RDM Weekly - Issue 045
A weekly roundup of Research Data Management resources.
Welcome to Issue 45 of the RDM Weekly Newsletter!
Thank you to everyone who has been sharing RDM Weekly! The newsletter received many new subscribers last week!
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
✅ Research Data Management Job Opportunities
Research data management related job opportunities that I have come across in the past week
✅ Just for Fun
A data management meme or other funny data management content
What’s New in RDM?
Resources from the past year
1. One of the Worst Things to Happen to Survey Design Has Been Convenience
Survey creation has become too convenient. Too fast. Too frictionless. Too detached from the thinking process. Tell the AI your goal. Click a button. Generate a survey. Ship it immediately. From 0 to launch in under 5 minutes. But in this post, Kirsten Lee Hill argues that convenience is encouraging people to skip the exact process that creates useful insights. Research (which includes surveying of any kind) is an area where thinking slower matters.
2. “There’s Going to Have to Be a Culture Shift”: Associate and Full Professors’ Perceptions and Experiences Related to Open Science Practices in Communication Sciences and Disorders
The purpose of this qualitative study was to expand upon the findings of Pfeiffer et al.'s (2025) study of the perceptions and experiences of assistant professors in communication sciences and disorders (CSD) related to open science (OS) by examining those of associate and full professors. Thirty-one faculty in CSD (15 associate professors and 16 full professors) each participated in one of four 1-hr virtual focus groups conducted via Zoom videoconferencing software. The researchers used both deductive and inductive coding methods to analyze the focus group data and develop categories and subcategories summarizing the discussions. The study found that associate and full professors in CSD perceive many of the same barriers and facilitators to engaging in OS as assistant professors; however, they uniquely highlighted the need for a cultural shift from the ways they were trained to enhance implementation of OS practices. This shift includes embedding education about OS early in academic training, clearly outlining benefits and incentives for engaging in OS, and providing opportunities for clinicians to partner with researchers in learning about and implementing these practices.
3. Indigenous AI Commons
This commons gathers what Indigenous communities are building, what researchers are documenting, and what history demands we remember — so the next generation of decisions about AI is shaped by those most affected by it. This living index includes upcoming events and trainings, example policies, peer-reviewed research, community solutions, Indigenous-built AI, and more.
4. “My First Pull Request”: A Beginners Guide to Open Source with PsychoPy
Make your first pull request and learn how to get started in the world of Open Source Projects! Contributing to Open Source Projects is a great way to familiarize yourself with coding ecosystems, improve the tools that you and your community use in your research and in general learn programming concepts. This free, online workshop (Thursday, June 18, 6 AM - 7 AM CDT) is designed to guide you through creating your first pull request, using PsychoPy as an example. This session is perfect for beginners and anyone curious about how open source collaboration works in cognitive science software.
5. Doing Open Social Science: A Guide for Researchers
Doing Open Social Science: A Guide for Researchers is the first comprehensive book setting out the principles and practices of open research, tailored specifically for those in the social science disciplines, at every career stage, offering practical advice on how to make research more transparent, trustworthy and reusable. Divided into four parts, this open access book explores the core principles and philosophy of open social science. Part II addresses how to improve the reproducibility of research through open approaches, including chapters on the principles and tools of documenting research as you go and on open data practices. Part III focuses on open practices within the qualitative social sciences. Chapters examine interview-based research, case studies and fieldwork, systematic documentation analysis, archival data and the role of openness in citizen (social) science. Part IV addresses shifting research cultures, with chapters on strategies for presenting research clearly and accessibly to maximise reach and impact and on open access publishing. The book ends with a discussion of the future of open social science. Ultimately, it argues, openness as a wider cultural change can renew the social sciences and the core foundations for academic progress in more dynamic and sustainable ways.
6. Working Smarter in R: Tips, Tricks & Real-World Lessons - Recording
In this recording, R-Ladies+ Remote hosted a relaxed panel discussion bringing together R users to share practical insights from their real-world experience. Rather than a formal talk, this session was a conversational discussion about the workflows, tools, habits, and small changes that can make everyday R work more efficient, reproducible, and enjoyable. The panel explored how people approach real-world analysis, from organising projects and cleaning messy data to debugging code, communicating results, and building more robust workflows. Resources mentioned throughout the discussion have been compiled here.
7. Notes from Dagstuhl: Biomedical Data Sharing
In this post, Damien Desfontaines writes about what he learned as a participant at a Dagstuhl seminar about privacy for biomedical data sharing. He shares his thoughts on the lack of differential privacy use, reliance on controlled access repositories, the use of mature risk mitigation, the use of AI, and more.
Oldies but Goodies
Older resources that are still helpful
1. Tracing Data: A Survey Investigating Disciplinary Differences in Data Citation
Data citations, or citations in reference lists to data, are increasingly seen as an important means to trace data reuse and incentivize data sharing. Although disciplinary differences in data citation practices have been well documented via scientometric approaches, we do not yet know how representative these practices are within disciplines. Nor do we yet have insight into researchers’ motivations for citing—or not citing—data in their academic work. Here, the authors present the results of the largest known survey (n = 2,492) to explicitly investigate data citation practices, preferences, and motivations, using a representative sample of academic authors by discipline, as represented in the Web of Science (WoS). They present findings about researchers’ current practices and motivations for reusing and citing data and also examine their preferences for how they would like their own data to be cited. They conclude by discussing disciplinary patterns in two broad clusters, focusing on patterns in the social sciences and humanities, and consider the implications of our results for tracing and rewarding data sharing and reuse.
2. LMU Open Science Center Self-Learning Course Catalog
The LMU Open Science Center recently refreshed their website, but the site still has all of the same helpful data management courses ranging from Maintaining Privacy with Open Data, to FAIR Data Management, to Generating Synthetic Data. New tutorials are also underway, such as Data Anonymity.
3. README Generator
This readme template is provided by McMaster University RDM Services for projects of any discipline and is adapted from Cornell's readme template and Francesco Varrato, Alain Borel and Chiara Gabella's "README file for Datasets - Best practices and template". This site provides a fillable template that guides you through common README sections. You can download your README as a TXT file.
4. SEER Webinar 3: Sharing Study Data: Implications for Education Researchers - Recording
In this 90-minute webinar, Ruth Neild from Mathematica presents an overview of IES’s guide, Sharing Study Data: A Guide for Education Researchers, and a panel of researchers will share their expertise and experiences with three key aspects of sharing study data 1) Managing disclosure risks, 2) Documenting and organizing data for other researchers, and 3) Depositing data for access by other researchers.
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
![r/ProgrammerHumor - What can you see? It's some form of Elvish. I can't read it. ^[\w-\.]+@([\w-]+\.)+[\w-]{2,4}$](https://substackcdn.com/image/fetch/$s_!0RFh!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff837ec94-4ef5-4647-bf2c-c9257b41ab1f_1072x1072.jpeg "r/ProgrammerHumor - What can you see? It's some form of Elvish. I can't read it. ^[\w-\.]+@([\w-]+\.)+[\w-]{2,4}$")
Sponsor
This newsletter is supported in part by the Eunice Kennedy Shriver National Institute Of Child Health & Human Development of the National Institutes of Health under Award Number R25HD114368. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health. Read more about the NIH Data Management for Data Sharing Workshop Project.
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