RDM Weekly - Issue 029
A weekly roundup of Research Data Management resources.
Welcome to Issue 29 of the RDM Weekly Newsletter!
If you are new to RDM Weekly, the content of this newsletter is divided into 4 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
✅ NEW! Research Data Management Job Opportunities
I recently decided that if I come across any research data management related job opportunities, that I will share the link here. I have no affiliation with these jobs.
✅ Just for Fun
A data management meme or other funny data management content
What’s New in RDM?
Resources from the past year
1. International Love Data Week Events
Love Data Week is an international celebration of data, taking place every year during the week of Valentine’s day. Universities, nonprofit organizations, government agencies, corporations and individuals are encouraged to host and participate in data-related events and activities. This year’s Love Data Week theme is “Where’s the Data?” – a way to get people thinking about data’s journey from collection through storage and preservation. This page, hosted by ICPSR, lists events happening around the world for Love Data Week (February 9-13, 2026). You can also submit your event to be added as well.
2. Bio-IT FAIR Data Hackathon
The Bio-IT World Hackathon (May 18-19, 2026 in Boston, MA) is a cornerstone of the Bio-IT World Conference & Expo, bringing together data scientists, software developers, and life science professionals to tackle real-world data challenges. Focused on Open Source and FAIR (Findable, Accessible, Interoperable, Reusable) principles, this two-day event fosters innovation and collaboration to deliver practical solutions. Facilitated by leaders from the NIH Common Fund Data Ecosystem (CFDE), this year’s event will focus on projects leveraging omics data and integrating CFDE tools, improving interoperability across datasets to accelerate discoveries. The CFDE ensures Common Fund data is accessible and reusable, providing researchers with a centralized online platform for integrating multiple resources seamlessly while enabling new insights and scalable solutions. Multiple prizes of up to $5,000 will be awarded to top teams for innovative, high-impact solutions that advance open science and biomedical data reuse. You can get involved by either submitting a proposal (due February 2, 2026) or by participating.
3. Ten Principles for Reliable, Efficient, and Adaptable Coding in Psychology and Cognitive Neuroscience
Writing code is becoming essential for psychology and neuroscience research, supporting increasingly advanced experimental designs, processing of ever-larger datasets and easy reproduction of scientific results. Despite its critical role, coding remains challenging for many researchers, as it is typically not part of formal academic training. The authors present a range of practices tailored to different levels of programming experience, from beginners to advanced users. The authors’ ten principles help researchers streamline and automate their projects, reduce human error, and improve the quality and reusability of their code. For principal investigators, the authors highlight the benefits of fostering a collaborative environment that values code sharing. Maintaining basic standards for code quality, reusability, and shareability is critical for increasing the trustworthiness and reliability of research in experimental psychology and cognitive neuroscience.
4. Data Management for Bioimaging – No-Cost, Easy-Access Tools for Edinburgh
In this guest blog post on the Edinburgh Research Data Blog, Dr. Ann Wheeler, Head of the Institute of Genetics and Cancer Advanced Imaging Resource, showcases their exemplar for metadata capture and data management workflows when working with bioimaging data.
5. Global Community Priorities for Agentic AI in Research - Community Consultation Results
In November 2025, the Research Data Alliance (RDA) in collaboration with Microsoft, launched a global community consultation to explore current use of agentic AI by researchers and get their perspectives on its value throughout the research lifecycle. For this consultation, agentic AI was defined as ‘artificial intelligence systems capable of autonomous operation with minimal human oversight’. This work builds upon RDA’s previous collaboration with Microsoft, which had identified agentic AI as a critical capability need amongst researchers and recommended investment in automated data preparation tools. Three proposed tools emerged as clear community priorities: the Literature Librarian, which would search literature using natural language queries integrated with library subscriptions; the Data Director, designed to support research data preparation and sharing in compliance with FAIR principles; and the Funding Finder, which would identify relevant funding opportunities and support application processes. Insights from this consultation will guide the next phase of work in 2026, which intends to focus on collaborative community development of an open, technology-agnostic blueprint for a priority agentic AI tool.
6. On “Simplifying” GDPR’s Definition of Personal Data
Last November, the European Commission published a proposal named "Digital Omnibus", to change and clarify multiple regulations. One of the proposed changes concerns the definition of personal data under GDPR (General Data Protection Regulation). It’s a critical definition, because if something is not personal data, then the GDPR doesn’t apply. So classifying something as not personal data — for example, by applying robust anonymization methods — essentially gives organizations a “get-out-of-data-protection-compliance-obligations” card. In this blog post, Damien Desfontaines want to make a simple point: this change would not lead to the desired goal of making it simpler for organizations to comply with data protection law. It would make it more difficult for organizations to achieve a good compliance posture, and more likely that they need expert help as part of their compliance programs.
7. R Package Development Advent Calendar 2025: A Complete Journey
The R package development ecosystem has evolved dramatically over the past decade.
Tools like usethis, pkgdown, and GitHub Actions have automated what used to be tedious, error-prone manual work. Yet many developers still follow outdated workflows, missing out on productivity gains and quality improvements. The advent calendar, from Athanasia Mo Mowinckel, was designed to bridge that gap—offering bite-sized, practical lessons that you can implement immediately in your packages. This post serves as a comprehensive reference guide, capturing all the insights, tools, and best practices shared throughout the advent calendar.
Oldies but Goodies
Older resources that are still helpful
1. Data Science Resources
This site is a collection of useful, freely-available data science resources and links, curated by Nicola Rennie. The site can be filtered by resource type (e.g., blog, video, podcast, data), category (e.g., R, visualisation, Quarto, Python, Git), and more.
2. How to Make a Data Dictionary
A data dictionary is critical to making your research more reproducible because it allows others to understand your data. The purpose of a data dictionary is to explain what all the variable names and values in your spreadsheet really mean. This brief tutorial, from the Center for Open Science, walks you through what fields to consider including and provides an example data dictionary.
3. Tidy Spreadsheets in Medical Research - Workshop Recording
In this 2021 recording from the R/Medicine Conference, Peter Higgins covers best practices for using medical data in spreadsheets like Excel and Google Sheets.
4. The Role of Human Fallibility in Psychological Research: A Survey of Mistakes in Data Management
Errors are an inevitable consequence of human fallibility, and researchers are no exception. Most researchers can recall major frustrations or serious time delays due to human errors while collecting, analyzing, or reporting data. This 2021 study is an exploration of mistakes made during the data-management process in psychological research. Authors surveyed 488 researchers regarding the type, frequency, seriousness, and outcome of mistakes that have occurred in their research team during the last 5 years. With these initial exploratory findings, authors do not aim to provide a description representative for psychological scientists but, rather, to lay the groundwork for a systematic investigation of human fallibility in research data management and the development of solutions to reduce errors and mitigate their impact.
Research Data Management Job Opportunities
These are data management job opportunities that I have seen posted in the last week. I have no affiliation with these organizations.
Just for Fun
dplyr} that replaces boring old function names with Boomer slang. Learn more: https://bradlindblad.github.io/boomerplyr/Thank you for reading! If you enjoy this content, please like, comment, or share this post! You can also support this work through Buy Me A Coffee.
Solid curation here, especially the Bio-IT hackathon emphasis on interoperability through CFDE tools. Bringing omics datasets together has been painful in my experience witout common schemas and endpoints, and seeing a concerted push toward practical FAIR implementation (not just aspirational principles) is refreshing. The $5k prizes dont hurt either for drawing real talent into solving integration bottelnecks.