Bibliografías temáticas / Data

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Literatura académica sobre el tema "Data"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 25 de julio de 2025

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Artículos de revistas sobre el tema "Data"

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Gade, Kishore. "Data Analytics: Data Privacy, Data Ethics, Data Monetization." International Journal of Science and Research (IJSR) 9, no. 2 (2020): 1953–59. http://dx.doi.org/10.21275/sr20027110931.

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Rawish Siddiqui, Muhammad. "Big Data vs. Traditional Data, Data Warehousing, AI, and Beyond." Chemistry Research and Practice 1, no. 2 (2024): 01–06. https://doi.org/10.64030/3065-906x.01.02.04.

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In the age of digital transformation, the rise of Big Data has fundamentally altered how organizations store, process, and utilize information. This whitepaper provides a comprehensive analysis comparing Big Data with traditional data systems, data warehousing, business intelligence (BI), artificial intelligence (AI), data science, and NoSQL databases. By exploring key differentiators such as volume, variety, velocity, and processing capabilities, this paper aims to shed light on how Big Data has reshaped modern technology infrastructures and its role in advancing analytics, decision-making, a
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Nicolae, Sfetcu. "Megadatele (Big Data) pe Internet." IT & C 1, no. 1 (2022): 23–27. https://doi.org/10.58679/IT47091.

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Termenul Big Data se referă la extragerea, manipularea și analiza unor seturi de date care sunt prea mari pentru a fi tratate în mod obișnuit. Din această cauză se utilizează software special și, în multe cazuri, și calculatoare și echipamente hardware special dedicate. În general la aceste date analiza se face statistic. Pe baza analizei datelor respective se fac de obicei predicții ale unor grupuri de persoane sau alte entități, pe baza comportamentului acestora în diverse situații și folosind tehnici analitice avansate. Se pot identifica astfel tendințe, necesități ș
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Gade, Kishore. "Data Analytics: Data Mesh Architecture and Its Implications for Data Management." International Journal of Science and Research (IJSR) 8, no. 11 (2019): 2061–67. http://dx.doi.org/10.21275/sr19113110630.

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Migdał-Najman, Kamila, and Krzysztof Najman. "BIG DATA = CLEAR + DIRTY + DARK DATA." Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, no. 469 (2017): 131–39. http://dx.doi.org/10.15611/pn.2017.469.13.

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Rakholiya, Kalpesh R., and Dr Dhaval Kathiriya. "Data Mining for Moving Object Data." Indian Journal of Applied Research 2, no. 3 (2011): 111–13. http://dx.doi.org/10.15373/2249555x/dec2012/34.

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Arputhamary, B., and L. Arockiam. "Data Integration in Big Data Environment." Bonfring International Journal of Data Mining 5, no. 1 (2015): 01–05. http://dx.doi.org/10.9756/bijdm.8001.

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Chomboon, K., N. Kaoungku, K. Kerdprasop, and N. Kerdprasop. "Data Mining in Semantic Web Data." International Journal of Computer Theory and Engineering 6, no. 6 (2014): 472–75. http://dx.doi.org/10.7763/ijcte.2014.v6.912.

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Zvyagin, L. S. "DATA MINING: BIG DATA AND DATA SCIENCE." SOFT MEASUREMENTS AND COMPUTING 5, no. 54 (2022): 81–90. http://dx.doi.org/10.36871/2618-9976.2022.05.006.

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Data mining is the process of discovering information that can be used in large amounts of data. This method uses mathematical analysis, which helps to identify patterns and trends in the data. Such patterns cannot be noticed during normal data viewing due to the complexity of the relationships that arise with a large amount of data. All of them are a set of tools and methods that help humanity in the changing world around us. It is becoming more and more voluminous, we receive huge aggregates of data on various processes. Big Data and Data Science allow large companies to systematize informat
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Remize, Michel. "La data pour dada." Archimag N°310, no. 10 (2017): 1. http://dx.doi.org/10.3917/arma.310.0001.

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Tesis sobre el tema "Data"

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Riminucci, Stefania. "COVID-19,Open data e data visualization:interazione con dati epidemiologici." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21577/.

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L’obiettivo di questa tesi è quello di analizzare l’efficacia di diverse strategie di data visualization utilizzate per presentare open data in forma grafica alla popolazione. Come caso di studio, si è presa in considerazione la pandemia di COVID-19, e le molteplici visualizzazioni che sfruttano gli open data messi a disposizione dalle comunità scientifiche, offrendo informazioni sull'evoluzione dei contagi a livello nazionale e internazionale. Per valutare l’efficacia delle diverse visualizzazioni, è stato sviluppato e proposto al pubblico un questionario per la raccolta di dati per ave
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Mondaini, Luca. "Data Visualization di dati spazio-temporali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16853/.

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Questa tesi propone, dopo aver introdotto le tipologie di dato esistenti e i concetti di Big Data, Open Data e Data Visualization, due obiettivi differenti: ottenere informazioni utilizzando le API di Google Maps, più precisamente, latitudine e longitudine di ogni indirizzo di domicilio degli studenti immatricolati in UniBo e la visualizzazione di questi dati, all'interno di un'applicazione web, mediante istogrammi e mappe digitali interattivi con l'utilizzo di tecniche di Data Visualization.
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Yu, Wenyuan. "Improving data quality : data consistency, deduplication, currency and accuracy." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8899.

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Data quality is one of the key problems in data management. An unprecedented amount of data has been accumulated and has become a valuable asset of an organization. The value of the data relies greatly on its quality. However, data is often dirty in real life. It may be inconsistent, duplicated, stale, inaccurate or incomplete, which can reduce its usability and increase the cost of businesses. Consequently the need for improving data quality arises, which comprises of five central issues of improving data quality, namely, data consistency, data deduplication, data currency, data accuracy and
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Long, Christopher C. "Data Processing for NASA's TDRSS DAMA Channel." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611474.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California<br>Presently, NASA's Space Network (SN) does not have the ability to receive random messages from satellites using the system. Scheduling of the service must be done by the owner of the spacecraft through Goddard Space Flight Center (GSFC). The goal of NASA is to improve the current system so that random messages, that are generated on board the satellite, can be received by the SN. The messages will be requests for service that the satellites control sy
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Budd, Chris. "Data Protection and Data Elimination." International Foundation for Telemetering, 2015. http://hdl.handle.net/10150/596395.

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ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV<br>Data security is becoming increasingly important in all areas of storage. The news services frequently have stories about lost or stolen storage devices and the panic it causes. Data security in an SSD usually involves two components: data protection and data elimination. Data protection includes passwords to protect against unauthorized access and encryption to protect against recovering data fro
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Furrier, Sean Alexander, and Sean Alexander Furrier. "Communicating Data: Data-Driven Storytelling." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/624989.

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Data is more abundant than ever, yet its utility is diminished by a lack of understanding and difficulty in communicating insights. This thesis seeks to test the effectiveness of data-driven storytelling as a means to solve this disconnect. Research conducted includes reading previous literature on the subject, interviewing journalists and data practitioners as well as learning to use various software tools. This research focuses on communicating engaging stories by finding, cleaning, analyzing and visualizing data using R, Python, Excel, Tableau, Carto and other software tools. The res
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Chitondo, Pepukayi David Junior. "Data policies for big health data and personal health data." Thesis, Cape Peninsula University of Technology, 2016. http://hdl.handle.net/20.500.11838/2479.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2016.<br>Health information policies are constantly becoming a key feature in directing information usage in healthcare. After the passing of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009 and the Affordable Care Act (ACA) passed in 2010, in the United States, there has been an increase in health systems innovations. Coupling this health systems hype is the current buzz concept in Information Technology, „Big data‟. The prospects of big data are full of potential, eve
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BRASCHI, GIACOMO. "La circolazione dei dati e l'analisi big data." Doctoral thesis, Università degli studi di Pavia, 2019. http://hdl.handle.net/11571/1244327.

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Descrizione degli strumenti giuridici che regolano la circolazione dei dati e analisi dei possibili sviluppi normativi auspicabile per favori la circolazione dei dati<br>Description of the legal instruments that regulate the circulation of data and analysis of possible legislative developments desirable to favor the circulation of data
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Perovich, Laura J. (Laura Jones). "Data Experiences : novel interfaces for data engagement using environmental health data." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/95612.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 71-81).<br>For the past twenty years, the data visualization movement has reworked the way we engage with information. It has brought fresh excitement to researchers and reached broad audiences. But what comes next for data? I seek to create example "Data Experiences" that will contribute to developing new spaces of information engagement. Using data from Silent Spring Institute
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Wang, Yi. "Data Management and Data Processing Support on Array-Based Scientific Data." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1436157356.

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Libros sobre el tema "Data"

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Cooper, Richard, and Jessie Kennedy, eds. Data Management. Data, Data Everywhere. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73390-4.

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Jakarta Raya (Indonesia). Badan Kesatuan Bangsa., ed. Kumpulan data: Data kerawanan, data narkoba, data tawuran pelajar. Badan Kesatuan Bangsa, Prop. DKI Jakarta, 2002.

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Monino, Jean-Louis, and Soraya Sedkaoui. Big Data, Open Data and Data Development. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119285199.

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Zhu, Zhen. Data Warehousing, Data Lakes and Data Lakehouses. Edited by Aeron Zentner. SAGE Publications, Inc., 2024. http://dx.doi.org/10.4135/9781071937471.

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Panchal, Ajay G. Data mining economic data. UMIST, 1998.

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Autodata, ed. Diesel data: Technical data. Autodata Limited, 1992.

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Press, Scripps College, and ArjoWiggins (Firm), eds. Good data, bad data. Scripps College Press, 2014.

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Andrews, D. F., and A. M. Herzberg. Data. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5098-2.

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Herian, Robert. Data. Routledge, 2021. http://dx.doi.org/10.4324/9781003162001.

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Tyagi, Amit Kumar. Data Science and Data Analytics. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003111290.

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Capítulos de libros sobre el tema "Data"

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Pastore y Piontti, Ana, Nicola Perra, Luca Rossi, Nicole Samay, and Alessandro Vespignani. "DATA, DATA, AND MORE DATA." In Charting the Next Pandemic. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93290-3_2.

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Domokos, László. "Data About Data." In Physical Property Prediction in Organic Chemistry. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-74140-1_3.

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Furner, Jonathan. "“Data”: The data." In Information Cultures in the Digital Age. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-14681-8_17.

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Luckin, Rose, Karine George, and Mutlu Cukurova. "Data, data everywhere." In AI for School Teachers. CRC Press, 2022. http://dx.doi.org/10.1201/9781003193173-3.

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Bahman, Zohuri, and Mossavar-Rahmani Farhang. "Data Warehousing, Data Mining, Data Modeling, and Data Analytics." In A Model to Forecast Future Paradigms. Apple Academic Press, 2019. http://dx.doi.org/10.1201/9781003000662-3.

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Busulwa, Richard, and Nina Evans. "Data, data management, data analytics, and data science technologies." In Digital Transformation in Accounting. Routledge, 2021. http://dx.doi.org/10.4324/9780429344589-18.

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Irti, Claudia. "Personal Data, Non-personal Data, Anonymised Data, Pseudonymised Data, De-identified Data." In Services and Business Process Reengineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3049-1_5.

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Herian, Robert. "Being in data." In Data. Routledge, 2021. http://dx.doi.org/10.4324/9781003162001-4.

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Herian, Robert. "Proximate data – a conclusion." In Data. Routledge, 2021. http://dx.doi.org/10.4324/9781003162001-6.

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Herian, Robert. "Introduction." In Data. Routledge, 2021. http://dx.doi.org/10.4324/9781003162001-1.

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Actas de conferencias sobre el tema "Data"

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Zhang, Xiaofeng, Zhangyang Wang, Dong Liu, and Qing Ling. "DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683197.

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Yi, Zhang, Zhao Hongkai, Wei Yongyu, Xia Yulian, and Zhang Xiaoyan. "Improved DataX Data Synchronization Technique for Distribution Grid Data Middleware Implementation." In 2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2024. http://dx.doi.org/10.1109/eebda60612.2024.10485928.

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Alfred, Rayner. "DARA: Data Summarisation with Feature Construction." In 2008 Second Asia International Conference on Modelling & Simulation (AMS). IEEE, 2008. http://dx.doi.org/10.1109/ams.2008.131.

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Hunger, Casen, Lluis Vilanova, Charalampos Papamanthou, Yoav Etsion, and Mohit Tiwari. "DATS - Data Containers for Web Applications." In ASPLOS '18: Architectural Support for Programming Languages and Operating Systems. ACM, 2018. http://dx.doi.org/10.1145/3173162.3173213.

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Ashok, Vikas, and Ravi Mukkamala. "Data mining without data." In the 10th annual ACM workshop. ACM Press, 2011. http://dx.doi.org/10.1145/2046556.2046578.

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Cassavia, Nunziato, Pietro Dicosta, Elio Masciari, and Domenico Saccà. "Data Preparation for Tourist Data Big Data Warehousing." In Special Session on Knowledge Discovery meets Information Systems: Experiences and Lessons Learned Dealing with Real-life Scenarios. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005144004190426.

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Sanchez, Susan M. "DATA FARMING: BETTER DATA, NOT JUST BIG DATA." In 2018 Winter Simulation Conference (WSC). IEEE, 2018. http://dx.doi.org/10.1109/wsc.2018.8632383.

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Lokesh, M., A. Keerthi Devi, U. Dinesh Chowdary, P. V. N. S. Divya Lakshmi, and G. Rama Koteswara Rao. "Data Redundancy, Data Phishing, and Data Cloud Backup." In 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2023. http://dx.doi.org/10.1109/icecct56650.2023.10179679.

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Koehler, Martin, Alex Bogatu, Cristina Civili, et al. "Data context informed data wrangling." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258015.

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Leadbetter, Adam, Damian Smyth, Robert Fuller, Eoin O'Grady, and Adam Shepherd. "Where big data meets linked data: Applying standard data models to environmental data streams." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840943.

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Informes sobre el tema "Data"

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Martin, Mark, Lance Vowell, Ian King, and Chris Augustus. Automated Data Cleansing in Data Harvesting and Data Migration. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/949761.

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Russell, H. A. J., N. Benoit, and D. Paradis. Data collection and data sources. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/298871.

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P.L. Cloke. Data Qualification Report For: Thermodynamic Data File, DATA0.YMP.R0 For Geochemical Code, EQ3/6? Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/899946.

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Musick, R., T. Critchlow, M. Ganesh, Z. Fidelis, A. Zemla, and T. Slezak. Data Foundry: Data Warehousing and Integration for Scientific Data Management. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/793555.

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Zhang, Jovan Yang, Hari Viswanathan, Jeffery Hyman, and Richard Middleton. Data Analytics of Hydraulic Fracturing Data. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1304742.

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Bishop, Bradley Wade. Data from Data Services Librarians Study. University of Tennessee, Knoxville Libraries, 2020. http://dx.doi.org/10.7290/m29yhy5qen.

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Bishop, Bradley Wade. Data from Data Management Plan Compliance. University of Tennessee, Knoxville Libraries, 2020. http://dx.doi.org/10.7290/pebuwhcq7l.

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Dosch, Brianne, and Tyler Martindate. Data from Business Journals Data Sharing. University of Tennessee, Knoxville Libraries, 2019. http://dx.doi.org/10.7290/pyxdnl2g0z.

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Tafolla, Tanya, Eappen Nelluvelil, Jacob Moore, Daniel Dunning, Nathaniel Morgan, and Robert Robey. MATAR: Data-Oriented Sparse Data Representation. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1773304.

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Hoitink, D. J., and K. W. Burk. Climatological data summary 1994, with historical data. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/90676.

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