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Готові списки джерел за темами / Data collection Methods

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Добірка наукової літератури з теми "Data collection Methods"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 19 лютого 2022

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Статті в журналах з теми "Data collection Methods"

1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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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

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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Дисертації з теми "Data collection Methods"

1

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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2

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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3

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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4

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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5

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.
Ph.D.
Other
Sciences
Modeling and Simulation PhD

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6

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.
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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7

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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8

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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9

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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10

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.
M.S.M.E.
Department of Mechanical, Materials and Aerospace Engineering;
Engineering and Computer Science
Mechanical Engineering

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Книги з теми "Data collection Methods"

1

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

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2

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

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3

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

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4

1965-, Bradley Melissa, ed. Data collection methods: Semi-structured interviews and focus groups. Santa Monica, CA: RAND, 2009.

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5

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

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6

Harrell, Margaret C. Data collection methods: Semi-structured interviews and focus groups. Santa Monica, CA: RAND, 2009.

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7

Cuadra, Ernesto. Data collection strategies and methods for monitoring student flows. New York: World Bank, 1992.

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8

Needs assessment: A systematic approach to data collection. Urbana: Sagamore Publishing, 2011.

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9

Edwards, W. Sherman. Questionnaires and data collection methods for the institutional population component. Rockville, MD: Dept. of Health & Human Services, Public Health Service, National Center for Health Services Research and Health Care Technology Assessment, 1989.

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10

Ryus, Paul, Erin Ferguson, Kelly M. Laustsen, Robert J. Schneider, Frank R. Proulx, Tony Hull, and Luis Miranda-Moreno. Methods and Technologies for Pedestrian and Bicycle Volume Data Collection. Washington, D.C.: Transportation Research Board, 2014. http://dx.doi.org/10.17226/23429.

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Частини книг з теми "Data collection Methods"

1

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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2

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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3

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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4

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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5

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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6

Bhatta, Basudeb. "Collection of Data." In Research Methods in Remote Sensing, 43–59. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6594-8_3.

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7

Dormann, Christian, and Christina Guthier. "Longitudinal data collection." In Advanced Research Methods for Applied Psychology, 146–57. Abingdon, Oxon; New York, NY: Routledge, [2018]: Routledge, 2018. http://dx.doi.org/10.4324/9781315517971-15.

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8

D’Arcy, Alexandra. "Advances in Sociolinguistic Transcription Methods." In Data Collection in Sociolinguistics, 189–92. Second edition | New York, NY : Routledge, [2018]: Routledge, 2017. http://dx.doi.org/10.4324/9781315535258-38.

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9

Hughes, John A., and W. W. Sharrock. "Data Collection versus Analysis." In Theory and Methods in Sociology, 257–76. London: Macmillan Education UK, 2007. http://dx.doi.org/10.1007/978-1-137-11160-9_11.

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10

Indrayan, Abhaya. "Methodology of Data Collection." In Research Methods for Medical Graduates, 121–46. Boca Raton : CRC Press, [2020]: CRC Press, 2019. http://dx.doi.org/10.1201/9780429435034-7.

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Тези доповідей конференцій з теми "Data collection Methods"

1

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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2

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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3

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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4

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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5

Goodman, Clifford. "Current methods for technology evaluation: primary data collection and synthetic methods." In Health Care Technology Policy II: The Role of Technology in the Cost of Health Care: Providing the Solutions, edited by Warren S. Grundfest. SPIE, 1995. http://dx.doi.org/10.1117/12.225332.

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6

Yoshihisa, Tomok, Tadashi Nakano, Shun N. Watanabe, and Tatsuya Suda. "Bee-inspired data collection methods for P2P streaming systems." In 3d International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems. ICST, 2008. http://dx.doi.org/10.4108/icst.bionetics2008.4719.

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7

Ning, Qiang, Hao Wu, Pradeep Dasigi, Dheeru Dua, Matt Gardner, Robert L. Logan IV, Ana Marasović, and Zhen Nie. "Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ." In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.emnlp-demos.17.

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Di Meo, Antonella, Barbara Borzi, Marta Faravelli, Diego Polli, Marco Pagano, and Alessio Cantoni. "VULNERABILITY DATA COLLECTION AND DEFINITION OF SEISMIC RISK FOR CITIES IN PALESTINE." In 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering Methods in Structural Dynamics and Earthquake Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2017. http://dx.doi.org/10.7712/120117.5736.16994.

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9

Sharma, Vaibhav, Beni Shpringer, Sung Min Yang, Martin Bolger, Sodiq Adewole, D. Brown, and Erfaneh Gharavi. "Data Collection Methods for Building a Free Response Training Simulation." In 2019 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 2019. http://dx.doi.org/10.1109/sieds.2019.8735621.

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Peleshchyshyn, Andriy, and Oleg Mastykash. "Analysis of the methods of data collection on social networks." In 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). IEEE, 2017. http://dx.doi.org/10.1109/stc-csit.2017.8098762.

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Звіти організацій з теми "Data collection Methods"

1

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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2

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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3

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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4

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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5

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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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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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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Mullen, Nasim A., Jina Li, and Brett C. Singer. Participant Assisted Data Collection Methods in the California Healthy Homes Indoor Air Quality Study of 2011-13. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1221052.

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Mullen, Nasim, Jina Li, and Brett Singer. Participant Assisted Data Collection Methods in the California Healthy Homes Indoor Air Quality Study of 2011-13. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1165269.

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