LibGuides: DDS-7250: Module 4

Module 4 Required Resources

The purpose of this list of articles is to support the development of essential knowledge and skills in the areas of data selection, cleaning, validation, and presentation—critical components of effective data science practice. By exploring these topics, you will gain a great understanding of how to identify and extract relevant data, address inconsistencies and errors through cleaning methods, ensure data reliability and accuracy through validation model techniques, and communicate findings effectively using appropriate visualization and presentation strategies. Together, these articles provide a structured foundation for developing proficiency in handling data throughout its lifecycle, enabling informed decision-making and robust analytical outcomes in academic and professional research settings.

Is generalization hindering the adoption of your findings?
Heldal, R. (2024). Is generalization hindering the adoption of your findings? In Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM '24), 348–358. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3674805.3686694

This resource highlights challenges and insights into generalization in empirical software engineering, providing critical analysis relevant for researchers in the field.
Ethical Considerations in Data Collection and Analysis
Okorie, G. N., Udeh, C. A., Adaga, E. M., DaraOjimba, O. D., & Oriekhoe, O. I. (2024). Ethical considerations in data collection and analysis: A review. International Journal of Applied Research in Social Sciences, 6(1), 1–22.

This review article discusses ethical challenges in modern data collection and analysis, making it essential for researchers navigating ethical dilemmas in their work.
Data Quality Assessment in the Wild: Findings from GitHub
Ustunboyacioglu, I., Kumara, I., Di Nucci, D., Tamburri, D. A., & Van Den Heuvel, W.-J. (2024). Data quality assessment in the wild: Findings from GitHub. In Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering (EASE '24), 120–129. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3661167.3661213

This paper investigates data quality on GitHub, offering practical insights for software engineers and data scientists.
Data Cleaning and Machine Learning: A Systematic Literature Review
Côté, P.-O., Nikanjam, A., Ahmed, N., Humeniuk, D., & Khomh, F. (2024). Data cleaning and machine learning: A systematic literature review. Automated Software Engineering: An International Journal, 31(2). https://doi.org/10.1007/s10515-024-00453-w

This systematic review provides an in-depth analysis of data cleaning practices in machine learning, making it invaluable for automation and software engineering professionals.
Emerging Ethics: Teaching Communication Design in Professional Writing Courses
Eble, M. F., McCullouch, S. B., Amador, S., & Blackmon, C. R. (2024). Emerging ethics: Teaching communication design in professional writing courses. In Proceedings of the 42nd ACM International Conference on Design of Communication (SIGDOC '24), 199–205. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3641237.3691670

This paper addresses ethical considerations in professional writing education, emphasizing the integration of communication design in ethical discourse.
Misconduct and Questionable Research Practices
Isbell, D. R., Brown, D., Chen, M., Derrick, D. J., Ghanem, R., Arvizu, M. N. G., Schnur, E., Zhang, M., & Plonsky, L. (2022). Misconduct and questionable research practices: The ethics of quantitative data handling and reporting in applied linguistics. The Modern Language Journal, 106, 172–195. https://doi.org/10.1111/modl.12760

This article evaluates ethical concerns in quantitative data handling within applied linguistics, providing actionable recommendations for researchers.
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
Schwethelm, K., Kaiser, J., Kuntzer, J., Yigitsoy, M., Rueckert, D., & Kaissis, G. (2024). Differentially private active learning: Balancing effective data selection and privacy. arXiv. http://arxiv.org/abs/2410.00542

This preprint examines techniques for achieving privacy in active learning frameworks, with implications for privacy-conscious machine learning development.
"The Data Says Otherwise" — Towards Automated Fact-checking and Communication of Data Claims
Fu, Y., Guo, S., Hoffswell, J., Bursztyn, V. S., Rossi, R., & Stasko, J. (2024). "The data says otherwise" — Towards automated fact-checking and communication of data claims. In Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST '24), Article 134, 1–20. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3654777.3676359

This paper explores automated tools for fact-checking data claims, making it highly relevant for data communication professionals.
Informative Data Selection with Uncertainty for Multimodal Object Detection
Zhang, X., Li, Z., Zou, Z., Gao, X., Xiong, Y., Jin, D., Li, J., & Liu, H. (2024). Informative data selection with uncertainty for multimodal object detection. IEEE Transactions on Neural Networks and Learning Systems, 35(10), 13561–13573. https://doi.org/10.1109/TNNLS.2023.3270159

This article addresses challenges in multimodal object detection, presenting innovative approaches for data selection with uncertainty.

Optional Resources

To democratize research with sensitive data, we should make synthetic data more accessible
Van Kesteren, E.-J. (2024). To democratize research with sensitive data, we should make synthetic data more accessible. Patterns (New York, N.Y.), 5(9), 101049. https://doi.org/10.1016/j.patter.2024.101049

This article advocates for the broader accessibility of synthetic data, emphasizing its potential to transform research with sensitive datasets.
Python Data Cleaning Cookbook - Second Edition
Walker, M. (2024). Python Data Cleaning Cookbook - Second Edition. Packt Publishing. https://www.oreilly.com/library/view/-/9781803239873/

This practical guide provides hands-on recipes for cleaning and preprocessing data using Python, making it a valuable resource for data analysts and scientists.
Data Cleaning and Exploration with Machine Learning
Walker, M. (2022). Data Cleaning and Exploration with Machine Learning. Packt Publishing. https://www.oreilly.com/library/view/-/9781803241678/

This book offers insights into leveraging machine learning techniques for data cleaning and exploration, bridging gaps between data preparation and analysis.
Data Storytelling in Data Visualization
Shao, H., Martinez-Maldonado, R., Echeverria, V., Yan, L., & Gasevic, D. (2024). Data storytelling in data visualization: Does it enhance the efficiency and effectiveness of information retrieval and insights comprehension? In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI '24), Article 195, 1–21. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3613904.3643022

This study investigates the role of storytelling in data visualization, offering insights into its impact on data comprehension and communication.
Beyond Numbers: Creating Analogies to Enhance Data Comprehension and Communication with Generative AI
Chen, Q., Shuai, W., Zhang, J., Sun, Z., & Cao, N. (2024). Beyond numbers: Creating analogies to enhance data comprehension and communication with generative AI. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI '24), Article 377, 1–14. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3613904.3642480

This paper explores the use of generative AI for creating analogies, enhancing the clarity and impact of data communication.
The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph
Wu, M., Vu, T.-T., Qu, L., & Haffari, G. (2024). The best of both worlds: Bridging quality and diversity in data selection with bipartite graph. arXiv. http://arxiv.org/abs/2410.12458

This preprint introduces a bipartite graph-based approach to balance data quality and diversity in selection processes.
Rethinking Data Selection at Scale: Random Selection is Almost All You Need
Xia, T., Yu, B., Dang, K., Yang, A., Wu, Y., Tian, Y., Chang, Y., & Lin, J. (2024). Rethinking data selection at scale: Random selection is almost all you need. arXiv. http://arxiv.org/abs/2410.09335

This paper reexamines data selection strategies, arguing for the surprising effectiveness of random selection in large-scale scenarios.
Compute-Constrained Data Selection
Yin, J. O., & Rush, A. M. (2024). Compute-constrained data selection. arXiv. http://arxiv.org/abs/2410.16208

This preprint presents strategies for optimizing data selection under compute constraints, valuable for resource-limited AI development.
Privacy-Preserving Data Selection for Horizontal and Vertical Federated Learning
Zhang, L., Li, A., Peng, H., Han, F., Huang, F., & Li, X. (2024). Privacy-preserving data selection for horizontal and vertical federated learning. IEEE Transactions on Parallel and Distributed Systems, 35(11), 2054–2068. https://doi.org/10.1109/TPDS.2024.3439709

This article explores privacy-preserving methods in federated learning, addressing challenges in secure data sharing and collaboration.
Exploratory Data Analysis with Python Cookbook
Oluleye, A. (2023). Exploratory Data Analysis with Python Cookbook. Packt Publishing. https://search.ebscohost.com/login.aspx?direct=true&AuthType=sso&db=edseee&AN=edseee.10251345&site=eds-live&scope=site&custid=natuniv

This cookbook offers over 50 Python-based recipes for exploring, visualizing, and extracting insights from structured and unstructured data.
Validation Methods to Promote Real-world Applicability of Machine Learning in Medicine
Rafiq, R. B., Modave, F., Guha, S., & Albert, M. V. (2021). Validation methods to promote real-world applicability of machine learning in medicine. In Proceedings of the 2020 3rd International Conference on Digital Medicine and Image Processing (DMIP '20), 13–19. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3441369.3441372

This paper discusses validation techniques to ensure the real-world applicability of machine learning in medical contexts.
Explainable Online Validation of Machine Learning Models for Practical Applications
Fuhl, W. et al. (2021). Explainable online validation of machine learning models for practical applications. In Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), 3304–3311. https://doi.org/10.1109/ICPR48806.2021.9412959

This study introduces methods for explainable online validation of ML models, emphasizing their use in real-world applications.
Data Evaluation and Enhancement for Quality Improvement of Machine Learning
Chen, H., Chen, J., & Ding, J. (2021). Data evaluation and enhancement for quality improvement of machine learning. IEEE Transactions on Reliability, 70(2), 831–847. https://doi.org/10.1109/TR.2021.3070863

This article explores data evaluation and enhancement techniques, focusing on improving machine learning model reliability.