Megan Johnston, Kristin Atwood, Ruth Hall, Jean K. Mah, Colleen Maxwell, et al. "Neurological Registry Data Collection Methods and Configuration." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 40, S2 (July 2013): S27—S31. http://dx.doi.org/10.1017/s0317167100017133.

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20

Bowles, K. A., E. H. Skinner, D. Mitchell, R. Haas, M. Ho, K. Salter, K. May, et al. "Data Collection Methods in Health Services Research." Applied Clinical Informatics 06, no. 01 (2015): 96–109. http://dx.doi.org/10.4338/aci-2014-10-ra-0097.

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Summary Background: Hospital length of stay and discharge destination are important outcome measures in evaluating effectiveness and efficiency of health services. Although hospital administrative data are readily used as a data collection source in health services research, no research has assessed this data collection method against other commonly used methods. Objective: Determine if administrative data from electronic patient management programs are an effective data collection method for key hospital outcome measures when compared with alternative hospital data collection methods. Method: Prospective observational study comparing the completeness of data capture and level of agreement between three data collection methods; manual data collection from ward-based sources, administrative data from an electronic patient management program (i.PM), and inpatient medical record review (gold standard) for hospital length of stay and discharge destination. Results: Manual data collection from ward-based sources captured only 376 (69%) of the 542 in-patient episodes captured from the hospital administrative electronic patient management program. Administrative data from the electronic patient management program had the highest levels of agreement with inpatient medical record review for both length of stay (93.4%) and discharge destination (91%) data. Conclusion: This is the first paper to demonstrate differences between data collection methods for hospital length of stay and discharge destination. Administrative data from an electronic patient management program showed the highest level of completeness of capture and level of agreement with the gold standard of inpatient medical record review for both length of stay and discharge destination, and therefore may be an acceptable data collection method for these measures. Citation: Sarkies MN, Bowles K-A, Skinner EH, Mitchell D, Haas R, Ho M, Salter K, May K, Markham D, O’Brien L, Plumb S, Haines T.P. Data collection methods in health services research – hospital length of stay and discharge destination. Appl Clin Inf 2015; 6: 96–109http://dx.doi.org/10.4338/ACI-2014-10-RA-0097

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21

Robinson, Andrew L., Carolyn G. Emden, Tobias D. Croft, Gillian C. Vosper, Jean A. Elder, Christine Stirling, and James C. Vickers. "Mixed Methods Data Collection in Dementia Research." Journal of Mixed Methods Research 5, no. 4 (August 29, 2011): 330–44. http://dx.doi.org/10.1177/1558689811416940.

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22

Laing, David K., Andrew W. Hartshorne, Roger Cook, and Geoffrey Robinson. "A Fiber Data Collection for Forensic Scientists—Collection and Examination Methods." Journal of Forensic Sciences 32, no. 2 (March 1, 1987): 11139J. http://dx.doi.org/10.1520/jfs11139j.

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23

Rubeena and Minhajullah Maseeh. "INNOVATIVE DATA COLLECTION METHODS FOR RESEARCH IN THE DIGITAL ERA." VIDYA - A JOURNAL OF GUJARAT UNIVERSITY 2, no. 2 (September 13, 2023): 125–29. http://dx.doi.org/10.47413/vidya.v2i2.224.

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Data collection is a very important stage in social science research. In social science research, the data collection stage is very patient and challenging. The data collection methods are an important part of the research. During the collection of data, a researcher should identify the appropriate method of data collection based on the type and source of the data. If the researcher does not use the proper data collection method, they will encounter many difficulties while gathering the data, which may have an impact on their research. Therefore, the selection of data collection methods should be done with the utmost care. Before the COVID-19 pandemic, collecting face-to-face primary data from the research population was better. But when the social distancing era started, data collection in the traditional way in the field of teacher education became a challenging task for researchers. The researchers used several online data collection methods to deal with these challenges. Therefore, in this paper, researchers tried to identify online data collection methods for conducting the study or investigation in teacher education during the COVID-19 era.

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24

A., Botchkaryov. "STRUCTURAL ADAPTATION OF DATA COLLECTION PROCESSES IN AUTONOMOUS DISTRIBUTED SYSTEMS USING REINFORCEMENT LEARNING METHODS." Computer systems and network 2, no. 1 (March 23, 2017): 13–26. http://dx.doi.org/10.23939/csn2020.01.013.

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A method of structural adaptation of data collection processes has been developed based on reinforcement learning of the decision block on the choice of actions at the structural and functional level subordinated to it, which provides a more efficient distribution of measuring and computing resources, higher reliability and survivability of information collection subsystems of an autonomous distributed system compared to methods of parametric adaptation. In particular, according to the results of experimental studies, the average amount of information collected in one step using the method of structural adaptation is 23.2% more than in the case of using the methods of parametric adaptation. At the same time, the amount of computational costs for the work of the structural adaptation method is on average 42.3% more than for the work of parametric adaptation methods. The reliability of the work of the method of structural adaptation was studied using the efficiency preservation coefficient for different values of the failure rate of data collection processes. Using the recovery rate coefficient for various values of relative simultaneous sudden failures, the survivability of a set of data collection processes organized by the method of structural adaptation has been investigated. In terms of reliability, the structural adaptation method exceeds the parametric adaptation methods by an average of 21.1%. The average survivability rate for the method of structural adaptation is greater than for methods of parametric adaptation by 18.4%. Key words: autonomous distributed system, data collection process, structural adaptation, reinforcement learning

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25

A., Botchkaryov. "FUNCTIONAL COORDINATION OF METHODS OF ORGANIZATION OF ADAPTIVE DATA COLLECTION PROCESSES AND METHODS OF SPATIAL SELF-ORGANIZATION OF MOBILE AGENTS." Computer systems and network 2, no. 1 (March 23, 2017): 27–32. http://dx.doi.org/10.23939/csn2020.01.027.

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The way of functional coordination of methods of organization adaptive data collection processes and methods of spatial self-organization of mobile agents by parallel execution of the corresponding data collection processes and the process of motion control of a mobile agent using the proposed protocol of their interaction and the algorithm of parallel execution planning is proposed. The method allows to speed up the calculations in the decision block of the mobile agent by an average of 40.6%. Key words: functional coordination, adaptive data collection process, spatial self-organization, mobile agents

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26

Mwita, Kelvin. "Factors to consider when choosing data collection methods." International Journal of Research in Business and Social Science (2147- 4478) 11, no. 5 (June 29, 2022): 532–38. http://dx.doi.org/10.20525/ijrbs.v11i5.1842.

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Novice and experienced researchers may encounter numerous methodological challenges when it comes to data collection. Data have to be collected from the right sources using appropriate data collection tools to make research studies impactful. There are numerous data collection methods that researchers may use but not all methods are appropriate for all research studies. Choosing inappropriate research methods may result in invalid research findings and lead to erroneous conclusions. This means researchers have to carefully choose data collection methods to make their studies achieve their respective goals. This study intended to identify factors that researchers have to consider in choosing data collection methods. A systematic literature review was used to analyze 28 journal articles published between 2018 and 2022. From the analysis the study identified ten (10) factors that researchers have to consider in choosing data collection methods. These factors are; the research goal, the scope of the study, sample size, type of data, time and user-friendliness to the subjects. Other factors are research approach, safety and security of a researcher, the need for triangulation and theoretical framework. Before choosing methods for data collection, it is important to put into consideration these factors to increase the chances of achieving research objectives and answering research questions of research projects.

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27

Rice, Eric, Ian W. Holloway, Anamika Barman-Adhikari, Dahlia Fuentes, C. Hendricks Brown, and Lawrence A. Palinkas. "A Mixed Methods Approach to Network Data Collection." Field Methods 26, no. 3 (January 27, 2014): 252–68. http://dx.doi.org/10.1177/1525822x13518168.

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28

Morrison, Diane M., Barbara C. Leigh, and Mary Rogers Gillmore. "Daily data collection: A comparison of three methods." Journal of Sex Research 36, no. 1 (February 1999): 76–81. http://dx.doi.org/10.1080/00224499909551970.

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29

Kholifah, Eling Retno, and Muhammad Sofwan. "Comparison of Data Collection Methods: Advantages and Disadvantages." Jurnal Kepemimpinan dan Pengurusan Sekolah 9, no. 4 (December 21, 2024): 519–25. https://doi.org/10.34125/jkps.v9i4.628.

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This article aims to analyze the differences in data collection techniques in the research as well as identify the advantages and disadvantages of various methods, including surveys, interviews, observations, document analysis, experiments, and focus group discussions (FGDs). The research method used is a literature study, which involves tracing and analyzing written sources to understand the application of each technique. The results show that surveys offer efficiency and a wide range, but often neglect the depth of information needed to understand social contexts. Interviews and FGDs provide deeper insights, although they take time and can be influenced by researcher biases. Observation methods provide a good contextual understanding, but are subjective, while document analysis can present relevant historical data but must be critically evaluated. Experiments allow for the identification of clear cause-and-effect relationships, but are often carried out under artificial conditions that do not reflect reality. Taking into account technological developments, such as online surveys and mobile apps, this article concludes that a deep understanding of data collection techniques can help researchers design more effective and ethical research, resulting in findings that benefit society.

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Al Rashdan, Ahmad, and Shawn St. Germain. "Methods of Data Collection in Nuclear Power Plants." Nuclear Technology 205, no. 8 (May 31, 2019): 1062–74. http://dx.doi.org/10.1080/00295450.2019.1610637.

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31

Phillips, Sarah, Mathias Dillen, Laura Green, Quentin Groom, and Marie-Helene Weech. "Evaluating Methods for Transcribing Specimen Labels." Biodiversity Information Science and Standards 3 (June 21, 2019): e37306. https://doi.org/10.3897/biss.3.37306.

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Distributed Systems of Scientific Collections (DiSSCo) a pan-European Research Infrastructure will facilitate the production of tens of millions of digital images of natural history specimens each year. The labels of these specimens contain valuable information for research, but their transcription can be difficult and time-consuming, with often hard to read handwritten labels. Whilst accurate label transcription is only one step along the way to create a specimen record fit for different research uses, it is an extremely important one. It would be very time-consuming to have to return to recheck label information for even a very small proportion of specimens. Once a specimen label is transcribed correctly, it becomes much easier to enhance the record with additional information from other sources, e.g. from literature or collector itineraries. It also becomes feasible to determine the point of collection from the textual information on the label by a process known as georeferencing, or even to find inaccuracies within the label itself. Under the auspices of the project Innovation and Consolidation for Large Scale Digitisation of Natural Heritage (ICEDIG), we compared different manual approaches to transcription of collection labels. Using herbarium specimens as an example, the quality of transcribed data by: in-house trained institute staff, outsourcing to a commercial company or transcription by the general public through online crowdsourcing platforms was compared through two transcription pilots. The first pilot consisted of 200 S<em>olanum</em> specimen images from the Royal Botanic Gardens Kew in the UK and 200 from Meise Botanic Garden in Belgium. This particular genus was chosen as both institutes had specimens from which the label data had already been transcribed through the digitisation company Picturae, completed by Alembo. The Kew specimens had also been transcribed in-house by staff employed as digitisation officers or curators and by an independant researcher. The images from both institutes were uploaded to two crowdsourcing platforms: DigiVol and DoeDat. In a second pilot, multiple European institutions holding botanical collections were approached to provide a sample of 200 digitally imaged herbarium sheet specimens to upload to multiple crowdsourcing platforms. Specimens from 7 institutions were uploaded for transcription to 5 different crowdsourcing platforms: DigiVol, DoeDat, Die Herbonauten, Les Herbonautes and Notes from Nature. For both pilots, key transcription data were assessed and common errors in label transcription identified. Reasons for these errors will be discussed along with possible mechanisms to improve the accuracy of the transcriptions. The need for standards for transcription is identified and recommendations made.

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32

Bafarasat, Abbas Ziafati, Eduardo Oliveira, and Anna Growe. "A researcher companion of data collection and validation methods." Urban Resilience and Sustainability 1, no. 2 (2023): 91–106. http://dx.doi.org/10.3934/urs.2023007.

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<abstract> <p>This paper aims to fill a gap between textbooks and papers on data collection methods. Many undergraduate study programs include modules on data collection methods. They provide students with systematic insight into data collection methods. However, a few years later, when students want to apply this training in their research, they may have forgotten unapplied parts! Because researchers might not devote themselves to adequate re-study of data collection textbooks, they sometimes choose a data collection method without a systematic view of other methods and the wider framework of data collection. Researchers might devote their available time to selective study about applying their chosen data collection method, but a loss of overall methodological insight might have negative implications for the research. The present paper aims to help with this problem. It provides a researcher companion of data collection and validation methods in a systematic textbook style but within a concise paper with essential details and examples. The paper can be used by researchers of different disciplines as a standalone methodological guide or a legend for informed searching of more detail in relevant references.</p> </abstract>

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33

Awa, Tiam, Gueye Ibrahima, Wade Ahmed-Mouhamadou, Sidibe Samba, and Niang Oumar. "Design and implementation of an offline-first road crash data collection system." World Journal of Advanced Research and Reviews 13, no. 2 (February 28, 2022): 095–107. https://doi.org/10.5281/zenodo.6245136.

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Road traffic accidents (RTAs) are a global burden that particularly affects people in developing countries. According to the World Health Organization, having reliable and precise data is necessary to raise the alert on the scale of road accidents and convince decision-makers of the need to take action. Each year, nearly 700 people lose their lives due to accidents on Senegalese roads. In Senegal, dis-aggregated data related to accidents are rare due to very significant under-recording. Most of these data are in a non-computerized form and their collection methods differ from one side to the other among road safety actors. The task of collecting data is simply considered of secondary importance for certain road safety actors. The disparity between the collection methods as well as collected data and the non computerized collection processes limit the possibility of having a real knowledge of the phenomenon of road accidents. In this work, we present a global solution of a road crash data collection and management system. In particular, we will focus here on the collection component with an application that aims to federate data collection processes of all road safety actors in Senegal. It uses a collection guide designed after a review of road accident data guides in Senegal and in other regions of the world. It also implements a parallel collection methodology, i.e the possibility of several agents working on collecting data for the same accident.

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34

Du, Kang, Dalei Song, and Yimei Zeng. "Research on Multi-Sensor Data Collection and Mapping Methods." International Journal of Computer Science and Information Technology 2, no. 1 (March 24, 2024): 341–47. http://dx.doi.org/10.62051/ijcsit.v2n1.35.

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In today's information society, the importance of collecting data through multiple channels continues to increase. This article focuses on the use of drones for large-area forest information collection and tree analysis. In complex environments, there are a series of problems in obtaining tree mapping information with a single sensor, such as single use scenarios, low accuracy, limited viewing angles, and high requirements for computing power. To address these challenges, this paper proposes a Bayesian-based multi-sensor fusion strategy to overcome the limitations of a single sensor by integrating depth camera and lidar data. Experimental results show that the proposed method significantly improves the accuracy of mapping surrounding trees and the ability to collect information. The multi-sensor fusion strategy effectively reduces the problem of repeated data processing and improves the comprehensiveness of collected data. It meets the requirements of real-time performance and accurate collection of surrounding scenes.

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35

Jiang, Ling Yun, and Zhi Biao Wang. "Data Collection and Model Construction Methods for Reverse Engineering." Advanced Materials Research 102-104 (March 2010): 189–93. http://dx.doi.org/10.4028/www.scientific.net/amr.102-104.189.

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The process of creating a CAD model from an object is mainly made up of two steps: the data collection through digital measurement and the construction of parameterized and revisable model. This paper discusses the measuring process and technical problems of the Coordinate Measuring Machine (CMM) and non-contact sensor. Through comparative analysis, we determine the application scope of those approaches in measuring different dimensions of the same objects considering the time efficiency and tolerance requirement. This paper divide the objects into two categories: freeform feature objects and regular feature objects. As for the freeform feature objects, people could fit wrap-around B-spline surfaces to construct the model. Regular feature objects for mass produce contain machined surfaces which should be precisely measured and modeled. The model of regular feature object should be constructed by three-dimensional modeling software, so that it is parametric and revisable for changing and improving the original design. Sizes and position of important surfaces of the model are acquired from CMM, and those of non-important features are fitted though point cloud processing. Some profile can’t be measured directly from CMM but should be precise, so this paper proposed two methods to construct profile line and analyze error by comparing it with point cloud.

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36

Morgan, Karen J., S. R. Johnson, Robert L. Rizek, Robert Reese, and Gary L. Stampley. "Collection of food intake data: An evaluation of methods." Journal of the American Dietetic Association 87, no. 7 (July 1987): 888–96. http://dx.doi.org/10.1016/s0002-8223(21)03211-9.

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37

Goneppanavar, Umesh, Zulfiqar Ali, SBala Bhaskar, and JigeeshuV Divatia. "Types of data, methods of collection, handling and distribution." Airway 2, no. 1 (2019): 36. http://dx.doi.org/10.4103/arwy.arwy_11_19.

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38

Grack Nelson, Amy, and Sarah Cohn. "Data Collection Methods for Evaluating Museum Programs and Exhibitions." Journal of Museum Education 40, no. 1 (March 2015): 27–36. http://dx.doi.org/10.1080/10598650.2015.11510830.

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39

Meurer, W. J., S. M. Frederiksen, J. J. Majersik, L. Zhang, A. Sandretto, and P. A. Scott. "Qualitative Data Collection and Analysis Methods: The INSTINCT Trial." Academic Emergency Medicine 14, no. 11 (November 1, 2007): 1064–71. http://dx.doi.org/10.1197/j.aem.2007.05.005.

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40

Strelluf, Christopher. "Book Review: Data Collection in Sociolinguistics: Methods and Applications." Journal of English Linguistics 41, no. 4 (December 2013): 380–84. http://dx.doi.org/10.1177/0075424213506012.

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41

Rice, Michelle C., and Jane Fellows. "COMPARING TWO METHODS OF PREVALENCE AND INCIDENCE DATA COLLECTION." Journal of Wound, Ostomy and Continence Nursing 31, Supplement (May 2004): S19—S20. http://dx.doi.org/10.1097/00152192-200405001-00055.

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42

Garner, J. B., and J. Uren. "THE USE OF PHOTOGRAPHIC METHODS FOR TRAFFIC DATA COLLECTION." Photogrammetric Record 7, no. 41 (August 26, 2006): 555–67. http://dx.doi.org/10.1111/j.1477-9730.1973.tb01189.x.

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43

Grove, Stephen J., and Raymond P. Fisk. "Observational data collection methods for services marketing: An overview." Journal of the Academy of Marketing Science 20, no. 3 (June 1992): 217–24. http://dx.doi.org/10.1007/bf02723408.

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44

Pucken, Valerie-Beau, Michèle Bodmer, Benjamin Lovis, Julie Pont, Giulia Savioli, Filipe Maximiano Sousa, and Gertraud Schüpbach-Regula. "Antimicrobial consumption: Comparison of three different data collection methods." Preventive Veterinary Medicine 186 (January 2021): 105221. http://dx.doi.org/10.1016/j.prevetmed.2020.105221.

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45

darovsky, Alex. "Workshop on strategy and methods for AD data collection." Synchrotron Radiation News 9, no. 5 (September 1996): 8. http://dx.doi.org/10.1080/08940889608602895.

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46

Lebedeva, Ol'ga. "OVERVIEW DATA COLLECTION METHODS FOR EVALUATION OF TRIP MATRIX." Bulletin of the Angarsk State Technical University 1, no. 16 (December 27, 2022): 134–38. http://dx.doi.org/10.36629/2686-777x-2022-1-16-134-138.

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Resumen

Traffic flow management on the road network is one of the key areas of transport and logistics activities, which involves various enterprises, third-party companies and regulatory authorities. Traffic safety and regularity of trips directly depend on the quality and clarity of the organization of the transport and logistics process. In a competitive environment, assessing the strengths and weaknesses of existing data collection methods becomes a significant advantage, allowing us to apply those methods that allow us to achieve our goals. To solve this problem, it is proposed to jointly and consistently use methods for restoring the correspondence matrix within the framework of the technological process of transport and logistics hubs. Based on the results of the analysis, specific proposals have been developed, the implementation of which will improve the efficiency of the transportation process based on the technically competent development and implementation of a methodology for assessing transport demand

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47

Cheliotis, Giorgos, Xuesong Lu, and Song Yi. "Reliability of Data Collection Methods in Social Media Research." Proceedings of the International AAAI Conference on Web and Social Media 9, no. 1 (August 3, 2021): 586–89. http://dx.doi.org/10.1609/icwsm.v9i1.14669.

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In this paper we argue for the systematic assessment and reporting of the reliability of data collection methods that rely on the automated collection of data from third party social media sites, where the researcher does not have direct access to meaning or control over data quality. Building on a long tradition of reliability assessment in the sciences and specifically in social science, we propose methods for the assessment of reliability, also for textual data that is increasingly mined from social media for the purposes of studying online populations.

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48

Quandt, Sara A., and Thomas A. Arcury. "Qualitative methods in arthritis research: Overview and data collection." Arthritis Care & Research 10, no. 4 (August 1997): 273–81. http://dx.doi.org/10.1002/art.1790100409.

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49

Campbell, N. J., P. Youngs, and I. Anderson. "A UK survey of obstetric anaesthetic data collection methods." International Journal of Obstetric Anesthesia 22, no. 2 (April 2013): 167–68. http://dx.doi.org/10.1016/j.ijoa.2013.01.009.

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50

Cheng, Anchi, and Yue Yu. "Recent advances in data collection for Cryo-EM methods." Current Opinion in Structural Biology 86 (June 2024): 102795. http://dx.doi.org/10.1016/j.sbi.2024.102795.

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