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Journal articles on the topic 'Database model'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

MacMullen, W. John. "Inter-database annotation linkages in model organism databases." Proceedings of the American Society for Information Science and Technology 42, no. 1 (October 18, 2006): n/a. http://dx.doi.org/10.1002/meet.14504201268.

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Wu, Yun Na, Jiang Shuai Li, and Jia Li Wang. "Design for Database of Energy Project Management Based on Portfolio." Advanced Materials Research 211-212 (February 2011): 62–67. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.62.

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With the continuous development of energy projects and the actual needs of the project, project portfolio management technique is known by people more and more. However, current databases of energy project management system are too different. This paper studies actual demand of energy project database, taking portfolio management theory as the basic, and use database modeling technology to build database’s conceptual data model, logical data model and physics data model based on the portfolio of energy project management. These models can be very good instruction of energy database design and construction, and will support energy project portfolio management system design to some guidance.

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3

Peryt, M., and T. Traczyk. "Equipment Database Data Model." Acta Physica Polonica B Proceedings Supplement 11, no. 4 (2018): 685. http://dx.doi.org/10.5506/aphyspolbsupp.11.685.

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Savnik, Iztok, and Franc Novak. "A construction database model." Microprocessing and Microprogramming 27, no. 1-5 (August 1989): 121–26. http://dx.doi.org/10.1016/0165-6074(89)90032-x.

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5

Lee, Minkyu. "An Enhancement of Japanese Acoustic Model using Korean Speech Database." JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA 32, no. 5 (2013): 438. http://dx.doi.org/10.7776/ask.2013.32.5.438.

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6

Stover, N. A., R. S. Punia, M. S. Bowen, S. B. Dolins, and T. G. Clark. "Tetrahymena genome database Wiki: a community-maintained model organism database." Database 2012 (March 20, 2012): bas007. http://dx.doi.org/10.1093/database/bas007.

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7

Nie, Feng Ying, Fang Fei Wu, and Li Yong Wan. "Study on Integration Model of Heterogeneous Database Based on XML Technology." Advanced Materials Research 912-914 (April 2014): 1403–6. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1403.

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In order to solve the problem of data integration and sharing for heterogeneous database at various departments from enterprises, this paper analyses thoroughly how to use the XML technology to mask differences between the heterogeneous databases. This paper designs an integration model based on the various heterogeneous databases by using the XML database as the middle database. The integration model is applied in the fault diagnosis system, and a data-exchange platform based on BizTalk is designed. On the heterogeneous database model, the fault diagnosis system can meets the need of multiple heterogeneous databases.

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He, Yutong, Di Tian, Hongxia Wang, Li Yao, Miao Yu, and Pengfei Chen. "A universal and multi-dimensional model for analytical data on geological samples." Geoscientific Instrumentation, Methods and Data Systems 8, no. 2 (October 30, 2019): 277–84. http://dx.doi.org/10.5194/gi-8-277-2019.

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Abstract. To promote the sharing and reutilization of geoanalytical data, various geoanalytical databases have been established over the last 30 years. Data models, which form the core of a database, are themselves the subjects of intensive studies. Data models determine the contents stored in the databases and applications of the databases. However, most geoanalytical data models have been designed for specific geological applications, which has led to strong heterogeneity between databases. It is therefore difficult for researchers to communicate and integrate geoanalytical data between databases. In particular, every time a new database is constructed, the time-consuming process of redesigning a data model significantly increases the development cycle. This study introduces a new data model that is universally applicable and highly efficient. The data model is applied to various geoanalytical methods and corresponding applications, and comprehensive analytical data contents together with associated background metadata are summarized and catalogued. Universal data attributes are then designed based on these metadata, which means that the model can be used for any geoanalytical database. Additionally, a multi-dimensional data mode is adopted, providing geological researchers with the ability to analyze geoanalytical data from six or more dimensions with high efficiency. Part of the model is implemented with the typical database system (MySQL) and comprehensive comparison experiments with existing geoanalytical data model are presented. The result unambiguously proves that the data model developed in this paper exceeds existing models in efficiency.

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Spyratos, Nicolas. "The partition model: a deductive database model." ACM Transactions on Database Systems 12, no. 1 (March 1987): 1–37. http://dx.doi.org/10.1145/12047.22718.

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Zhang, Xue Min, and Hua Bei Nie. "Storage Model of Native XML Database Based on Object-Oriented Database." Applied Mechanics and Materials 241-244 (December 2012): 2812–15. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2812.

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Storage for XML is a main research of Native XML database. Native XML Database has many different physical storage patterns, and their corresponding storage models are different. Based on defining the conceptions of Native XML Database, storage tactics and storage model, the paper describe the storage model based on object oriented database.

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Alshammari, Bandar M. "Security Assessment Model for Database Relational Designs." Journal of Medical Imaging and Health Informatics 9, no. 6 (August 1, 2019): 1171–80. http://dx.doi.org/10.1166/jmihi.2019.2715.

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The increasing number of data breaches has led many organizations to focus on securing their IT infrastructures and application architectures. However, the main causes of many of the latest attacks are not associated with these two architectures. The damage caused by most of the recent attacks could have been minimized if more attention was given to enhancing the security of all components of the database architecture. The existing enterprise database architecture frameworks do not consider this issue a priority; hence, it has received minimal attention. The enterprise database architecture is the most important architecture because it is responsible for defining how all types of data, whether security-critical or not, are stored and accessed. This paper focuses on addressing the lack of a complete solution to help enterprise system architects to address the security of their organizations from early stages. The novelty of this approach is that it specifies how to modify the required artifacts by the enterprise database architecture to address security-critical data. The approach also defines a number of security measurements that help enterprise architects in measuring the security of the organization database based on those artifacts. These metrics are developed based on the results of a cybersecurity experiment conducted on 100 randomly selected open-source websites. The paper's contributions also consist of the definition of a number of security refactoring rules that specify how to modify current enterprise databases to make them more secure. This paper uses an existing relational diagram for a health clinic database to illustrate the application of the model to an existing database. The validity and applicability of these metrics and refactoring rules are proved using an experiment conducted on a number of security-related databases.

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McWherter, David, Mitchell Peabody, William C. Regli, and Ali Shokoufandeh. "Solid Model Databases: Techniques and Empirical Results." Journal of Computing and Information Science in Engineering 1, no. 4 (October 1, 2001): 300–310. http://dx.doi.org/10.1115/1.1430233.

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This paper presents techniques for managing solid models in relational database management systems. Our goal is to enable support for traditional database operations (sorting, distance metrics, range queries, nearest neighbors, etc) on large databases of solid models. We introduce an approach to compare models based on shape using information extracted from the model boundary representation into Model Signature Graphs. We show how the Model Signature Graphs can be used to compute topological distances among models and how to use these measures to create metric spaces for indexing and clustering of solid models. We believe this work will begin to bridge the solid modeling and database communities, enabling new paradigms for interrogation of CAD datasets based on the engineering content of solid models.

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Rani, K. Sandhya, M. Sai Chaitanya, and G. Sai Kiran. "A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1467–70. http://dx.doi.org/10.31142/ijtsrd11402.

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Win, Aye Mon, and Khin Lay Myint. "Database Security Model using Access Control Mechanism in Student Data Management." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 529–31. http://dx.doi.org/10.31142/ijtsrd22820.

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15

Kunda, Douglas, and Hazael Phiri. "A Comparative Study of NoSQL and Relational Database." Zambia ICT Journal 1, no. 1 (December 11, 2017): 1–4. http://dx.doi.org/10.33260/zictjournal.v1i1.8.

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Relational Database and NoSQL are competing types of database models. The former has been in existence since 1979 and the latter since the year 2000. The demands of modern applications especially in web 2.0, 3.0 and big data have made NoSQL a popular database of choice. Choosing an appropriate database model to use is an important decision that developers must make based on the features of a given database model. This paper compares the features of Relational Databases and NoSQL to establish which database is better at supporting demands of modern applications. The paper also brings out the challenges of NoSQL. Finally, the paper concludes by determining whether Relational Databases would completely be replaced by NoSQL database models. The findings revealed that, Relational Databases are based on ACID model which emphasizes better consistency, security and offers a standard query language. However, Relational Databases have poor scalability, weak performance, cost more, face availability challenges when supporting large number of users and handle limited volume of data. NoSQL, on the other hand is based on the BASE model, which emphasizes greater scalability and provides a flexible schema, offers better performance, mostly open source, cheap but, lacks a standard query language and does not provide adequate security mechanisms. Both databases will continue to exist alongside each other with none being better than the other. The choice of the database to use will depend on the nature of the application being developed. Each database type has its own challenges and strengths, with relational database lacking of support for unstructured data while NoSQL lacks standardization and has poor security. Modern applications in web 2.0, 3.0 and big data are well suited to use NoSQL but, there are still many applications that rely on Relational Databases.

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Keseler, I. M., M. Skrzypek, D. Weerasinghe, A. Y. Chen, C. Fulcher, G. W. Li, K. C. Lemmer, et al. "Curation accuracy of model organism databases." Database 2014 (June 12, 2014): bau058. http://dx.doi.org/10.1093/database/bau058.

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17

Torra, Vicenç, Mariam Taha, and Guillermo Navarro-Arribas. "The space of models in machine learning: using Markov chains to model transitions." Progress in Artificial Intelligence 10, no. 3 (April 12, 2021): 321–32. http://dx.doi.org/10.1007/s13748-021-00242-6.

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AbstractMachine and statistical learning is about constructing models from data. Data is usually understood as a set of records, a database. Nevertheless, databases are not static but change over time. We can understand this as follows: there is a space of possible databases and a database during its lifetime transits this space. Therefore, we may consider transitions between databases, and the database space. NoSQL databases also fit with this representation. In addition, when we learn models from databases, we can also consider the space of models. Naturally, there are relationships between the space of data and the space of models. Any transition in the space of data may correspond to a transition in the space of models. We argue that a better understanding of the space of data and the space of models, as well as the relationships between these two spaces is basic for machine and statistical learning. The relationship between these two spaces can be exploited in several contexts as, e.g., in model selection and data privacy. We consider that this relationship between spaces is also fundamental to understand generalization and overfitting. In this paper, we develop these ideas. Then, we consider a distance on the space of models based on a distance on the space of data. More particularly, we consider distance distribution functions and probabilistic metric spaces on the space of data and the space of models. Our modelization of changes in databases is based on Markov chains and transition matrices. This modelization is used in the definition of distances. We provide examples of our definitions.

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18

Renwick, R. M. "A Model for Database Design." American Journal of Occupational Therapy 45, no. 9 (September 1, 1991): 827–32. http://dx.doi.org/10.5014/ajot.45.9.827.

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19

Alarie, A., and C. Morisset. "EXTENSIVE ONLINE SHOCK MODEL DATABASE." Revista Mexicana de Astronomía y Astrofísica 55, no. 2 (September 13, 2019): 377–92. http://dx.doi.org/10.22201/ia.01851101p.2019.55.02.21.

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We present a new database of fully radiative shock models calculated with the shock and photoionization code mappings v. The database architecture is built to contain diverse shock grids comprising of multiple shock parameters. It can be easily accessible through the MySQL protocol. Intensities of spectral lines from infrared to X-rays are stored along with other useful outputs such as the ionic fractions/temperature, integrated densities, etc. A web page was created in order to explore interactively the database as it evolves with time. Examples of its usage are given using the Python language.

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20

Dubaj Price, Michelle, and Daryl D. Hurd. "WormBase: A Model Organism Database." Medical Reference Services Quarterly 38, no. 1 (January 2, 2019): 70–80. http://dx.doi.org/10.1080/02763869.2019.1548896.

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21

Kroenke, D. M. "Beyond the relational database model." Computer 38, no. 5 (May 2005): 89–90. http://dx.doi.org/10.1109/mc.2005.151.

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22

Castrignano, T. "The PMDB Protein Model Database." Nucleic Acids Research 34, no. 90001 (January 1, 2006): D306—D309. http://dx.doi.org/10.1093/nar/gkj105.

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23

Savnik, Iztok, Tomaž Mohorič, Tomi Dolenc, and Franc Novak. "Database model for design data." ACM SIGPLAN OOPS Messenger 4, no. 3 (July 1993): 26–40. http://dx.doi.org/10.1145/165507.165517.

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24

Motzkin, Dalia. "An optimal physical database model." Mathematical Modelling 8 (1987): 240–44. http://dx.doi.org/10.1016/0270-0255(87)90581-1.

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25

Ristic, Sonja, Slavica Aleksic, Milan Celikovic, and Ivan Lukovic. "Generic and standard database constraint meta-models." Computer Science and Information Systems 11, no. 2 (2014): 679–96. http://dx.doi.org/10.2298/csis140216037r.

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Many software engineering activities entail dealing with legacy information systems. When these systems become too costly to maintain, or when new technologies need to be incorporated, they need to be replaced or somehow reengineered. This can be done with significantly reduced amount of effort and cost if the conceptual models of these systems are available. Reverse engineering is the process of analyzing a subject system to create representations of the system at a higher level of abstraction. Relational databases are a common source of reverse engineering. Starting from a physical database schema, that is recorded into relational database schema data repository, the conceptual database schema or logical database schema could be extracted. The extraction process may be seen as a chain of model-to-model transformations that trace model elements from a model at the lower level of abstraction to a model at the higher level of abstraction, achieved through meta-modeling. In the paper we present generic and standard database constraint meta-models, focusing on multi-relational database constraints captured in a legacy database. These meta-models are aimed at support of model transformations to create conceptual models, as a useful source for the system reengineering process.

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Deng, Lu, Wei Hong Han, Dong Liu, and Ying Xiong. "The Model of Fuzzy Retrieval Based on External Index." Applied Mechanics and Materials 380-384 (August 2013): 1605–8. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1605.

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With the rapid development of the Internet, database is getting more and more attention for its advantages of high concurrency, high scalability and high availability. In order to solve the problem that how to retrieving the data in the database efficiently and accurately, a model of fuzzy retrieval based on external index is proposed in this paper. By this external index, the efficiency of the NoSQL databases retrieval whose column not be appointed is greatly improved. As an example, the Cassandra database is adopted to store the data and the external index is stored in the national databases. A community information management system is utilized to show the feasibility of the model. The results show that the model can save a lot of time in retrieval whose column not be appointed. Moreover, this model can also be used to some other NoSQL databases.

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Bello, S. M., J. E. Richardson, A. P. Davis, T. C. Wiegers, C. J. Mattingly, M. E. Dolan, C. L. Smith, J. A. Blake, and J. T. Eppig. "Disease model curation improvements at Mouse Genome Informatics." Database 2012 (March 20, 2012): bar063. http://dx.doi.org/10.1093/database/bar063.

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28

Blattberg, Robert C., and Stephen J. Hoch. "Database Models and Managerial Intuition: 50% Model + 50% Manager." Management Science 36, no. 8 (August 1990): 887–99. http://dx.doi.org/10.1287/mnsc.36.8.887.

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29

Fildes, Robert. "Database models and managerial intuition: 50% model +50% manager." International Journal of Forecasting 7, no. 2 (August 1991): 251–52. http://dx.doi.org/10.1016/0169-2070(91)90066-5.

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30

Lin, Xue Yun. "A Relational Database Complexity Computing Model." Key Engineering Materials 474-476 (April 2011): 1464–69. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1464.

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In this paper we have proposed a model to compute the complexity of small relational database application developed in MS Access. The model was built using different database objects. These objects were categorized based upon the basis of weights assigned to their complexity determining factors. We have implemented our model in a tool called SMARtS. We used our model to compute complexity of eight database projects. To evaluate our model, we analyzed the calculated complexity and actual effort. We found that our model successfully captured the complexity of different database projects. Correlation analysis has shown that complexities computed by SMARtS and the actual efforts are strongly correlated.

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Renard, K. G., and V. A. Ferreira. "RUSLE Model Description and Database Sensitivity." Journal of Environmental Quality 22, no. 3 (July 1993): 458–66. http://dx.doi.org/10.2134/jeq1993.00472425002200030009x.

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32

Vyawahare, H. R., P. P. Karde, and V. M. Thakare. "Hybrid Database Model For Efficient Performance." Procedia Computer Science 152 (2019): 172–78. http://dx.doi.org/10.1016/j.procs.2019.05.040.

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33

Papazoglou, M. P., and B. J. Krämer. "A database model for object dynamics." VLDB Journal The International Journal on Very Large Data Bases 6, no. 2 (May 1, 1997): 73–96. http://dx.doi.org/10.1007/s007780050034.

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34

Papazoglou, M. P., and B. J. Krämer. "A database model for object dynamics." VLDB Journal The International Journal on Very Large Data Bases 6, no. 3 (August 1, 1997): 257–60. http://dx.doi.org/10.1007/s007780050044.

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35

Zhuge, Hai, Yunpeng Xing, and Peng Shi. "Resource space model, OWL and database." ACM Transactions on Internet Technology 8, no. 4 (September 2008): 1–31. http://dx.doi.org/10.1145/1391949.1391954.

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36

Kolker, Eugene, Roger Higdon, Winston Haynes, Dean Welch, William Broomall, Doron Lancet, Larissa Stanberry, and Natali Kolker. "MOPED: Model Organism Protein Expression Database." Nucleic Acids Research 40, no. D1 (December 1, 2011): D1093—D1099. http://dx.doi.org/10.1093/nar/gkr1177.

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37

Anvari, Morteza. "A database model for medical consultation." Journal of the American Society for Information Science 42, no. 8 (September 1991): 554–57. http://dx.doi.org/10.1002/(sici)1097-4571(199109)42:8<554::aid-asi3>3.0.co;2-j.

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38

Pratt, J. Michael, and Maxine Cohen. "A process-oriented scientific database model." ACM SIGMOD Record 21, no. 3 (September 1992): 17–25. http://dx.doi.org/10.1145/140979.140987.

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39

Tsuchiya, Masahiro, Michael P. Mariani, and James D. Brom. "Distributed database management model and validation." IEEE Transactions on Software Engineering SE-12, no. 4 (April 1986): 511–20. http://dx.doi.org/10.1109/tse.1986.6312898.

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40

Gyssens, M., J. Paredaens, J. van den Bussche, and D. van Gucht. "A graph-oriented object database model." IEEE Transactions on Knowledge and Data Engineering 6, no. 4 (1994): 572–86. http://dx.doi.org/10.1109/69.298174.

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41

Abiteboul, Serge, and Richard Hull. "IFO: a formal semantic database model." ACM Transactions on Database Systems 12, no. 4 (November 1987): 525–65. http://dx.doi.org/10.1145/32204.32205.

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42

HE, Zhen-Ying. "A Data Model for XML Database." Journal of Software 17, no. 4 (2006): 759. http://dx.doi.org/10.1360/jos170759.

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43

Wang, Xiao Gang, Xin Zhan Li, and Yue Li. "A Pattern Making System Based on Clothes Databases." Applied Mechanics and Materials 55-57 (May 2011): 1073–78. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.1073.

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In this article based on databases of 3D female bodies emulator, 3D jacket emulator, textile material parameters, historical photos and pattern parameters, an automatic pattern making and its 3D emulator model support system of woman warm jacket was developed. In this system different databases were set up and mathematic models of body block and pattern were founded, which they were the main algorithms of program. Program of connecting databases for pattern making was developed and its 3D emulator model was realized synchronization. Based on 20 variables for the female body, the net body block is first be made for individuality and its 3D model also is offered. Then according to the parameters from textile material database and pattern database, pattern is automatically made on the base of the body block. At the same time its 3D jacket model can be presented to user by this system. In this system, managing and connecting different types of databases were come true. This new automatic designing method differs with the traditional pattern making, which introduced the database technology to make works automatically and present 3D emulator model synchronously. This open system is offered user with spreading the database possible and connecting with ready CAD system.

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Li, Changqing, and Jianhua Gu. "A Paged Prefetching Model for Join Operations of Cross-Databases." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 6 (December 2020): 1225–34. http://dx.doi.org/10.1051/jnwpu/20203861225.

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As the applications of big data are increasing, the NoSQL (Not only Structured Query Language) database management systems have been developed rapidly. How to integrate NoSQL database and relational database effectively has become one of the research hotspots. In the existing research results, the paged query method used for join operations of these heterogeneous multi-databases can produce a large delay. In view of this deficiency, this paper presents a paged prefetching model, and focuses on its basic composition, prefetching mode and operation mechanism. A prototype system is designed and developed. The effect of this model is verified, and the expected targets are achieved. Compared with the non-prefetched paging query method, the outstanding contribution of this research result is that it can reduce the delay of paging query and thus improve the efficiency of the join operations of cross-databases.

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Revathi S, Thanga, N. Ramaraj, and S. Chithra. "Tracy–Singh Product and Genetic Whale Optimization Algorithm for Retrievable Data Perturbation for Privacy Preserved Data Publishing in Cloud Computing." Computer Journal 63, no. 2 (November 17, 2019): 239–53. http://dx.doi.org/10.1093/comjnl/bxz101.

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Abstract This paper proposes a retrievable data perturbation model for overcoming the challenges in cloud computing. Initially, genetic whale optimization algorithm (genetic WOA) is developed by integrating genetic algorithm (GA) and WOA for generating the optimized secret key. Then, the input data and the optimized secret key are given to the Tracy–Singh product-based model for transforming the original database into perturbed database. Finally, the perturbed database can be retrieved by the client, if and only if the client knows the secret key. The performance of the proposed model is analyzed using three databases, namely, chess, T10I4D100K and retail databases from the FIMI data set based on the performance metrics, privacy and utility. Also, the proposed model is compared with the existing methods, such as Retrievable General Additive Data Perturbation, GA and WOA, for the key values 128 and 256. For the key value 128, the proposed model has the better privacy and utility of 0.18 and 0.83 while using the chess database. For the key value 256, the proposed model has the better privacy and utility of 0.18 and 0.85, using retail database. From the analysis, it can be shown that the proposed model has better privacy and utility values than the existing models.

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46

Lyngbaek, Peter, and Victor Vianu. "Mapping a semantic database model to the relational model." ACM SIGMOD Record 16, no. 3 (December 1987): 132–42. http://dx.doi.org/10.1145/38714.38732.

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47

Prinz, Astrid A., Cyrus P. Billimoria, and Eve Marder. "Alternative to Hand-Tuning Conductance-Based Models: Construction and Analysis of Databases of Model Neurons." Journal of Neurophysiology 90, no. 6 (December 2003): 3998–4015. http://dx.doi.org/10.1152/jn.00641.2003.

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Conventionally, the parameters of neuronal models are hand-tuned using trial-and-error searches to produce a desired behavior. Here, we present an alternative approach. We have generated a database of about 1.7 million single-compartment model neurons by independently varying 8 maximal membrane conductances based on measurements from lobster stomatogastric neurons. We classified the spontaneous electrical activity of each model neuron and its responsiveness to inputs during runtime with an adaptive algorithm and saved a reduced version of each neuron's activity pattern. Our analysis of the distribution of different activity types (silent, spiking, bursting, irregular) in the 8-dimensional conductance space indicates that the coarse grid of conductance values we chose is sufficient to capture the salient features of the distribution. The database can be searched for different combinations of neuron properties such as activity type, spike or burst frequency, resting potential, frequency–current relation, and phase-response curve. We demonstrate how the database can be screened for models that reproduce the behavior of a specific biological neuron and show that the contents of the database can give insight into the way a neuron's membrane conductances determine its activity pattern and response properties. Similar databases can be constructed to explore parameter spaces in multicompartmental models or small networks, or to examine the effects of changes in the voltage dependence of currents. In all cases, database searches can provide insight into how neuronal and network properties depend on the values of the parameters in the models.

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48

Pillai, L., P. Chouvarine, C. O. Tudor, C. J. Schmidt, K. Vijay-Shanker, and F. M. McCarthy. "Developing a biocuration workflow for AgBase, a non-model organism database." Database 2012 (November 17, 2012): bas038. http://dx.doi.org/10.1093/database/bas038.

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Karr, J. R., N. C. Phillips, and M. W. Covert. "WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions." Database 2014 (September 16, 2014): bau095. http://dx.doi.org/10.1093/database/bau095.

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Shimoyama, Mary, Jennifer R. Smith, Jeff De Pons, Marek Tutaj, Pawjai Khampang, Wenzhou Hong, Christy B. Erbe, Garth D. Ehrlich, Lauren O. Bakaletz, and Joseph E. Kerschner. "The Chinchilla Research Resource Database: resource for an otolaryngology disease model." Database 2016 (2016): baw073. http://dx.doi.org/10.1093/database/baw073.

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