RDM Weekly - Issue 027
A weekly roundup of Research Data Management resources.
Happy New Year and Welcome to Issue 27 of the RDM Weekly Newsletter!
It was tough taking two weeks off from the newsletter. Over that time I collected so many resources that I wanted to share, making it really difficult to choose what to include this week!
If you are new to RDM Weekly, the content of this newsletter is divided into 3 categories:
✅ What’s New in RDM?
These are resources that have come out within the last year or so
✅ Oldies but Goodies
These are resources that came out over a year ago but continue to be excellent ones to refer to as needed
✅ Just for Fun
A data management meme or other funny data management content
What’s New in RDM?
Resources from the past year
1. Open Science: My Insights Into Data Sharing, Preregistration, and Replication
After a decade of implementing open science practices as a principal investigator, mentor, data repository founder, and Editor-in-Chief, Sara Hart has learned that the question isn’t whether researchers should adopt these practices, but how to adapt them meaningfully. This commentary, based on a talk given at the 2024 CSBBCS conference, argues for two key principles: first, open science implementation must be context-dependent rather than one-size-fits-all, and second, practical research realities require flexible approaches to idealized policies.
2. 50-State Comparison: State Data Dictionaries and Manuals
States often publish data dictionaries or manuals that showcase which data elements their systems collect. This resource links to every public education-to-workforce data dictionary, data manual, or related document the Education Commission of the States identified in early care and education, K-12, postsecondary and workforce data systems across all 50 states and the District of Columbia. Not only is this resource useful if you work with these type of data, but it’s also insightful for seeing the various ways states document and standardize their data.
3. Building Trust Through Data Accountability
In this article from the Stanford Social Innovation Review, the authors argue that a values-driven, accountable approach to data isn't optional—it's essential. There is a crisis of distrust that extends far beyond individual privacy concerns or interactions on social media. It reaches into the civic data systems that shape our everyday lives. The authors believe that values must guide how we collect, interpret, and use data and that accountability agreements are how we put these values into practice.
4. Data Nudges of 2025: Year in Review
I recently came across the monthly Data Nudges newsletter from Research Data Service at the University of Illinois Urbana-Champaign and it is filled with helpful information. Most recently the newsletter included a year in review where they discussed their three main themes for the year: Data Ethics and Policy, Accessibility and Rescue, and Practical Management, and they linked to associated newsletters. You can view all past newsletters in their archive.
5. Taming Gnarly Nested Data With purr::modify_tree
This post is about an R function from the {purrr} package called modify_tree. Like many other {purrr} functions, it’s a function for processing lists. But unlike functions like map and friends that move through lists iteratively, modify_tree traverses its input recursively (helpful for processing nested lists — think JSON). The article should give readers a better understanding of what modify_tree is and what it does, as well as what the arguments are and when each should be used.
6. Can a CC License Constrain Fair Use or Other Copyright Limitations or Exemptions?
For all their ubiquity, CC licenses are also misunderstood. The very utility that makes them powerful — their ability to simplify and pre-authorize use — can obscure a key fact: a CC license can only grant rights that users otherwise would not have. It cannot restrict what is already allowed under fair use, right of first sale, or other copyright exceptions or limitations. This article explores how the distinction is being lost in public and academic discussions of open access, particularly in debates over the use of scholarly materials to train large language models (LLMs).
7. How to Use Notion for Academic Research
I’ve always said that project management and data management are intertwined and as such, finding tools that improve our ability to manage projects is also a big win for data management. In this blog post, Robert Kubinec walks through how he uses Notion to help coordinate his projects and provides tips on ways this tool may benefit your work as well. Along similar lines, Kelly Williams also recently gave a talk on how she uses a different tool, Asana, to help manage her projects, coordinate team efforts, and standardize documentation. You can learn more about her experiences in her slide deck.
Oldies but Goodies
Older resources that are still helpful
1. Data Cleaning for Data Sharing
This three-part blog post series was originally created in honor of Love Data Week 2023. Part 1 of the series discusses how to standardize the data cleaning process and reviews common steps in a cleaning process. Part 2 (Creating a Data Cleaning Workflow) discusses how to organize a data cleaning process in a more standardized way that ensures your process is reproducible and your data is reliable. Part 3 (Cleaning Sample Data in a Standardized Way) pulls it all together by providing a hypothetical cleaning scenario and walking through all of the recommended steps using an example dataset.
2. FAIR Research Data Management Tutorial
This interactive course, from the LMU Open Science Center, is centered around the FAIR principles and their practical application in managing your research data efficiently. This tutorial will artificially advance your experience of navigating other researcher’s data, thereby hoping to point out good and bad practices that shall soon inspire your own data management. The tutorial evolves around four published articles and their datasets and includes associated hands-on exercises.
3. Datasets for Data Cleaning Practice
Looking for datasets to practice data cleaning or preprocessing on? Look no further!Each of these datasets needs a little bit of TLC before it’s ready for different analysis techniques. For each dataset, the author of this blog post included a link to where you can access it, a brief description of what’s in it, and an “issues” section describing what needs to be done or fixed in order for it to fit easily into a data analysis pipeline.
4. Data Management Manual for Clinical Trials Units New Starters
This manual was created in an effort to identify and document standard data management processes between three UKCRC Clinical Trial Units focusing on new starters. It provides tons of great content that could be replicated in other organizations. The manual covers an overview of clinical trials, data management regulations, database design, quality control, and more. There are links to many other helpful resources from this network on the UKCRC CTU website, including a thorough REDCap Manual.
Just for Fun
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