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Relevant bibliographies by topics / Data combination

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Academic literature on the topic 'Data combination'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "Data combination"

1

Ting, Kai Ming, Boon Toh Low, and Ian H. Witten. "Learning from Batched Data: Model Combination Versus Data Combination." Knowledge and Information Systems 1, no. 1 (1999): 83–106. http://dx.doi.org/10.1007/bf03325092.

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Dong, Xiuze, Yunchuan Guo, Fenghua Li, Liju Dong, and Arshad Khan. "Combination Model of Heterogeneous Data for Security Measurement." JUCS - Journal of Universal Computer Science 25, no. (3) (2019): 270–81. https://doi.org/10.3217/jucs-025-03-0270.

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Measuring security is a core step for guaranteeing security of network and information systems. Due to massiveness and heterogeneity of measurement data, it is difficult to classify and combine them on demand. In this paper, considering implication relationship of metrics, we propose a combination model and combination policy for security measurement. Several examples demonstrate the effectiveness of our model.

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3

Yin Liu, Yin Liu, and Yang Zhang Yin Liu. "A Weighted Evidence Combination Method for Multisensor Data Fusion." 網際網路技術學刊 23, no. 3 (2022): 553–60. http://dx.doi.org/10.53106/160792642022052303013.

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<p>Multisensor information fusion exerts a key part in lots of practical usages. Dempster-shafer evidence theory has drawn extensive attention in many scopes of information fusion due to its flexibility and effectiveness in dealing with uncertain data without aforehand data. But when combining highly contradictory evidence with Dempster’s combinatorial principles, it can result in counterintuitive results. To solve the issue, the study proposes a multi-sensor data weighted evidence combination fusion method based on inter-evidence difference measure. Firstly, different measures including

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Zagidullin, Bulat, Jehad Aldahdooh, Shuyu Zheng, et al. "DrugComb: an integrative cancer drug combination data portal." Nucleic Acids Research 47, W1 (2019): W43—W51. http://dx.doi.org/10.1093/nar/gkz337.

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AbstractDrug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi)

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Doneus, M., and W. Neubauer. "2D combination of prospection data." Archaeological Prospection 5, no. 1 (1998): 29–56. http://dx.doi.org/10.1002/(sici)1099-0763(199803)5:1<29::aid-arp92>3.0.co;2-t.

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Zhao, Wei, Kris Sachsenmeier, Lanju Zhang, Erin Sult, Robert E. Hollingsworth, and Harry Yang. "A New Bliss Independence Model to Analyze Drug Combination Data." Journal of Biomolecular Screening 19, no. 5 (2014): 817–21. http://dx.doi.org/10.1177/1087057114521867.

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The Bliss independence model is widely used to analyze drug combination data when screening for candidate drug combinations. The method compares the observed combination response ( YO) with the predicted combination response ( YP), which was obtained based on the assumption that there is no effect from drug-drug interactions. Typically, the combination effect is declared synergistic if YO is greater than YP. However, this method lacks statistical rigor because it does not take into account the variability of the response measures and can frequently cause false-positive claims. In this article,

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Tang, Jing. "DrugComb - an integrative cancer drug combination data portal (v1.5)." Nucleic Acids Res. 2021 49, W1 (2025): W174—W184. https://doi.org/10.1093/nar/gkab438.

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Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.org) where the r

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8

Buxton, B. F., W. B. Langdon, and S. J. Barrett. "Data Fusion by Intelligent Classifier Combination." Measurement and Control 34, no. 8 (2001): 229–34. http://dx.doi.org/10.1177/002029400103400802.

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Bhattacharya, Debopam. "Evaluating treatment protocols using data combination." Journal of Econometrics 173, no. 2 (2013): 160–74. http://dx.doi.org/10.1016/j.jeconom.2012.11.003.

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Valton, Kamberaj. "CATEGORICAL DATA ANALYSIS USING LOGISTIC REGRESSION." https://link.springer.com/journal/10898 3 (September 7, 2021): 11. https://doi.org/10.5281/zenodo.5534800.

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The categorized data that will be analyzed in this paper will be of the type that will use the logistic regression method. The rest of the paper will focus on logistic regression and then the combination of these in the title of the topic mentioned above. Logistic regression is a technique widely used for categorical data analysis, offering increased flexibility compared to traditional intersection analysis. A binary result can be predicted using one or more categorical variables, continuous variables or combinations.

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Dissertations / Theses on the topic "Data combination"

1

Hoenicke, Jochen. "Combination of processes, data, and time /." Oldenburg : Univ., Fak. II, Dep. für Informatik, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014970023&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Nilsson, Jens. "Transformation and Combination in Data-Driven Dependency Parcing." Doctoral thesis, Växjö universitet, Matematiska och systemtekniska institutionen, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-6776.

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This thesis deals with automatic syntactic analysis of natural languagetext, also known as parsing. The parsing approach is data-driven, whichmeans that parsers are constructed by means of machine learning, lookingat training data in the form of annotated natural language sentences. The syntactic framework used in the thesis is dependency-based. Robustness is one of the characteristics of the data-driven approaches investigated here.The overall aim of this thesis is to maintain robustness while increasing accuracy.The content of the thesis falls naturally into two tracks, a transformation trac

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Guo, Hong Yu. "Multiple classifier combination through ensembles and data generation." Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/26648.

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This thesis introduces new approaches, namely the DataBoost and DataBoost-IM algorithms, to extend Boosting algorithms' predictive performance. The DataBoost algorithm is designed to assist Boosting algorithms to avoid over-emphasizing hard examples. In the DataBoost algorithm, new synthetic data with bias information towards hard examples are added to the original training set when training the component classifiers. The DataBoost approach was evaluated against ten data sets, using both decision trees and neural networks as base classifiers. The experiments show promising results, in terms of

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Holmberg, Andreas. "Combination analysis of multispectral and radar satellite data." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185506.

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Remote sensing technologies, such as satellite imagery, have proven to be a powerful tool for land cover classification when combined with machine learning algorithms. Depending on which type of sensor is used for the imagery, different properties of land cover classes may be distinguished. Because of this, a data set containing a combination of data from different sensors could potentially further improve the classification accuracy. To determine if adding data from the radar sensor on the satellite constellation Sentinel-1 to data from the multispectral optical sensor on the satellite conste

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Boomkamp, Henno J. "Combination of altimetry data from different satellite missions." Thesis, Aston University, 1998. http://publications.aston.ac.uk/14151/.

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Substantial altimetry datasets collected by different satellites have only become available during the past five years, but the future will bring a variety of new altimetry missions, both parallel and consecutive in time. The characteristics of each produced dataset vary with the different orbital heights and inclinations of the spacecraft, as well as with the technical properties of the radar instrument. An integral analysis of datasets with different properties offers advantages both in terms of data quantity and data quality. This thesis is concerned with the development of the means for su

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Cook, G. D. "Data selection and model combination in connectionist speech recognition." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597919.

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The hybrid connectionist-hidden Markov model (HMM) approach to large vocabulary continuous speech recognition has been shown to be competitive with HMM based systems. However, the recent availability of extremely large amounts of acoustic training data has highlighted a problem with the connectionist acoustic modelling paradigm. The effective use of such large amounts of data is difficult due to the computational requirements of training large connectionist models. This dissertation details research aimed at increasing the performance of connectionist acoustic models through the effective use

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Li, Jinwen. "Detailed marine gravity field determination by combination of heterogeneous data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq20874.pdf.

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Lewis, Cannada Andrew. "The Unreasonable Usefulness of Approximation by Linear Combination." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83866.

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Through the exploitation of data-sparsity ---a catch all term for savings gained from a variety of approximations--- it is possible to reduce the computational cost of accurate electronic structure calculations to linear. Meaning, that the total time to solution for the calculation grows at the same rate as the number of particles that are correlated. Multiple techniques for exploiting data-sparsity are discussed, with a focus on those that can be systematically improved by tightening numerical parameters such that as the parameter approaches zero the approximation becomes

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Kamarudin, Md Nor. "Local geoid determination from a combination of gravity and GPS data." Thesis, University of Newcastle Upon Tyne, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363535.

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Crosby, Jacy Rebecca. "Modeling and Synergy Testing of Drug Combination Data: A Pharmacokinetic Analysis." UNF Digital Commons, 2008. http://digitalcommons.unf.edu/etd/143.

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In this paper, we present and implement a method to assess the mathematical synergy of two-drug combinations based on a stochastic model. The drugs in question are two isomers that are applied to the human eye via a liquid eye drop. Techniques applied to the data in this paper can be applied to other two-drug combination studies. We derive the mean and the variance terms of the drug combination "effects" in closed form using Ito's method of stochastic differential equations. The model fit of the data to the individual subject is examined by both statistical and graphical methods. Two estimatio

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Books on the topic "Data combination"

1

Grant, D. B. Combination of terrestrial and GPS data for earth deformation studies. School of Surveying, University of New South Wales, 1990.

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2

Harvey, Bruce R. The combination of VLBI and ground data for geodesy and geophysics. School of Surveying, University of New South Wales, 1985.

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3

Liwicki, Marcus. Recognition of whiteboard notes: Online, offline and combination. World Scientific, 2008.

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Horst, Bunke, ed. Recognition of whiteboard notes: Online, offline and combination. World Scientific, 2008.

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5

Stoner, James W. Estimating pavement damage from longer and heavier combination vehicles. Midwest Transportation Center, 1994.

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6

Archinal, Brent Allen. Determination of Earth rotation by the combination of data from different space geodetic systems. Dept. of Geodetic Science and Surveying, Ohio State University, 1987.

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7

Golding, Andrew R. Pronouncing names by a combination of rule-based and case-based reasoning. Dept. of Computer Science, Stanford University, 1992.

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8

Graham, Bryan S. Efficient estimation of data combination models by the method of auxiliary-to-study tilting (ast). National Bureau of Economic Research, 2011.

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9

Berry, Steven. Differentiated products demand systems from a combination of micro and macro data: The new car market. National Bureau of Economic Research, 1998.

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10

McDaniel, T. L. The Development of an AVI/WIM data base and the demonstration of a combination of bridge WIM and portable AVI data: Final report. Oregon Dept. of Transportation, 1985.

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Book chapters on the topic "Data combination"

1

Appriou, Alain. "Combination of Sources." In Uncertainty Theories and Multisensor Data Fusion. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118578636.ch5.

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Wu, Shengli. "The Linear Combination Method." In Data Fusion in Information Retrieval. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28866-1_5.

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Guo, Xi, Chuan Xiao, and Yoshiharu Ishikawa. "Combination Skyline Queries." In Transactions on Large-Scale Data- and Knowledge-Centered Systems VI. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34179-3_1.

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Jamnia, Ali. "Data, Measurements, Tests and Experiments." In Design of Electromechanical and Combination Products, 2nd ed. CRC Press, 2023. http://dx.doi.org/10.1201/9781003301523-24.

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Tronci, Roberto, Giorgio Giacinto, and Fabio Roli. "Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method." In Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03070-3_13.

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Ghasemi Hamed, Mohammad, and Ahmad Akbari. "Hierarchical Bayesian Classifier Combination." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96136-1_10.

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Cecotti, Hubert, and Abdel Belaïd. "Hybrid OCR Combination for Ancient Documents." In Pattern Recognition and Data Mining. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_71.

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Humston, Elizabeth M., and Robert E. Synovec. "Comprehensive Chromatography Data Interpretation Technologies." In Comprehensive Chromatography in Combination with Mass Spectrometry. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118003466.ch12.

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Bandelt, H. J. "Combination of data in phylogenetic analysis." In Systematics and Evolution of the Ranunculiflorae. Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-6612-3_38.

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Kam Ho, Tin. "Data Complexity Analysis for Classifier Combination." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_6.

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Conference papers on the topic "Data combination"

1

Wang, Jiale, Jian Li, Jikuan Tang, Chaoyue Sun, Shiqi Wang, and Dongqing Yang. "Cycle Slip Detection and Repair Method for Dual-Frequency Ionosphere-Free Combination and MW Combination Assisted by INS." In 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). IEEE, 2024. https://doi.org/10.1109/icsidp62679.2024.10868851.

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Wu, Shiwen, and Jian Zhao. "Interest-aware Item Combination Prediction." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671764.

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Lin, Tzu-Heng, and Chen Gao. "Session-aware Item-combination Recommendation with Transformer Network." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671485.

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Saari, Mika, Petri Rantanen, and Sami Hyrynsalmi. "Software Hardware Combination for Data Gathering." In 2020 IEEE 10th International Conference on Intelligent Systems (IS). IEEE, 2020. http://dx.doi.org/10.1109/is48319.2020.9199960.

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Aslandogan, Y. A., and G. A. Mahajani. "Evidence combination in medical data mining." In International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. IEEE, 2004. http://dx.doi.org/10.1109/itcc.2004.1286697.

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Ku, Tai-Yeon, Wan-ki Park, and Il-Woo Lee. "Big Data Service Combination for Efficient Energy Data Analytics." In Cloud-Computing and Super-Computing 2016. Science & Engineering Research Support soCiety, 2016. http://dx.doi.org/10.14257/astl.2016.139.90.

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Fawcett, R. "Combination coding: a new entropy coding technique." In Proceedings of Data Compression Conference - DCC '96. IEEE, 1996. http://dx.doi.org/10.1109/dcc.1996.488366.

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K. Lu, W., J. W. He, H. S. Shi, Z. X. Li, X. T. Zhang, and Y. L. Wu. "An Adaptive Over / Under Data Combination Method." In 74th EAGE Conference and Exhibition incorporating EUROPEC 2012. EAGE Publications BV, 2012. http://dx.doi.org/10.3997/2214-4609.20148351.

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"BENEFICIAL SEQUENTIAL COMBINATION OF DATA MINING ALGORITHMS." In 8th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2006. http://dx.doi.org/10.5220/0002495501350143.

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Rendon, Juan, and Lilian M. de Menezes. "Structural Combination of Neural Network Models." In 2016 IEEE 16th International Conference on Data Mining: Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0064.

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Reports on the topic "Data combination"

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D'Haultfoeuille, Xavier, Christophe Gaillac, and Arnaud Maurel. Partially Linear Models under Data Combination. National Bureau of Economic Research, 2022. http://dx.doi.org/10.3386/w29953.

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Yakovlev, Evgeny, Denis Nekipelov, and Tatiana V. Komarova. Identification, data combination and the risk of disclosure. Institute for Fiscal Studies, 2011. http://dx.doi.org/10.1920/wp.cem.2011.3811.

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Meyer, Bruce, and Nikolas Mittag. An Empirical Total Survey Error Decomposition Using Data Combination. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w25737.

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Lukach, J. (Test and gather data on sweep spike combination tillage tool). Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/5136774.

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Graham, Bryan, Cristine Campos de Xavier Pinto, and Daniel Egel. Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST). National Bureau of Economic Research, 2011. http://dx.doi.org/10.3386/w16928.

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York, J. C., K. M. Remund, G. Chen, B. C. Simpson, and T. M. Brown. Bayesian methods for the combination of core sampling data with historical models for tank characterization. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/105105.

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ZHAO, D., and P. OCONNOR. DESIGN OF 2.4 GHZ CMOS DIRECT CONVERSION LNA AND MIXER COMBINATION FOR WIRLESS DATA LINK TRANSCEIVER. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/794023.

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Berry, Steven, James Levinsohn, and Ariel Pakes. Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market. National Bureau of Economic Research, 1998. http://dx.doi.org/10.3386/w6481.

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Lukach, J. [Test and gather data on sweep spike combination tillage tool]. Quarterly technical report, August 1991--March 1992. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10153304.

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Myers, S., M. Begnaud, B. Young, A. Chiang, and I. Bondar. Summary of NDC Capacity Building Workshop and Regional Seismic Travel Time (RSTT) in combination with Data Sharing and Integration Training. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1888640.

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