RDM Weekly - Issue 043
A weekly roundup of Research Data Management resources.
Welcome to Issue 43 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. EConsent vs Traditional Consent Among Prospective Biobank Participants: A Randomized Study Within a Study Trial
Electronic consent (eConsent) has the potential to cost-effectively scale research recruitment and improve equity in access to research benefits, but it is unclear whether it achieves comparable informedness compared to traditional consent. In a randomized, controlled, non-inferiority trial (N = 604; ClinicalTrials.gov ID NCT04131062), the authors compared a human conversation-based consent process to an eConsent platform similar to that used by NIH’s All of Us Research Program and by studies conducted via Apple ResearchKit. Average comprehension scores of participants randomized to eConsent were non-inferior (N = 233; M = 85.8, SD = 14.7) to those randomized to traditional consent (N = 371; M = 76.5, SD = 22.3; t(600.6) = 9.51, p < 0.001). One limitation was imbalance in the proportion of participants in each arm who initiated the survey via which we collected our outcome measures. Still researchers should give serious consideration to adoption of eConsent as an alternative or supplement to traditional consent. Indeed, the pattern of results the authors observed suggests that eConsent might yield more informed research enrollment decisions.
2. The Advantage of Big Team Science: Lessons Learned From Cognitive Science
The credibility revolution in psychology and related sciences contributed to the adoption of large-scale research initiatives known as Big Team Science (BTS). BTS has made significant advances in addressing issues of replication, statistical power, and diversity through the use of larger samples and more representative cross-cultural data. However, while these collaborations hold great potential, they also introduce unique challenges related to their scale. Drawing on experiences from successful BTS projects, the authors identified and outlined key strategies for overcoming diversity, volunteering, and capacity challenges. The authors emphasize the need for clear role definitions, structured and preregistered workflows, centralized project management, and transparent decision documenting to prevent common pitfalls. Of note for this newsletter, the article discusses the ways in which inconsistent data management is challenging for BTS research. Ultimately, the authors call for reflection on the strengths and limitations of BTS to enhance the quality, generalizability, and impact of research across disciplines.
3. Measuring Research Data Reuse in Scholarly Publications Using Generative Artificial Intelligence: Open Science Indicator Development and Preliminary Results
Numerous metascience studies and other initiatives have begun to monitor the prevalence of open science practices when it is more important to understand the 'downstream' effects or impacts of open science. PLOS and DataSeer have developed a new LLM-based indicator to measure an important effect of open science: the reuse of research data. This study’s results show a data reuse rate of 43%, which is higher than established bibliometric techniques. The authors show that data reuse can be measured at scale using LLMs and generative artificial intelligence. The positive effects of research data sharing and reuse may currently be underestimated.
4. Introduction to SQL
This course from Greg Wilson contains lessons ranging from introduction to databases, to selecting, filtering, aggregation and joins, as well as advanced topics. The workshop also includes database files for practice exercises.
5. Developing Principles to Guide Artificial Intelligence (AI) Use in Education Research
American Institutes for Research (AIR) is asking you to share your input on AI education research principles. As AI becomes more widely used in education, researchers are not only studying its impact in schools but also using AI for research activities like coding data, generating test items, conducting analysis, and drafting reports. AIR’s AI Education Research Principles Design Lab convened researchers, industry leaders, and funders to develop shared, adaptable principles that apply across research types and stages. The aim is to complement existing research standards by promoting transparency, trust, and ethical, high‑quality use of AI—maximizing benefits while minimizing risks. You can review the Draft for Public Comment: Principles for the Use of Artificial Intelligence in Education Research. AIR has now opened a public comment process to gather input from the education research community. All feedback will be reviewed, with revisions addressing the most common and relevant themes. Please share your input using this form by May 8, 2026.
6. Living Well with Data: Stewardship as a Just and Viable Paradigm
In this report, Reema Patel argues for a fundamental shift in mindset to address deep-seated challenges in data governance. The report maps ten different mental models, ie ways of thinking about data governance, and demonstrates that many are failing. In the report, Reema proposes Data Stewardship as the foundational ‘meta-mental model’ for a just and viable future. The report proposes a new, relational approach to data – through stewardship. Data Stewardship reframes data not as a resource (or ‘the new oil’), but as a relationship and a ‘living system’ or ‘the new soil’ that requires continuous, reflexive care. This approach grounds data governance in ethical responsibility, collective benefit, and long-term relational care, rejecting extractive or colonial logics.
7. UC Open Research Day 2026
The University of Cincinnati Libraries Research and Data Services, Xavier University Department of Speech, Language and Communicative Behavior, College of Allied Health Sciences Department of Communication Sciences & Disorders, and UC Center for Public Engagement with Science invite you to an Open Research Day on May 27, 2026, from 9 am to 4 pm. This free event will be held in person at the University of Cincinnati Faculty Enrichment Center. Recordings will be made available after the events. Anyone interested in learning about or sharing experiences and expertise related to open scholarship practices and principles is welcome. This event will feature a keynote address, lightning talks, podium/panel presentations, and a discussion group to foster Open Research efforts and community at the University of Cincinnati and beyond.
Oldies but Goodies
Older resources that are still helpful
1. Data Privacy Handbook
The Data Privacy Handbook is a practical guide on handling personal data in scientific research, primarily written for Utrecht University researchers and research support staff in the Netherlands. It is an initiative of Research Data Management Support, in collaboration with privacy and data experts at Utrecht University. The Data Privacy Handbook consists of a knowledge base which explains how the EU General Data Protection Regulation applies to scientific research, an overview of privacy-enhancing techniques & tools and practical guidance on their implementation, and use cases in the form of research projects with privacy-related issues, for which a reusable solution (e.g., tool, workflow) is shared.
2. The CLEAR Principle: Organizing Data and Metadata into Semantically Meaningful Types of FAIR Digital Objects to Increase their Human Explorability and Cognitive Interoperability
Ensuring the FAIRness (Findable, Accessible, Interoperable, Reusable) of data and metadata is an important goal in both research and industry. Knowledge graphs and ontologies have been central in achieving this goal, with interoperability of data and metadata receiving much attention. This paper argues that the emphasis on machine-actionability has overshadowed the essential need for human-actionability of data and metadata, and provides three examples that describe the lack of human-actionability within knowledge graphs. This paper propagates the incorporation of cognitive interoperability as another vital layer within the European Open Science Cloud Interoperability Framework and discusses the relation between human explorability of data and metadata and their cognitive interoperability. It suggests adding the CLEAR Principle to support the cognitive interoperability and human contextual explorability of data and metadata. The subsequent sections present the concept of semantic units, elucidating their important role in attaining CLEAR. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs, each represented with its own resource that constitutes a FAIR Digital Object (FDO) and that instantiates a corresponding FDO class. Various categories of FDOs are distinguished. Each semantic unit can be displayed in a user interface either as a mind-map-like graph or as natural language text.
3. Exploring Missing Values in naniar
Missing values, indicated by (or coerced to) NA in R, are common in environmental data due to equipment malfunction, survey non-response, human error, resource limitations, and any number of other unforeseen hiccups that can occur during data collection. Despite their ubiquity, NAs are rarely considered in exploratory data analysis, and are commonly “dealt with” (read: disappeared) by listwise deletion. Listwise deletion (in which any row with an NA is removed) may be the best method for handling missings, but also omits valuable existing observations, reduces statistical power, and depending on the mechanism of missingness can increase bias in parameter estimates. Exploring and thinking critically about missing data is an important and often overlooked part of exploratory data analysis that can help us to understand what data are missing and why, so that we choose an appropriate method for handling them. In this tutorial, Allison Horst shows you how to move beyond is.na() to learn other useful tools and approaches for exploring and visualizing missing values with helpful functions in the naniar package by Dr. Nick Tierney. Practice exercises included!
4. Drowning in Research Data: Addressing Data Management Literacy of Graduate Students
Graduate students work in increasingly complex research environments where advances in technology and research methodologies result in gathering and analyzing large amounts of data. Proper management (organization, protection, preservation, sharing) of this research data is essential for productivity, securing grant funding, enabling collaboration and ensuring the future use of data. There are numerous studies of faculty researchers and their research data management practices but relatively few on graduate students, who are also key members of research teams. In particular, little has been studied on how graduate students learn about research data management and how that relates to their research behaviours. In this 2013 paper the authors discuss findings from a research study of social sciences and science graduate students' levels of research data management literacy, which include attitudes and behaviours, and formal and informal education experiences. Using an online survey of Canadian graduate students in the social sciences and science, the authors were able to reach a large number of students across the country and to gather sufficient responses to allow them to offer some insights on the overall graduate student research data management landscape.
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.
None this week
Just for Fun
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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