For those learning about fair use, this is a specific example of how fair use may be used in research for text data mining. The book also explores basic copyright and fair use more generally, as well as the specifics of text data mining.
From the "about" section of the book:

"This book explores the legal literacies covered during the virtual Building Legal Literacies for Text Data Mining Institute, including copyright (both U.S. and international law), technological protection measures, privacy, and ethical considerations. It describes in detail how we developed and delivered the 4-day institute, and also provides ideas for hosting shorter literacy teaching sessions. Finally, we offer reflections and take-aways on the Institute."

DASHlink is a virtual laboratory for scientists and engineers to disseminate results and collaborate on research problems in health management technologies for aeronautics systems. Managed by the Integrated Vehicle Health Management project within NASA's Aviation Safety program, the Web site is designed to be a resource for anyone interested in data mining, IVHM, aeronautics and NASA.

A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments.
This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

The Data Renaissance delves into the complexities of data's role in various industries and its broader impact on society. It highlights the challenges in investigating data practices, citing examples like TikTok, where algorithms and data handling are closely guarded secrets. The content, contributed by students under the guidance of an expert, covers a wide range of topics, including the ethical aspects of generative AI in education and the workplace, and case studies reflecting real-world experiences. This evolving text, intended to be updated with each class, serves as a dynamic resource for educators and students alike, offering insights and discussion guides for an in-depth understanding of the ever-changing landscape of data in our digital age.

Our final database video. This one looks at some odds and ends. We examine: Data Warehouse, Data Mining, Big Data. I also talk about the ethics of data mining from the NSA and CDC, and how they are different.

We also give out top picks for the lesson.

Links from Video:
•http://www.w3schools.com/sql/
•What is Database & SQL by Guru99 http://youtu.be/FR4QIeZaPeM
•What is a database http://youtu.be/t8jgX1f8kc4
•MySQL Database For Beginners https://www.udemy.com/mysql-database-for-beginners2/

This course examines the theory and practice of using computational methods in the emerging field of digital humanities. It develops an understanding of key digital humanities concepts, such as data representation, digital archives, information visualization, and user interaction through the study of contemporary research, in conjunction with working on real-world projects for scholarly, educational, and public needs. Students create prototypes, write design papers, and conduct user studies.

Effective Research Data Management (RDM) is a key component of research integrity and reproducible research, and its importance is increasingly emphasised by funding bodies, governments, and research institutions around the world. However, many researchers are unfamiliar with RDM best practices, and research support staff are faced with the difficult task of delivering support to researchers across different disciplines and career stages. What strategies can institutions use to solve these problems?

Engaging Researchers with Data Management is an invaluable collection of 24 case studies, drawn from institutions across the globe, that demonstrate clearly and practically how to engage the research community with RDM. These case studies together illustrate the variety of innovative strategies research institutions have developed to engage with their researchers about managing research data. Each study is presented concisely and clearly, highlighting the essential ingredients that led to its success and challenges encountered along the way. By interviewing key staff about their experiences and the organisational context, the authors of this book have created an essential resource for organisations looking to increase engagement with their research communities.

This handbook is a collaboration by research institutions, for research institutions. It aims not only to inspire and engage, but also to help drive cultural change towards better data management. It has been written for anyone interested in RDM, or simply, good research practice.

15.768 Management of Services: Concepts, Design, and Delivery explores the use of operations tools and perspectives in the service sector, including both for-profit and not-for-profit organizations. The course builds on conceptual frameworks and cases from a wide range of service operations, selected from health care, hospitality, internet services, supply chain, transportation, retailing, food service, entertainment, financial services, humanitarian services, government services, and others.

Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the “information overload” that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.

This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics.

This class is an applications-oriented course covering the modeling of large-scale systems in decision-making domains and the optimization of such systems using state-of-the-art optimization tools. Application domains include: transportation and logistics planning, pattern classification and image processing, data mining, design of structures, scheduling in large systems, supply-chain management, financial engineering, and telecommunications systems planning. Modeling tools and techniques include linear, network, discrete and nonlinear optimization, heuristic methods, sensitivity and post-optimality analysis, decomposition methods for large-scale systems, and stochastic optimization.
This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5223 (System Optimisation: Models and Computation).

Students learn basic data analysis tools and techniques in AP Statistics, but often don’t work with large sets of real-world data. This project gives students exposure to how data is analyzed in many of America’s top corporations, universities and banks. By using multiple input variables, students learn how to develop realistic prediction models for the demand for goods and services.

This interactive lesson helps students understand how companies use algorithms to sort job applicants. It also encourages students to reflect on how digital data mining also can contribute to the hiring process. Students examine resumes and digital data to consider the ways in which our data may open or close opportunities in an increasingly digitized hiring market.

Open data is a vital pillar of open science and a key enabler for reproducibility, data reuse, and novel discoveries. Enforcement of open-data policies, however, largely relies on manual efforts, which invariably lag behind the increasingly automated generation of biological data. To address this problem, we developed a general approach to automatically identify datasets overdue for public release by applying text mining to identify dataset references in published articles and parse query results from repositories to determine if the datasets remain private. We demonstrate the effectiveness of this approach on 2 popular National Center for Biotechnology Information (NCBI) repositories: Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA). Our Wide-Open system identified a large number of overdue datasets, which spurred administrators to respond directly by releasing 400 datasets in one week.