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Journal articles on the topic 'Data engineering and data science'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

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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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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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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7

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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8

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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9

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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10

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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11

Gao, Hanyu, Li-Tao Zhu, Zheng-Hong Luo, Marco A. Fraga, and I.-Ming Hsing. "Machine Learning and Data Science in Chemical Engineering." Industrial & Engineering Chemistry Research 61, no. 24 (June 22, 2022): 8357–58. http://dx.doi.org/10.1021/acs.iecr.2c01788.

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12

Gupta, Suraj, Diana Aga, Amy Pruden, Liqing Zhang, and Peter Vikesland. "Data Analytics for Environmental Science and Engineering Research." Environmental Science & Technology 55, no. 16 (August 2, 2021): 10895–907. http://dx.doi.org/10.1021/acs.est.1c01026.

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13

Rocha, Prof Carlos Alberto Martins da, and Prof Dr Luis Borges Gouveia. "Information Engineering: Strategic decision based on data science." International Journal of Advanced Engineering Research and Science 8, no. 6 (2021): 203–12. http://dx.doi.org/10.22161/ijaers.86.23.

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14

Hagedorn, Christina, Tanner Sorensen, Adam Lammert, Asterios Toutios, Louis Goldstein, Dani Byrd, and Shrikanth Narayanan. "Engineering Innovation in Speech Science: Data and Technologies." Perspectives of the ASHA Special Interest Groups 4, no. 2 (April 15, 2019): 411–20. http://dx.doi.org/10.1044/2018_pers-sig19-2018-0003.

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Purpose As increasing amounts and types of speech data become accessible, health care and technology industries increasingly demand quantitative insight into speech content. The potential for speech data to provide insight into cognitive, affective, and psychological health states and behavior crucially depends on the ability to integrate speech data into the scientific process. Current engineering methods for acquiring, analyzing, and modeling speech data present the opportunity to integrate speech data into the scientific process. Additionally, machine learning systems recognize patterns in data that can facilitate hypothesis generation, data analysis, and statistical modeling. The goals of the present article are (a) to review developments across these domains that have allowed real-time magnetic resonance imaging to shed light on aspects of atypical speech articulation; (b) in a parallel vein, to discuss how advancements in signal processing have allowed for an improved understanding of communication markers associated with autism spectrum disorder; and (c) to highlight the clinical significance and implications of the application of these technological advancements to each of these areas. Conclusion The collaboration of engineers, speech scientists, and clinicians has resulted in (a) the development of biologically inspired technology that has been proven useful for both small- and large-scale analyses, (b) a deepened practical and theoretical understanding of both typical and impaired speech production, and (c) the establishment and enhancement of diagnostic and therapeutic tools, all having far-reaching, interdisciplinary significance. Supplemental Material https://doi.org/10.23641/asha.7740191

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Simmhan, Yogesh, Lavanya Ramakrishnan, Gabriel Antoniu, and Carole Goble. "Cloud computing for data-driven science and engineering." Concurrency and Computation: Practice and Experience 28, no. 4 (November 10, 2015): 947–49. http://dx.doi.org/10.1002/cpe.3668.

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16

Koribalski, Bärbel S. "Open Astronomy and Big Data Science." Proceedings of the International Astronomical Union 15, S367 (December 2019): 227–30. http://dx.doi.org/10.1017/s1743921321000879.

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AbstractOpen Astronomy is an important and valuable goal, including the availability of refereed science papers and user-friendly public astronomy data archives. The latter allow and encourage interested researchers from around the world to visualise, analyse and possibly download data from many different science and frequency domains. With the enormous growth of data volumes and complexity, open archives are essential to explore ideas and make discoveries. Open source software is equally important for many reasons, including reproducibility and collaboration. I will present examples of open archive and software tools, including the CSIRO ASKAP Science Data Archive (CASDA), the Local Volume HI Survey (LVHIS), the 3D Source Finding Application (SoFiA) and the Busy Function (BF). Astronomy is international and includes or links to an incredibly wide range of sciences, computing, engineering, and education. Its open nature can serve as an example for world-wide interdisciplinary collaborations.

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17

Schenkel, Ralf, Stefanie Scherzinger, and Marina Tropmann-Frick. "„Data Engineering“ in der Hochschullehre." Datenbank-Spektrum 21, no. 3 (October 29, 2021): 251–53. http://dx.doi.org/10.1007/s13222-021-00395-7.

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ZusammenfassungDas Themenheft zu „Data Engineering for Data Science“ gibt uns Anlass, die Rolle dieses Themas in der akademischen Datenbanklehre im Rahmen einer kleinen Umfrage zu erfassen. In diesem Artikel geben wir die Ergebnisse gesammelt wieder. Uns haben 17 Rückmeldungen aus der GI-Fachgruppe Datenbanksysteme erreicht. Im Vergleich zu einer früheren Umfrage zur Lehre im Bereich „Cloud“, 2014 im Datenbankspektrum vorgestellt, zeichnet sich ab, dass Data-Engineering-Inhalte zunehmend auch in grundständigen Lehrveranstaltungen gelehrt werden, sowie außerhalb der Kerninformatik. Data Engineering scheint sich als ein Querschnittsthema zu etablieren, das nicht nur den Masterstudiengängen vorbehalten ist.

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18

Lewis, M. J. "Food engineering data book." Food Chemistry 32, no. 4 (January 1989): 319–20. http://dx.doi.org/10.1016/0308-8146(89)90090-3.

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19

Müller-Wondorf, Rolf. "Data Science steigert Anlageneffizienz." Logistik für Unternehmen 33, no. 01-02 (2019): 26–27. http://dx.doi.org/10.37544/0930-7834-2019-01-02-26.

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Kundensupport |Als Lieferant von Hardwarelösungen hat sich Vanderlande in den vergangenen Jahrzehnten international bereits einen sehr guten Ruf erarbeitet. Jetzt beschäftigt sich der Anlagenbauer zunehmend auch mit Software und Datenverarbeitung. Dafür gibt es gute Gründe, wie Rob Qualm – Market Director Parcel beim niederländischen Anbieter – im Gespräch mit unserer Zeitschrift verriet. Die Fragen stellte Rolf Müller-Wondorf.

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20

Wang, Chunpeng, Ullrich Steiner, and Alessandro Sepe. "Synchrotron Big Data Science." Small 14, no. 46 (September 17, 2018): 1802291. http://dx.doi.org/10.1002/smll.201802291.

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21

Subhadra, Akella. "The Art of Data Science and Big Data Analytics: Inspecting and Transforming Data." Asian Journal of Computer Science and Technology 9, no. 1 (May 5, 2020): 45–56. http://dx.doi.org/10.51983/ajcst-2020.9.1.2151.

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Data Science is associated with new discoveries, the discovery of value from the data. It is a practice of deriving insights and developing business strategies through transformation of data in to useful information. It has been evaluated as a scientific field and research evolution in disciplines like statistics, computing science, intelligence science, and practical transformation in the domains like science, engineering, public sector, business and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. In this paper we entitled epicycles of analysis, formal modeling, from data analysis to data science, data analytics -A keystone of data science, The Big data is not a single technology but an amalgamation of old and new technologies that assistance companies gain actionable awareness. The big data is vital because it manages, store and manipulates large amount of data at the desirable speed and time. Big data addresses detached requirements, in other words the amalgamate of multiple un-associated datasets, processing of large amounts of amorphous data and harvesting of unseen information in a time-sensitive generation. As businesses struggle to stay up with changing market requirements, some companies are finding creative ways to use Big Data to their growing business needs and increasingly complex problems. As organizations evolve their processes and see the opportunities that Big Data can provide, they struggle to beyond traditional Business Intelligence activities, like using data to populate reports and dashboards, and move toward Data Science- driven projects that plan to answer more open-ended and sophisticated questions. Although some organizations are fortunate to have data scientists, most are not, because there is a growing talent gap that makes finding and hiring data scientists in a timely manner is difficult. This paper, aimed to demonstrate a close view about Data science, big data, including big data concepts like data storage, data processing, and data analysis of these technological developments, we also provide brief description about big data analytics and its characteristics , data structures, data analytics life cycle, emphasizes critical points on these issues.

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22

Subhadra, Akella. "The Art of Data Science and Big Data Analytics: Inspecting and Transforming Data." Asian Journal of Electrical Sciences 9, no. 1 (May 5, 2020): 1–12. http://dx.doi.org/10.51983/ajes-2020.9.1.2374.

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Data Science is associated with new discoveries, the discovery of value from the data. It is a practice of deriving insights and developing business strategies through transformation of data in to useful information. It has been evaluated as a scientific field and research evolution in disciplines like statistics, computing science , intelligence science , and practical transformation in the domains like science, engineering, public sector, business and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. In this paper we entitled epicycles of analysis, formal modeling, from data analysis to data science, data analytics -A keystone of data science, The Big data is not a single technology but an amalgamation of old and new technologies that assistance companies gain actionable awareness. The big data is vital because it manages, store and manipulates large amount of data at the desirable speed and time. In particular, big data addresses detached requirements, in other words the amalgamate of multiple un-associated datasets, processing of large amounts of amorphous data and harvesting of unseen information in a time-sensitive generation. As businesses struggle to stay up with changing market requirements, some companies are finding creative ways to use Big Data to their growing business needs and increasingly complex problems. As organizations evolve their processes and see the opportunities that Big Data can provide, they struggle to beyond traditional Business Intelligence activities, like using data to populate reports and dashboards, and move toward Data Science- driven projects that plan to answer more open-ended and sophisticated questions. Although some organizations are fortunate to have data scientists, most are not, because there is a growing talent gap that makes finding and hiring data scientists in a timely manner is difficult. This paper, aimed to demonstrate a close view about Data science, big data, including big data concepts like data storage, data processing, and data analysis of these technological developments, we also provide brief description about big data analytics and its characteristics , data structures, data analytics life cycle, emphasizes critical points on these issues.

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23

Saltz, Jeffrey S., and Ivan Shamshurin. "Exploring pair programming beyond computer science: a case study in its use in data science/data engineering." International Journal of Higher Education and Sustainability 2, no. 4 (2019): 265. http://dx.doi.org/10.1504/ijhes.2019.10025048.

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24

Saltz, Jeffrey S., and Ivan Shamshurin. "Exploring pair programming beyond computer science: a case study in its use in data science/data engineering." International Journal of Higher Education and Sustainability 2, no. 4 (2019): 265. http://dx.doi.org/10.1504/ijhes.2019.103360.

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25

Bellatreche, Ladjel, and A. Min Tjoa. "Special issue on advances in data, information and knowledge engineering in data science era." Computing 104, no. 4 (February 28, 2022): 711–15. http://dx.doi.org/10.1007/s00607-021-01032-7.

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26

Wang, Yingxu. "Big Data Algebra (BDA): A Denotational Mathematical Structure for Big Data Science and Engineering." Journal of Advanced Mathematics and Applications 5, no. 1 (June 1, 2016): 3–25. http://dx.doi.org/10.1166/jama.2016.1096.

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27

Ahmad, Yanif, Randal Burns, Michael Kazhdan, Charles Meneveau, Alex Szalay, and Andreas Terzis. "Scientific data management at the Johns Hopkins institute for data intensive engineering and science." ACM SIGMOD Record 39, no. 3 (February 8, 2011): 18–23. http://dx.doi.org/10.1145/1942776.1942782.

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28

Aiken, P. H. "Reverse engineering of data." IBM Systems Journal 37, no. 2 (1998): 246–69. http://dx.doi.org/10.1147/sj.372.0246.

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29

Satyro, Marco A. "Life, data, and everything." Pure and Applied Chemistry 79, no. 8 (January 1, 2007): 1403–17. http://dx.doi.org/10.1351/pac200779081403.

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The use of thermodynamics for the solution of chemical and process engineering problems is determined by a blend of science, engineering, art, and grit. Although chemical engineering thermodynamics benefits from an ever-increasing body of basic scientific knowledge, the solution of real problems still depends very much on the knowledge of individuals, their creativity, and their determination to recommend solutions. The essence of success is determined by a combination of two apparently simple components-physical property data and how they are used.

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30

NAIDU, CH VENKATA APPALA. "Data Sythesys and Knowledge Engineering." Journal of Research on the Lepidoptera 51, no. 1 (March 10, 2020): 527–35. http://dx.doi.org/10.36872/lepi/v51i1/301047.

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31

Reichwald, Ralf, and Juan-Ignacıo Conrat. "Engineering Change Data Management." Zeitschrift für wirtschaftlichen Fabrikbetrieb 91, no. 9 (September 1, 1996): 398–401. http://dx.doi.org/10.1515/zwf-1996-910911.

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32

Hüngsberg, W. "Die Engineering Data Message." Zeitschrift für wirtschaftlichen Fabrikbetrieb 90, no. 9 (September 1, 1995): 452–54. http://dx.doi.org/10.1515/zwf-1995-900923.

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33

Ferguson, Andrew L. "Machine learning and data science in soft materials engineering." Journal of Physics: Condensed Matter 30, no. 4 (December 22, 2017): 043002. http://dx.doi.org/10.1088/1361-648x/aa98bd.

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34

Sangaiah, Arun Kumar, Hoang Pham, Mu-Yen Chen, Huimin Lu, and Francesco Mercaldo. "Cognitive data science methods and models for engineering applications." Soft Computing 23, no. 19 (August 6, 2019): 9045–48. http://dx.doi.org/10.1007/s00500-019-04262-2.

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35

Beck, David A. C., James M. Carothers, Venkat R. Subramanian, and Jim Pfaendtner. "Data science: Accelerating innovation and discovery in chemical engineering." AIChE Journal 62, no. 5 (February 28, 2016): 1402–16. http://dx.doi.org/10.1002/aic.15192.

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36

Montáns, Francisco J., Francisco Chinesta, Rafael Gómez-Bombarelli, and J. Nathan Kutz. "Data-driven modeling and learning in science and engineering." Comptes Rendus Mécanique 347, no. 11 (November 2019): 845–55. http://dx.doi.org/10.1016/j.crme.2019.11.009.

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37

Tsujimoto, T. "Science Brought by JASMINE Data." EAS Publications Series 45 (2010): 445–48. http://dx.doi.org/10.1051/eas/1045080.

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38

Szczypiorski, Krzysztof. "Cybersecurity and Data Science." Electronics 11, no. 15 (July 25, 2022): 2309. http://dx.doi.org/10.3390/electronics11152309.

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39

Shuey and Wiederhold. "Data Engineering and Information Systems." Computer 19, no. 1 (January 1986): 18–30. http://dx.doi.org/10.1109/mc.1986.1663030.

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40

Williams, Unislawa, Robert Brown, Marilyn Davis, Tinaz Pavri, and Fatemeh Shafiei. "Teaching Data Science in Political Science: Integrating Methods with Substantive Curriculum." PS: Political Science & Politics 54, no. 2 (January 29, 2021): 336–39. http://dx.doi.org/10.1017/s1049096520001687.

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ABSTRACTThe importance of data science in society today is undeniable, and now is the time to prepare data science talent (National Academies of Sciences, Engineering, and Medicine 2018). Data science demands collaboration, but collaboration within political science departments has been weak in teaching data science. Bridging substantive and methods courses can critically aid in teaching data science because it facilitates this collaboration. Our innovation is to integrate data science into both substantive and methods courses through a dedicated data science course and modules on data science topics taught in substantive courses. This approach allows not only for more opportunities for teaching and practice of data science methods but also helps students to understand how social, economic, and political biases and incentives can affect their data.

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Lytras, Miltiadis D., and Anna Visvizi. "Big Data Research for Social Science and Social Impact." Sustainability 12, no. 1 (December 24, 2019): 180. http://dx.doi.org/10.3390/su12010180.

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This Special Issue of Sustainability devoted to the topic of “Big Data Research for Social Sciences and Social Impact” attracted significant attention of scholars, practitioners, and policy-makers from all over the world. Locating themselves at the cross-section of advanced information systems and computer science research and insights from social science and engineering, all papers included in this Special Issue contribute to the debate on the use of big data in social sciences and big data social impact. By promoting a debate on the multifaceted challenges that our societies are exposed to today, this Special Issue offers an in-depth, integrative, well-organized, comparative study into the most recent developments shaping the future directions of interdisciplinary research and policymaking.

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42

Wang, Yingxu. "On the Cognitive and Theoretical Foundations of Big Data Science and Engineering." New Mathematics and Natural Computation 13, no. 02 (July 2017): 101–17. http://dx.doi.org/10.1142/s1793005717400026.

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Big data play an indispensable role not only in the cognitive mechanisms of human sensation, quantification, qualification, estimation, memory, and reasoning, but also in a wide range of engineering applications. A basic study on the theoretical foundations of big data science is presented with a coherent set of general principles and analytic methodologies for big data systems. Cognitive foundations of big data are explored in order to formally explain the origination and nature of big data. A set of mathematical models of big data are created that rigorously elicit the general essences and patterns of big data across pervasive domains in sciences, engineering, and societies. A significant finding towards big data science is that big data systems in nature are a recursive [Formula: see text]-dimensional-typed hyperstructure (RNTHS) rather than pure numbers. The fundamental topological property of big data reveals a set of denotational mathematical solutions for dealing with inherited complexities and unprecedented challenges in big data engineering.

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Galpin, Ixent. "Data science: an emerging discipline." CITAS 2, no. 1 (July 1, 2016): 39–46. http://dx.doi.org/10.15332/24224529.5178.

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The role of data scientist has been described as the “sexiest job of the 21st Century”. While possibly there is a degree of hype associated with such a claim, there are factors at play such as the unprecedented growth in the amount of data being generated. This paper characterises the already established disciplines which underpin data science, viz., data engineering, statistics, and data mining. Following a characterisation of the previous fields, data science is found to be most closely related to data mining. However, in contrast to data mining, data science promises to operate over datasets that exhibit significant challenges in terms of the four Vs: Volume, Variety, Velocity and Veracity. This paper notes that the current emphasis, both in industry and academia, is on the first three Vs, which pose mainly scientific or technological challenges, rather than Veracity, which is a truly scientific (and arguably a more complex) challenge. Data Science can be seen to have a more ambitious objective than what traditionally data mining has: as a science, data science aims to lead to the creation of new theories and knowledge. This paper notes that, ironically, the veracity dimension, which is arguably the closest one relating to this objective, is being neglected. Despite the current media frenzy about data science, the paper concludes that more time is needed to see whether it will emerge as discipline in its own right.

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44

Poulova, Petra, and Ivana Simonova. "Innovations in Data Engineering Subjects." Advanced Science Letters 23, no. 6 (June 1, 2017): 5090–93. http://dx.doi.org/10.1166/asl.2017.7316.

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Chuprina, Svetlana, Vassil Alexandrov, and Nia Alexandrov. "Using Ontology Engineering Methods to Improve Computer Science and Data Science Skills." Procedia Computer Science 80 (2016): 1780–90. http://dx.doi.org/10.1016/j.procs.2016.05.447.

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46

Gökalp, Mert Onuralp, Ebru Gökalp, Kerem Kayabay, Altan Koçyiğit, and P. Erhan Eren. "Data-driven manufacturing: An assessment model for data science maturity." Journal of Manufacturing Systems 60 (July 2021): 527–46. http://dx.doi.org/10.1016/j.jmsy.2021.07.011.

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47

Vilar, Polona, and Vlasta Zabukovec. "Research data management and research data literacy in Slovenian science." Journal of Documentation 75, no. 1 (January 14, 2019): 24–43. http://dx.doi.org/10.1108/jd-03-2018-0042.

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PurposeThe purpose of this paper is to investigate the differences between scientific disciplines (SDs) in Slovenia in research data literacy (RDL) and research data management (RDM) to form recommendations regarding how to move things forward on the institutional and national level.Design/methodology/approachPurposive sample of active researchers was used from widest possible range of SD. Data were collected from April 21 to August 7, 2017, using 24-question online survey (5 demographic, 19 content questions (single/multiple choice and Likert scale type). Bivariate (ANOVA) and multivariate methods (clustering) were used.FindingsThe authors identified three perception-related and four behavior-related connections; this gave three clusters per area. First, perceptions – skeptical group, mainly social (SocS) and natural sciences (NatS): no clear RDM and ethical issues standpoints, do not agree that every university needs a data management plan (DMP). Careful group, again including mainly SocS and NatS: RDM is problematic and linked to ethical dilemmas, positive toward institutional DMPs. Convinced group, mainly from humanities (HUM), NatS, engineering (ENG) and medicine and health sciences (MedHeS): no problems regarding RDM, agrees this is an ethical question, is positive toward institutional DMP’s. Second, behaviors – sparse group, mainly from MedHeS, NatS and HUM, some agricultural scientists (AgS), and some SocS and ENG: do not tag data sets with metadata, do not use file-naming conventions/standards. Frequent group – many ENG, SocS, moderate numbers of NatS, very few AgS and only a few MedHeS and HUM: often use file-naming conventions/standards, version-control systems, have experience with public-domain data, are reluctant to use metadata with their RD. Slender group, mainly from AgS and NatS, moderate numbers of ENG, SocS and HUM, but no MedHeS: often use public-domain data, other three activities are rare.Research limitations/implicationsResearch could be expanded to a wider population, include other stakeholders and use qualitative methods.Practical implicationsResults are useful for international comparisons but also give foundations and recommendations on institutional and national RDM and RDL policies, implementations, and how to bring academic libraries into the picture. Identified differences suggest that different educational, awareness-raising and participatory approaches are needed for each group.Originality/valueThe findings offer valuable insight into RDM and RDL of Slovenian scientists, which have not yet been investigated in Slovenia.

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Laure, Erwin, Heinz Stockinger, and Kurt Stockinger. "Performance engineering in data Grids." Concurrency and Computation: Practice and Experience 17, no. 2-4 (2005): 171–91. http://dx.doi.org/10.1002/cpe.923.

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Bhatt, Ritikesh, Toufiq Shaikh, Dr Sandeep Patil, Mohnish Harwani, and Bibhu Kumar. "Automating Medical Data and Using Data Science For Heart Disease Prediction." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3933–36. http://dx.doi.org/10.22214/ijraset.2022.43278.

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Abstract: Technology has aided the improvement of individual health, healthcare, biomedical research as well as public health. Therefore, healthcare institutions are seeking to develop integrated information-management environments to consolidate the inevitable application of big data to health care. There exist various entry points into the medical world where computational tools assist patient care matters; reporting results of tests, allowing direct entry of orders or patient information by clinicians, facilitating access to transcribed reports, and in some cases supporting telemedicine applications, because of disorganized and incomplete patient records pose an obstacle to patient care. The most common medium by which records of medical history are kept is paper making data management a severe impediment to productivity. However, the promise of a more efficient healthcare service is obvious through the use of automated health records management systems. Heart disease is a common disease that is overlooked by most. In this study, we discuss how a person can figure out if they need to go to a doctor for a health check-up for any heart-related issues using machine learning algorithms. Keywords: Data Science, Statistics, Python, Data mining, Machine learning, Analytics, Big Data, Disease Prediction, Firebase, Supervised Learning, Unsupervised Learning, ElectrocardioGram(ECG).

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Cuello, Verónica, Gonzalo Zarza, Maria Corradini, and Michael Rogers. "Data Science & Engineering into Food Science: A novel Big Data Platform for Low Molecular Weight Gelators’ Behavioral Analysis." Journal of Computer Science and Technology 20, no. 2 (October 29, 2020): e08. http://dx.doi.org/10.24215/16666038.20.e08.

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The objective of this article is to introduce a comprehensiveend-to-end solution aimed at enabling the applicationof state-of-the-art Data Science and Analyticmethodologies to a food science related problem. Theproblem refers to the automation of load, homogenization,complex processing and real-time accessibility tolow molecular-weight gelators (LMWGs) data to gaininsights into their assembly behavior, i.e. whether agel can be mixed with an appropriate solvent or not.Most of the work within the field of Colloidal andFood Science in relation to LMWGs have centered onidentifying adequate solvents that can generate stablegels and evaluating how the LMWG characteristics canaffect gelation. As a result, extensive databases havebeen methodically and manually registered, storingresults from different laboratory experiments. Thecomplexity of those databases, and the errors causedby manual data entry, can interfere with the analysisand visualization of relations and patterns, limiting theutility of the experimental work.Due to the above mentioned, we have proposed ascalable and flexible Big Data solution to enable theunification, homogenization and availability of the datathrough the application of tools and methodologies.This approach contributes to optimize data acquisitionduring LMWG research and reduce redundant data processingand analysis, while also enabling researchersto explore a wider range of testing conditions and pushforward the frontier in Food Science research.

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