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Automatic Documentation and Mathematical Linguistics

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01.02.2023 | INFORMATION ANALYSIS

About Representation and Evaluation of the Scientific Data, Numerical and Non-Numerical Nature in the Properties of Materials Research

verfasst von: A. O. Erkimbaev, V. Yu. Zitserman, G. A. Kobzev, A. V. Kosinov

Erschienen in: Automatic Documentation and Mathematical Linguistics | Ausgabe 1/2023

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Abstract—The paper considers issues in presenting and assessing the quality of scientific data in the materials science domain. Problems with numerical and non-numerical data of various types are presented using examples of data on the properties of reactor materials. Difficulties encountered in the description of nominal and ordinal data types in materials science are considered. A review of existing schemes for assessing data quality and their applicability in the considered examples of real data is carried out.

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Zaidel’, A.N., Oshibki izmerenii fizicheskikh velichin (Measurement Errors of Physical Quantities), Moscow: Lan’, 2009, 3rd ed.

Taylor, J.R., An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements, Univ. Sci. Books, Physics Teacher, 1981, 2nd ed.

Erkimbaev, A.O., Zitserman, V.Yu., and Kobzev, G.A., The intensive use of digital data in modern natural science, Autom. Doc. Math. Linguist., 2017, vol. 51, no. 5, p. 201–213. https://doi.org/10.3103/S0005105517050028CrossRef

Erkimbaev, A.O., Zitserman, V.Yu., Kobzev, G.A., and Kosinov, A.V., Curation of digital scientific data, Sci. Tech. Inf. Process., 2019, vol. 46, no. 3, pp. 192–203. https://doi.org/10.3103/S0147688219030092CrossRef

Wang, J. and Tao, Q., Machine learning: The state of the art, IEEE Intell. Syst., 2008, vol. 23, no. 6, pp. 49–55. https://doi.org/10.1109/MIS.2008.107CrossRef

What is big data? Definition of big data, Oracle Cloud Infrastructure. https://www.oracle.com/cis/big-data/ what-is-big-data/. Cited November 6, 2022.

Korotaev, P., Novoselov, I., Yanilkin, A., and Shapeev, A., Accessing thermal conductivity of complex compounds by machine learning interatomic potentials, Phys. Rev. B, 2019, vol. 100, p. 144308. https://doi.org/10.1103/PhysRevB.100.144308CrossRef

Wen, C., Zhang, Y., Wang, C., Xue, D., Bai, Ya., Antonov, S., Dai, L., Lookman, T., and Su, Yu., Machine learning assisted design of high entropy alloys with desired property, Acta Mater., 2019, vol. 70, pp. 109–117. https://doi.org/10.1016/j.actamat.2019.03.010CrossRef

Dub, A.V. and Rudskoi, A.I., New materials for nuclear power, Herald Russ. Acad. Sci., 2021, vol. 1, no. 3, pp. 292–296. https://doi.org/10.1134/S1019331621030047CrossRef

10.

Bol’shakov, A.A. and Karimov, R.N., Metody obrabotki mnogomernykh dannykh i vremennykh ryadov. Uchebnoe posobie dlya vuzov (Methods for Processing Multidimensional Data and Time Series: Textbook for Universities), Moscow: Goryachaya Liniya-Telekom, 2015, 2nd ed.

11.

Nelson, R.R., Todd, P.A., and Wixom, B.H., Antecedents of information and system quality: An empirical examination within the context of data warehousing, J. Manage. Inf. Syst., 2005, vol. 21, no. 4, pp. 199–235. https://doi.org/10.1080/07421222.2005.11045823CrossRef

12.

Batini, C. and Scannapieca, M., Data Quality: Concepts, Methodologies and Techniques, Data-Centric Systems and Applications, Berlin: Springer, 2006. https://doi.org/10.1007/3-540-33173-5CrossRef

13.

Lei, Y., Uren, V., and Motta, E., A framework for evaluating semantic metadata, Proc. 4th Int. Conf. on Knowledge Capture, Whistler, Canada, 2007, Sleeman, D. and Barker, K., Eds., New York: Association for Computing Machinery, 2006, pp. 135–142. https://doi.org/10.1145/1298406.1298431

14.

Categorical data: Machine learning, neural networks, artificial intelligence. https://machinelearningmastery. ru/understanding-feature-engineering-part-2-categorical-data-f54324193e63/. Cited November 11, 2022.

15.

Sin’kov, D.V. and Vanichkin, A.D., Coding the categorical data for use in machine learning, Molodoi Uchenyi, 2020, no. 21, pp. 70–72.

16.

Ding, J., Li, X., Kang, X., and Gudivada, V.N., A case study of the augmentation and evaluation of training data for deep learning, J. Data Inf. Qual., 2019, vol. 11, no. 4, pp. 1–22. https://doi.org/10.1145/3317573CrossRef

17.

Ashley, K., Data quality and curation, Data Sci. J., 2013, vol. 12, p. GRDI65–GRDI68. https://doi.org/10.2481/dsj.GRDI-011

18.

Cai, L. and Zhu, Y., The challenges of data quality and data quality assessment in the big data era, Data Sci. J., 2015, vol. 14, no. 2, pp. 1–10. https://doi.org/10.5334/dsj-2015-002CrossRef

19.

Wang, R.Y. and Strong, D.M., Beyond accuracy: What data quality means to data consumers, J. Manage. Inf. Syst., 1996, vol. 12, no. 4, pp. 5–33. https://doi.org/10.1080/07421222.1996.11518099CrossRef

20.

Rao, D., Gudivada, V.N., and Raghavan, V.V., Data quality issues in big data, 2015 IEEE Int. Conf. on Big Data (Big Data), Santa Clara, Calif., 2015, IEEE, 2015, pp. 2654–2660. https://doi.org/10.1109/BigData.2015.7364065

21.

Eletskii, A.V., Erkimbaev, A.O., Zitserman, V.Yu., Kobzev, G.A., and Trakhtengerts, M.S., Thermophysical properties of nanoobjects: Data classification and validity evaluation, High Temp., 2012, vol. 50, no. 4, pp. 488–495. https://doi.org/10.1134/S0018151X1203008XCrossRef

22.

Fadahunsi, K.Ph., O’Connor, S., Akinlua, J.T., Wark, P.A., Gallagher, J., Carroll, Ch., Car, J., Majeed, A., and O’Donoghue, J., Information quality frameworks for digital health technologies: Systematic review, J. Med. Internet Res., 2021, vol. 23, no. 5, p. e23479. https://doi.org/10.2196/23479CrossRef

23.

Trushin, Yu.V., Fizicheskie osnovy materialovedeniya. Uchebnoe posobie (Physical Foundations of Materials Science: Textbook), St. Petersburg: Izd-vo Akad. Univ., 2015.

24.

Ivannikov, V.P., Osnovy materialovedeniya. Konstruktsionnye materialy i tekhnologii. Uchebnoe posobie (Foundations of Materials Science: Structural Materials and Technologies), Moscow: Infra-Inzheneriya, 2022.

25.

Abramovich, M.D., Radiatsionnoe materialovedenie na AES (Radiation Materials Science at Nuclear Power Stations), Moscow: Energoatomizdat, 1984.

26.

Was, G.S., Fundamentals of Radiation Materials Science: Metals and Alloys, Berlin: Springer, 2007. https://doi.org/10.1007/978-3-540-49472-0CrossRef

27.

Makarov, E.I., Regularities of radiation creep of 08Kh18N10T steel irradiated to high neutron damaging doses at temperatures from 330 to 420°C, Cand. Sci. (Eng.) Dissertation, Ul’yanovsk, 2020.

28.

Neustroev, V.S., Low-temperature radiation damaging of austenitic steels irradiated in research and energy reactors, Doctoral (Eng.) Dissertation, Moscow, 2006.

29.

Elizarov, A.M., Lipachev, E.K., and Malakhal’tsev, M.A., Osnovy MathML. Predstavlenie matematicheskikh tekstov v Internet (Foundations of MathML: Representation of Mathematical Texts in Internet), Kazan: KMO, 2008.

30.

Chirico, R.D., Frenkel, M., Diky, V.V., Marsh, K.N., and Wilhoit, R.C., ThermoML—An XML-based approach for storage and exchange of experimental and critically evaluated thermophysical and thermochemical property data. 2. Uncertainties, J. Chem. Eng. Data, 2003, vol. 48, no. 5, pp. 1344–1359. https://doi.org/10.1021/je034088iCrossRef

Titel: About Representation and Evaluation of the Scientific Data, Numerical and Non-Numerical Nature in the Properties of Materials Research
verfasst von: A. O. Erkimbaev
V. Yu. Zitserman
G. A. Kobzev
A. V. Kosinov
Publikationsdatum: 01.02.2023
Verlag: Pleiades Publishing
Erschienen in: Automatic Documentation and Mathematical Linguistics / Ausgabe 1/2023
Print ISSN: 0005-1055
Elektronische ISSN: 1934-8371
DOI: https://doi.org/10.3103/S0005105523010077

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