Relevant bibliographies by topics / Analysis of experimental data

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

Academic literature on the topic 'Analysis of experimental data'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Analysis of experimental data"

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MILOSAN, Ioan. "STATISTICAL PROCESSING OF EXPERIMENTAL DATA USING ANALYSIS OF VARIANCE." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 18, no. 1 (June 24, 2016): 489–96. http://dx.doi.org/10.19062/2247-3173.2016.18.1.67.

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K, Kanniga, and Akhil Varma. "Experimental Setup and Analysis of Data Logging Software Specifications." International Journal of Psychosocial Rehabilitation 23, no. 4 (July 20, 2019): 361–67. http://dx.doi.org/10.37200/ijpr/v23i4/pr190195.

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Klasson, K. Thomas. "Experimental data analysis." Applied Biochemistry and Biotechnology 63-65, no. 1 (March 1997): 339–48. http://dx.doi.org/10.1007/bf02920435.

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Bhar, Lalmohan, and Sanjay Kumar Prasad. "Bayesian Analysis of Experimental Data." Pakistan Journal of Statistics and Operation Research 9, no. 2 (October 21, 2013): 225. http://dx.doi.org/10.18187/pjsor.v9i2.377.

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Freckleton, Robert P., and Mark Rees. "Comparative analysis of experimental data." Methods in Ecology and Evolution 10, no. 8 (June 5, 2019): 1308–21. http://dx.doi.org/10.1111/2041-210x.13164.

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Wang, P. C. "Analysis of Experimental Poisson Data." Communications in Statistics - Theory and Methods 36, no. 6 (May 2, 2007): 1059–68. http://dx.doi.org/10.1080/03610920601076594.

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Eno, Larry, Johan G. B. Beumee, and Herschel Rabitz. "Sensitivity analysis of experimental data." Applied Mathematics and Computation 16, no. 2 (February 1985): 153–63. http://dx.doi.org/10.1016/0096-3003(85)90005-0.

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Cornbleet, Joanne. "Replicate Analysis of Coagulation Data? Inappropriate Analysis of Experimental Data." American Journal of Clinical Pathology 83, no. 6 (June 1, 1985): 776–77. http://dx.doi.org/10.1093/ajcp/83.6.776b.

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TAKAGI, Kazuhisa, Yasushi MUTO, Takao ISHIZUKA, Noriyuki WATANABE, and Masanori ARITOMI. "ICONE19-43590 Evaluation of supercritical CO_2 centrifugal compressor experimental data by CFD analysis." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2011.19 (2011): _ICONE1943. http://dx.doi.org/10.1299/jsmeicone.2011.19._icone1943_240.

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Arkhangel'skii, I. V., and V. V. Lavrov. "Statistical analysis of thermoanalytical experimental data." Journal of thermal analysis 50, no. 3 (October 1997): 455–62. http://dx.doi.org/10.1007/bf01980505.

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Dissertations / Theses on the topic "Analysis of experimental data"

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Morone, Daniel Justin Reese. "Progressive Collapse: Simplified Analysis Using Experimental Data." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1354602937.

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Elvik, Gary G. "Frequency response analysis of T-ACS experimental data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA384673.

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Urgueira, Antonio Paulo Vale. "Dynamic analysis of coupled structures using experimental data." Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/46590.

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Henning, Gustav. "Visualization of neural data : Dynamic representation and analysis of accumulated experimental data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166770.

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The scientific method is an integral part of the investigation and exploration of hypotheses. Although procedures may vary from one field to the next, most have common identifiable stages. Today, there is no lack of tools that illustrate data in different graphical mediums. This thesis focuses instead on the type of tools that researchers use to investigate their hypotheses’ validity.When a sufficient amount of data is gathered, it can be presented for analysis in meaningful ways to illustrate patterns or abnormalities that would otherwise go unnoticed when only viewed in raw numbers. However useful static visualization of data can be when presented in ascientific paper, researchers are often overwhelmed by the number of plots and graphs that can be made using only a sliver of data. Therefore, this thesis will introduce software which purpose is to demonstrate the needs of researchers inanalyzing data from repeated experiments in order to speed up the process of recognizing variations between them.
Den vetenskapliga metoden är en integral del av undersökningen och utforskandet av hypoteser. Medan procedurer varierar mellan fält liknar de varandra i stora drag. Idag finns det ingen brist på verktyg som visualiserar data i olika grafiska kontexter. Istället fokuserar denna tes på de typ av verktyg som forskare använder för att undersöka integriteten av hypoteser.           När tillräckligt med data samlats finns det olika sätt att presentera denna på ett meningsfullt sätt för att demonstrera mönster och avvikelser som skulle förbli osedda i endast siffror.             Hurvida användbar statisk visualisering av data är som grafik till vetenskapliga rapporter gäller nödvändigtvis inte samma sak vid analys på grund av de många kombinationer av visualisering som ofta finns. Mjukvara kommer att introduceras för att demonstrera behovet av dynamisk representation vid analys av ackumulerad data för att påskynda upptäckten av mönster och avvikelser.
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Mantrala, Ravi K. "Squeak and rattle detection a comparative experimental data analysis /." Cincinnati, Ohio : University of Cincinnati, 2008. http://www.ohiolink.edu/etd/view.cgi?acc%5Fnum=ucin1204678221.

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Thesis (M.S.)--University of Cincinnati, 2008.
Advisor: Dr. Randall J Allemang. Title from electronic thesis title page (viewed Apr.30, 2008). Includes abstract. Keywords: Wavelet Transform, Morlet, Squeak and Rattle. Includes bibliographical references.
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MANTRALA, RAVI K. "Squeak and Rattle Detection: A Comparative Experimental Data Analysis." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1204678221.

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Kang, Lulu. "Computer and physical experiments: design, modeling, and multivariate interpolation." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34805.

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Many problems in science and engineering are solved through experimental investigations. Because experiments can be costly and time consuming, it is important to efficiently design the experiment so that maximum information about the problem can be obtained. It is also important to devise efficient statistical methods to analyze the experimental data so that none of the information is lost. This thesis makes contributions on several aspects in the field of design and analysis of experiments. It consists of two parts. The first part focuses on physical experiments, and the second part on computer experiments. The first part on physical experiments contains three works. The first work develops Bayesian experimental designs for robustness studies, which can be applied in industries for quality improvement. The existing methods rely on modifying effect hierarchy principle to give more importance to control-by-noise interactions, which can violate the true effect order of a system because the order should not depend on the objective of an experiment. The proposed Bayesian approach uses a prior distribution to capture the effect hierarchy property and then uses an optimal design criterion to satisfy the robustness objectives. The second work extends the above Bayesian approach to blocked experimental designs. The third work proposes a new modeling and design strategy for mixture-of-mixtures experiments and applies it in the optimization of Pringles potato crisps. The proposed model substantially reduces the number of parameters in the existing multiple-Scheffé model and thus, helps the engineers to design much smaller experiments. The second part on computer experiments introduces two new methods for analyzing the data. The first is an interpolation method called regression-based inverse distance weighting (RIDW) method, which is shown to overcome some of the computational and numerical problems associated with kriging, particularly in dealing with large data and/or high dimensional problems. In the second work, we introduce a general nonparametric regression method, called kernel sum regression. More importantly, we make an interesting discovery by showing that a particular form of this regression method becomes an interpolation method, which can be used to analyze computer experiments with deterministic outputs.
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Newton, Wesley E. "Data Analysis Using Experimental Design Model Factorial Analysis of Variance/Covariance (DMAOVC.BAS)." DigitalCommons@USU, 1985. https://digitalcommons.usu.edu/etd/6378.

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DMAOVC.BAS is a computer program written in the compiler version of microsoft basic which performs factorial analysis of variance/covariance with expected mean squares. The program accommodates factorial and other hierarchical experimental designs with balanced sets of data. The program is writ ten for use on most modest sized microprocessors, in which the compiler is available. The program is parameter file driven where the parameter file consists of the response variable structure, the experimental design model expressed in a similar structure as seen in most textbooks, information concerning the factors (i.e. fixed or random, and the number of levels), and necessary information to perform covariance analysis. The results of the analysis are written to separate files in a format that can be used for reporting purposes and further computations if needed.
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Jaques, Alonso V. "Development of advanced methods for analysis of experimental data in diffusion." abstract and full text PDF (UNR users only), 2009. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3387810.

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Aman, Carolyn J. "The devaluation of women's work: Analysis of national and experimental data." Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/289050.

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Different explanations have been given for the sex gap in pay between male and female occupations. Comparable worth proponents argue predominantly female occupations pay less than comparable male occupations because of their sex composition, that occupations' sex composition affects their wages. In contrast, Reskin and Roos (1990) and Strober (1984) argue the correspondence between occupational sex composition and wages is due to employers' preferences for male workers. Given first choice of occupations, males choose the better compensated occupations, which results in a causal effect of occupational wages on sex composition. Despite these opposing causal claims, few studies have attempted to ascertain the causal order between occupational sex composition and wages. This research focuses on the relationship between occupational sex composition and wages during the 1980s. Consistent with causal assumption of comparable worth proponents, analyses of Current Population Survey data (Study 1) support a causal effect of occupational sex composition on wages. Study 1 demonstrates that sex composition has a linear effect on wages for females and a nonlinear effect on wages for males. For both males and females, sex composition has a negative effect on wages over the entire range of sex composition. Study 2 revisits the causality question using 1980 and 1990 Census data, supplemented by additional controls from other data sets and finds a nonlinear effect of occupational sex composition on wages for females, but not for males. A negative effect of wages on sex composition was not found in any of the models. These results suggest that males may be less susceptible to the negative effects of sex composition than females. Study III uses an experimental study to determine if a "devaluation by association process" accounts for the lower wages of female occupations. The study found males but not females engage in a devaluation by association process, but neither males nor females devalue occupations based on their association with women. This may be indicative of a decline in the importance of sex as a diffuse status characteristic. The combined results of these studies suggest cautious optimism as far as reducing the sex gap in pay is concerned.
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Books on the topic "Analysis of experimental data"

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Data analysis for experimental design. New York: Guilford Press, 2008.

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American, Society of Heating Refrigerating and Air-Conditioning Engineers. Engineering analysis of experimental data. Atlanta, Ga: ASHRAE, 2005.

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Foundations of experimental data analysis. Boca Raton: CRC Press, 1992.

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Milliken, George A. Analysis of messy data. New York: Van Nostrand Reinhold, 1989.

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Miliken, George A. Analysis of messy data. New York: Chapman & Hall, 1992.

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Milliken, George A. Analysis of messy data. New York: Chapman & Hall, 1989.

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J, Keough Michael, ed. Experimental design and data analysis for biologists. Cambridge, UK: Cambridge University Press, 2003.

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Shaw, Benjamin D. Uncertainty Analysis of Experimental Data with R. Boca Raton : CRC Press, 2017.: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315366715.

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Elvik, Gary G. Frequency response analysis of T-ACS experimental data. Monterey, Calif: Naval Postgraduate School, 2000.

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Cross section and experimental data analysis using EViews. Hoboken, NJ: John Wiley & Sons, 2011.

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Book chapters on the topic "Analysis of experimental data"

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Dormann, Carsten. "Experimental Design." In Environmental Data Analysis, 185–205. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2_14.

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Klasson, K. Thomas. "Experimental Data Analysis." In Biotechnology for Fuels and Chemicals, 339–48. Totowa, NJ: Humana Press, 1997. http://dx.doi.org/10.1007/978-1-4612-2312-2_29.

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Viana, Marlos A. G., and Vasudevan Lakshminarayanan. "Symmetry and Experimental Data." In Dihedral Fourier Analysis, 1–9. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5562-2_1.

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Jähne, Bernd, Michael Klar, and Markus Jehle. "Data Analysis." In Springer Handbook of Experimental Fluid Mechanics, 1437–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-30299-5_25.

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Calvert, J. R., and R. A. Farrar. "Analysis of Experimental Data." In An Engineering Data Book, 36–39. London: Macmillan Education UK, 1999. http://dx.doi.org/10.1007/978-1-349-11310-1_4.

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Reddy, M. Venkataswamy. "Experimental Data Analysis: Anova." In Statistical Methods in Psychiatry Research and SPSS, 127–38. Second edition. | Toronto ; New Jersey : Apple Academic Press, 2018.: Apple Academic Press, 2019. http://dx.doi.org/10.1201/9780429023309-10.

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Abarbanel, Henry D. I. "Analysis of Experimental Data." In Understanding Complex Systems, 199–220. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7218-6_6.

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Yandell, Brian S. "Experimental Design." In Practical Data Analysis for Designed Experiments, 35–45. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4899-3035-4_3.

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Chaudhuri, Arindam. "Experimental Data Utilized." In Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks, 21–22. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7474-6_4.

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Shikano, Susumu, Thomas Bräuninger, and Michael Stoffel. "Statistical Analysis of Experimental Data." In Experimental Political Science, 163–77. London: Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9781137016645_8.

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Conference papers on the topic "Analysis of experimental data"

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Dolan, Kevin T. "A New Surrogate for Experimental Data Analysis." In EXPERIMENTAL CHAOS: 6th Experimental Chaos Conference. AIP, 2002. http://dx.doi.org/10.1063/1.1487532.

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Simonotto, Jennifer D. "Nonlinear Synchronization Analysis of Spatiotemporal Heart Data." In EXPERIMENTAL CHAOS: 7th Experimental Chaos Conference. AIP, 2003. http://dx.doi.org/10.1063/1.1612217.

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Ridgway, Jeffrey. "Analysis of Ocean Electromagnetic Data Using a Hilbert Spectrum Approach." In EXPERIMENTAL CHAOS: 7th Experimental Chaos Conference. AIP, 2003. http://dx.doi.org/10.1063/1.1612230.

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Gao, Byron J., and Alexander Katrompas. "A Preliminary Experimental Analysis on RateMyProfessors." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378446.

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VOSS, H. U., and J. KURTHS. "ANALYSIS OF SPATIOTEMPORAL DATA WITH NONPARAMETRIC REGRESSION AND MAXIMAL CORRELATION." In 5th Experimental Chaos Conference. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812811516_0020.

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Wu, Yanzhao, Wenqi Cao, Semih Sahin, and Ling Liu. "Experimental Characterizations and Analysis of Deep Learning Frameworks." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8621930.

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"An Integrated Application to Support the Implementation of Measurements and Data Analysis." In Experimental Mechanics of Solids. Materials Research Forum LLC, 2019. http://dx.doi.org/10.21741/9781644900215-17.

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Hamad, Yousif A., Mohammed N. Qasim, Ayvar A. Rashid, and Mohammed E. Seno. "Algorithms of Experimental Medical Data Analysis." In 2020 International Conference on Computer Science and Software Engineering (CSASE). IEEE, 2020. http://dx.doi.org/10.1109/csase48920.2020.9142094.

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Abramova, A. Yu. "DATA ANALYSIS IN BIOMEDICAL EXPERIMENTAL RESEARCH." In MODERN PROBLEMS IN SYSTEMIC REGULATION OF PHYSIOLOGICAL FUNCTIONS. NPG Publishing, 2019. http://dx.doi.org/10.24108/5-2019-confnf-9.

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Korotkikh, V. L. "Wavelet Analysis of E852 Experimental Data." In HADRON SPECTROSCOPY: Ninth International Conference on Hadron Spectroscopy. AIP, 2002. http://dx.doi.org/10.1063/1.1482503.

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Reports on the topic "Analysis of experimental data"

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Makaruk, Hanna. Uncertainty in Experimental Data Analysis. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1734696.

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Candy, J. V., L. M. Stoops, S. N. Franco, and M. C. Emmons. MODAL FREQUENCY TRACKING: Performance Analysis on Noisy Experimental Ground Test Data. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1438608.

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Jongsoo Yoo and Masaaki Yamada. Experimental Evaluation of Multi-spacecraft Data Analysis Techniques in a Laboratory Plasma. Office of Scientific and Technical Information (OSTI), March 2012. http://dx.doi.org/10.2172/1037450.

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Davis, Daniel. Analysis of stimulated Raman scattering using diode data at the Pecos experimental platform. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1734496.

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Hodgkiss, W. LCESHELF Experiment Support and Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, March 1997. http://dx.doi.org/10.21236/ada333271.

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Hodgkiss, William S. SWellEX Experiment Planning and Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, March 1996. http://dx.doi.org/10.21236/ada310598.

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Terry, W. K., J. K. Jewell, J. B. Briggs, T. A. Taiwo, W. S. Park, and H. S. Khalil. Preliminary assessment of existing experimental data for validation ofreactor physics codes and data for NGNP design and analysis. Office of Scientific and Technical Information (OSTI), October 2005. http://dx.doi.org/10.2172/861614.

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Callen, J. D. TFTR Experimental Data Analysis Collaboration. Annual progress report, November 15, 1992--November 14, 1993. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/10108163.

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Lee, Hai-Woong, and Thomas F. George. Analysis of Experimental Data for Neutralization of Low-Energy Ions at a Solid Surface. Fort Belvoir, VA: Defense Technical Information Center, January 1986. http://dx.doi.org/10.21236/ada163205.

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Parker, Jason, and Fred Schauer. Data Analysis and Compression Techniques for Megabyte-Data PDE Experiments. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada454352.

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