RDM Weekly - Issue 038
A weekly roundup of Research Data Management resources.
Welcome to Issue 38 of the RDM Weekly Newsletter!
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. Indigenous Data Sovereignty Toolkit
This toolkit was designed to support individuals, organizations, communities, and governments in using data in ways that uphold Indigenous data sovereignty and promotes inclusive, culturally grounded practices. Grounded in principles of data sovereignty and culturally-relevant gender-based analysis (CRGBA), this toolkit offers guidance on how to approach data in ways that centre community, relationships and accountability.
2. Significant Changes to the NIH Data Management and Sharing Plan Requirements Starting in May
Starting May 25, 2026 the NIH will require researchers to submit a data management and sharing plan based on updated elements. This post from University of Cincinnati Libraries provides a really useful comparison of the current and the updated version questions. Similarly, Bernard Becker Medical Library at Washington University in St. Louis also recently put out a helpful post on the NIH DMS Plan Format Updates, 2026 Edition: What to Know.
3. Awareness and Use of Open Research Practices: An International Survey of Researchers Across Disciplines
The current study explores international researcher’s awareness and use of open research practice and variations across regions, disciplines, methodologies, and career level. A total of 3,017 researchers (45 countries; 24 disciplines) completed the Brief Open Research Survey, reporting their awareness and use of eleven common open research practices and factors that would support their adoption. Respondents reported high awareness of Open Access Publishing, Preprints, and Open Data and awareness only fell below 50% for Research Co-production and Registered Reports. Use was high for Open Access Publishing, but fell below 50% for Preprints, Open Data, Open Research, Open Materials, Open Peer Review, Open Code, Preregistration, Research Co-production, Replication, and Registered Reports. Awareness and use varied across the sampled regions (e.g., Europe vs. Asia), disciplines (e.g., Psychology vs. General & Others in Sciences), methodologies (e.g., quantitative vs. qualitative), and career stages (e.g., PhD students vs. Professors). Respondents reported that the top five supportive strategies of open research were incentives from funders, institutions and regulators; dedicated funding; recognition in promotion and recruitment criteria; more training; and more information.
4. Data Stewardship Handbook
The Data Steward Handbook is an ELIXIR Research Data Management (RDM) Community resource that offers practical guidance to data stewards on relevant topics in their daily job, encouraging the use of existing resources and best practices to improve local data management. Among other things, it includes real-world examples of how other data stewards run data management activities in their institute, with tips and ideas that help others.
5. Research Data Management Micro-Guides: Concise, Practical Guidance for Researchers and Students
A set of concise micro-guides on research data management, covering planning, documentation, storage, preservation, sharing, and FAIR principles. Designed to support researchers and students in implementing good scientific practice and preparing Data Management Plans. The materials can be adapted for reuse in other institutional contexts.
6. Bestiary of Questionable Research Practices in Psychology
Questionable research practices (QRPs) pose a significant threat to the quality of scientific research. However, historically, they remain ill-defined, and a comprehensive list of QRPs is lacking. In this article, we address this concern by defining, collecting, and categorizing QRPs using a community-consensus method. Collaborators of the study agreed on the following definition: QRPs are ways of producing, maintaining, sharing, analyzing, or interpreting data that are likely to produce misleading conclusions, typically in the interest of the researcher. QRPs are not normally considered to include research practices that are prohibited or proscribed in the researcher’s field (e.g., fraud, research misconduct). Neither do they include random researcher error (e.g., accidental data loss). Drawing from both iterative discussions and existing literature, the authors collected, defined, and categorized 40 QRPs for quantitative research. The authors also considered attributes such as potential harms, detectability, clues, and preventive measures for each QRP. The results suggest that QRPs are pervasive and versatile and have the potential to undermine all stages of the scientific enterprise.
7. Using Git With Your SAS Projects - Webinar
SAS programmers: wouldn’t it be great if you could improve coding quality all the while maximizing efficiency? On April 2nd at 11am ET, this free webinar from SAS will show you how to use modern source control systems like Git for version control, collaboration and integration with tools and processes that will enhance efficiency.
Oldies but Goodies
Older resources that are still helpful
1. Creating REDCap Data Dictionaries Using ChatGPT 4.0
Those who have built REDCap projects for their own research team or company (or perhaps worse, for someone else’s), know that it is a tedious, time-consuming task. With the ability to upload documents to LLMs such as ChatGPT (OpenAI, 2023), REDCap administrators now have the ability to construct survey instruments–and entire projects–within minutes. With a few simple prompts, ChatGPT can transform a blank, template data dictionary, to a fully functional REDCap instrument. This article reviews that process.
2. From CSV to Database with Python
Imagine you wanted to do some data analysis, but also wanted to avoid importing any external libraries into Python. This is not just an academic exercise. A large part of the challenge of working in Python is managing dependencies, environments, and breaking updates. Sticking with base Python and the libraries it ships with would make your code considerably more robust and lower maintenance. We can do exactly this with SQLite3. This post will extend the first database created in this earlier post and take advantage of the improved mental model outlined in this post.
3. Resources for Practicing Open Science with Qualitative Research in Education
This resource list grew out of a hackathon at the Virtual Unconference on Open Scholarship Practices in Education Research. This list contains resources for researchers, editors, and reviewers interested in practicing open science principles, particularly in education research. This list is not exhaustive but meant as a starting point for individuals wanting to learn more about doing open science work specifically for qualitative research.
4. Frictionless Data
Frictionless Data is a progressive open-source framework for building data infrastructure – data management, data integration, data flows, etc. It includes various data standards and provides software to work with data. You can use Frictionless to describe your data (add metadata and schemas), validate your data, and transform your data. You can also write custom data standards based on the Frictionless specifications. These slides from Kyle Husmann help further explain how Frictionless works.
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.
Utrecht University - Consultant Research Data Management & Communication Adviser
Australian National University - Archivist, Australian Data Archive
Just for Fun
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.