Welcome to Issue 15 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. Data Tracking in Neurodivergent Samples Guide
I recently came across this excellent guide from a team of researchers working on the MathMIND project. The guide is designed to help researchers of neurodivergent populations, including participants with intellectual disability, create data tracking systems to accurately describe attrition and the characteristics of their samples. The guide, along with the worksheets found in their OSF project, provides a practical tutorial for setting up a system to to track data collection efforts. While the guide is specific to monitoring data collected from neurodivergent populations, I think it can be helpful for broadly understanding how to track other data collection efforts as well.
2. Education Researchers’ Beliefs and Barriers Towards Data Sharing
Data sharing is increasingly becoming a highly encouraged or required practice for any federally funded research projects. However, the uptake of these practices in education science has been minimal. Research suggests that many researchers believe data sharing should be practiced always or often, but also suggests that many researchers rarely practice data sharing. This disconnect indicates a general lack of understanding around data sharing and suggests there are salient barriers that prevent education researchers from engaging in the practice. This work examines a) the prevalence of positive attitudes and perceived barriers to data sharing in a sample of education researchers, and b) if there is a difference between the perceived barriers for researchers who have different levels of data sharing experience.
3. Considerations for the Provision of Synthetic Forms of Secure Data
Synthetic data is an emerging tool for those learning and/or planning to use secure data and for collaborators on secure data projects. Other work is being done to develop use cases, shared terminology, and tools to assess utility and risk. The purpose of this paper is to guide synthetic data providers on considerations for the creation, provision and sharing of synthetic datasets where their primary use will be for learning, tool development, project collaboration and planning research using secure data. It is aimed at data providers with an interest in improving the efficiency of use of secure data, whilst maintaining its privacy and public acceptability.
4. 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. We present a range of practices tailored to different levels of programming experience, from beginners to advanced users. The articles’ 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.
5. New AI Features Available In Enterprise REDCap System
Three new features have been enabled in the Enterprise REDCap system, powered by AI. These AI tools include text generation (have the AI wordsmith your questions, instructions, alerts or survey invitations), translations (automatically translate both your instruments and the REDCap interface into a language of your choosing), and summarization (get a quick summary of any free text field across your survey responses). Wake Health has a dedicated, self-guided training course to learn more about these AI tools.
6. Webinar: Creating an NIH Data Management and Sharing Plan with ICPSR
Are you preparing a renewal, resubmission, or upcoming NIH grant application? Join ICPSR, on October 30th, 2025 at 9am CST, for a practical virtual workshop designed to help you navigate the requirements of the NIH’s Data Management and Sharing (DMS) Policy. This interactive session will guide you through the essential components of creating an effective DMS Plan and highlight the value of transparent data sharing. You’ll gain insights into the NIH’s data sharing policies, learn how to de-identify and prepare both restricted- and public-use data files, and discover ICPSR’s many resources to support your research, whether you work with qualitative or quantitative data.
Oldies but Goodies
Older resources that are still helpful
1. When Online Content Disappears
The internet is an unimaginably vast repository of modern life, with hundreds of billions of indexed webpages. But even as users across the world rely on the web to access books, images, news articles and other resources, this content sometimes disappears from view. This 2024 Pew Research Center analysis shows just how fleeting online content actually is.
2. Science as Amateur Software Development
In contrast to other fields, like say landscaping or software engineering, science as a profession is largely *unprofessional*—apprentice scientists are taught less about how to work responsibly than about how to earn promotions. This results in ubiquitous and costly errors. Software development has become indispensable to scientific work. In this 2020 talk Richard McElreath asks how it can become even more useful by transferring some aspects of its professionalism, the day-to-day tracking and back-tracking and testing that is especially part of distributed, open-source software development. Science, after all, aspires to be distributed, open-source knowledge development.
3. Better Spreadsheets
This open access workshop is about formatting, not necessarily tips and tricks for Excel. The learning objectives of the course is be able to a) explain and utilize tidy principles to effectively organize your data, b) format your data to effectively utilize it in analyses, c) collaborate with data scientists more effectively with formatting tips, and d) explain data cleaning tools in R that can help.
4. ManyBabies Workshop: Project Management
You cannot have good data management without also having good project management. In this recording from February 2024, presenter Michael C. Frank (Stanford University) shares tips on how to organize project materials in a way that reduces confusion and errors, and makes collaborating and sharing easy. Slides for this talk are also available here.
Just for Fun
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I enjoyed every bit of it. The resources are useful. Thanks for sharing