About the Book
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
About the Author :
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.
Review :
Data Management for Researchers: Organize, Maintain and Share Your Data for Research Success joins others in the 'Research Skills' series with an in-depth guide to data management and manipulation created especially with the research community in mind.
As digital data sources and results translate to larger chunks of data and databases available for research purposes, it becomes necessary to develop different types of data management strategies that use a researcher's structure and purposes to best advantage. Data Management for Researchers is one of the few books on the market to delve into such basics as documenting data, improving analytical approaches, assuring security for sensitive data, and backing up work.
Examples and case histories pepper the approach, adding interest and real-world examples to validate the importance of the data management process in research circles. Without the proper protocols in place, data may be compromised, corrupted, or even lost - along with the PhD or study associated with it.
Any serious researcher working with data must make this book a priority read.
This concise book offers sound advice on all aspects of data management, in an accessible style. The intended audience is researchers, as the title makes clear. And I could easily imagine it being bought and recommended to PhD students to be read as part of their first year training. The author has a PhD in Chemistry, and most examples are drawn from the natural sciences, rather than social science, but much of the content would also be relevant to social scientists. It might be less easy for a humanities researcher to relate to, however.
The book is pretty comprehensive in its coverage of the topic. The opening chapter, gives some context for the importance of managing research data, with a little about the context of policy change, but Briney presents it mostly as a practical issue, with a concern for working effectively and not losing data. The book’s dedication is “In memory of data lost.” The chapter then reviews a range of definitions and I really liked the definition she herself coins that data is “anything you perform analysis on.” This is a really good universal definition, complementing the approaches that list lots of examples - though arguably the definition does not differentiate data from literature. She also discusses whether the word data should be singular or plural, opting for the former.
Chapter 2 considers the concept of the data lifecycle. She contrasts an old linear model ending in publishing, with a more complex, cyclic model, that repeats itself, through reuse. Usefully she also maps subsequent chapters to this lifecycle, as her “data roadmap”. This serves as a neat outline of the whole book.
Chapter 3 is about data management planning. Briney presents the key questions that need to be answered in any DMP. She then reviews policies that might affect the DMP, be they about privacy, retention, ownership etc. This section I found a bit general, but as she is trying to write for an international audience, and for researchers in many subjects, it would be hard to be more specific. I think if I were a researcher I would still be wondering how to be sure I had a comprehensive view of the policies that affected my own work. Also, she does not stress university policies very much. Case studies include DMP for the book itself.
Chapter 4 covers documentation including lab notebooks for recording the research process, protocols for methods, data dictionaries, metadata and standards (general and subject specific). She gives examples of standards for some of the main science disciplines – but again, given the proliferation of standards within research specialisms this is inevitably a quite broad brush stroke.
Chapter 5 explores file organization, including filenaming conventions and version control. There is also an introduction to databases. Chapter 6 is entitled “improving your data analysis”. There is advice here on processing data and on using spreadsheets effectively. It also offers advice on managing code, including how to use GitHub. This is getting further into advice on the research process itself.
Chapter 7 is on managing sensitive data, and includes advice on basic computer security, including encryption. She also outlines approaches to anonymization. Chapter 8 addresses storage and back-ups. Again the advice is very practical. Chapter 9 is about long term storage and preservation – and has especially useful advice on how to collect data with preservation in mind.
Chapter 10 explores data sharing. The first part of the chapter is mostly about issues around licensing data, followed by public sharing of data. Given the importance of open data in current policy agendas, this section seemed a bit buried. Chapter 11 deals with data reuse, explaining how to go about finding data for secondary analysis and how to cite such resources.
Thus the coverage of data management the book is pretty comprehensive; the structure logical. Briney writes from experience with a very practical focus on managing data effectively. The style is accessible and uncomplicated. She is not afraid to offer direct advice. The whole book is concise at around 200 pages. In addition, a really significant merit of the book is that there is a cheap pdf version is available.
The author is American, and in a few places - such as in chapter 10, in the section on copyright - she limits the scope of her advice to the USA. But generally she takes an international approach, e.g. in reporting relevant policy and legal aspects. Given that its intended readership is for such a wide audience in any country and any science discipline, there are places where the advice is a bit generic. A point the author might have made more strongly was how data management skills are an asset for employability. But I cannot think of much that is missing in terms of topics covered, for a science student. Given the researcher’s perspective it does not cover more directly LIS related activities such as producing a policy or developing services. But it would be informative for LIS staff to read it, and it is certainly one to recommend to PhD students, perhaps to complement Corti et al.’s more social science specific Managing and Sharing Research Data.
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.
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.
... 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.
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
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/
Briney takes the reader through a pragmatic and sensible route through the activities of data management.
This practical handbook can help bring new researchers quickly up-to-speed on the topic, as well as serve as a reference to meet specific data management needs they encounter throughout the data life cycle.
I cannot recommend this slender, seemingly innocent looking book enough – it will literally change how you think about data management.
[I] intend to give a copy of this book to each graduate student / trainee that joins my lab
