Готові списки джерел за темами / Data engineering and data science

Добірка наукової літератури з теми "Data engineering and data science"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Data engineering and data science"

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Klettke, Meike, and Uta Störl. "Four Generations in Data Engineering for Data Science." Datenbank-Spektrum 22, no. 1 (December 22, 2021): 59–66. http://dx.doi.org/10.1007/s13222-021-00399-3.

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Анотація:
AbstractData-driven methods and data science are important scientific methods in many research fields. All data science approaches require professional data engineering components. At the moment, computer science experts are needed for solving these data engineering tasks. Simultaneously, scientists from many fields (like natural sciences, medicine, environmental sciences, and engineering) want to analyse their data autonomously. The arising task for data engineering is the development of tools that can support an automated data curation and are utilisable for domain experts. In this article, we will introduce four generations of data engineering approaches classifying the data engineering technologies of the past and presence. We will show which data engineering tools are needed for the scientific landscape of the next decade.
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KRIEGER, JAMES. "Data on academic science/engineering updated." Chemical & Engineering News 65, no. 1 (January 5, 1987): 18. http://dx.doi.org/10.1021/cen-v065n001.p018a.

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3

Bertino, Elisa. "Introduction to Data Science and Engineering." Data Science and Engineering 1, no. 1 (February 25, 2016): 1–3. http://dx.doi.org/10.1007/s41019-016-0005-1.

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4

Kulkarni, Nishant. "Olympic Data Analysis using Data Science." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 855–61. http://dx.doi.org/10.22214/ijraset.2022.48046.

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Abstract: The Olympic games are international sports events with more than 200 nations participating in various competitions. The Sportspersons from various countries participate in competitions and make their countries proud of their excellence in sports. The primary objective of this paper is to analyze the Olympic dataset using python to compare overall performance of countries and to evaluate the contribution of each country in the Olympics. These analyses will give deeper insight into the performance of countries in Olympics over the years and helps sportspersons to quickly analyze their own and the competitor’s performance. In this paper, the exploratory data analysis techniques are used to provide comparison between performance of various countries and the contribution of each country in the Olympics. Visualization of Olympics dataset in many aspects provides the status of countries in Olympics and helps countries with poor performance to produce quality players and improve nation’s performance in Olympics. Despite a lot of hard work, many countries or players are unable to perform well during the events and grab medals whereas there are many countries that perform very well in the event and secure many medals. An analysis needs to be done by each country to evaluate the previous statistics which will detect the mistakes which they have done previously and will also help them in future development. Visualization of the data over various factors will provide us with the statistical view of the various factors which lead to the evolution of the Olympic Games and Improvement in the performance of various Countries/Players over time. The primary objective of this Research paper is to analyze the large Olympic dataset using Exploratory Data Analysis to evaluate the evolution of the Olympic Games over the years.
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5

Duever, Thomas A. "Data Science in the Chemical Engineering Curriculum." Processes 7, no. 11 (November 8, 2019): 830. http://dx.doi.org/10.3390/pr7110830.

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With the increasing availability of large amounts of data, methods that fall under the term data science are becoming important assets for chemical engineers to use. Methods, broadly speaking, are needed to carry out three tasks, namely data management, statistical and machine learning and data visualization. While claims have been made that data science is essentially statistics, consideration of the three tasks previously mentioned make it clear that it is really broader than just statistics alone and furthermore, statistical methods from a data-poor era are likely insufficient. While there have been many successful applications of data science methodologies, there are still many challenges that must be addressed. For example, just because a dataset is large, does not necessarily mean it is meaningful or information rich. From an organizational point of view, a lack of domain knowledge and a lack of a trained workforce among other issues are cited as barriers for the successful implementation of data science within an organization. Many of the methodologies employed in data science are familiar to chemical engineers; however, it is generally the case that not all the methods required to carry out data science projects are covered in an undergraduate chemical engineering program. One option to address this is to adjust the curriculum by modifying existing courses and introducing electives. Other examples include the introduction of a data science minor or a postgraduate certificate or a Master’s program in data science.
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Ashraf, Chowdhury, Nisarg Joshi, David A. C. Beck, and Jim Pfaendtner. "Data Science in Chemical Engineering: Applications to Molecular Science." Annual Review of Chemical and Biomolecular Engineering 12, no. 1 (June 7, 2021): 15–37. http://dx.doi.org/10.1146/annurev-chembioeng-101220-102232.

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Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science—the application tools for molecular discovery and property optimization at the atomic scale. We summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.
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Cressie, Noel. "Comment: When Is It Data Science and When Is It Data Engineering?" Journal of the American Statistical Association 115, no. 530 (April 2, 2020): 660–62. http://dx.doi.org/10.1080/01621459.2020.1762619.

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Kroll, Joshua A. "Data Science Data Governance [AI Ethics]." IEEE Security & Privacy 16, no. 6 (November 2018): 61–70. http://dx.doi.org/10.1109/msec.2018.2875329.

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Hering, Janet G. "From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering." Journal of Environmental Engineering 145, no. 8 (August 2019): 02519001. http://dx.doi.org/10.1061/(asce)ee.1943-7870.0001578.

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Gibert, Karina, Jeffery S. Horsburgh, Ioannis N. Athanasiadis, and Geoff Holmes. "Environmental Data Science." Environmental Modelling & Software 106 (August 2018): 4–12. http://dx.doi.org/10.1016/j.envsoft.2018.04.005.

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Дисертації з теми "Data engineering and data science"

1

Kanter, Max (James Max). "The data science machine : emulating human intelligence in data science endeavors." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/107031.

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Анотація:
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 87-88).
Data scientists are responsible for many tasks in the data analysis process including formulating the question, generating features, building a model, and disseminating the results. The Data Science Machine is a automated system that emulates a human data scientist's ability to generate predictive models from raw data. In this thesis, we propose the Deep Feature Synthesis algorithm for automatically generating features for relational datasets. We implement this algorithm and test it on 3 data science competitions that have participation from nearly 1000 data science enthusiasts. In 2 of the 3 competitions we beat a majority of competitors, and in the third, we achieve 94% of the best competitor's score. Finally, we take steps towards incorporating the Data Science Machine into the data science process by implementing and evaluating an interface for users to interact with the Data Science Machine.
by Max Kanter
M. Eng.
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2

Wason, Jasmin Lesley. "Automating data management in science and engineering." Thesis, University of Southampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396143.

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Smith, Micah J. (Micah Jacob). "Scaling collaborative open data science." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117819.

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Анотація:
Thesis: S.M. in Computer Science, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 103-107).
Large-scale, collaborative, open data science projects have the potential to address important societal problems using the tools of predictive machine learning. However, no suitable framework exists to develop such projects collaboratively and openly, at scale. In this thesis, I discuss the deficiencies of current approaches and then develop new approaches for this problem through systems, algorithms, and interfaces. A central theme is the restructuring of data science projects into scalable, fundamental units of contribution. I focus on feature engineering, structuring contributions as the creation of independent units of feature function source code. This then facilitates the integration of many submissions by diverse collaborators into a single, unified, machine learning model, where contributions can be rigorously validated and verified to ensure reproducibility and trustworthiness. I validate this concept by designing and implementing a cloud-based collaborative feature engineering platform, Feature- Hub, as well as an associated discussion platform for real-time collaboration. The platform is validated through an extensive user study and modeling performance is benchmarked against data science competition results. In the process, I also collect and analyze a novel data set on the feature engineering source code submitted by crowd data scientist workers of varying backgrounds around the world. Within this context, I discuss paths forward for collaborative data science.
by Micah J. Smith.
S.M. in Computer Science
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4

Yang, Ying. "Interactive Data Management and Data Analysis." Thesis, State University of New York at Buffalo, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10288109.

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Анотація:

Everyone today has a big data problem. Data is everywhere and in different formats, they can be referred to as data lakes, data streams, or data swamps. To extract knowledge or insights from the data or to support decision-making, we need to go through a process of collecting, cleaning, managing and analyzing the data. In this process, data cleaning and data analysis are two of the most important and time-consuming components.

One common challenge in these two components is a lack of interaction. The data cleaning and data analysis are typically done as a batch process, operating on the whole dataset without any feedback. This leads to long, frustrating delays during which users have no idea if the process is effective. Lacking interaction, human expert effort is needed to make decisions on which algorithms or parameters to use in the systems for these two components.

We should teach computers to talk to humans, not the other way around. This dissertation focuses on building systems --- Mimir and CIA --- that help user conduct data cleaning and analysis through interaction. Mimir is a system that allows users to clean big data in a cost- and time-efficient way through interaction, a process I call on-demand ETL. Convergent inference algorithms (CIA) are a family of inference algorithms in probabilistic graphical models (PGM) that enjoys the benefit of both exact and approximate inference algorithms through interaction.

Mimir provides a general language for user to express different data cleaning needs. It acts as a shim layer that wraps around the database making it possible for the bulk of the ETL process to remain within a classical deterministic system. Mimir also helps users to measure the quality of an analysis result and provides rankings for cleaning tasks to improve the result quality in a cost efficient manner. CIA focuses on providing user interaction through the process of inference in PGMs. The goal of CIA is to free users from the upfront commitment to either approximate or exact inference, and provide user more control over time/accuracy trade-offs to direct decision-making and computation instance allocations. This dissertation describes the Mimir and CIA frameworks to demonstrate that it is feasible to build efficient interactive data management and data analysis systems.

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5

Gertner, Yael. "Private data base access schemes avoiding data distribution." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/42730.

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Li, Richard D. (Richard Ding) 1978. "Web clickstream data analysis using a dimensional data warehouse." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86671.

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Анотація:
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2001.
Includes bibliographical references (leaves 83-84).
by Richard D. Li.
M.Eng.
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Ramanayaka, Mudiyanselage Asanga. "Data Engineering and Failure Prediction for Hard Drive S.M.A.R.T. Data." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1594957948648404.

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Derksen, Timothy J. (Timothy John). "Processing of outliers and missing data in multivariate manufacturing data." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/38800.

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Анотація:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
Includes bibliographical references (leaf 64).
by Timothy J. Derksen.
M.Eng.
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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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Chiesa, Alessandro. "Proof-carrying data." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61151.

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Анотація:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Page 96 blank. Cataloged from PDF version of thesis.
Includes bibliographical references (p. 87-95).
The security of systems can often be expressed as ensuring that some property is maintained at every step of a distributed computation conducted by untrusted parties. Special cases include integrity of programs running on untrusted platforms, various forms of confidentiality and side-channel resilience, and domain-specific invariants. We propose a new approach, proof-carrying data (PCD), which sidesteps the threat of faults and leakage by reasoning about properties of a computation's output data, regardless of the process that produced it. In PCD, the system designer prescribes the desired properties of a computation's outputs. Corresponding proofs are attached to every message flowing through the system, and are mutually verified by the system's components. Each such proof attests that the message's data and all of its history comply with the prescribed properties. We construct a general protocol compiler that generates, propagates, and verifies such proofs of compliance, while preserving the dynamics and efficiency of the original computation. Our main technical tool is the cryptographic construction of short non-interactive arguments (computationally-sound proofs) for statements whose truth depends on "hearsay evidence": previous arguments about other statements. To this end, we attain a particularly strong proof-of-knowledge property. We realize the above, under standard cryptographic assumptions, in a model where the prover has blackbox access to some simple functionality - essentially, a signature card.
by Alessandro Chiesa.
M.Eng.
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Книги з теми "Data engineering and data science"

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Cui, Zhen, Jinshan Pan, Shanshan Zhang, Liang Xiao, and Jian Yang, eds. Intelligence Science and Big Data Engineering. Visual Data Engineering. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36189-1.

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Lee, Roger, ed. Big Data, Cloud Computing, Data Science & Engineering. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-96803-2.

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Lee, Roger, ed. Big Data, Cloud Computing, and Data Science Engineering. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-24405-7.

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He, Xiaofei, Xinbo Gao, Yanning Zhang, Zhi-Hua Zhou, Zhi-Yong Liu, Baochuan Fu, Fuyuan Hu, and Zhancheng Zhang, eds. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23989-7.

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King, Tim. Data Network Engineering. Boston, MA: Springer US, 1999.

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Cheremisinoff, Paul N. Process engineering data book. Lancaster, PA: Technomic Pub., 1995.

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7

Unit, Engineering Sciences Data. Engineering sciences data: fatigue - fracture mechanics data. London: Engineering Sciences Data Unit, 1985.

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Unit, Engineering Sciences Data. Engineering sciences data: wind engineering. London: Engineering Sciences Data Unit, 1985.

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9

Madarshahian, Ramin, and Francois Hemez, eds. Data Science in Engineering, Volume 9. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-76004-5.

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Polkowski, Zdzislaw, Sambit Kumar Mishra, and Julian Vasilev. Data Science in Engineering and Management. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003216278.

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Частини книг з теми "Data engineering and data science"

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Quix, Christoph. "Data Engineering." In Data Science, 85–104. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-33403-1_5.

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Papp, Stefan, and Bernhard Ortner. "Data Engineering." In Handbuch Data Science und KI, 112–44. 2nd ed. München: Carl Hanser Verlag GmbH & Co. KG, 2022. http://dx.doi.org/10.3139/9783446472457.004.

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Varga, Ervin. "Data Engineering." In Practical Data Science with Python 3, 29–71. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4859-1_2.

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Papp, Stefan, and Bernhard Ortner. "Data Engineering." In The Handbook of Data Science and AI, 101–30. München: Carl Hanser Verlag GmbH & Co. KG, 2022. http://dx.doi.org/10.3139/9781569908877.004.

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Soh, Julian, and Priyanshi Singh. "Data Preparation and Data Engineering Basics." In Data Science Solutions on Azure, 65–115. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6405-8_3.

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Soviany, Sorin, and Cristina Soviany. "Feature Engineering." In Principles of Data Science, 79–103. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43981-1_5.

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Mahalle, Parikshit Narendra, Gitanjali Rahul Shinde, Priya Dudhale Pise, and Jyoti Yogesh Deshmukh. "Data Science in Civil Engineering and Mechanical Engineering." In Studies in Big Data, 87–99. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5160-1_6.

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Patgiri, Ripon, and Sabuzima Nayak. "Big Biomedical Data Engineering." In Principles of Data Science, 31–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43981-1_3.

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Duboue, Pablo. "Feature Engineering." In Applied Data Science in Tourism, 109–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88389-8_7.

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Vuppalapati, Chandrasekar. "Data Engineering and Exploratory Data Analysis Techniques." In International Series in Operations Research & Management Science, 75–158. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77485-1_2.

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Тези доповідей конференцій з теми "Data engineering and data science"

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Oyamada, Masafumi. "Extracting Feature Engineering Knowledge from Data Science Notebooks." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006522.

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Glotzer, Sharon C. "Data Science for Assembly Engineering." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3469649.

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Menzies, Tim, Ekrem Kocaguneli, Fayola Peters, Burak Turhan, and Leandro L. Minku. "Data science for software engineering." In 2013 35th International Conference on Software Engineering (ICSE). IEEE, 2013. http://dx.doi.org/10.1109/icse.2013.6606752.

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Lam, Hoang Thanh, Beat Buesser, Hong Min, Tran Ngoc Minh, Martin Wistuba, Udayan Khurana, Gregory Bramble, Theodoros Salonidis, Dakuo Wang, and Horst Samulowitz. "Automated Data Science for Relational Data." In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. http://dx.doi.org/10.1109/icde51399.2021.00305.

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Drummond, David E. "Open sourcing education for Data Engineering and Data Science." In 2016 IEEE Frontiers in Education Conference (FIE). IEEE, 2016. http://dx.doi.org/10.1109/fie.2016.7757517.

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Cruz, Lito Perez. "When Data Science Becomes Software Engineering." In 9th International Conference on Knowledge Engineering and Ontology Development. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006508502260232.

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Chakravaram, Venkamaraju, Vidya Sagar Rao G., Jangirala Srinivas, and Sunitha Ratnakaram. "The Role of Big Data, Data Science and Data Analytics in Financial Engineering." In the 2019 International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3341620.3341630.

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Leung, Carson K., Yubo Chen, Siyuan Shang, and Deyu Deng. "Big Data Science on COVID-19 Data." In 2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE). IEEE, 2020. http://dx.doi.org/10.1109/bigdatase50710.2020.00010.

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Haas, Laura. "Leveraging Data and People to Accelerate Data Science." In 2017 IEEE 33rd International Conference on Data Engineering (ICDE). IEEE, 2017. http://dx.doi.org/10.1109/icde.2017.9.

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Dubath, Pierre, Roland Walter, and Thierry Courvoisier. "INTEGRAL Science Data Center." In SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Brian D. Ramsey and Thomas A. Parnell. SPIE, 1996. http://dx.doi.org/10.1117/12.253992.

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Звіти організацій з теми "Data engineering and data science"

1

Greenberg, Jane, Samantha Grabus, Florence Hudson, Tim Kraska, Samuel Madden, René Bastón, and Katie Naum. The Northeast Big Data Innovation Hub: "Enabling Seamless Data Sharing in Industry and Academia" Workshop Report. Drexel University, March 2017. http://dx.doi.org/10.17918/d8159v.

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Increasingly, both industry and academia, in fields ranging from biology and social sciences to computing and engineering, are driven by data (Provost & Fawcett, 2013; Wixom, et al, 2014); and both commercial success and academic impact are dependent on having access to data. Many organizations collecting data lack the expertise required to process it (Hazen, et al, 2014), and, thus, pursue data sharing with researchers who can extract more value from data they own. For example, a biosciences company may benefit from a specific analysis technique a researcher has developed. At the same time, researchers are always on the search for real-world data sets to demonstrate the effectiveness of their methods. Unfortunately, many data sharing attempts fail, for reasons ranging from legal restrictions on how data can be used—to privacy policies, different cultural norms, and technological barriers. In fact, many data sharing partnerships that are vital to addressing pressing societal challenges in cities, health, energy, and the environment are not being pursued due to such obstacles. Addressing these data sharing challenges requires open, supportive dialogue across many sectors, including technology, policy, industry, and academia. Further, there is a crucial need for well-defined agreements that can be shared among key stakeholders, including researchers, technologists, legal representatives, and technology transfer officers. The Northeast Big Data Innovation Hub (NEBDIH) took an important step in this area with the recent "Enabling Seamless Data Sharing in Industry and Academia" workshop, held at Drexel University September 29-30, 2016. The workshop brought together representatives from these critical stakeholder communities to launch a national dialogue on challenges and opportunities in this complex space.
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2

Daniels, Matthew, Autumn Toney, Melissa Flagg, and Charles Yang. Machine Intelligence for Scientific Discovery and Engineering Invention. Center for Security and Emerging Technology, May 2021. http://dx.doi.org/10.51593/20200099.

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The advantages of nations depend in part on their access to new inventions—and modern applications of artificial intelligence can help accelerate the creation of new inventions in the years ahead. This data brief is a first step toward understanding how modern AI and machine learning have begun accelerating growth across a wide array of science and engineering disciplines in recent years.
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3

Halford, Alison. Working towards modern, affordable & sustainable energy systems in the context of displacement. Recommendations for researchers and practitioners. Coventry University, January 2020. http://dx.doi.org/10.18552/heed/2020/0001.

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This working paper is drawn from presentations and discussions that emerged during the ‘Agency of Change: Energy in the Displaced Context’ digital Conference held on Wednesday 4th November 2020. The conference was organised by the Centre of Data Science, Coventry University on behalf of the GCRF EPSRC Humanitarian Engineering and Energy for Displacement (HEED) project.
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4

Caplin, Andrew. Economic Data Engineering. Cambridge, MA: National Bureau of Economic Research, October 2021. http://dx.doi.org/10.3386/w29378.

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5

DEFENSE LOGISTICS AGENCY ALEXANDRIA VA. Data Quality Engineering Handbook. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada315573.

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6

Steeves, Brye, and Donald Montoya. NSDS Nuclear Science Data Solutions. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1663156.

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7

Feldgoise, Jacob, and Remco Zwetsloot. Estimating the Number of Chinese STEM Students in the United States. Center for Security and Emerging Technology, October 2020. http://dx.doi.org/10.51593/20200023.

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In recent years, concern has grown about the risks of Chinese nationals studying science, technology, engineering and mathematics (STEM) subjects at U.S. universities. This data brief estimates the number of Chinese students in the United States in detail, according to their fields of study and degree level. Among its findings: Chinese nationals comprise 16 percent of all graduate STEM students and 2 percent of undergraduate STEM students, lower proportions than were previously suggested in U.S. government reports.
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8

Bishop, Bradley Wade. Job analyses of earth science data librarians and data managers. University of Tennessee, Knoxville Libraries, February 2020. http://dx.doi.org/10.7290/mi9a8xvdto.

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9

Bidier, S., U. Khristenko, A. Kodakkal, C. Soriano, and R. Rossi. D7.4 Final report on Stochastic Optimization results. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.02.

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This deliverable report focuses on the final stochastic optimization results obtained within the EXAscale Quantification of Uncertainties for Technology and Science Simulation (ExaQUte) project. Details on a novel wind inlet generator that is able to incorporate local wind-field data through a deep-learned rapid distortion model and generates the turbulent wind data during run-time is presented in section 2. Section 3 presents the results of the overall stochastic optimization procedure applied to a twisted tapered tower with multiple design parameters within an uncertain synthetic wind field. Thereby, the significance of the developed methods and the obtained results are discussed and their integration in industrial wind-engineering workflows is outlined in section 4.
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10

Ishkov, Vitaly, N. Sergeyeva, L. Zabarinskaya, M. Nisilevich, E. Kedrov, and T. Krylova. Data on Solar Activity for Science. Balkan, Black sea and Caspian sea Regional Network for Space Weather Studies, July 2019. http://dx.doi.org/10.31401/sungeo.2019.01.01.

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