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Book Details
Abstract
A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data.
Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management.
Data Management for Researchers includes sections on:
* The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code.
* The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data.
* Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans.
* Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable.
* Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content.
* Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems.
* Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure.
* Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost.
* Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project.
* Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data.
* Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it.
This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation.
"An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline." —Robert Buntrock, Chemical Information Bulletin
... recommended as a textbook for graduate-level research techniques courses. It's an important resource for academic and special library shelves and a vital reference for anyone working with data.
Kristen LaBonte
Briney has written a useful primer on data management for researchers which provides practical advice throughout on managing data. It is easy to read and clearly structured. http://www.ariadne.ac.uk/issue75/cole
Gareth Cole, Loughborough University Library
For researchers and consumers of data who are often fraught with managing excess information, Briney's book offers valuable techniques, strategies and standards to help achieve proficient data management and successful outcomes. This book can be useful to both novice researchers and well-established scientists alike.
Mary F. Miles
Kristin Briney has a PhD in physical chemistry and a Master’s degree in library and information studies from the University of Wisconsin-Madison, and currently works in an academic library, advising researchers on data management planning. Her blog can be found at www.dataabinitio.com.
Apparently, NASA lost much of the early data from space exploration, including high quality video footage of the first moon landing. All the more reason to do as it says in the sub-title to the book.
Alan Crowden
Kristin Briney’s Data Management for Researchers is a book that should be on the shelf (physical or virtual) of every librarian, researcher and research administrator. Scientists, engineers, social scientists, humanists — anyone who’s work involves generating and keeping track of digital data. This is the book for you.
.... I recommend this book without hesitation for all academic libraries. Individual researchers, research administrators, funding agency employees and academic librarians would all find much useful information. Simply giving a copy to new graduate students is probably a worthwhile investment at any institution.
http://scienceblogs.com/confessions/2016/01/11/reading-diary-data-management-for-researchers-organize-maintain-and-share-your-data-for-research-success-by-kristin-briney/
John Dupuis, York University Library, Toronto
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| CONTENTS | viii | ||
| ABOUT THE AUTHOR | x | ||
| ACKNOWLEDGEMENTS | xi | ||
| 1 THE DATA PROBLEM | 1 | ||
| 1.1 WHY IS EVERYONE TALKING ABOUT DATA MANAGEMENT? | 2 | ||
| 1.2 WHAT IS DATA MANAGEMENT? | 3 | ||
| 1.2.1 Defining data | 3 | ||
| 1.2.2 Defining data management | 7 | ||
| 1.3 WHY SHOULD YOU DO DATA MANAGEMENT? | 7 | ||
| 2 THE DATA LIFECYCLE | 9 | ||
| 2.1 THE DATA LIFECYCLE | 9 | ||
| 2.1.1 The old data lifecycle | 9 | ||
| 2.1.2 The new data lifecycle | 11 | ||
| 2.2 THE DATA ROADMAP | 11 | ||
| 2.2.1 Following the data roadmap | 11 | ||
| 2.3 WHERE TO START WITH DATA MANAGEMENT | 13 | ||
| 2.4 CHAPTER SUMMARY | 15 | ||
| 3 PLANNING FOR DATA MANAGEMENT | 15 | ||
| 3.1 HOW TO PLAN FOR DATA MANAGEMENT | 15 | ||
| 3.1.1 The importance of planning for data management | 17 | ||
| 3.1.2 How to customize data management to your needs | 17 | ||
| 3.2 CREATING A DATA MANAGEMENT PLAN | 19 | ||
| 3.2.1 Why create a written data management plan? | 19 | ||
| 3.2.2 What a data management plan covers | 19 | ||
| 3.2.3 Creating a data management plan for your research | 23 | ||
| 3.3 DATA POLICIES | 23 | ||
| 3.3.1 Types of policies and where to find them | 23 | ||
| 3.3.2 Data privacy policies | 25 | ||
| 3.3.3 Data retention policies | 25 | ||
| 3.3.4 Data ownership policies | 27 | ||
| 3.3.5 Data and copyright | 29 | ||
| 3.3.6 Data management policies | 29 | ||
| 3.3.7 Data sharing policies | 29 | ||
| 3.4 CASE STUDIES | 31 | ||
| 3.4.1 Example data management plan for a Midwest ornithology project | 31 | ||
| 3.4.2 My data management plan for this book | 31 | ||
| 3.5 CHAPTER SUMMARY | 33 | ||
| 4 DOCUMENTATION | 35 | ||
| 4.1 RESEARCH NOTES AND LAB NOTEBOOKS | 35 | ||
| 4.1.1 Taking better notes | 35 | ||
| 4.1.2 Laboratory notebooks | 37 | ||
| 4.1.3 Electronic laboratory notebooks | 41 | ||
| 4.2 METHODS | 43 | ||
| 4.2.1 Definition of methods | 43 | ||
| 4.2.2 Evolving protocols | 45 | ||
| 4.2.3 Managing methods information | 45 | ||
| 4.3 OTHER USEFUL DOCUMENTATION FORMATS | 45 | ||
| 4.3.1 README.txt files | 45 | ||
| 4.3.2 Templates | 47 | ||
| 4.3.3 Data dictionaries | 47 | ||
| 4.3.4 Codebooks | 49 | ||
| 4.4 METADATA | 49 | ||
| 4.4.1 When to use metadata versus notes | 51 | ||
| 4.4.2 The basics of metadata | 51 | ||
| 4.4.3 Adopting a metadata schema | 55 | ||
| 4.5 STANDARDS | 57 | ||
| 4.5.1 General standards | 57 | ||
| 4.5.2 Scientific standards | 57 | ||
| 4.6 CHAPTER SUMMARY | 61 | ||
| 5 ORGANIZATION | 61 | ||
| 5.1 FILE ORGANIZATION | 61 | ||
| 5.1.1 Organizing digital information | 63 | ||
| 5.1.2 Organizing physical content | 63 | ||
| 5.1.3 Organizing related physical and digital information | 65 | ||
| 5.1.4 Indexes | 65 | ||
| 5.1.5 Organizing information for collaborations | 67 | ||
| 5.1.6 Organizing literature | 67 | ||
| 5.2 NAMING CONVENTIONS | 69 | ||
| 5.2.1 File naming | 69 | ||
| 5.2.2 File versioning | 71 | ||
| 5.3 DOCUMENTING YOUR CONVENTIONS | 73 | ||
| 5.3.1 What to document | 73 | ||
| 5.3.2 Where to document | 73 | ||
| 5.4 DATABASES | 75 | ||
| 5.4.1 How databases work | 75 | ||
| 5.4.2 Querying a database | 77 | ||
| 5.5 CHAPTER SUMMARY | 79 | ||
| 6 IMPROVING DATA ANALYSIS | 79 | ||
| 6.1 RAW VERSUS ANALYZED DATA | 79 | ||
| 6.1.1 Managing raw and analyzed data | 81 | ||
| 6.1.2 Documenting the analysis process | 81 | ||
| 6.2 PREPARING DATA FOR ANALYSIS | 81 | ||
| 6.2.1 Data quality control | 81 | ||
| 6.2.2 Spreadsheet best practices | 85 | ||
| 6.3 MANAGING YOUR RESEARCH CODE | 87 | ||
| 6.3.1 Coding best practices | 87 | ||
| 6.3.2 Version control | 89 | ||
| 6.3.3 Code sharing | 91 | ||
| 6.4 CHAPTER SUMMARY | 93 | ||
| 7 MANAGING SENSITIVE DATA | 93 | ||
| 7.1 TYPES OF SENSITIVE DATA | 93 | ||
| 7.1.1 National data privacy laws | 95 | ||
| 7.1.2 Ethics and sensitive data | 97 | ||
| 7.1.3 Other data categorized as sensitive | 97 | ||
| 7.2 KEEPING DATA SECURE | 97 | ||
| 7.2.1 Basic computer security | 99 | ||
| 7.2.2 Access | 101 | ||
| 7.2.3 Encryption | 103 | ||
| 7.2.4 Destroying data | 105 | ||
| 7.2.5 Personnel | 105 | ||
| 7.2.6 Training and keeping a security plan | 107 | ||
| 7.2.7 Summarization of the dos and don’ts | 107 | ||
| 7.3 ANONYMIZING DATA | 107 | ||
| 7.3.1 Types of personally identifiable information | 109 | ||
| 7.3.2 Masking data | 109 | ||
| 7.3.3 De-identifying data | 111 | ||
| 7.3.4 Other anonymization considerations | 114 | ||
| 7.4 CHAPTER SUMMARY | 115 | ||
| 8 STORAGE AND BACKUPS | 115 | ||
| 8.1 STORAGE | 115 | ||
| 8.1.1 Storage best practices | 117 | ||
| 8.1.2 Storage hardware | 117 | ||
| 8.1.3 Choosing storage | 119 | ||
| 8.1.4 Physical storage | 121 | ||
| 8.2 BACKUPS | 121 | ||
| 8.2.1 Backup best practices | 121 | ||
| 8.2.2 Backup considerations | 123 | ||
| 8.2.3 Test your backups | 123 | ||
| 8.2.4 Backing up analog data | 123 | ||
| 8.3 CASE STUDIES | 125 | ||
| 8.4 CHAPTER SUMMARY | 125 | ||
| 9 LONG-TERM STORAGE AND PRESERVATION | 127 | ||
| 9.1 WHAT TO RETAIN AND HOW LONG TO RETAIN IT | 127 | ||
| 9.1.1 Data retention policies | 127 | ||
| 9.1.2 Common sense data retention | 131 | ||
| 9.2 PREPARING YOUR DATA FOR THE LONG TERM | 131 | ||
| 9.2.1 Keeping fi les readable | 131 | ||
| 9.2.2 Keeping datasets interpretable | 135 | ||
| 9.2.3 Long-term data management | 137 | ||
| 9.3 OUTSOURCING DATA PRESERVATION | 137 | ||
| 9.4 CHAPTER SUMMARY | 139 | ||
| 10 SHARING DATA | 139 | ||
| 10.1 DATA AND INTELLECTUAL PROPERTY | 139 | ||
| 10.1.1 Data and copyright | 141 | ||
| 10.1.2 Licenses and contracts | 143 | ||
| 10.1.3 Patents | 143 | ||
| 10.1.4 Intellectual property and data sharing | 145 | ||
| 10.2 LOCAL DATA SHARING AND REUSE | 145 | ||
| 10.3 COLLABORATIONS | 145 | ||
| 10.4 PUBLIC DATA SHARING | 147 | ||
| 10.4.1 Reasons for public sharing | 147 | ||
| 10.4.2 Sources of public sharing requirements | 149 | ||
| 10.4.3 When and what to share | 151 | ||
| 10.4.4 Preparing your data for sharing | 153 | ||
| 10.4.5 How to share | 153 | ||
| 10.4.6 Licensing shared data | 157 | ||
| 10.5 GETTING CREDIT FOR SHARED DATA | 159 | ||
| 10.5.1 The basics of getting credit for your data | 159 | ||
| 10.5.2 Altmetrics | 161 | ||
| 10.6 CHAPTER SUMMARY | 161 | ||
| 11 DATA REUSE AND RESTARTING THE DATA LIFECYCLE | 163 | ||
| 11.1 FINDING AND REUSING DATA | 163 | ||
| 11.1.1 Finding data | 163 | ||
| 11.1.2 Data reuse rights | 165 | ||
| 11.1.3 Using someone else’s data | 165 | ||
| 11.2 CITING DATA | 167 | ||
| 11.2.1 Citation format | 167 | ||
| 11.2.2 Other citation considerations | 167 | ||
| 11.3 RESTARTING THE DATA LIFECYCLE | 169 | ||
| REFERENCES | 171 | ||
| INDEX | 185 |