Kristin Briney - Data Management for Researchers: Organize, maintain and share your data for research success

DATA MANAGEMENT FOR RESEARCHERS

Data Management for Researchers

Organize, Maintain and Share your Data
for Research Success

Kristin Briney

RESEARCH SKILLS SERIES

Pelagic Publishing | www.pelagicpublishing.com

Published by Pelagic Publishing
www.pelagicpublishing.com
PO Box 725, Exeter EX1 9QU, UK

Data Management for Researchers

ISBN 978-1-78427-011-7 (Pbk)

ISBN 978-1-78427-012-4 (Hbk)

ISBN 978-1-78427-013-1 (ePub)

ISBN 978-1-78427-014-8 (Mobi)

ISBN 978-1-78427-030-8 (PDF)

Copyright 2015 Kristin Briney

This book should be quoted as Briney, K. (2015) Data Management for Researchers: Organize, Maintain and Share your Data for Research Success. Exeter: Pelagic Publishing, UK.

All rights reserved. No part of this document may be produced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without prior permission from the publisher. While every effort has been made in the preparation of this book to ensure the accuracy of the information presented, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Pelagic Publishing, its agents and distributors will be held liable for any damage or loss caused or alleged to be caused directly or indirectly by this book.

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In memory of data lost

CONTENTS

ABOUT THE AUTHOR

Kristin Briney began her career as a research chemist before becoming more interested in (and frustrated by) the management of research data. She currently works in an academic library, advising researchers on data management planning and best practices for dealing with data. Kristin holds a PhD in physical chemistry and a Masters degree in library and information studies, both from the University of Wisconsin-Madison. She blogs about practical data management at www.dataabinitio.com.

ACKNOWLEDGEMENTS

Writing a book is a substantial project that cannot be done without significant support from other people.

I want to thank two people who particularly helped to make this book happen. A special thank you to Dorothea Salo for teaching me data management and encouraging me to write this book, and to Tim Gritten, my supervisor, for giving me time out of my normal duties to research and write.

Thank you also to my reviewers Ariel Andrea, Andrew Johnson, Trisha Adamus, Jeremy Higgins, Dorothea Salo, Abigail Goben, Amanda Whitmire, Margaret Henderson, and Brianna Marshall for reading over chapter drafts and giving me helpful feedback.

Im also grateful for the work done by the blog Retraction Watch. Without their chronicle of corrections to the scientific record, this book would be much less colorful.

Finally, thank you to my husband who read over drafts, offered a useful researcher perspective, and generally kept the house running while I wrote a book.

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; ONeal 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-too-common 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 dont 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-workers 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 Foundations 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