Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain. Most of the data stored worldwide is legacy data-data created some time in the past, for a particular purpose, and left in obsolete formats. As with keepsakes in an attic, we retain this information thinking it may have value in the future, though we have no current use for it. The case studies in this book, from such diverse fields as cosmology, quantum physics, high-energy physics, microbiology, psychiatry, medicine, and hospital administration, all serve to demonstrate how innovative people draw value from legacy data. By following the case examples, readers will learn how legacy data is restored, merged, and analyzed for purposes that were never imagined by the original data creators.

Includes bibliographical references and index.

Front Cover; Repurposing Legacy Data; Copyright Page; Contents; Author Biography; 1 Introduction; 1.1 Why Bother?; 1.2 What Is Data Repurposing?; 1.3 Data Worth Preserving; 1.4 Basic Data Repurposing Tools; 1.4.1 A Simple Text Editor; 1.4.2 Simple Programming Skills; 1.4.3 Data Visualization Utilities; 1.5 Personal Attributes of Data Repurposers; 1.5.1 Data Organization Methods; 1.5.2 Ability to Develop a Clear Understanding of the Goals of a Project; References; 2 Learning from the Masters; 2.1 New Physics from Old Data; 2.2 Repurposing the Physical and Abstract Property of Uniqueness.

2.3 Repurposing a 2,000-Year-Old Classification2.4 Decoding the Past; 2.5 What Makes Data Useful for Repurposing Projects?; References; 3 Dealing with Text; 3.1 Thus It Is Written; 3.2 Search and Retrieval; 3.3 Indexing Text; 3.4 Coding Text; References; 4 New Life for Old Data; 4.1 New Algorithms; 4.2 Taking Closer Looks; 4.3 Crossing Data Domains; References; 5 The Purpose of Data Analysis Is to Enable Data Reanalysis; 5.1 Every Initial Data Analysis on Complex Datasets Is Flawed; 5.2 Unrepeatability of Complex Analyses; 5.3 Obligation to Verify and Validate.

5.4 Asking What the Data Really MeansReferences; 6 Dark Legacy: Making Sense of Someone Else's Data; 6.1 Excavating Treasures from Lost and Abandoned Data Mines; 6.2 Nonstandard Standards; 6.3 Specifications, Not Standards; 6.4 Classifications and Ontologies; 6.5 Identity and Uniqueness; 6.6 When to Terminate (or Reconsider) a Data Repurposing Project; References; 7 Social and Economic Issues; 7.1 Data Sharing and Reproducible Research; 7.2 Acquiring and Storing Data; 7.3 Keeping Your Data Forever; 7.4 Data Immutability; 7.5 Privacy and Confidentiality; 7.6 The Economics of Data Repurposing.

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