RDM Weekly - Issue 003
A weekly roundup of Research Data Management resources.
Welcome to Issue 3 of the RDM Weekly Newsletter!
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. Webinar – Once Upon a Data Steward
This Webinar (July 10th, 2025, 8-9am CST/2-3pm BST), presented by Dr. Samantha Pearman-Kanza, explores the growing importance of data availability and management in modern research. The session will also introduce the emerging role of the Data Steward, a new type of digital Research Technical Professional (dRTP) who ensures data quality and usability throughout its lifecycle. The webinar also unveils CaSDaR (Careers and Skills for data-driven Research), a new four-year initiative designed to support and empower Data Stewards as central figures in the research lifecycle.
2. Sharing Qualitative Data
In this presentation, Crystal Steltenpohl, with the Center for Open Science, provides insight into how to respond to data privacy and sensitivity concerns regarding sharing qualitative data.
3. Recoding (Column) Values in Python
In this post, Ama Nyame-Mensah shows us the power of using a codebook (or data dictionary) to help streamline your data wrangling process by acting as a map or rulebook for your data. This post reviews how to use your codebook to more efficiently recode variable values in Python. Ama has written a complementary post about recoding in R as well!
4. Enabling Data-Driven Collaborative and Reproducible Environmental Synthesis Science
This manuscript shares the lessons learned from providing scientific computing support to over 600 researchers and discipline experts, helping them develop reproducible and scalable analytical workflows to process large amounts of heterogeneous data.
5. Podcast – Research Data: Handle With Care
This podcast explores research data management and its related fields. Created as a part of an NFDI4Health-sponsored workshop, it highlights the practical benefits of responsible and sustainable data management for researchers. Through expert insights and real-world examples, the podcast covers best practices, challenges, and strategies to make data Findable, Accessible, Interoperable, and Reusable (FAIR). Whether you're experienced or new to research data management, this podcast offers valuable perspectives on managing research data effectively and the benefits it brings to your work.
6. NIH Strategic Plan for Data Science
The National Institutes of Health recently released their 2025-2030 Strategic Plan for Data Science. The completed plan articulates NIH's official position on critical priorities including next-generation data infrastructure, interoperability standards implementation, emerging technology integration, and development of a multifaceted data science workforce prepared for tomorrow's challenges. The plan outlines 5 main goals, with Goal 1 focusing on doubling down on supporting and effectively implementing the NIH Data Management and Sharing Policy. You can read a brief summary of the 2025-2030 plan in this June blog post.
Oldies but Goodies
Older resources that are still helpful
1. {excluder} R package
If you have ever exported data from Qualtrics and been annoyed by the multiple header lines of metadata, this is the package for you. Functions such as remove_label_rows() can help you take care of that. The goal of {excluder} is to facilitate checking for, marking, and excluding rows of data frames for common exclusion criteria. This package applies to data collected from Qualtrics surveys, and default column names come from importing data with the {qualtRics} package.
2. Harvard Strategic Data Project Coding Style Guide
This style guide provides standard conventions for organizing a data project, including naming files, organizing folder structures, and naming variables. The document also includes a section on coding guidelines. While the guidelines are specific to Stata, the general practices are beneficial for working with any program. This document is a great example for how teams may consider setting up their own Style Guide.
3. R Data Wrangling Cheat Sheet
This guide, created by Earl Duncan, provides examples of common data wrangling tasks relying on two main R packages, {dplyr} and {tidyr}. It’s filled with tons of great examples of data cleaning tasks we encounter in much of our work.
4. Basic Data Entry Using Excel
In this brief video tutorial, Darren Dahly shows us how to take a paper form used for data collection, and set up an effective data entry system in Excel that results in a reliable, machine-readable dataset that can be used for analysis.
Just for Fun
Thank you for checking out the RDM Weekly Newsletter! If you enjoy this content, please like, comment, or share this post! You can also support this work through Buy Me A Coffee.