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Статті в журналах з теми "Data products"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Asyahri, Hadi Nasyuha, Zulham, and Rusydi Ibnu. "Implementation of K-means algorithm in data analysis." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 2 (2022): 307–13. https://doi.org/10.12928/telkomnika.v20i2.21986.

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Some large companies have difficulty in providing products even though the products are still available in the warehouse. Based on these problems, a solution is needed in managing cosmetic products and can find the right strategy so that it can increase business in the field of sales and improve sales services by using algorithms in data mining that can overcome these problems, such as clustering techniques that use the K-means clustering algorithm as a way to measure proximity data between cosmetic products based on transactions that have occurred. The specialty of the analysis of the managem

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2

Kavaliunas, John. "Census 2000 data products." Government Information Quarterly 17, no. 2 (2000): 209–22. http://dx.doi.org/10.1016/s0740-624x(00)00028-9.

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3

Rossow, William B., and Robert A. Schiffer. "ISCCP Cloud Data Products." Bulletin of the American Meteorological Society 72, no. 1 (1991): 2–20. http://dx.doi.org/10.1175/1520-0477(1991)072<0002:icdp>2.0.co;2.

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4

Siva Kishore Kancharla, Venkateswara. "Advancing Healthcare Data Exchange: The Role of AI and Cloud Analytics in Data Products." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 1553–58. http://dx.doi.org/10.21275/sr241125121613.

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5

Rhomadhona, Herfia, Winda Aprianti, and Jaka Permadi. "Penerapan Data Mining Terhadap Data Penjualan Prioduk Kopi Menggunakan Algoritma Apriori." Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan 10, no. 2 (2021): 65–73. http://dx.doi.org/10.31629/sustainable.v10i2.3792.

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Currently, the business competition in the culinary field is very tight. Second Home is a culinary business that sells various coffee products that are in demand by the public. The large number of various coffee products causes a lot of sales transaction data to be generated by Second Home. The sales data is not managed properly, causing accumulation and useless data. The level of useless data can be minimized by using a data mining approach. The Data Mining approach aims to provide new information from coffee product sales data. Apriori algorithm is one of the algorithms of data mining that i

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6

Chowdhary, K., and H. N. Najm. "Data free inference with processed data products." Statistics and Computing 26, no. 1-2 (2014): 149–69. http://dx.doi.org/10.1007/s11222-014-9484-y.

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7

R., Sangeetha, and A. Geetha Dr. "Effect of Organic Product Label on the Consumer Perception." International Journal of Engineering and Management Research 14, no. 1 (2024): 14–17. https://doi.org/10.5281/zenodo.10628203.

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In the organic market which is saturated in nature, the organic food consumption is seen to be growing to a considerable level. With such a prevalence, the market share for organic products has been seen to be increasing. The main element for the successful growth of the organic sector is the optimistic image which most of the consumers attribute to the organic products. Such an optimistic image is created through the labels possessed in the organic products. In this regard, the current study explores the influence of organic product label over the consumer perception regarding the organic pro

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8

Sovia, Rini, Abulwafa Muhammad, Syafri Arlis, Guslendra Guslendra, and Sarjon Defit. "Analysis of sales levels of pharmaceutical products by using data mining algorithm C45." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 476–84. https://doi.org/10.11591/ijeecs.v22.i1.pp476-484.

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This research was conducted to analyze the level of sales of pharmaceutical products at a Pharmacy. This is done to find out the types of products that have high and low sales levels. This study uses the C45 Data Mining Algorithm concept that will produce a conclusion on the prediction of sales of pharmaceutical products through data processing obtained from sales transactions at pharmacies. This C45 algorithm will form a decision tree that provides users with knowledge about products that are in great demand by consumers based on sales data and predetermined variables. The final result of the

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9

Geiger, Michaela. "Probleme lösen mit «Data Products»." PACKaktuell 38, no. 2 (2021): 18. http://dx.doi.org/10.51202/1664-6533-2021-2-018.

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Im verstärkten Wettbewerb müssen sich Unternehmen auf breiter Front digital aufstellen – in ihren Prozessen, Produkten und Leistungen. Inno-Pack.net, Initiative zur Innovationsförderung, zeigte Ende Januar 2021 in zwei Workshops die Möglichkeit einer neuen Herangehensweise auf.

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10

Berrada-Gomez, M. P., S. Rakotomalala, M. Bellec, S. Guillou, D. de Javel, and P. J. Ferret. "Exposure data for sunscreen products." Toxicology Letters 238, no. 2 (2015): S98. http://dx.doi.org/10.1016/j.toxlet.2015.08.324.

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11

PLANET, W. "NESDIS operational ozone data products." Global and Planetary Change 4, no. 1-3 (1991): 55–60. http://dx.doi.org/10.1016/0921-8181(91)90070-d.

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12

M., Arul Kumar, S. Gopalsamy Dr., Shanmuganathan S., Ganesan S., and Kumar k.Sathish. "INDIA'S FOREIGN TRADE PERFORMANCE OF AGRICULTURAL PRODUCTS IN MALDIVES." International Research Journal of Modernization in Engineering Technology and Science 03, no. 04 (2021): 1923–30. https://doi.org/10.5281/zenodo.4727892.

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The study seeks to establish a trade relationship between agricultural products in the Maldives. India has become a significant player globally, especially for rice, cotton, sugar, and buffalo meat. Notwithstanding these items, India has likewise become a sizeable exporter of soybean supper, guar gum, corn, and wheat, just as differing scope of different things. India is the second-biggest organic product maker on the planet. Production of horticulture crops is estimated at a record of 313.9 million metric tonnes (MMT) in 2018-19 as per third advance estimates. In their present study, an aggre

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13

Wang, Qiang, Jing Feng, Quan Sun, Zhengqiang Pan, and Jieru Meng. "Mechanical Products Reliability Assessment Based on the Structural Performance Degradation Data." International Journal of Materials, Mechanics and Manufacturing 3, no. 3 (2015): 166–69. http://dx.doi.org/10.7763/ijmmm.2015.v3.188.

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14

Kachhwaha, Deeksha, and Vani Agrawal. "Forecasting for Grocery Store Perishable Food Products Using Big Data Analytics." International Journal of Science and Research (IJSR) 14, no. 3 (2025): 111–17. https://doi.org/10.21275/sr25302073905.

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15

Pradeep, C., ANSV Murali Krishna, and G. Prasad. "A New Approach for Data Retrieval from Hierarchical File System to Handle Priority Satellite Data Products Dissemination." International Journal of Scientific Engineering and Research 9, no. 12 (2021): 26–30. https://doi.org/10.70729/se211223113009.

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16

I., V. "Data Engineering: using Data Analysis Techniques in Producing Data Driven Products." International Journal of Computer Applications 161, no. 1 (2017): 13–16. http://dx.doi.org/10.5120/ijca2017912712.

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17

Nieke, Jens, Daniel Schlapfer, Francesco Dell'Endice, Jason Brazile, and Klaus I. Itten. "Uniformity of Imaging Spectrometry Data Products." IEEE Transactions on Geoscience and Remote Sensing 46, no. 10 (2008): 3326–36. http://dx.doi.org/10.1109/tgrs.2008.918764.

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18

Purfini, A. P. "Complex Data Analysis for Products Bundling." IOP Conference Series: Materials Science and Engineering 407 (September 26, 2018): 012102. http://dx.doi.org/10.1088/1757-899x/407/1/012102.

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19

Rothmann, Mark D., Kallappa Koti, Kyung Yul Lee, Hong Laura Lu, and Yuan Li Shen. "Missing Data in Biologic Oncology Products." Journal of Biopharmaceutical Statistics 19, no. 6 (2009): 1074–84. http://dx.doi.org/10.1080/10543400903242993.

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20

Galonnier, Maude, Marie-Pierre Gomez-Berrada, Sonia Guillou, Aurelie Rielland, Dominique De Javel, and Pierre-Jacques Ferret. "Exposure data for family care products." Toxicology Letters 221 (August 2013): S223. http://dx.doi.org/10.1016/j.toxlet.2013.05.535.

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21

Gomez-Berrada, M., L. Crestey, C. Berge, S. Guillou, D. De Javel, and P. Ferret. "Exposure data for baby care products." Toxicology Letters 205 (August 2011): S88. http://dx.doi.org/10.1016/j.toxlet.2011.05.323.

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22

Nalin, A., and C. Herbin. "Exposure data for baby wipe products." Toxicology Letters 295 (October 2018): S221. http://dx.doi.org/10.1016/j.toxlet.2018.06.945.

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23

Wehr, T., and E. Attema. "Geophysical validation of envisat data products." Advances in Space Research 28, no. 1 (2001): 83–91. http://dx.doi.org/10.1016/s0273-1177(01)00297-6.

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24

Mish, William H., James L. Green, Mary G. Reph, and Mauricio Peredo. "ISTP science data systems and products." Space Science Reviews 71, no. 1-4 (1995): 815–77. http://dx.doi.org/10.1007/bf00751352.

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25

Baldwin, R. A., and M. J. Chung. "Managing engineering data for complex products." Research in Engineering Design 7, no. 4 (1995): 215–31. http://dx.doi.org/10.1007/bf01580459.

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26

PETTENGILL, G. H., P. G. FORD, W. T. K. JOHNSON, R. K. RANEY, and L. A. SODERBLOM. "Magellan: Radar Performance and Data Products." Science 252, no. 5003 (1991): 260–65. http://dx.doi.org/10.1126/science.252.5003.260.

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27

Krinsky, Daniel L. "Natural products: Show me the data." American Journal of Health-System Pharmacy 55, no. 2 (1998): 125. http://dx.doi.org/10.1093/ajhp/55.2.125.

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28

Sobol, Michael I. "Macintosh Security and Data Integrity Products." Information Systems Security 2, no. 1 (1993): 25–29. http://dx.doi.org/10.1080/19393559308551340.

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29

Dey, Debabrata, and Subodha Kumar. "Reassessing Data Quality for Information Products." Management Science 56, no. 12 (2010): 2316–22. http://dx.doi.org/10.1287/mnsc.1100.1261.

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30

Masuoka, E., A. Fleig, R. E. Wolfe, and F. Patt. "Key characteristics of MODIS data products." IEEE Transactions on Geoscience and Remote Sensing 36, no. 4 (1998): 1313–23. http://dx.doi.org/10.1109/36.701081.

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31

Doyle, J. "Comparing products using data envelopment analysis." Omega 19, no. 6 (1991): 631–38. http://dx.doi.org/10.1016/0305-0483(91)90012-i.

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32

Worley, Steven J., Scott D. Woodruff, Richard W. Reynolds, Sandra J. Lubker, and Neal Lott. "ICOADS release 2.1 data and products." International Journal of Climatology 25, no. 7 (2005): 823–42. http://dx.doi.org/10.1002/joc.1166.

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33

Peterseil, Johannes, Alexandre Belleflamme, Alessandro Oggioni, et al. "Advancing Scientific Research through Well-Defined and Standardized Data Products in eLTER." ARPHA Conference Abstracts 8 (May 28, 2025): e151888. https://doi.org/10.3897/aca.8.e151888.

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The scientific community increasingly relies on high-quality data products to drive research, enable cross-disciplinary studies and inform decision-making. The eLTER RI, as a European scale research infrastructure, aims to monitor the key characteristics of ecosystems and biodiversity and their response to global change and provide data streams as one of its services. In this context, the Whole System Approach (WAILS) and the derived eLTER Standard Observations (SOs) provide the scientific framework and protocols not only for data collection but also for integrating legacy data from long-term

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34

Hellyana, Corie Mei. "Implementasi Bahasa Phyton Untuk Clusterisasi Data Penjualan Menggunakan Metode K-Means." Informatics and Computer Engineering Journal 3, no. 2 (2023): 140–47. https://doi.org/10.31294/icej.v3i2.2290.

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In a business or trade, the process of keeping stock of goods is one way to provide customer satisfaction. To fulfill this, business owners must be able to analyze which goods or products are selling well, selling well and not selling well in the market, not an easy thing if the business has hundreds or even thousands of products sold every month. This problem can be solved by clustering sales products using the K-Means method. In the clustering process using the Python programming language which is object oriented which produces 3 clusters, where 1 cluster is for products that sell well, the

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35

Zi-Yang Ye, Zi-Yang Ye, Xuan Ji Zi-Yang Ye, Ming-Zi Ye Xuan Ji, Yu-Tong Shan Ming-Zi Ye, and Xiang-Rong Shi Yu-Tong Shan. "Data Analysis of Amazon Product Based on LSTM and GPR." 電腦學刊 33, no. 4 (2022): 015–27. http://dx.doi.org/10.53106/199115992022083304002.

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<p>In this paper, we propose a method that combines models such as GPR with PSO optimization to predict the time series data. We use LSTM and TOPSIS with entropy weight method modification to process vari-ous types of data from various aspects, taking into account both tabular and textual data, and to mine valuable contents from them. Based on shopping data, we analyze the historical situation and predict the future sales of products. So that we can recommend the most suitable products for customers. At the same time, for merchants, this paper provides directions for product optimization

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36

Malovichko, A. A., O. Ye Starovoit, M. V. Kolomiyets, L. P. Gabsatarova, and M. I. Ryzhikova. "CTBTO DATA AND DATA PRODUCTS IN SEISMIC MONITORING IN RUSSIA." NNC RK Bulletin, no. 2 (June 30, 2018): 5–10. http://dx.doi.org/10.52676/1729-7885-2018-2-5-10.

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The paper describes experience of Geophysical Service (GS) RAS gained from use of CTBTO data and its data products as well as of KNDC data and data products in seismic monitoring in Russia and in bordering areas in near-real time mode data processing in 2016–2017. It is demonstrated the usage of waveforms, arrivals and Bulletin SEL1 data when in location by EEAS events on Russian territory.

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37

Davern, Michael E., Bruce D. Meyer, and Nikolas K. Mittag. "Creating Improved Survey Data Products Using Linked Administrative-Survey Data." Journal of Survey Statistics and Methodology 7, no. 3 (2018): 440–63. http://dx.doi.org/10.1093/jssam/smy017.

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AbstractRecent research linking administrative to survey data has laid the groundwork for improvements in survey data products. However, the opportunities have not been fully realized yet. In this article, our main objective is to use administrative-survey linked microdata to demonstrate the potential of data linkage to reduce survey error through model-based blended imputation methods. We use parametric models based on the linked data to create imputed values of Medicaid enrollment and food stamp (SNAP) receipt. This approach to blending data from surveys and administrative data through model

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38

Martin, John. "Data, Data Everywhere." Mechanical Engineering 137, no. 07 (2015): 46–51. http://dx.doi.org/10.1115/1.2015-jul-3.

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This article explores evolution of product lifecycle management (PLM) and its advantages. PLM is commonly defined as a set of applications that enable the creation, design, and development of new products through rollout, servicing, upgrade, and end of life. PLM seller Dassault Systèmes, for example, said its 3DExperience platform is compliant with more than 40 standards requested by industry, including web, communication, visualization, and security standards. Most PLM software is able to generate reports from information located in a single system; but only skilled users are able to access,

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39

Masiello, Guido, Carmine Serio, Thomas Deleporte, et al. "Comparison of IASI water vapour products over complex terrain with COPS campaign data." Meteorologische Zeitschrift 22, no. 4 (2013): 471–87. http://dx.doi.org/10.1127/0941-2948/2013/0430.

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40

Zhang, Shengjia, Hongchun Zhu, Jie Li, Yanrui Yang, and Haiying Liu. "Data-Free Area Detection and Evaluation for Marine Satellite Data Products." Remote Sensing 14, no. 15 (2022): 3815. http://dx.doi.org/10.3390/rs14153815.

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The uncertainty verification of satellite ocean color products and the bias analysis of multiple data are both indispensable in the evaluation of ocean color products. Incidentally, ocean color products often have missing information that causes the methods mentioned above to be difficult to evaluate these data effectively. We propose an analysis and evaluation method based on data-free area. The objective of this study is to evaluate the quality of ocean color products with respect to information integrity and continuity. First, we use an improved Spectral Angle Mapper, also called ISAM. It c

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41

Flewelling, H. A., E. A. Magnier, K. C. Chambers, et al. "The Pan-STARRS1 Database and Data Products." Astrophysical Journal Supplement Series 251, no. 1 (2020): 7. http://dx.doi.org/10.3847/1538-4365/abb82d.

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42

Yang, Hu, Robbie Iacovazzi, Ninghai Sun, et al. "ATMS Radiance Data Products’ Calibration and Evaluation." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1–11. http://dx.doi.org/10.1109/tgrs.2021.3123576.

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43

Braverman, A., and L. Di Girolamo. "MISR global data products: a new approach." IEEE Transactions on Geoscience and Remote Sensing 40, no. 7 (2002): 1626–36. http://dx.doi.org/10.1109/tgrs.2002.801159.

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44

Cochran, Lainy D., Aubrey C. Eckert, Brian Hunt, and Terry Kraus. "Uncertainty Analysis of Consequence Management Data Products." Health Physics 118, no. 4 (2020): 382–95. http://dx.doi.org/10.1097/hp.0000000000001133.

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45

Wu, Bao Zhong, Jing Qiu, Guo Xi Li, Jing Zhong Gong, and Xin Yin. "Data-Driven Tolerance Analysis for Products Assembly." Advanced Materials Research 590 (November 2012): 287–96. http://dx.doi.org/10.4028/www.scientific.net/amr.590.287.

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Tolerance characteristics have an important influence on products dynamic properties. To realize dynamic properties oriented tolerance analysis and design of mechanical products, a data-driven based tolerance analysis method was proposed. Firstly, based on the cloud model theory, an assemble cloud model was established, and a new method of assembly data processing and knowledge mining was proposed. Secondly, the assembly cloud model was used to analysis the measured data from practical assembly process and search the alignment laws. Finally, using the above mentioned methods, product key parts

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46

Zibordi, Giuseppe, Frederic Melin, and Jean-Francois Berthon. "A Regional Assessment of OLCI Data Products." IEEE Geoscience and Remote Sensing Letters 15, no. 10 (2018): 1490–94. http://dx.doi.org/10.1109/lgrs.2018.2849329.

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47

Danilova, Daria Dmitrievna, and Oleg Yurievich Sabinin. "DATA ANALYSIS AND VISUALIZATION OF SIMILAR PRODUCTS." Theoretical & Applied Science 134, no. 06 (2024): 12–16. http://dx.doi.org/10.15863/tas.2024.06.134.2.

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48

Slater, James A., Graham Garvey, Carolyn Johnston, et al. "The SRTM Data “Finishing” Process and Products." Photogrammetric Engineering & Remote Sensing 72, no. 3 (2006): 237–47. http://dx.doi.org/10.14358/pers.72.3.237.

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49

Bogart, R. S., C. Baldner, S. Basu, D. A. Haber, and M. C. Rabello-Soares. "HMI ring diagram analysis II. Data products." Journal of Physics: Conference Series 271 (January 1, 2011): 012009. http://dx.doi.org/10.1088/1742-6596/271/1/012009.

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50

&NA;. "Free Data Sheets on Silicone Medical Products." Journal of Clinical Engineering 18, no. 4 (1993): 315. http://dx.doi.org/10.1097/00004669-199307000-00007.

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