Despite established funder or journal obligations for data preservation and sharing, data management education is still somewhat haphazardly taught through self-education, mentoring or one-time workshops offered by an academic library or as a single lecture in a responsible conduct of research course or a methodology course. This was demonstrated by Raszewski et al. (2021) in a survey results of data management education for doctoral nursing programmes, in which faculty reported that students received data management guidance primarily from their individual faculty supervisors or in limited class sessions. A similar survey by Tenopir et al. (2016), found science educators reporting a lack of classroom time and instructional materials, leaving them to feel unprepared to provide adequate data management education.

Over the past fifteen years, associated educational materials have been created to primarily support graduate students and faculty researchers. Concurrently, there have been more than thirty books published which were aimed primarily at academic librarians about how to teach and create services surrounding research data management, which are out of scope for this article. However, as data management has shifted from being a novel area for funding and educational development to being an expected and established part of training programmes, much of the early momentum put into developing data management education has been lost. For example, the curricula initially developed to meet training needs after early policy changes in 2010-2012 in the UK and US are no longer being updated or supported and, thus, do not meet current researcher or trainee needs. The original NECDMC, MANTRA and DataOne Curricula are all over a decade old (DataONE 2016, Lamar Soutter Library - University of Massachusetts Medical School 2017, The University of Edinburgh 2022).

In addition to mentoring or single lectures, current data management education for researchers takes the form of online courses, webinars, websites, articles and, occasionally, books. Such books fill an important need for individuals and classes seeking to learn about data management. Frequently, though, data management is not the entire topic of a book, but instead is integrated with other methodological materials or is only tangentially mentioned amongst software-specific guidance. This creates barriers for learners seeking topical or discipline-focused texts and for educators looking for a full semester course textbook.

There is ongoing need for longer form explorations of data management which include updates related to funder, policy and disciplinary changes and which provide a more significant investigation into areas of data management for specific disciplines or broadly for groups like STEM graduate students. To understand the gaps, documentation is needed of the book-length options aimed at researchers or students (as opposed to librarians). In this annotated bibliography, we explore the landscape of books addressing data management for researchers in order to evaluate the available materials and identify areas where updated book-length resources might be beneficial.

We started the bibliography of data management books with a list of known books, as both authors regularly teach on the topic and reference such material. To supplement the existing list, in Summer 2023, we searched our respective academic library catalogues, Amazon.com, Bookshop.org and Google Play for books on data management using the terms including “data management”, “data curation” and “data literacy” along with using subject headings and bibliographies of identified books. We also noted relevant recommended books on individual book pages on the commercial websites. These search lists were repeated and updated in May 2024.

From the initial list of books returned from our search terms, we narrowed the selection down to English-language books on data management written for researchers and research trainees. Books on data management for information technology professions were excluded from the results. Similarly, books about library data services or librarians teaching data management were excluded. We also excluded manuals that solely focused on a specific tool or software instead of data management principles. Once narrowed to books for researchers, we included books no matter the research discipline, either broad (e.g. social sciences) or narrow (e.g. linguistics).

The authors borrowed each book on this short list from our libraries or via interlibrary loan or, where available, viewed an open access copy of the book. We recorded metadata about each book and checked its ebook availability from GOBI Library Solutions (GOBI) (a book and ebook vendor commonly used by academic libraries), Amazon and Google Play. Cost information was collected from GOBI in May 2024; where a book was available in paperback and hardback, only the less expensive paperback price was recorded. After calibrating our coding scheme on a chosen text, the authors divided the final list of books. For each book, the author reviewed the content, assessed the intended audience and coded it by the data management topics covered. Codings were based on stages of the data management lifecycle from the UK Data Archive’s Data Lifecycle (UK Data Service 2024), as well as commonly addressed data management topics such as the lifecycle itself and data management plans. The list of topic codes is as follows:

• Why Manage Data
• The Data Lifecycle
• Creating Data
• Processing Data
• Analysing Data
• Data Documentation
• Data Storage
• Preserving Data
• Giving Access to Data
• Re-Using Data
• Data Management Plans (DMPs)
• Data Policies.

Code definitions are provided in Appendix A. At this point in the coding process, we excluded additional books that only tangentially covered data management in less than about 20% of their content or at least one whole chapter. For the final list of included books, see Table 1; the list of excluded titles is provided in Appendix B.

After coding, the authors summarised each book’s coverage of data management and how it relates to the larger bibliography. Summaries were later normalised between both authors for consistency in coverage and analysis.

Table 1 lists the 17 data management books for researchers and research trainees included in this bibliography, sorted by year. The majority of the books were published after 2011 when the Research Councils UK (RCUK) put forth its Common Principles on Data Policy (RCUK 2014) and the United States of America's National Science Foundation (NSF) implemented its Data Management Policy (National Science Foundation 2024), both of which set off a trend for the increased focus on data management and sharing in research. The pre-2011 titles originate from the same lead author, William Michener, who has been deeply involved in the long-term curation of research data in ecology.

Notable amongst the publications is the diversity of publishing houses and academic presses producing these titles. Many presses have just one book addressing this subject with the exception of SAGE Publications. SAGE has three books in this bibliography, as well as a number of books on the excluded titles list (see Appendix B), demonstrating significant interest in the area of research data management publications.

Availability of each of the 17 books is listed in Table 2. Notably, recent texts are not consistently available for libraries to purchase as ebooks through the GOBI vendor, which creates potential barriers if a library wishes to acquire it for general usage or a faculty member requests the library to purchase it in order to assign it for their class. For individual purchase, Kindle and Google Play ebooks are more consistently available for books published after 2015.

Cost of the books varies widely. Five of the print books (29%) cost over $100, with three (18%) of these over $200. Only six of the 17 books (35%) cost less than $50 to purchase in print. Two of the publications (12%) have open access versions. While libraries may be able to afford more expensive editions, cost is likely a factor for individuals looking to purchase a data management book for his/her own collection.

Table 3 lists the data management topics covered by each of the books. There does not appear to be any trends in topical coverage over time, with the exception that DMPs were not covered prior to 2012; this is understandable as most funding agencies only started to require such plans around that time. Otherwise, books about data management cover most aspects of the data lifecycle, no matter the publication date. Indeed, Michener and Brunt (2000) contains an illustration of a data lifecycle, well before such lifecycles became ubiquitous in teaching data management in the 2010s (Cox and Tam 2018).

The most commonly covered topics were Processing Data (14 of 17, 82%) and Giving Access to Data (13 of 17, 76%). The least commonly covered topics were DMPs (6 of 17, 35%) and Data Policies (6 of 17, 35%), which are not part of the data lifecycle, but are becoming a required condition of much grant-funded research. Several books addressed other related topics, such as data visualisations, managing sensitive data and data ethics, which were not coded here.

The following annotated bibliography includes the citation, target audience of the text and a summary of each book.

Baykoucheva, S. (2015). Managing scientific information and research data. Chandos Publishing.

• Target Audience: Scientists, emphasis on chemistry.
• Summary: Baykoucheva has written a book that broadly conveys the science librarian's many areas of expertise to scientific researchers. This includes not only data management, but also publishing ethics, searching databases, measuring impact (including altmetrics) and leveraging unique identifiers. Data management is mostly covered in one chapter, "Coping with 'Big Data': eScience", which is approached from the perspective of sharing, preserving and citing data, as well as writing data management plans for grant applications. The book will easily become dated given its focus on specific tools and platforms.

Berenson, K. R. (2018). Managing your research data and documentation. American Psychological Association.

• Target Audience: Behavioural, health and social scientists.
• Summary: Berenson's book gives a practical overview of collecting, processing and analysing social science data in an organised and replicable way. The book provides examples based on the SPSS statistical package, but the recommended strategies are not limited to that tool. Berenson lays a solid foundation in data organisation, naming and replicability, but in a limited social science context absent of larger connections to data management and sharing. This book would be valuable for orientating a new research student to the practicalities of collecting and analysing social science data.

Berez-Kroeker, A. L., McDonnell, B. J., Koller, E., & Collister, L. B. (Eds.). (2021). The open handbook of linguistic data management. MIT Press.

• Target Audience: Linguists.
• Summary: In an ideal world, we would have a book like this for every research discipline. This handbook covers the basics of data management for the discipline of linguistics, including everything from the data lifecycle and how to preserve data to the ethics of working with indigenous languages and data's role in the tenure process. This is useful information in and of itself, but only comprises the first quarter of the book. The remainder showcases over 40 case studies of data management from across linguistics, discussing managing audio, video, text, code, databases and more. The result is practical guidance combined with a wide-ranging snapshot of data management within one discipline.

Briney, K. (2015). Data management for researchers: Organize, maintain and share your data for research success. Pelagic Publishing.

• Target Audience: Interdisciplinary researchers, emphasis on STEM.
• Summary: This book is an accessible introductory text to guide students and researchers through basic data management activities. It is unusual in that it addresses data management practically and generally for a broad audience, with an emphasis on STEM research. A significant focus of the book is on data organisation and documentation and the development of a data management plan, with the idea of helping researchers be able to then share or preserve their data more easily. While a solid introduction, it will need to be supplemented with readings to meet evolving policy and practice changes.

Cooper, H. M. (2016). Ethical choices in research: Managing data, writing reports and publishing results in the social sciences. American Psychological Association.

• Target Audience: Interdisciplinary researchers, emphasis on psychology.
• Summary: This book centres on ethical research, a portion of which is the role of data. There is one chapter on data management, a second on misconduct with data and a third on ethics in data analysis; together, these chapters offer an important perspective on the ethical collection and use of research data, a different focus than many other data management books. While the book makes considerations for ethics in human-subjects research and draws deeply from a wealth of American Psychological Association resources, the work is broad enough to be useful to researchers in other fields.

Corti, L., Van den Eynden, V., Bishop, L., & Woollard, M. (2020). Managing and sharing research data: A guide to good practice (Second edition). SAGE Publications.

• Target Audience: Interdisciplinary students, emphasis on social sciences.
• Summary: This book, presented by a team from the UK Data Services, is designed to assist students and early career researchers in understanding the activities of research data management throughout the data lifecycle. The emphasis, rather than on any specific discipline, is on general good data practices, such as formatting and organising data, using non-proprietary formats, considering ethical limitations with de-identification. It is one of the only books that goes into de-identification and risk of disclosure. Examples of policy and case studies are predominantly from the UK, with additional European issues also addressed – only minimal examples are given related to the United States.

Herzog, D. (2016). Data literacy: A user’s guide. SAGE Publications.

• Target Audience: Undergraduate students, emphasis on journalism, communications and computer science students.
• Summary: Herzog’s book is aimed at teaching undergraduate students and provides a unique exploration of data literacy through the lens of reusing data to meet journalistic or other communication needs. Rather than emphasising capturing data or advanced analytical techniques, instead, the author is focused on finding and using public datasets in order to help students learn to navigate government or other public datasets for the first time. Data management is touched upon, but from the perspective of a secondary data user. This is a very introductory book, providing some simple data cleaning, munging and visualisation ideas.

Hoffmann, J. P. (2017). Principles of data management and presentation. University of California Press.

• Target Audience: Social and behavioural scientists.
• Summary: Hoffman’s book offers a social and behavioural science perspective on data management and presentation, providing examples using the software Stata, SPSS and SAS. While data management principles are mostly limited to one chapter in the book, the book does provide an entire chapter on finding and reusing secondary data – a topic that infrequently receives full attention, likely due to rapid changes in availability. This book also provides fundamentals of data presentation in tables and figures, a useful complement to standard data management topics.

Michener, W. K. (1986). Research data management in the ecological sciences. University of South Carolina Press.

• Target Audience: Ecologists.
• Summary: This book is a summary of papers presented at a symposium held in 1984 to "describe standard data management techniques" along with current technology and future goals. The book is divided into a series of sections including an overview of data management in the field of ecology, data discovery, database development and challenges related to data quality assurance, working with GIS, hydrological and meteorological data and working with then-current analytical tools. A final section summarises and looks ahead to the future. The book maintains relevancy, with only the names of tools needing to be exchanged for current ones, while the challenges and issues with data standards and processes continue to remain.

Michener, W. K., & Brunt, J. W. (2000). Ecological data: Design, management, and processing. Blackwell Science.

• Target Audience: Ecologists.
• Summary: "Ecological data: design, management and processing" continues to be highly relevant to current data management concerns. While the book's references to technology are certainly out of date, the principles behind how data should be managed and management choices should be made are sound. The book's limited focus is on ecological data, but it covers a broad range of data management topics within that purview, even providing a very forward thinking chapter on how to create a data archive for long-term data preservation and sharing (not surprising given the editors' involvement with the LTER data archive project). This title remains a useful touchstone on data management.

Michener, W. K., Brunt, J. W., & Stafford, S. G. (1994). Environmental information management and analysis: Ecosystem to global scales. Taylor & Francis.

• Target Audience: Environmental researchers, emphasis on forest health, GIS and global biosphere.
• Summary: This book explores a variety of aspects related to environmental research including currently available databases, conducting QA/QI, issues with data sharing, creating large databases and models, working with GIS and other modelling software and emerging analysis techniques. Data management is embedded across different case studies not so much as guidance to researchers, but exploring the current challenges and current state of affairs.

Paulus, T. M., & Lester, J. N. (2021). Doing qualitative research in a digital world. SAGE Publications.

• Target Audience: Interdisciplinary researchers, emphasis on qualitative methodology.
• Summary: This book, in its second edition, would easily serve as the foundational text for a social science research methods course. The authors provide a broad overview of activities surrounding the conduct of qualitative research including using analysis software, creating paperless literature reviews, gathering data in digital spaces and using a variety of tools and then sharing findings. Through vignettes, different methods of using software or analysis are introduced and explored. Data management topics focus mostly on collecting or gaining access to data and performing analysis using standard qualitative software. Data management, sharing and reuse are briefly touched upon, but are not addressed in a comprehensive enough manner to prepare students to fully manage their data.

Pryor, G. (2018). Managing research data. Facet Publishing.

• Target Audience: Librarians, interdisciplinary researchers and university administrators.
• Summary: Pryor's foundational edited collection provides an overview of the current state of research data management in the early 2010s. This collection focuses on available services and perspectives from the UK (specifically England and Scotland) with additional details from the United States, Australia and Europe. While the primary audience of the book, as described by the editor, was information professionals, the book has been broadened to have a more general research focus. Limitations of this book include the geographic locations. It is also reflective of the time period of its publication, which predates many significant funder and policy changes in the past decade.

Smalheiser, N. R. (2017). Data literacy: How to make your experiments robust and reproducible. Elsevier Academic Press.

• Target Audience: Undergraduate STEM students.
• Summary: Smalheiser’s text is focused on providing practical examples to support undergraduate STEM students who are developing good experimental practices. The focus is narrowly centred around research in a basic science lab. The text addresses experimental design, quantitative data exploration, basic statistics, sharing data and emerging changes in scientific publishing. Aspects of data management show up in the various chapters, but are not a specific focus.

Sibinga, C. T. S. (2018). Ensuring research integrity and the ethical management of data. IGI Global, Information Science Reference.

• Target Audience: Interdisciplinary research, provides case studies with global perspective.
• Summary: This contributed volume is primarily a research ethics book that touches on data management, meaning that ethics provide the framework through which data management strategies are contextualised. Data management is discussed in about half chapters of the book centre (the remainder focuses on ethical research), with data strategies mainly covering data collection, processing and analysis – though one chapter covers documentation and informed consent. One of the strengths of this book is the international nature of the contributors, who write from four different continents and provide viewpoints outside of the North American and European perspectives that are more common in books on data management.

Weidmann, N. B. (2023). Data management for social scientists: From files to databases. Cambridge University Press.

• Target Audience: Social scientists.
• Summary: This book is about data processing in the social sciences, which is described in the book as the data lifecycle phase between data collection and data analysis. This primarily means educating the reader on the structure of many types of social science data, from spreadsheets to databases and spatial data to textual data. There is less focus on tools for data processing, though the author does review the basics of data processing in R and R's tidyverse library. Tucked in between the descriptions of the theoretical structure of data and corresponding real-world examples are bits of knowledge on other data management topics, though the book does not cover data management comprehensively.

Zozus, M. (2017). The data book: Collection and management of research data. CRC Press.

• Target Audience: Interdisciplinary researchers, emphasis on data-heavy disciplines.
• Summary: Zozus offers a comprehensive data management book that has a strong theoretical and technical foundation. For example, the book covers different models for conceptualising "data", various international data standards and technicalities for workflow diagrams, in addition to more regularly covered data management concepts. This book fills the niche of a hefty, information-science perspective on managing research data.

Despite the extent to which data management has become a critical part of grant applications and obligations, we could only identify 17 books which met our criteria for inclusion in this bibliography. Comparably, in library and information science alone, we are aware of nearly double that number of data management-in-libraries titles published within the past decade. Further, even for the books which are included, data management is the central focus in only 10 titles. Researcher and research trainee books frequently consider “data management” to be mostly about data analysis rather than any of the other activities of the data lifecycle. This is supported by the fact that creating, processing, analysing, documenting and providing access to data were the most commonly addressed topics in these books (in addition to why researchers should manage data in the first place). This limits the utility of these books to teach good data management practices or to help researchers and research trainees identify discipline-specific best practices.

Another concern is that only four of the 17 data management books reviewed were published in the past five years, while there has been significant changes in funder and data policy, which is likely to further evolve drastically with the addition of interest in datasets to underlie data science, machine learning and other algorithmic research techniques. All of these changes drive a need for books about data management for researchers and research trainees which are likely to address foundational issues comprehensively and outlast the availability of resources such as courses, website lists and other online materials which are much more subject to change. The current trend towards developing Open Education Resource style books might provide a mechanism that would combine both the comprehensiveness and narrative of a book format with the opportunity to make continual updates, but the sustainability for this has not been investigated.

There is great variety in the disciplinary coverage of these books. Several books have a limited disciplinary focus – for example, William Michener wrote comprehensively about data management in the ecological sciences – which are of great use in their individual subject areas. While there are a number of books which focus on data management in the social sciences, the same is not replicated across life or basic sciences or engineering. Additionally, as various humanities disciplines continue to play catch-up in the area of data management, books should be published for these researchers and trainees. Future data management books for researchers should take these disciplinary gaps into account.

There is also an educational need for updated cross-disciplinary material to serve as textbooks and introductions to data management. The most recent generalist book is the second edition of Managing and sharing research data by Corti et al. (2019). An updated introductory text for graduate students is overdue. For undergraduates, there is no currently available foundational text on data management, though Herzog (2016) and Smalheiser (2017) cover some data management topics for this audience. While market research has not been investigated for an undergraduate text, the need has been documented beginning over fifteen years ago (Partlo 2009, Partlo 2015). With the continual growth of data handling at the graduate level and in the workforce, there is need for undergraduate-focused data management education materials. The COVID-19 pandemic may have delayed publishing of such textbooks or directed writers to other topics but, due to the rapidly evolving policy landscape, there is the risk of reliance on older texts on data management by researchers and research trainees.

Finally, cost is a significant issue which future authors and publishers must take into account cost. Most books in this bibliography are cost prohibitive for an individual buyer. To counter this, we hope to see continuation of the recent trend of offering open access versions of data management books.

A review of the available books that address research data management reveals a limited number of books which are aimed at an academic researcher and research trainee audience and less than a dozen that focus on the topic. There are significant opportunities to create disciplinary-specific introductory texts that would support either classroom instruction and individual learners in understanding the history and foundation of data management in their respective fields. Additionally, introductory textbooks about data management are needed to provide up-to-date information about this continually evolving field.

Why Manage Data: The book addressed why data management is an important topic and why researchers should care to perform data management activities.

The Data Lifecycle: The book defined a data lifecycle (i.e. a model, often shown as a diagram, that demonstrates how data move through multiple stages over the course of its lifetime); lifecycles could be cyclical or linear and there were no requirements for specific categories to occur in any lifecycle.

Creating Data: The book discussed discipline or methodology-specific data management activities used while collecting data for the research project.

Processing Data: The book included information on how to properly handle data while they were being cleaned and prepared for analysis.

Analysing Data: The book discussed strategies for analysing data to answer a specific research question. This might include statistical analysis, qualitative coding or other techniques.

Data Documentation: The book addressed how information about the data and their collection and analysis, should be recorded to support transparency and reproducibility of the research process.

Data Storage: The book gave guidance on how data should be stored physically or electronically and backed up, potentially including information on how to prevent loss or unauthorised access to the data.

Preserving Data: The book included content on how to maintain data after the end of the active research project, with preservation being conducted either by the original researchers or by another entity.

Giving Access to Data: The book described how to properly share data with others, including informal sharing with other research teams up through sharing data with the public.

Re-Using Data: The book covered how to find data generated by other research teams, use such data and/or give credit for such reuse.

Data Management Plans (DMPs): The book defined “data management plans”, described their place within the data lifecycle and/or gave guidance on how to write a plan.

Data Policies: The book outlined policies that can apply to research data, such as funder policy, institutional policy and/or journal policy.

Bazeley, P., & Jackson, K. (2013). Qualitative data analysis with NVivo (Second edition). SAGE Publications.

Coffey, A., & Atkinson, P. (1996). Making sense of qualitative data: Complementary research strategies. SAGE Publications.

El-Mazny, A. (2014). Biomedical statistics: Research methods and data management. Createspace Independent P.

Fleming, G., & Bruce, P. C. (2021). Responsible data science: Transparency and fairness in algorithms. John Wiley & Sons, Incorporated.

Fogarty, B. J. (2023). Quantitative social science data with R: An introduction (Second edition). SAGE Publications.

Friese, S. (2019). Qualitative data analysis with ATLAS.ti (Third edition). SAGE Publications.

Morrow, J. (2021). Be data literate: The data literacy skills everyone needs to succeed. Kogan Page Limited.

Perry, S. M. (Ed.). (2018). Maximizing social science research through publicly accessible data sets. Information Science Reference.

Rensi, G., & Claxton, H. D. (1972). A data collection and processing procedure for evaluating a research program. Pacific Southwest Forest and Range Experiment Station, Forest Service, U.S. Dept. of Agriculture.

Richards, L. (2021). Handling qualitative data: A practical guide (Fourth edition). SAGE Publications.

Salmona, M., Kaczynski, D., & Lieber, E. (2020). Qualitative and mixed methods data analysis using Dedoose: A practical approach for research across the social sciences. SAGE Publications.

Sommer, R., & Sommer, B. B. (2002). A practical guide to behavioral research: Tools and techniques (Fifth edition). Oxford University Press.

Thomson, R. E., & Emery, W. J. (2014). Data analysis methods in physical oceanography (Third edition). Elsevier.

Kristin Briney is the author of one of the books in the bibliography. She did not code or review her own book for this article.