Foundational Practices of Research Data Management

If you’re a regular reader of my blog, you’ll know that one of my goals is for all researchers to adopt the basic data management practices that make conducting research easier. I’ve written a whole book on data management, done videos, created checklists, written numerous blog posts, etc., but it will never be enough until researchers are regularly taught these skills. Until that point, I’ll keep sending the gospel of data out in the world in different formats, hoping to reach new audiences.

My latest iteration of educating about the principles of data management is in the form of a research article in RIO. I really like the article format because it’s just enough space to provide a broad overview of the basic data management practices. And if readers want to learn more, we’ve provided a handy list of citations!

The new article covers 10 practices of data management that my coauthors and I consider to be foundational:

  • Practice 1: Keep sufficient documentation
  • Practice 2: Organize files and name them consistently
  • Practice 3: Version the Files
  • Practice 4: Create a security plan, when applicable
  • Practice 5: Define roles and responsibilities
  • Practice 6: Back up the data
  • Practice 7: Identify tool constraints
  • Practice 8: Close out the project
  • Practice 9: Put the data in a repository
  • Practice 10: Write these conventions down [in a data management plan]

This is by no means the complete scope of data management but rather a good introduction. Honestly, if you implement all ten practices into your research, you’re going to be doing very well with your data.

So if you or a peer are looking for a general introduction to research data management, check out my new article “Foundational Practices of Research Data Management.”

Citation: Briney KA, Coates H, Goben A (2020) Foundational Practices of Research Data Management. Research Ideas and Outcomes 6: e56508.

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Book Review: How Charts Lie

How Charts Lie cover image

Continuing in my pandemic reading of data books, next up is “How Charts Lie: Getting Smarter about Visual Information” by Alberto Cairo. (I didn’t plan to be a predominately book review blog, but I need a way to channel the pandemic anxiety, so here we are.)

This book is a little different than other visualization books I’ve been reading because it focuses on visual literacy (which Cairo calls “graphicacy”) instead of chart design. Because charts appear by their nature more authoritative (they show “facts” and make such information easy to understand), we need to train ourselves to critically assess the information displayed. This book provides the framework for an individual to engage with and dissect the charts we regularly see in the news and on social media and decide what’s accurate.

Cairo uses his experience as a chart designer and chart consumer to break down the major ways that charts lie. Each type of lie gets covered in its own chapter in the book:

  • Poor design
  • Displaying dubious data
  • Displaying insufficient data
  • Concealing or confusing uncertainty
  • Suggesting misleading patterns

You’ll notice that these mistakes aren’t all about chart design; many chart issues concern the data that’s being visualized, including everything from displaying percentages instead of absolute numbers on a map to vetting data sources. Cairo provides ways to think through the many mistakes that are made in data selection, because even the prettiest and easiest-to-read chart can lie to us by getting the data wrong .

What’s nice about the book is that it doesn’t assume that charts are intentionally lying to us. Sometimes designers make honest mistakes and sometimes trade-offs have to be made. Cairo walks the reader through exemplar visualizations and shows us how different choices affect the accuracy and design of the chart. By discussing the data selection and visualization decision process as well as showing how these choices affect the final design, Cairo provides the reader with the mental scaffolding to critically assess charts.

As with any data book, Cairo uses plenty of examples throughout this book. What I found interesting is how many of these examples were drawn from recent politics; the book actually starts by dissecting a graphic that Donald Trump shared in April 2017. While I appreciate the American cultural touchstones (and it’s nice to rage at some of the bad charts we’ve seen in recent years), I do worry that this book will lose some of its relevance over time.

Overall, this is a good book for any information consumer to read and will also help visualization designers learn to avoid pitfalls and assess design trade-offs. I would also recommend it to my fellow librarians who do information literacy instruction; the visual literacy discussed in this book is a perfect compliment to the work we’re already doing with students around assessing text-based resources.

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Project Close Out Checklist for Research Data

Researchers tend to think about data management at key times during a project, such as when writing a data management plan for grant funding and when preparing for data collection. But there’s one other critical time for data management in the project lifecycle: when a project ends and/or a researcher leaves the project.

I’ve actually blogged about project close out twice before (here and here) because it’s an area where I’ve had my own successes and failures. I’ve lost data in projects where I didn’t do data close out and have saved myself several large headaches on projects where I did close out. But here’s the important thing: project close out isn’t actually that difficult, it’s just that there is hardly any guidance on how to do it.

Enter the “Project Close Out Checklist for Research Data“! Born out of a discussion with Jonathan Petters and Abigail Goben at the RDAP Summit in 2020, this checklist describes a range of activities for helping ensure that research data are properly managed at the end of a project. Activities include: making stewardship decisions, preparing files for archiving, sharing data, and setting aside important files in a “FINAL” folder.

Two versions of the checklist are available: a Caltech Library branded version and a generic editable version. I’m sharing the checklist under a CC BY license, so please reuse and remix with attribution.

My hope is that this checklist will help researchers be able to use their data well into the future!

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Recent Publications

It’s always nice to have new publications to put up on the blog, especially when they’re all things I’ve been working on for at least a year. If you’re interested in privacy and data and libraries, I hope you check them out!

Briney, Kristin, Becky Yoose, John Mark Ockerbloom, and Shea Swauger. “A Practical Guide to Performing a Library User Data Risk Assessment in Library-Built Systems.” Digital Library Federation, May 2020.

Libraries collect data about the people they serve every day. While some data collection is necessary to provide services, responsible data management is essential to protect the privacy of our users and uphold our professional values. One of the ways to ensure responsible data management is to perform a Data Risk Assessment. A Data Risk Assessment is a process of identifying data the library collects about users, understanding how it manages that data, identifying the risks associated with that data, and then selecting an appropriate risk mitigation strategy.

Jones, K. M. L., Asher, A., Goben, A., Perry, M. R., Salo, D., Briney, K. A., & Robertshaw, M. B. (forthcoming). “We’re being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education. Journal of the Association for Information Science and Technology.

Higher education institutions are continuing to develop their capacity for learning analytics (LA), which is a sociotechnical data‐mining and analytic practice. Institutions rarely inform their students about LA practices, and there exist significant privacy concerns. Without a clear student voice in the design of LA, institutions put themselves in an ethical gray area. To help fill this gap in practice and add to the growing literature on students’ privacy perspectives, this study reports findings from over 100 interviews with undergraduate students at eight U.S. higher education institutions. Findings demonstrate that students lacked awareness of educational data‐mining and analytic practices, as well as the data on which they rely. Students see potential in LA, but they presented nuanced arguments about when and with whom data should be shared; they also expressed why informed consent was valuable and necessary. The study uncovered perspectives on institutional trust that were heretofore unknown, as well as what actions might violate that trust. Institutions must balance their desire to implement LA with their obligation to educate students about their analytic practices and treat them as partners in the design of analytic strategies reliant on student data in order to protect their intellectual privacy.

Jones, K. M. L., Briney, K. A., Goben, A., Salo, D., Asher, A., & Perry, M. R. (2020). A comprehensive primer to library learning analytics practices, initiatives, and privacy issues. College & Research Libraries, 81(3), 570–591.

Universities are pursuing learning analytics practices to improve returns from their investments, develop behavioral and academic interventions to improve student success, and address political and financial pressures. Academic libraries are additionally undertaking learning analytics to demonstrate value to stakeholders, assess learning gains from instruction, and analyze student-library usage, et cetera. The adoption of these techniques leads to many professional ethics issues and practical concerns related to privacy. In this narrative literature review, we provide a foundational background in the field of learning analytics, library adoption of these practices, and identify ethical and practical privacy issues.

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