Data Management for Researchers: Organize, maintain and share your data for research success (Research Skills) - Softcover

Briney, Kristin

Softcover

ISBN 10: 1784270113 ISBN 13: 9781784270117

Verlag: Pelagic Publishing, 2015

Inhaltsangabe

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

Über diesen Titel

Über die Autorin bzw. den Autor

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.

Auszug. © Genehmigter Nachdruck. Alle Rechte vorbehalten.

Data Management for Researchers

Organize, Maintain and Share your Data for Research Success

By Kristin Briney

Pelagic Publishing

Copyright © 2015 Kristin Briney
All rights reserved.

ISBN: 978-1-78427-011-7

Contents

About the author, ix, 
Acknowledgements, x, 
Chapter 1 The data problem, 1, 
Chapter 2 The data lifecycle, 9, 
Chapter 3 Planning for data management, 16, 
Chapter 4 Documentation, 35, 
Chapter 5 Organization, 62, 
Chapter 6 Improving data analysis, 80, 
Chapter 7 Managing sensitive data, 94, 
Chapter 8 Storage and backups, 116, 
Chapter 9 Long-term storage and preservation, 127, 
Chapter 10 Sharing data, 140, 
Chapter 11 Data reuse and restarting the data lifecycle, 163, 
References, 171, 
Index, 186, 

CHAPTER 1

THE DATA PROBLEM

On July 20, 1969, Neil Armstrong climbed out of his spacecraft and placed his feet on the moon. The landing was broadcast live all over the world and was a significant event in both scientific and human history. Today, we can still watch the grainy video of the moon landing but what we cannot do is watch the original, higher quality footage or examine some of the data from this mission. This is because much of the data from early space exploration is lost forever.

Among the lost data are the original Apollo 11 tapes containing high-quality video footage of the moon landing. Their loss first came to light in 2006 (Macey 2006) and NASA personnel spent the next three years searching for the tapes across multiple continents before concluding that they were likely wiped and reused for data storage sometime in the 1970s (NASA 2009; O'Neal 2009; Pearlman 2009). Other data from this era fared better but at the cost of significant time and money. The Lunar Orbiter Image Recovery Project (LOIRP 2014), for example, spent years and well over a half a million dollars recovering images taken of the moon by the five Lunar Orbiter spacecraft missions preparing for the moon landing in 1969 (Wood 2009; Turi 2014). The project required finding specialized and obsolete hardware to read the original magnetic tapes, reconstructing how to process the raw data into high-quality images, decoding the labeling scheme on each of the tapes, and doing all of this with little to no documentation. Only the cultural importance of the data on these tapes, such as the first image of the earth as seen from the moon, made such efforts worthwhile.

The story of this momentous occasion in scientific history ends with an all-toocommon example of failing to plan for data management. Almost 50 years later, researchers are still inadvertently destroying data or having trouble finding data that still exists. A recent study of biology data, for example, found that data disappears at a rate of 17% per year after publishing the results (Vines et al. 2014). Another estimate says that 31% of all PC users have suffered complete data loss due to events outside of their control; this correlates with 6% of PCs losing data in any given year (Anon 2014a). Unfortunately, very few of us have significant resources – as with the lunar data projects – to recover our own data when something happens to it. Lost, misplaced, and even difficult to understand data represents a real cost in terms of time and money. Fortunately, there are practices you can use to make it easier to find and use your data when you need it; those practices are collectively called "data management".

At its most essential, data management is about taking care of your data better so that you don't experience small frustrations when actively working with your data, like having trouble finding documentation for a particular dataset, or bigger problems after a project ends, like lost data. Having well-managed data means that you can find a particular dataset, will have all of the notes you need, can prevent a security breach, can easily use a co-worker's data, and can manage the chaos of an ever-growing number of digital files. Basically, many of the little headaches that researchers often encounter around data during the research process can be prevented through good data management. Just as you need to periodically clean your home, so too should you do regular upkeep on your data.

The good news is that dealing with your digital research data does not have to be difficult, though it is different than managing analog content. This book will show you many practices you can use to take care of your research data better. The ultimate goal is for you to be able to easily find and use your data when needed, whether it is historic 50-year-old data or the critical dissertation data you collected last week.

1.1 WHY IS EVERYONE TALKING ABOUT DATA MANAGEMENT?

"Data management" is a relatively new term within research, arising in the mid-2000s with funder requirements for both data management and data sharing. Such mandates gained momentum in the UK with the 2011 Common Principles on Data Policy from Research Councils UK (Research Councils UK 2011) and in the United States with the National Science Foundation's data management plan requirement in 2011 (NSF 2013). Data management and sharing policies are now becoming commonplace in science, with recent adoption by journals such as Science (Science/AAAS 2014), Nature (Nature Publishing Group 2006), and PLOS (Bloom 2013). The overall trend is for increased data management but let's examine why this trend exists in the first place.

We cannot discuss the rise in data management requirements without examining its partner, data sharing. The two concepts often pair together in addressing similar problems in the scientific process, such as limited resources, reproducibility issues, and advancing science at a faster rate (Borgman 2012). The pairing also occurs because well-managed data requires less preparation for sharing. Taken as a whole, most of the reasons why you are now required to manage and share your data are external, though there are many personal benefits to having well managed research data, which we will examine throughout the book.

One of the main reasons behind the implementation of data management and sharing requirements relates to money. The rise of data management requirements roughly coincided with the global economic recession of the late 2000s when many research funding groups faced smaller budgets. With limited resources, funders want to be sure that researchers are making the best use of those resources, for example, by preventing the common occurrence of losing data at the end of a project (Vines et al. 2014). Public funders face additional pressure to make research products like articles and data available to the public who support the research; the current default is that these resources are locked behind paywalls, or are not even made available in the first place. By requiring data management and sharing, funders can not only stem the loss of important data but also provide accountability to those ultimately paying for the research. As an added benefit, any data reuse – either by the original researcher or other researchers – means that the same amount of money will result in more research because data usually costs more to collect than to reuse. Therefore, many research funders see data management and sharing requirements as advantageous.

Another key reason for data management and sharing policies is the prevalence of digital data in scientific research. Research data is digital on a scale never seen before which opens up a whole new...

Details zur Ausgabe

• Verlag: Pelagic Publishing
• Erscheinungsdatum: 2015
• Sprache: Englisch
• ISBN 10: 1784270113
• ISBN 13: 9781784270117
• Einband: Taschenbuch
• Anzahl der Seiten: 204
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: GPSR Kontakt; gpsr@libri.de; ; Europaallee 1; Bad Hersfeld; 36244; Deutschland