2025 Wrap Up

We’ve thankfully reached the end of 2025. It’s been a rollercoaster of a year, with lots of ups and downs both personally and professionally. On the professional side, I’ve had some really solid highlights, so it seems fitting to review them in a blog post.

Publications

I had a very good publication year. My third book came out in December:

I also published two articles: a bibliography of data management books for researchers (a list that I recently made out of date); and an article about developing the exercises in my new book.

And I almost forgot about the book chapter that came out this year (books take forever between the writing and publication):

Public Access

I spent a good part of 2025, and plan to spend a good part of 2026, supporting the new public access policies from US funding agencies. As a librarian, it’s been incredibly frustrating to be caught between funder requirements for public access and publisher open access policies that often conflict. I think that most people agree that the current scholarly publishing industry is too expensive and isn’t working, but it’s really messy to be working in this area as we try to transition to something better. For everyone’s sake, I hope 2026 is easier in this area.

ASL

I’m currently 80% done with a certificate in American Sign Language (ASL); I have to take one more class this spring, ASL 4, in order to finish. I think it’s a great idea for someone in public service to know how to communicate with Deaf patrons, which is why I’m taking advantage of my university’s tuition assistance to work on this certificate. I don’t think I’ll ever be fluent in ASL, but I’m definitely more comfortable communicating in this language and have enjoyed learning about Deaf culture.

Looking Forward

I’m ended 2025 with some good news: I just signed a contract to write my fourth book. I’ve already drafted a few chapters and will share more information once I draft more. I can tell you it’s about data management and sharing, which is probably not a surprise.

I hope you have a restful holiday season and a wonderful 2026.

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The Data Management Workbook

I am thrilled to share that my book, The Data Management Workbook, will be published next month on December 2, 2025 by Pelagic Publishing.

The Data Management Workbook is a collection of 24 hands-on exercises to help you improve your data management. For example, if you learned about the concept of file naming conventions in my first book, Data Management for Researchers, an exercise in the Workbook actually walks you through the steps to create a customized file naming convention for your research. The goal is for the book to help you implement data management strategies that fit your research workflows.

The Workbook is the traditionally published and updated version of educational resource, The Research Data Management Workbook, which I blogged about previously. Do note that the downloadable versions of the old version have gone away, though the online edition is still up. This published edition not only is updated and polished, but also contains six completely new exercises. I’m really pleased with the updated version of the Workbook and I enjoyed spending the extra time and effort to make the exercises that much better for everyone.

Do you want a copy of The Data Management Workbook? You can order the book from the publisher, Pelagic, or find it on Amazon. Or encourage your library to buy a copy, so multiple people can enjoy it.

I hope you enjoy this book and it helps you improve your data management!

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Data Management for Collaborations

I’ve written a lot about the fundamentals of data management, usually from the viewpoint of a single researcher trying to make their data a little easier to deal with. However, a lot of research is collaborative, so it’s worth taking a little time to detail the data management practices that benefit collaborative research.

I actually co-wrote a paper a few years ago about the data management processes for a large collaborative project that I was a member of, the Data Doubles project. While a big part of the article centered on the project’s living data management plans (DMPs), I want to get explicit about some of the data management strategies that were particularly helpful:

  1. Common storage
    • Collaborative research requires a common storage area where researchers can expect to find shared files. It’s important that everyone knows where this storage is and has access to it.
  2. File organization
    • Shared files are even more likely to be disorganized than data files from a single researcher. It’s critical for collaborators to work out a system for organizing files so that everyone knows where data is expected to be within the storage system. A file organization system will save everyone so much time when searching for a specific file, especially if you didn’t create it yourself.
  3. File naming
    • File naming is the last piece of the storage-file organization-file naming trifecta that will help files move seamlessly between collaborators. If everyone knows and uses a shared file naming convention, it becomes easy for anyone to identify files at a glance and know what data has already been collected. Have someone propose a naming scheme then refine it as a group to help get buy-in.
  4. Documentation
    • I’m a big fan of having an index/inventory for data files when doing collaborative research. Of course you’ll need other project documentation like standard research notes, but an index provides a couple extra benefits: it allows for an alternate method to discover and locate data files, and allows collaborators to track the process of data collection. A spreadsheet works great for an index.
  5. Permissions
    • It’s extra important in collaborative research to be clear what people can and cannot do with the project data. This might be as formal as a Data Use Agreement but could also just be a discussion about how project members should ask for permission before reusing data for other research projects.
  6. Living DMP
    • The living DMP basically collects all of the above into one document to help collaborators remember data management decisions. The document should be reviewed and updated occasionally and is especially useful for onboarding new project members.
  7. Data manager
    • If the project is large enough, it’s worthwhile to designate someone as the “data manager.” This person might: propose the file organization and naming systems; update the data index; draft the living DMP; remind everyone of data management strategies; and clean up disorganized data, as needed. Not every project needs a data manager, but it’s usually good to make data management someone’s responsibility or else it might be no one’s responsibility and never get done.

I’m sure there are more data management strategies that are useful for collaborative projects, but I think these are some of the most basic. What have you found helpful when managing data in collaborative research?

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Data Management Books for Researchers

I’m inordinately proud of my first book, Data Management for Researchers, which is officially 10 years old this month! A lot has changed in the last decade, to the point where I really do need to update this book (though that is going to have to wait due to other exciting book news).

While I will always be partial to my own book, my colleague Abigail Goben and I were curious about what other data management books are available for researchers right now. The answer to this question can be found in an annotated bibliography that we published earlier this year.

Our article describes 17 data management books published between 1986-2023 that fully or partially cover data management strategies for a research audience. We list prices, open access availability, target audiences, and topics covered. The article also provides a brief summary of all 17 books, to help you decide which book is right for your needs. Basically, it’s a high-level overview of all of the books available for researchers that cover data management strategies.

As an author of one of the books on the list, I’m happy to see such a variety of offerings in the area of data management, though I know that there are gaps yet to be filled. If my book doesn’t speak to your personal research needs (or you’re just curious what’s available), I encourage you to check out the bibliography to find the right book for you!

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Indigenous Data and CARE

I just finished reading An Indigenous Peoples’ History of the United States, which was a great overview of the history of indigenous-US relations. Warning: the book is difficult to read at times, as it describes the long history of genocide against indigenous nations in America. That said, it’s important for non-indigenous, especially white, people to recognize this genocide and not look away from this history.

The brutal history of indigenous-US relations (as well as similar relations between other colonial powers and the indigenous people inhabiting the lands colonized) creates a long shadow that impacts many modern topics, including data management and sharing. This is especially the case when managing and sharing data pertaining to indigenous people, their cultures, and their knowledges of the world.

Historically, research about indigenous people, their cultures, and traditional knowledge has been exploitative, with a power differential between the [typically white] researchers and the indigenous people being studied. Researchers extracted knowledge from tribes with little-to-no benefit – and often harm – to the tribes themselves. With the rise of modern indigenous rights movements, tribes have been striving to correct such power imbalances and assert their sovereignty, including in the areas of research and data.

Indigenous data is too broad of a topic to fully cover in a single blog post (I’ll instead refer you to the book Indigenous Data Sovereignty and Policy, which is available Open Access), but at the very least we need to discuss “CARE”. We hear a lot about FAIR data (Findable, Accessible, Interoperable, and Reusable), but the CARE Principles for Indigenous Data Governance are equally important.

The CARE principles were developed by a global alliance of indigenous groups and are made up of 12 principles in four areas:

  • Collective benefit,
  • Authority to control,
  • Responsibility,
  • Ethics.

The Principles recognize:

  • that indigenous ways of knowing are different than western scientific knowledge systems;
  • that research must be conducted in partnership with tribes, from the very beginning of a project;
  • that tribes must benefit from research, both in terms of individual development as well as using data for tribal governance;
  • that tribes have the authority to control what happens to the data collected;
  • and more.

I encourage you to fully read the CARE Principles to better understand all of the included guidance. In short, conducting research on and about indigenous people, their cultures, and their knowledge systems must be handled in a very different way than how research methodology is typically taught in U.S universities. This is because such research must be collaborative with tribes, consider tribal world views, and be disseminated in ways beneficial to tribes.

While the CARE Principles do not apply to all types of research, the ideas behind the CARE Principles are taking root in many institutions that support research using indigenous data. For example, the U.S. National Institutes of Health (NIH) sometimes funds research pertaining to indigenous peoples. When the NIH Data Management and Sharing Policy went into effect in 2023, the policy had a separate policy supplement on the Responsible Management and Sharing of American Indian/Alaska Native Participant Data, as the usual terms of the policy did not apply to this type of data. This is just one instance of how we are starting to treat indigenous data differently, and with a lot more care, than other types of research data.

If you in any way touch indigenous data, I encourage you to read the full CARE Principles and dig deeper into the ways that working with this type of data is different from how other types of data are managed and shared.

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Rescheduled Data Accessibility Webinar

I unfortunately had a medical event right before November’s scheduled figshare webinar on “Making repositories and data digitally accessible.” I ended up having surgery in December and now that I’m feeling much better, we have rescheduled the webinar for February.

The webinar is on now on Tuesday, February 11, 2025 at 8am PT / 11am ET / 4pm GMT. You can register here for the webinar.

I hope you can join us at the new time.

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