Relevant bibliographies by topics / Data collection Methods

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data collection Methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Data collection Methods"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Data collection Methods"

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Murphy, John Frank Davis Mark E. Davis Mark E. "Methods for collection and processing of gene expression data /." Diss., Pasadena, Calif. : California Institute of Technology, 2005. http://resolver.caltech.edu/CaltechETD:etd-06252004-101813.

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Haas, Georg-Christoph [Verfasser], and Bella [Akademischer Betreuer] Struminskaya. "Modernization of data collection methods / Georg-Christoph Haas ; Betreuer: Bella Struminskaya." Mannheim : Universitätsbibliothek Mannheim, 2021. http://d-nb.info/1239558260/34.

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Fullman, Leah Irene. "Craniofacial Teams' Data Collection and Reporting Methods for Videonasendoscopy and Videofluoroscopy." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/557.

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Videonasendoscopy and videofluoroscopy are two instruments used by craniofacial teams for assessing velopharyngeal function. Various methods have been proposed throughout the literature for collecting and reporting data from these two types of imaging studies. The purpose of this investigation is to survey craniofacial teams regarding current use of videonasendoscopy and videofluoroscopy in the clinical setting. The results show the videonasendoscopy is more frequently used than videofluoroscopy and that estimations are a more common data collection/reporting method than objective measurements. The data also show that a wide variety of methods are currently in use and only a small number of teams use the standardizing method proposed by the international working group (Golding-Kushner, et al., 1990).
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Siddiqui, Muazzam. "DATA MINING METHODS FOR MALWARE DETECTION." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2783.

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This research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to find out the best features and build models that can classify a given program into a malware or a clean class. Our research is closely related to information retrieval and classification techniques and borrows a number of ideas from the field. We used a vector space model to represent the programs in our collection. Our data mining framework includes two separate and distinct classes of experiments. The first are the supervised learning experiments that used a dataset, consisting of several thousand malicious and clean program samples to train, validate and test, an array of classifiers. In the second class of experiments, we proposed using sequential association analysis for feature selection and automatic signature extraction. With our experiments, we were able to achieve as high as 98.4% detection rate and as low as 1.9% false positive rate on novel malwares.<br>Ph.D.<br>Other<br>Sciences<br>Modeling and Simulation PhD
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Cyr, J. "The Pitfalls and Promise of Focus Groups as a Data Collection Method." SAGE PUBLICATIONS INC, 2015. http://hdl.handle.net/10150/615820.

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Despite their long trajectory in the social sciences, few systematic works analyze how often and for what purposes focus groups appear in published works. This study fills this gap by undertaking a meta-analysis of focus group use over the last 10 years. It makes several contributions to our understanding of when and why focus groups are used in the social sciences. First, the study explains that focus groups generate data at three units of analysis, namely, the individual, the group, and the interaction. Although most researchers rely upon the individual unit of analysis, the method’s comparative advantage lies in the group and interactive units. Second, it reveals strong affinities between each unit of analysis and the primary motivation for using focus groups as a data collection method. The individual unit of analysis is appropriate for triangulation; the group unit is appropriate as a pretest; and the interactive unit is appropriate for exploration. Finally, it offers a set of guidelines that researchers should adopt when presenting focus groups as part of their research design. Researchers should, first, state the main purpose of the focus group in a research design; second, identify the primary unit of analysis exploited; and finally, list the questions used to collect data in the focus group.
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Nenneman, Milton. "An examination of state and local fusion centers and data collection methods." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://bosun.nps.edu/uhtbin/hyperion-image.exe/08Mar%5FNenneman.pdf.

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Thesis (M.A. in Security Studies (Homeland and Security Defense))--Naval Postgraduate School, March 2008.<br>Thesis Advisor(s): Simeral, Robert. "March 2008." Description based on title screen as viewed on May 5, 2008. Includes bibliographical references (p. 119-122). Also available in print.
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Osowski, Christopher John. "Developing data collection methods to inform the quantitative design of cycle infrastructure." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/416629/.

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Increased share of urban travel by bicycle is widely desired as a cost-effective and environmentally-beneficial means of travel, and one which has the potential to reduce road congestion and improve health outcomes. Recent rapid cycling growth in cities such as London has served to highlight the lack of robust empirically-backed quantitative literature to inform the practitioner, and the consequential barrier to the delivery of enabling infrastructure of the scale required to meet that demand. Even simple measures vary by orders of magnitude in the literature and some depend on intuitively na¨ıve assumptions, so a simulation (based on the Social Force Model) was defined and implemented to test the key underpinning (non-interaction) assumption of the Highway Capacity Manual’s quantitative definition of cycle level of service. The simulations indicate that an assumption of non-interaction between cyclists results in an outcome intrinsically at odds with fundamental traffic flow theory. Both the literature and simulation process serve to highlight the lack of existing appropriate empirical data and behavioural understanding. Furthermore, collecting such data is difficult, expensive and not easily scalable using current methods. Consequently, a methodology for the collection of key cyclist parameters from generic video data was created, and can be applied to bespoke video surveys and existing CCTV capture, across a variety of modes, and at a fraction of the cost of human operators. In addition, a bicycle simulator is developed which can test cyclist behaviour in a replicable manner and in a range of circumstances. The design and construction process is detailed, and a proof-of-concept, validated against real data, is presented. Subject to some minor improvements identified, the simulator can now be used more widely for the collection of behavioural data. These methodologies provide new and practical capabilities for the collection and application of cyclist data, and a greater understanding of cycle behaviour.
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He, Yi. "An Analysis of Airborne Data Collection Methods for Updating Highway Feature Inventory." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/5016.

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Highway assets, including traffic signs, traffic signals, light poles, and guardrails, are important components of transportation networks. They guide, warn and protect drivers, and regulate traffic. To manage and maintain the regular operation of the highway system, state departments of transportation (DOTs) need reliable and up-to-date information about the location and condition of highway assets. Different methodologies have been employed to collect road inventory data. Currently, ground-based technologies are widely used to help DOTs to continually update their road database, while air-based methods are not commonly used. One possible reason is that the initial investment for air-based methods is relatively high; another is the lack of a systematic and effective approach to extract road features from raw airborne light detection and ranging (LiDAR) data and aerial image data. However, for large-area inventories (e.g., a whole state highway inventory), the total cost of using aerial mapping is actually much lower than other methods considering the time and personnel needed. Moreover, unmanned aerial vehicles (UAVs) are easily accessible and inexpensive, which makes it possible to reduce costs for aerial mapping. The focus of this project is to analyze the capability and strengths of airborne data collection system in highway inventory data collection. In this research, a field experiment was conducted by the Remote Sensing Service Laboratory (RSSL), Utah State University (USU), to collect airborne data. Two kinds of methodologies were proposed for data processing, namely ArcGIS-based algorithm for airborne LiDAR data, and MATLAB-based procedure for aerial photography. The results proved the feasibility and high efficiency of airborne data collection method for updating highway inventory database.
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Palmer, Kurt D. "Data collection plans and meta models for chemical process flowsheet simulators." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/24511.

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Mitteff, Eric. "AUTOMATED ADAPTIVE DATA CENTER GENERATION FOR MESHLESS METHODS." Master's thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2635.

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Meshless methods have recently received much attention but are yet to reach their full potential as the required problem setup (i.e. collocation point distribution) is still significant and far from automated. The distribution of points still closely resembles the nodes of finite volume-type meshes and the free parameter, c, of the radial-basis expansion functions (RBF) still must be tailored specifically to a problem. The localized meshless collocation method investigated requires a local influence region, or topology, used as the expansion medium to produce the required field derivatives. Tests have shown a regular cartesian point distribution produces optimal results, however, in order to maintain a locally cartesian point distribution a recursive quadtree scheme is herein proposed. The quadtree method allows modeling of irregular geometries and refinement of regions of interest and it lends itself for full automation, thus, reducing problem setup efforts. Furthermore, the construction of the localized expansion regions is closely tied up to the point distribution process and, hence, incorporated into the automated sequence. This also allows for the optimization of the RBF free parameter on a local basis to achieve a desired level of accuracy in the expansion. In addition, an optimized auto-segmentation process is adopted to distribute and balance the problem loads throughout a parallel computational environment while minimizing communication requirements.<br>M.S.M.E.<br>Department of Mechanical, Materials and Aerospace Engineering;<br>Engineering and Computer Science<br>Mechanical Engineering
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Books on the topic "Data collection Methods"

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Jessup, Eric. Truck trip data collection methods. Salem, OR: Oregon Dept. of Transportation, Research Unit, 2004.

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B, Skarpness, Tornow C, Battelle Memorial Institute, United States. Federal Highway Administration. Office of Highway Information Management, and United States. Federal Highway Administration, eds. Improved vehicle occupancy data collection methods. [Washington, DC]: The Office, 1997.

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Tinsley, Robert N. Data collection methods in retailing research. Birmingham: Aston University. Management Centre, 1986.

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Food and Agricultural Organization of the United Nations., ed. National methods of agricultural price data collection. Rome: Food and Agriculture Organization of the United Nations, 1986.

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Roger, Sapsford, and Jupp Victor, eds. Data collection and analysis. London: Sage in association with Open University, 1996.

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French, Alexander. Traffic data collection and analysis: Methods and procedures. Washington, D.C: Transportation Research Board, National Research Council, 1986.

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United States. Agency for International Development, ed. Rapid, low-cost data collection methods for A.I.D. Washington, D.C: U.S. Agency for International Development, 1987.

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O, Martin Michael, Mullis Ina V. S, Bruneforth Michael, and Third International Mathematics and Science Study., eds. Quality assurance in data collection. Chestnut Hill, Mass: Center for the Study of Testing, Evaluation, and Educational Policy, Boston College, 1996.

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Cuadra, Ernesto. Data collection strategies and methods for monitoring student flows. New York: World Bank, 1991.

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Williams, Owen R. Methods for collection and analysis of fluvial-sediment data. [Washington, D.C.?]: U.S. Dept. of Agriculture, Forest Service, Watersshed and Air Management Staff, 1988.

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Book chapters on the topic "Data collection Methods"

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Baker, Michael J. "Data collection methods." In Research for Marketing, 159–208. London: Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-21230-9_8.

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Hanvey, Chester. "Data Collection Methods." In Wage and Hour Law, 19–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74612-8_2.

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Orkin, Kate. "Data Collection Methods." In Encyclopedia of Quality of Life and Well-Being Research, 1430–32. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_664.

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Swanson, Richard A., and Thomas J. Chermack. "Data Collection Methods." In ANALYSIS, 75–89. New York: Routledge, 2024. http://dx.doi.org/10.4324/9781003518532-8.

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Jansen, Bernard J., Kholoud K. Aldous, Joni Salminen, Hind Almerekhi, and Soon-gyo Jung. "Data Collection Methods." In Understanding Audiences, Customers, and Users via Analytics, 33–52. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-41933-1_4.

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Orkin, Kate. "Data Collection Methods." In Encyclopedia of Quality of Life and Well-Being Research, 1582–84. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_664.

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Wesner, Simone. "Data Collection Methods." In Researching the Creative and Cultural Industries, 56–74. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003161912-4.

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Heap, Vicky, and Jaime Waters. "Data collection methods." In Mixed Methods in Criminology, 141–76. Abingdon, Oxon ; New York, NY : Routledge, 2018.: Routledge, 2019. http://dx.doi.org/10.4324/9781315143354-8.

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Li, Yanmei, and Sumei Zhang. "Data Collection." In Applied Research Methods in Urban and Regional Planning, 37–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93574-0_4.

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Drummond, Avril. "Collection of data." In Research Methods for Therapists, 78–99. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-2979-2_4.

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Conference papers on the topic "Data collection Methods"

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Skalkin, Anton, and Anton Romanov. "Automation Methods of Data Collection and Analysis for Managing Complex IT Infrastructures." In 2025 International Russian Smart Industry Conference (SmartIndustryCon), 263–68. IEEE, 2025. https://doi.org/10.1109/smartindustrycon65166.2025.10986101.

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Walker, Joe. "SSPC-PA2: New Electronic Data Collection Solutions." In SSPC 2014 Greencoat, 1–9. SSPC, 2014. https://doi.org/10.5006/s2014-00064.

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Advances in technology have lead to vast changes in Dry Film Thickness (DFT) testing methods. These changes have not only improved the overall user experience and repeatability of measurements, but also have necessitated changes in international standards. Traditionally, industries used both visual and analog devices in conjunction with paper reporting systems to ensure that QA/QC requirements were being met. In the early 1980's, the microprocessor became widely available for application in portable test equipment. Manufacturers, in conjunction with the technology industry, developed the first electronic inspection tools. With the advent of affordable mainframes and the swell of PC availability and usage, instruments were, for the first time, able to be linked in real time to DOS programs. The era of electronic Statistical Process Control (SPC) was born. Due to physical and networking limitations, electronic SPC was primarily used in factories and other large-scale production facilities. Gradually, the market demanded that data capture be made more portable, and instruments with onboard memory and data download capability were introduced which ushered in the age of inspection instruments with features heretofore only found in computers.
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Mudiraj, Nakkala Srinivas, and Satwinder Singh. "Hindi Text-to-Image Generation: A Diverse Data Collection Methods, Annotation Approaches and Challenges." In 2024 Second International Conference on Advanced Computing & Communication Technologies (ICACCTech), 47–55. IEEE, 2024. https://doi.org/10.1109/icacctech65084.2024.00019.

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Zhou, Desheng, Liqiang Yao, Zhen Shang, Yingren Wang, and Bo Chen. "A Study of Parallel Configurable Collection Methods for Grid Data Information Containing Imbalance Samples." In 2025 2nd International Conference on Smart Grid and Artificial Intelligence (SGAI), 37–40. IEEE, 2025. https://doi.org/10.1109/sgai64825.2025.11009403.

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Seitnazarov, Kuanish K., Biybinaz K. Turemuratova, and Anvar K. Aytanov. "Stages and Methods of Data Collection for Developing an Artificial Intelligence Model for Recognizing Letters of the Karakalpak Sign Language." In 2024 IEEE 25th International Conference of Young Professionals in Electron Devices and Materials (EDM), 2530–34. IEEE, 2024. http://dx.doi.org/10.1109/edm61683.2024.10615102.

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Semkova, V., N. Otuka, S. P. Simakov, and V. Zerkin. "Experimental nuclear reaction data collection EXFOR." In 2011 2nd International Conference on Advancements in Nuclear Instrumentation, Measurement Methods and their Applications (ANIMMA). IEEE, 2011. http://dx.doi.org/10.1109/animma.2011.6172861.

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Gao, Lijn, Hang Yin, Yuhua Wei, and Le Wang. "Data Collection Methods Based on Mobile Sink Node." In 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/ameii-15.2015.32.

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Mihalinac, Sandra, Maja Ahac, Saša Ahac, and Miroslav Šimun. "Overview of emerging road traffic data collection methods." In 6th International Conference on Road and Rail Infrastructure. University of Zagreb Faculty of Civil Engineering, 2021. http://dx.doi.org/10.5592/co/cetra.2020.1210.

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It is a well-known fact that the data on road traffic flow characteristics is essential for sustainable road network management. First road traffic volume counts date back to the 1950s when short-term periodic road traffic counts were carried out in cities worldwide. Manual traffic counting is one of the oldest and most technologically simple methods to obtain data on road traffic volume and its composition. Today, because of the ever-growing road transport demand, it has become clear that the development of Intelligent Transport Systems (ITS) is vital to increase safety and tackle increasing emission and congestion problems. The introduction of ITS highly depends on the quality and quantity of traffic data. Under the growing requirement of long-term traffic flow information, various traffic data collection methods have evolved. They allow systematic recording of the traffic flow volume and composition but also vehicle speed, total gross weight, number of axles, axle load and travel destination. This data, which is collected continuously over longer periods, enables a detailed analysis of traffic flows, and represents the basis for decision making in planning, designing, construction and maintenance of road infrastructure. This paper gives an overview of traditional and emerging traffic data collection methods - both fixed and mobile - and the analysis of the current road traffic data collection methods used on the Croatian road network, in terms of their potential and limitations.
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Cao, Ling, Pei Gang Liu, and Nian Yan Huang. "Study on building electricity consumption data collection methods." In 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/icmemtc-16.2016.130.

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Kim, Sunghwan Mac, Stephen Wan, Cecile Paris, Jin Brian, and Bella Robinson. "The Effects of Data Collection Methods in Twitter." In Proceedings of the First Workshop on NLP and Computational Social Science. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/w16-5611.

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Reports on the topic "Data collection Methods"

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Sandstrom, Mary M., Geoffrey W. Brown, Daniel N. Preston, Colin J. Pollard, Kirstin F. Warner, Daniel L. Remmers, Daniel N. Sorensen, et al. Integrated Data Collection Analysis (IDCA) Program - SSST Testing Methods. Office of Scientific and Technical Information (OSTI), March 2013. http://dx.doi.org/10.2172/1305869.

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Mitchell, J. L., Winston Bennett, Weissmuller Jr., Gosc J. J., Waldroop R. L., and Patricia. Computer-Based Job and Occupational Data Collection Methods: Feasibility Study. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada388029.

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Perk, Victoria. Handbook of Automated Data Collection Methods for the National Transit Database. Tampa, FL: University of South Florida, October 2003. http://dx.doi.org/10.5038/cutr-nctr-rr-2002-06.

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Haring, Christopher. Data collection tools for river geomorphology studies : LiDAR and traditional methods. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42502.

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The purpose of this review is to highlight LiDAR data usage for geomorphic studies and compare to other remote sensing technologies. This review further identifies survey efficiencies and issues that can be problematic in using LiDAR digital elevation models (DEMs) in completing surveys and geomorphic analysis. US Army Corps of Engineers (USACE) geospatial data collection guidance (EM 1110-1-1000) (USACE 2015) aligns with the American Society for Photogrammetry and Remote Sensing Positional Accuracy Standards for Digital Geospatial Data (ASPRS 2014). Geomorphic assessment technologies are rapidly evolving, and LiDAR data collection methods are at the forefront. The FluvialGeomorph (FG) toolbox, developed to support USACE watershed planning, is a recent example of the use of LiDAR high-resolution terrain data to provide a new, efficient approach for rapid watershed assessments (Haring et al. 2020; Haring and Biedenharn 2021). However, there are advantages and disadvantages in using LiDAR data compared to other remote sensing technologies and traditional topographic field survey methods.
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Al Rashdan, Ahmad Y., and Shawn W. St. Germain. Automation of Data Collection Methods for Online Monitoring of Nuclear Power Plants. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1475451.

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Tokuhiro, Akiro, Art Ruggles, and David Pointer. Data Collection Methods for Validation of Advanced Multi-Resolution Fast Reactor Simulations. Office of Scientific and Technical Information (OSTI), January 2015. http://dx.doi.org/10.2172/1169913.

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Rowland, Mary M., Larry D. Bryant, Bruce K. Johnson, James H. Noyes, Michael J. Wisdom, and Jack Ward Thomas. The Starkey project: history, facilities, and data collection methods for ungulate research. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 1997. http://dx.doi.org/10.2737/pnw-gtr-396.

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Strachan, Anna Louise. The Impact of Covid-19 on Research Methods and Approaches. Institute of Development Studies (IDS), January 2021. http://dx.doi.org/10.19088/cc.2021.002.

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The Covid-19 pandemic, and measures to contain the spread of the virus, such as border closures, quarantine requirements, mandatory PCR (Polymerase Chain Reaction) tests, curfews, and social distancing requirements, have had a significant impact on research methods and approaches. Most of the available literature assumes that remote data collection is the only viable means of collecting primary data during the pandemic, so that is the focus of this report. While there is an extensive discussion of challenges associated with undertaking primary data collection during this time, there are also several commentaries and opinion pieces that highlight the opportunities and positive aspects of remote data collection.
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Guy, Bryan, and Jon Fricker. Guidelines for Data Collection Techniques and Methods for Roadside Station Origin-Destination Studies. West Lafayette, IN: Purdue University, 2005. http://dx.doi.org/10.5703/1288284313368.

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10

Page-Dumroese, Deborah S., Ann M. Abbott, and Thomas M. Rice. Forest Soil Disturbance Monitoring Protocol: Volume II: Supplementary methods, statistics, and data collection. Washington, DC: U.S. Department of Agriculture, Forest Service, 2009. http://dx.doi.org/10.2737/wo-gtr-82b.

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